diff --git a/lib/python3.11/site-packages/cryptography/__about__.py b/lib/python3.11/site-packages/cryptography/__about__.py new file mode 100644 index 0000000000000000000000000000000000000000..c81901c0ab9a8d07842000edfb07b9857ee78b1d --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/__about__.py @@ -0,0 +1,17 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +__all__ = [ + "__author__", + "__copyright__", + "__version__", +] + +__version__ = "48.0.0" + + +__author__ = "The Python Cryptographic Authority and individual contributors" +__copyright__ = f"Copyright 2013-2026 {__author__}" diff --git a/lib/python3.11/site-packages/cryptography/__init__.py b/lib/python3.11/site-packages/cryptography/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d374f752dfd53c6f9a12c69309fdcb6eee765251 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/__init__.py @@ -0,0 +1,13 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +from cryptography.__about__ import __author__, __copyright__, __version__ + +__all__ = [ + "__author__", + "__copyright__", + "__version__", +] diff --git a/lib/python3.11/site-packages/cryptography/exceptions.py b/lib/python3.11/site-packages/cryptography/exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..fe125ea9a763ad731ba99e4761d6623e1d2c639c --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/exceptions.py @@ -0,0 +1,52 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +import typing + +from cryptography.hazmat.bindings._rust import exceptions as rust_exceptions + +if typing.TYPE_CHECKING: + from cryptography.hazmat.bindings._rust import openssl as rust_openssl + +_Reasons = rust_exceptions._Reasons + + +class UnsupportedAlgorithm(Exception): + def __init__(self, message: str, reason: _Reasons | None = None) -> None: + super().__init__(message) + self._reason = reason + + +class AlreadyFinalized(Exception): + pass + + +class AlreadyUpdated(Exception): + pass + + +class NotYetFinalized(Exception): + pass + + +class InvalidTag(Exception): + pass + + +class InvalidSignature(Exception): + pass + + +class InternalError(Exception): + def __init__( + self, msg: str, err_code: list[rust_openssl.OpenSSLError] + ) -> None: + super().__init__(msg) + self.err_code = err_code + + +class InvalidKey(Exception): + pass diff --git a/lib/python3.11/site-packages/cryptography/fernet.py b/lib/python3.11/site-packages/cryptography/fernet.py new file mode 100644 index 0000000000000000000000000000000000000000..c6744ae384e3497d2855b32398f8ee59b972f371 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/fernet.py @@ -0,0 +1,224 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +import base64 +import binascii +import os +import time +import typing +from collections.abc import Iterable + +from cryptography import utils +from cryptography.exceptions import InvalidSignature +from cryptography.hazmat.primitives import hashes, padding +from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes +from cryptography.hazmat.primitives.hmac import HMAC + + +class InvalidToken(Exception): + pass + + +_MAX_CLOCK_SKEW = 60 + + +class Fernet: + def __init__( + self, + key: bytes | str, + backend: typing.Any = None, + ) -> None: + try: + key = base64.urlsafe_b64decode(key) + except binascii.Error as exc: + raise ValueError( + "Fernet key must be 32 url-safe base64-encoded bytes." + ) from exc + if len(key) != 32: + raise ValueError( + "Fernet key must be 32 url-safe base64-encoded bytes." + ) + + self._signing_key = key[:16] + self._encryption_key = key[16:] + + @classmethod + def generate_key(cls) -> bytes: + return base64.urlsafe_b64encode(os.urandom(32)) + + def encrypt(self, data: bytes) -> bytes: + return self.encrypt_at_time(data, int(time.time())) + + def encrypt_at_time(self, data: bytes, current_time: int) -> bytes: + iv = os.urandom(16) + return self._encrypt_from_parts(data, current_time, iv) + + def _encrypt_from_parts( + self, data: bytes, current_time: int, iv: bytes + ) -> bytes: + utils._check_bytes("data", data) + + padder = padding.PKCS7(algorithms.AES.block_size).padder() + padded_data = padder.update(data) + padder.finalize() + encryptor = Cipher( + algorithms.AES(self._encryption_key), + modes.CBC(iv), + ).encryptor() + ciphertext = encryptor.update(padded_data) + encryptor.finalize() + + basic_parts = ( + b"\x80" + + current_time.to_bytes(length=8, byteorder="big") + + iv + + ciphertext + ) + + h = HMAC(self._signing_key, hashes.SHA256()) + h.update(basic_parts) + hmac = h.finalize() + return base64.urlsafe_b64encode(basic_parts + hmac) + + def decrypt(self, token: bytes | str, ttl: int | None = None) -> bytes: + timestamp, data = Fernet._get_unverified_token_data(token) + if ttl is None: + time_info = None + else: + time_info = (ttl, int(time.time())) + return self._decrypt_data(data, timestamp, time_info) + + def decrypt_at_time( + self, token: bytes | str, ttl: int, current_time: int + ) -> bytes: + if ttl is None: + raise ValueError( + "decrypt_at_time() can only be used with a non-None ttl" + ) + timestamp, data = Fernet._get_unverified_token_data(token) + return self._decrypt_data(data, timestamp, (ttl, current_time)) + + def extract_timestamp(self, token: bytes | str) -> int: + timestamp, data = Fernet._get_unverified_token_data(token) + # Verify the token was not tampered with. + self._verify_signature(data) + return timestamp + + @staticmethod + def _get_unverified_token_data(token: bytes | str) -> tuple[int, bytes]: + if not isinstance(token, (str, bytes)): + raise TypeError("token must be bytes or str") + + try: + data = base64.urlsafe_b64decode(token) + except (TypeError, binascii.Error): + raise InvalidToken + + if not data or data[0] != 0x80: + raise InvalidToken + + if len(data) < 9: + raise InvalidToken + + timestamp = int.from_bytes(data[1:9], byteorder="big") + return timestamp, data + + def _verify_signature(self, data: bytes) -> None: + h = HMAC(self._signing_key, hashes.SHA256()) + h.update(data[:-32]) + try: + h.verify(data[-32:]) + except InvalidSignature: + raise InvalidToken + + def _decrypt_data( + self, + data: bytes, + timestamp: int, + time_info: tuple[int, int] | None, + ) -> bytes: + if time_info is not None: + ttl, current_time = time_info + if timestamp + ttl < current_time: + raise InvalidToken + + if current_time + _MAX_CLOCK_SKEW < timestamp: + raise InvalidToken + + self._verify_signature(data) + + iv = data[9:25] + ciphertext = data[25:-32] + decryptor = Cipher( + algorithms.AES(self._encryption_key), modes.CBC(iv) + ).decryptor() + plaintext_padded = decryptor.update(ciphertext) + try: + plaintext_padded += decryptor.finalize() + except ValueError: + raise InvalidToken + unpadder = padding.PKCS7(algorithms.AES.block_size).unpadder() + + unpadded = unpadder.update(plaintext_padded) + try: + unpadded += unpadder.finalize() + except ValueError: + raise InvalidToken + return unpadded + + +class MultiFernet: + def __init__(self, fernets: Iterable[Fernet]): + fernets = list(fernets) + if not fernets: + raise ValueError( + "MultiFernet requires at least one Fernet instance" + ) + self._fernets = fernets + + def encrypt(self, msg: bytes) -> bytes: + return self.encrypt_at_time(msg, int(time.time())) + + def encrypt_at_time(self, msg: bytes, current_time: int) -> bytes: + return self._fernets[0].encrypt_at_time(msg, current_time) + + def rotate(self, msg: bytes | str) -> bytes: + timestamp, data = Fernet._get_unverified_token_data(msg) + for f in self._fernets: + try: + p = f._decrypt_data(data, timestamp, None) + break + except InvalidToken: + pass + else: + raise InvalidToken + + iv = os.urandom(16) + return self._fernets[0]._encrypt_from_parts(p, timestamp, iv) + + def decrypt(self, msg: bytes | str, ttl: int | None = None) -> bytes: + for f in self._fernets: + try: + return f.decrypt(msg, ttl) + except InvalidToken: + pass + raise InvalidToken + + def decrypt_at_time( + self, msg: bytes | str, ttl: int, current_time: int + ) -> bytes: + for f in self._fernets: + try: + return f.decrypt_at_time(msg, ttl, current_time) + except InvalidToken: + pass + raise InvalidToken + + def extract_timestamp(self, msg: bytes | str) -> int: + for f in self._fernets: + try: + return f.extract_timestamp(msg) + except InvalidToken: + pass + raise InvalidToken diff --git a/lib/python3.11/site-packages/cryptography/hazmat/__init__.py b/lib/python3.11/site-packages/cryptography/hazmat/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b9f1187011bdaa0720bc462564582393700f3d4a --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/__init__.py @@ -0,0 +1,13 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +""" +Hazardous Materials + +This is a "Hazardous Materials" module. You should ONLY use it if you're +100% absolutely sure that you know what you're doing because this module +is full of land mines, dragons, and dinosaurs with laser guns. +""" diff --git a/lib/python3.11/site-packages/cryptography/hazmat/_oid.py b/lib/python3.11/site-packages/cryptography/hazmat/_oid.py new file mode 100644 index 0000000000000000000000000000000000000000..4bf138d4f80b8e83e0657577e338270df18fcdcd --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/_oid.py @@ -0,0 +1,356 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +from cryptography.hazmat.bindings._rust import ( + ObjectIdentifier as ObjectIdentifier, +) +from cryptography.hazmat.primitives import hashes + + +class ExtensionOID: + SUBJECT_DIRECTORY_ATTRIBUTES = ObjectIdentifier("2.5.29.9") + SUBJECT_KEY_IDENTIFIER = ObjectIdentifier("2.5.29.14") + KEY_USAGE = ObjectIdentifier("2.5.29.15") + PRIVATE_KEY_USAGE_PERIOD = ObjectIdentifier("2.5.29.16") + SUBJECT_ALTERNATIVE_NAME = ObjectIdentifier("2.5.29.17") + ISSUER_ALTERNATIVE_NAME = ObjectIdentifier("2.5.29.18") + BASIC_CONSTRAINTS = ObjectIdentifier("2.5.29.19") + NAME_CONSTRAINTS = ObjectIdentifier("2.5.29.30") + CRL_DISTRIBUTION_POINTS = ObjectIdentifier("2.5.29.31") + CERTIFICATE_POLICIES = ObjectIdentifier("2.5.29.32") + POLICY_MAPPINGS = ObjectIdentifier("2.5.29.33") + AUTHORITY_KEY_IDENTIFIER = ObjectIdentifier("2.5.29.35") + POLICY_CONSTRAINTS = ObjectIdentifier("2.5.29.36") + EXTENDED_KEY_USAGE = ObjectIdentifier("2.5.29.37") + FRESHEST_CRL = ObjectIdentifier("2.5.29.46") + INHIBIT_ANY_POLICY = ObjectIdentifier("2.5.29.54") + ISSUING_DISTRIBUTION_POINT = ObjectIdentifier("2.5.29.28") + AUTHORITY_INFORMATION_ACCESS = ObjectIdentifier("1.3.6.1.5.5.7.1.1") + SUBJECT_INFORMATION_ACCESS = ObjectIdentifier("1.3.6.1.5.5.7.1.11") + OCSP_NO_CHECK = ObjectIdentifier("1.3.6.1.5.5.7.48.1.5") + TLS_FEATURE = ObjectIdentifier("1.3.6.1.5.5.7.1.24") + CRL_NUMBER = ObjectIdentifier("2.5.29.20") + DELTA_CRL_INDICATOR = ObjectIdentifier("2.5.29.27") + PRECERT_SIGNED_CERTIFICATE_TIMESTAMPS = ObjectIdentifier( + "1.3.6.1.4.1.11129.2.4.2" + ) + PRECERT_POISON = ObjectIdentifier("1.3.6.1.4.1.11129.2.4.3") + SIGNED_CERTIFICATE_TIMESTAMPS = ObjectIdentifier("1.3.6.1.4.1.11129.2.4.5") + MS_CERTIFICATE_TEMPLATE = ObjectIdentifier("1.3.6.1.4.1.311.21.7") + ADMISSIONS = ObjectIdentifier("1.3.36.8.3.3") + + +class OCSPExtensionOID: + NONCE = ObjectIdentifier("1.3.6.1.5.5.7.48.1.2") + ACCEPTABLE_RESPONSES = ObjectIdentifier("1.3.6.1.5.5.7.48.1.4") + + +class CRLEntryExtensionOID: + CERTIFICATE_ISSUER = ObjectIdentifier("2.5.29.29") + CRL_REASON = ObjectIdentifier("2.5.29.21") + INVALIDITY_DATE = ObjectIdentifier("2.5.29.24") + + +class NameOID: + COMMON_NAME = ObjectIdentifier("2.5.4.3") + COUNTRY_NAME = ObjectIdentifier("2.5.4.6") + LOCALITY_NAME = ObjectIdentifier("2.5.4.7") + STATE_OR_PROVINCE_NAME = ObjectIdentifier("2.5.4.8") + STREET_ADDRESS = ObjectIdentifier("2.5.4.9") + ORGANIZATION_IDENTIFIER = ObjectIdentifier("2.5.4.97") + ORGANIZATION_NAME = ObjectIdentifier("2.5.4.10") + ORGANIZATIONAL_UNIT_NAME = ObjectIdentifier("2.5.4.11") + SERIAL_NUMBER = ObjectIdentifier("2.5.4.5") + SURNAME = ObjectIdentifier("2.5.4.4") + GIVEN_NAME = ObjectIdentifier("2.5.4.42") + TITLE = ObjectIdentifier("2.5.4.12") + INITIALS = ObjectIdentifier("2.5.4.43") + GENERATION_QUALIFIER = ObjectIdentifier("2.5.4.44") + X500_UNIQUE_IDENTIFIER = ObjectIdentifier("2.5.4.45") + DN_QUALIFIER = ObjectIdentifier("2.5.4.46") + PSEUDONYM = ObjectIdentifier("2.5.4.65") + USER_ID = ObjectIdentifier("0.9.2342.19200300.100.1.1") + DOMAIN_COMPONENT = ObjectIdentifier("0.9.2342.19200300.100.1.25") + EMAIL_ADDRESS = ObjectIdentifier("1.2.840.113549.1.9.1") + JURISDICTION_COUNTRY_NAME = ObjectIdentifier("1.3.6.1.4.1.311.60.2.1.3") + JURISDICTION_LOCALITY_NAME = ObjectIdentifier("1.3.6.1.4.1.311.60.2.1.1") + JURISDICTION_STATE_OR_PROVINCE_NAME = ObjectIdentifier( + "1.3.6.1.4.1.311.60.2.1.2" + ) + BUSINESS_CATEGORY = ObjectIdentifier("2.5.4.15") + POSTAL_ADDRESS = ObjectIdentifier("2.5.4.16") + POSTAL_CODE = ObjectIdentifier("2.5.4.17") + INN = ObjectIdentifier("1.2.643.3.131.1.1") + OGRN = ObjectIdentifier("1.2.643.100.1") + SNILS = ObjectIdentifier("1.2.643.100.3") + UNSTRUCTURED_NAME = ObjectIdentifier("1.2.840.113549.1.9.2") + + +class SignatureAlgorithmOID: + RSA_WITH_MD5 = ObjectIdentifier("1.2.840.113549.1.1.4") + RSA_WITH_SHA1 = ObjectIdentifier("1.2.840.113549.1.1.5") + # This is an alternate OID for RSA with SHA1 that is occasionally seen + _RSA_WITH_SHA1 = ObjectIdentifier("1.3.14.3.2.29") + RSA_WITH_SHA224 = ObjectIdentifier("1.2.840.113549.1.1.14") + RSA_WITH_SHA256 = ObjectIdentifier("1.2.840.113549.1.1.11") + RSA_WITH_SHA384 = ObjectIdentifier("1.2.840.113549.1.1.12") + RSA_WITH_SHA512 = ObjectIdentifier("1.2.840.113549.1.1.13") + RSA_WITH_SHA3_224 = ObjectIdentifier("2.16.840.1.101.3.4.3.13") + RSA_WITH_SHA3_256 = ObjectIdentifier("2.16.840.1.101.3.4.3.14") + RSA_WITH_SHA3_384 = ObjectIdentifier("2.16.840.1.101.3.4.3.15") + RSA_WITH_SHA3_512 = ObjectIdentifier("2.16.840.1.101.3.4.3.16") + RSASSA_PSS = ObjectIdentifier("1.2.840.113549.1.1.10") + ECDSA_WITH_SHA1 = ObjectIdentifier("1.2.840.10045.4.1") + ECDSA_WITH_SHA224 = ObjectIdentifier("1.2.840.10045.4.3.1") + ECDSA_WITH_SHA256 = ObjectIdentifier("1.2.840.10045.4.3.2") + ECDSA_WITH_SHA384 = ObjectIdentifier("1.2.840.10045.4.3.3") + ECDSA_WITH_SHA512 = ObjectIdentifier("1.2.840.10045.4.3.4") + ECDSA_WITH_SHA3_224 = ObjectIdentifier("2.16.840.1.101.3.4.3.9") + ECDSA_WITH_SHA3_256 = ObjectIdentifier("2.16.840.1.101.3.4.3.10") + ECDSA_WITH_SHA3_384 = ObjectIdentifier("2.16.840.1.101.3.4.3.11") + ECDSA_WITH_SHA3_512 = ObjectIdentifier("2.16.840.1.101.3.4.3.12") + DSA_WITH_SHA1 = ObjectIdentifier("1.2.840.10040.4.3") + DSA_WITH_SHA224 = ObjectIdentifier("2.16.840.1.101.3.4.3.1") + DSA_WITH_SHA256 = ObjectIdentifier("2.16.840.1.101.3.4.3.2") + DSA_WITH_SHA384 = ObjectIdentifier("2.16.840.1.101.3.4.3.3") + DSA_WITH_SHA512 = ObjectIdentifier("2.16.840.1.101.3.4.3.4") + ED25519 = ObjectIdentifier("1.3.101.112") + ED448 = ObjectIdentifier("1.3.101.113") + GOSTR3411_94_WITH_3410_2001 = ObjectIdentifier("1.2.643.2.2.3") + GOSTR3410_2012_WITH_3411_2012_256 = ObjectIdentifier("1.2.643.7.1.1.3.2") + GOSTR3410_2012_WITH_3411_2012_512 = ObjectIdentifier("1.2.643.7.1.1.3.3") + + +_SIG_OIDS_TO_HASH: dict[ObjectIdentifier, hashes.HashAlgorithm | None] = { + SignatureAlgorithmOID.RSA_WITH_MD5: hashes.MD5(), + SignatureAlgorithmOID.RSA_WITH_SHA1: hashes.SHA1(), + SignatureAlgorithmOID._RSA_WITH_SHA1: hashes.SHA1(), + SignatureAlgorithmOID.RSA_WITH_SHA224: hashes.SHA224(), + SignatureAlgorithmOID.RSA_WITH_SHA256: hashes.SHA256(), + SignatureAlgorithmOID.RSA_WITH_SHA384: hashes.SHA384(), + SignatureAlgorithmOID.RSA_WITH_SHA512: hashes.SHA512(), + SignatureAlgorithmOID.RSA_WITH_SHA3_224: hashes.SHA3_224(), + SignatureAlgorithmOID.RSA_WITH_SHA3_256: hashes.SHA3_256(), + SignatureAlgorithmOID.RSA_WITH_SHA3_384: hashes.SHA3_384(), + SignatureAlgorithmOID.RSA_WITH_SHA3_512: hashes.SHA3_512(), + SignatureAlgorithmOID.ECDSA_WITH_SHA1: hashes.SHA1(), + SignatureAlgorithmOID.ECDSA_WITH_SHA224: hashes.SHA224(), + SignatureAlgorithmOID.ECDSA_WITH_SHA256: hashes.SHA256(), + SignatureAlgorithmOID.ECDSA_WITH_SHA384: hashes.SHA384(), + SignatureAlgorithmOID.ECDSA_WITH_SHA512: hashes.SHA512(), + SignatureAlgorithmOID.ECDSA_WITH_SHA3_224: hashes.SHA3_224(), + SignatureAlgorithmOID.ECDSA_WITH_SHA3_256: hashes.SHA3_256(), + SignatureAlgorithmOID.ECDSA_WITH_SHA3_384: hashes.SHA3_384(), + SignatureAlgorithmOID.ECDSA_WITH_SHA3_512: hashes.SHA3_512(), + SignatureAlgorithmOID.DSA_WITH_SHA1: hashes.SHA1(), + SignatureAlgorithmOID.DSA_WITH_SHA224: hashes.SHA224(), + SignatureAlgorithmOID.DSA_WITH_SHA256: hashes.SHA256(), + SignatureAlgorithmOID.ED25519: None, + SignatureAlgorithmOID.ED448: None, + SignatureAlgorithmOID.GOSTR3411_94_WITH_3410_2001: None, + SignatureAlgorithmOID.GOSTR3410_2012_WITH_3411_2012_256: None, + SignatureAlgorithmOID.GOSTR3410_2012_WITH_3411_2012_512: None, +} + + +class HashAlgorithmOID: + SHA1 = ObjectIdentifier("1.3.14.3.2.26") + SHA224 = ObjectIdentifier("2.16.840.1.101.3.4.2.4") + SHA256 = ObjectIdentifier("2.16.840.1.101.3.4.2.1") + SHA384 = ObjectIdentifier("2.16.840.1.101.3.4.2.2") + SHA512 = ObjectIdentifier("2.16.840.1.101.3.4.2.3") + SHA3_224 = ObjectIdentifier("1.3.6.1.4.1.37476.3.2.1.99.7.224") + SHA3_256 = ObjectIdentifier("1.3.6.1.4.1.37476.3.2.1.99.7.256") + SHA3_384 = ObjectIdentifier("1.3.6.1.4.1.37476.3.2.1.99.7.384") + SHA3_512 = ObjectIdentifier("1.3.6.1.4.1.37476.3.2.1.99.7.512") + SHA3_224_NIST = ObjectIdentifier("2.16.840.1.101.3.4.2.7") + SHA3_256_NIST = ObjectIdentifier("2.16.840.1.101.3.4.2.8") + SHA3_384_NIST = ObjectIdentifier("2.16.840.1.101.3.4.2.9") + SHA3_512_NIST = ObjectIdentifier("2.16.840.1.101.3.4.2.10") + + +class PublicKeyAlgorithmOID: + DSA = ObjectIdentifier("1.2.840.10040.4.1") + EC_PUBLIC_KEY = ObjectIdentifier("1.2.840.10045.2.1") + RSAES_PKCS1_v1_5 = ObjectIdentifier("1.2.840.113549.1.1.1") + RSASSA_PSS = ObjectIdentifier("1.2.840.113549.1.1.10") + X25519 = ObjectIdentifier("1.3.101.110") + X448 = ObjectIdentifier("1.3.101.111") + ED25519 = ObjectIdentifier("1.3.101.112") + ED448 = ObjectIdentifier("1.3.101.113") + + +class ExtendedKeyUsageOID: + SERVER_AUTH = ObjectIdentifier("1.3.6.1.5.5.7.3.1") + CLIENT_AUTH = ObjectIdentifier("1.3.6.1.5.5.7.3.2") + CODE_SIGNING = ObjectIdentifier("1.3.6.1.5.5.7.3.3") + EMAIL_PROTECTION = ObjectIdentifier("1.3.6.1.5.5.7.3.4") + TIME_STAMPING = ObjectIdentifier("1.3.6.1.5.5.7.3.8") + OCSP_SIGNING = ObjectIdentifier("1.3.6.1.5.5.7.3.9") + ANY_EXTENDED_KEY_USAGE = ObjectIdentifier("2.5.29.37.0") + SMARTCARD_LOGON = ObjectIdentifier("1.3.6.1.4.1.311.20.2.2") + KERBEROS_PKINIT_KDC = ObjectIdentifier("1.3.6.1.5.2.3.5") + IPSEC_IKE = ObjectIdentifier("1.3.6.1.5.5.7.3.17") + BUNDLE_SECURITY = ObjectIdentifier("1.3.6.1.5.5.7.3.35") + CERTIFICATE_TRANSPARENCY = ObjectIdentifier("1.3.6.1.4.1.11129.2.4.4") + + +class OtherNameFormOID: + PERMANENT_IDENTIFIER = ObjectIdentifier("1.3.6.1.5.5.7.8.3") + HW_MODULE_NAME = ObjectIdentifier("1.3.6.1.5.5.7.8.4") + DNS_SRV = ObjectIdentifier("1.3.6.1.5.5.7.8.7") + NAI_REALM = ObjectIdentifier("1.3.6.1.5.5.7.8.8") + SMTP_UTF8_MAILBOX = ObjectIdentifier("1.3.6.1.5.5.7.8.9") + ACP_NODE_NAME = ObjectIdentifier("1.3.6.1.5.5.7.8.10") + BUNDLE_EID = ObjectIdentifier("1.3.6.1.5.5.7.8.11") + + +class AuthorityInformationAccessOID: + CA_ISSUERS = ObjectIdentifier("1.3.6.1.5.5.7.48.2") + OCSP = ObjectIdentifier("1.3.6.1.5.5.7.48.1") + + +class SubjectInformationAccessOID: + CA_REPOSITORY = ObjectIdentifier("1.3.6.1.5.5.7.48.5") + + +class CertificatePoliciesOID: + CPS_QUALIFIER = ObjectIdentifier("1.3.6.1.5.5.7.2.1") + CPS_USER_NOTICE = ObjectIdentifier("1.3.6.1.5.5.7.2.2") + ANY_POLICY = ObjectIdentifier("2.5.29.32.0") + + +class AttributeOID: + CHALLENGE_PASSWORD = ObjectIdentifier("1.2.840.113549.1.9.7") + UNSTRUCTURED_NAME = ObjectIdentifier("1.2.840.113549.1.9.2") + + +_OID_NAMES = { + NameOID.COMMON_NAME: "commonName", + NameOID.COUNTRY_NAME: "countryName", + NameOID.LOCALITY_NAME: "localityName", + NameOID.STATE_OR_PROVINCE_NAME: "stateOrProvinceName", + NameOID.STREET_ADDRESS: "streetAddress", + NameOID.ORGANIZATION_NAME: "organizationName", + NameOID.ORGANIZATIONAL_UNIT_NAME: "organizationalUnitName", + NameOID.SERIAL_NUMBER: "serialNumber", + NameOID.SURNAME: "surname", + NameOID.GIVEN_NAME: "givenName", + NameOID.TITLE: "title", + NameOID.GENERATION_QUALIFIER: "generationQualifier", + NameOID.X500_UNIQUE_IDENTIFIER: "x500UniqueIdentifier", + NameOID.DN_QUALIFIER: "dnQualifier", + NameOID.PSEUDONYM: "pseudonym", + NameOID.USER_ID: "userID", + NameOID.DOMAIN_COMPONENT: "domainComponent", + NameOID.EMAIL_ADDRESS: "emailAddress", + NameOID.JURISDICTION_COUNTRY_NAME: "jurisdictionCountryName", + NameOID.JURISDICTION_LOCALITY_NAME: "jurisdictionLocalityName", + NameOID.JURISDICTION_STATE_OR_PROVINCE_NAME: ( + "jurisdictionStateOrProvinceName" + ), + NameOID.BUSINESS_CATEGORY: "businessCategory", + NameOID.POSTAL_ADDRESS: "postalAddress", + NameOID.POSTAL_CODE: "postalCode", + NameOID.INN: "INN", + NameOID.OGRN: "OGRN", + NameOID.SNILS: "SNILS", + NameOID.UNSTRUCTURED_NAME: "unstructuredName", + SignatureAlgorithmOID.RSA_WITH_MD5: "md5WithRSAEncryption", + SignatureAlgorithmOID.RSA_WITH_SHA1: "sha1WithRSAEncryption", + SignatureAlgorithmOID.RSA_WITH_SHA224: "sha224WithRSAEncryption", + SignatureAlgorithmOID.RSA_WITH_SHA256: "sha256WithRSAEncryption", + SignatureAlgorithmOID.RSA_WITH_SHA384: "sha384WithRSAEncryption", + SignatureAlgorithmOID.RSA_WITH_SHA512: "sha512WithRSAEncryption", + SignatureAlgorithmOID.RSASSA_PSS: "rsassaPss", + SignatureAlgorithmOID.ECDSA_WITH_SHA1: "ecdsa-with-SHA1", + SignatureAlgorithmOID.ECDSA_WITH_SHA224: "ecdsa-with-SHA224", + SignatureAlgorithmOID.ECDSA_WITH_SHA256: "ecdsa-with-SHA256", + SignatureAlgorithmOID.ECDSA_WITH_SHA384: "ecdsa-with-SHA384", + SignatureAlgorithmOID.ECDSA_WITH_SHA512: "ecdsa-with-SHA512", + SignatureAlgorithmOID.DSA_WITH_SHA1: "dsa-with-sha1", + SignatureAlgorithmOID.DSA_WITH_SHA224: "dsa-with-sha224", + SignatureAlgorithmOID.DSA_WITH_SHA256: "dsa-with-sha256", + SignatureAlgorithmOID.ED25519: "ed25519", + SignatureAlgorithmOID.ED448: "ed448", + SignatureAlgorithmOID.GOSTR3411_94_WITH_3410_2001: ( + "GOST R 34.11-94 with GOST R 34.10-2001" + ), + SignatureAlgorithmOID.GOSTR3410_2012_WITH_3411_2012_256: ( + "GOST R 34.10-2012 with GOST R 34.11-2012 (256 bit)" + ), + SignatureAlgorithmOID.GOSTR3410_2012_WITH_3411_2012_512: ( + "GOST R 34.10-2012 with GOST R 34.11-2012 (512 bit)" + ), + HashAlgorithmOID.SHA1: "sha1", + HashAlgorithmOID.SHA224: "sha224", + HashAlgorithmOID.SHA256: "sha256", + HashAlgorithmOID.SHA384: "sha384", + HashAlgorithmOID.SHA512: "sha512", + HashAlgorithmOID.SHA3_224: "sha3_224", + HashAlgorithmOID.SHA3_256: "sha3_256", + HashAlgorithmOID.SHA3_384: "sha3_384", + HashAlgorithmOID.SHA3_512: "sha3_512", + HashAlgorithmOID.SHA3_224_NIST: "sha3_224", + HashAlgorithmOID.SHA3_256_NIST: "sha3_256", + HashAlgorithmOID.SHA3_384_NIST: "sha3_384", + HashAlgorithmOID.SHA3_512_NIST: "sha3_512", + PublicKeyAlgorithmOID.DSA: "dsaEncryption", + PublicKeyAlgorithmOID.EC_PUBLIC_KEY: "id-ecPublicKey", + PublicKeyAlgorithmOID.RSAES_PKCS1_v1_5: "rsaEncryption", + PublicKeyAlgorithmOID.X25519: "X25519", + PublicKeyAlgorithmOID.X448: "X448", + ExtendedKeyUsageOID.SERVER_AUTH: "serverAuth", + ExtendedKeyUsageOID.CLIENT_AUTH: "clientAuth", + ExtendedKeyUsageOID.CODE_SIGNING: "codeSigning", + ExtendedKeyUsageOID.EMAIL_PROTECTION: "emailProtection", + ExtendedKeyUsageOID.TIME_STAMPING: "timeStamping", + ExtendedKeyUsageOID.OCSP_SIGNING: "OCSPSigning", + ExtendedKeyUsageOID.SMARTCARD_LOGON: "msSmartcardLogin", + ExtendedKeyUsageOID.KERBEROS_PKINIT_KDC: "pkInitKDC", + ExtensionOID.SUBJECT_DIRECTORY_ATTRIBUTES: "subjectDirectoryAttributes", + ExtensionOID.SUBJECT_KEY_IDENTIFIER: "subjectKeyIdentifier", + ExtensionOID.KEY_USAGE: "keyUsage", + ExtensionOID.PRIVATE_KEY_USAGE_PERIOD: "privateKeyUsagePeriod", + ExtensionOID.SUBJECT_ALTERNATIVE_NAME: "subjectAltName", + ExtensionOID.ISSUER_ALTERNATIVE_NAME: "issuerAltName", + ExtensionOID.BASIC_CONSTRAINTS: "basicConstraints", + ExtensionOID.PRECERT_SIGNED_CERTIFICATE_TIMESTAMPS: ( + "signedCertificateTimestampList" + ), + ExtensionOID.SIGNED_CERTIFICATE_TIMESTAMPS: ( + "signedCertificateTimestampList" + ), + ExtensionOID.PRECERT_POISON: "ctPoison", + ExtensionOID.MS_CERTIFICATE_TEMPLATE: "msCertificateTemplate", + ExtensionOID.ADMISSIONS: "Admissions", + CRLEntryExtensionOID.CRL_REASON: "cRLReason", + CRLEntryExtensionOID.INVALIDITY_DATE: "invalidityDate", + CRLEntryExtensionOID.CERTIFICATE_ISSUER: "certificateIssuer", + ExtensionOID.NAME_CONSTRAINTS: "nameConstraints", + ExtensionOID.CRL_DISTRIBUTION_POINTS: "cRLDistributionPoints", + ExtensionOID.CERTIFICATE_POLICIES: "certificatePolicies", + ExtensionOID.POLICY_MAPPINGS: "policyMappings", + ExtensionOID.AUTHORITY_KEY_IDENTIFIER: "authorityKeyIdentifier", + ExtensionOID.POLICY_CONSTRAINTS: "policyConstraints", + ExtensionOID.EXTENDED_KEY_USAGE: "extendedKeyUsage", + ExtensionOID.FRESHEST_CRL: "freshestCRL", + ExtensionOID.INHIBIT_ANY_POLICY: "inhibitAnyPolicy", + ExtensionOID.ISSUING_DISTRIBUTION_POINT: "issuingDistributionPoint", + ExtensionOID.AUTHORITY_INFORMATION_ACCESS: "authorityInfoAccess", + ExtensionOID.SUBJECT_INFORMATION_ACCESS: "subjectInfoAccess", + ExtensionOID.OCSP_NO_CHECK: "OCSPNoCheck", + ExtensionOID.CRL_NUMBER: "cRLNumber", + ExtensionOID.DELTA_CRL_INDICATOR: "deltaCRLIndicator", + ExtensionOID.TLS_FEATURE: "TLSFeature", + AuthorityInformationAccessOID.OCSP: "OCSP", + AuthorityInformationAccessOID.CA_ISSUERS: "caIssuers", + SubjectInformationAccessOID.CA_REPOSITORY: "caRepository", + CertificatePoliciesOID.CPS_QUALIFIER: "id-qt-cps", + CertificatePoliciesOID.CPS_USER_NOTICE: "id-qt-unotice", + OCSPExtensionOID.NONCE: "OCSPNonce", + AttributeOID.CHALLENGE_PASSWORD: "challengePassword", +} diff --git a/lib/python3.11/site-packages/cryptography/hazmat/asn1/__init__.py b/lib/python3.11/site-packages/cryptography/hazmat/asn1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..ac3d4bd8590b56f20ac1d9e8cd68221f47cce16b --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/asn1/__init__.py @@ -0,0 +1,43 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from cryptography.hazmat.asn1.asn1 import ( + TLV, + BitString, + Default, + Explicit, + GeneralizedTime, + IA5String, + Implicit, + Null, + PrintableString, + SetOf, + Size, + UTCTime, + Variant, + decode_der, + encode_der, + sequence, + set, +) + +__all__ = [ + "TLV", + "BitString", + "Default", + "Explicit", + "GeneralizedTime", + "IA5String", + "Implicit", + "Null", + "PrintableString", + "SetOf", + "Size", + "UTCTime", + "Variant", + "decode_der", + "encode_der", + "sequence", + "set", +] diff --git a/lib/python3.11/site-packages/cryptography/hazmat/asn1/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/cryptography/hazmat/asn1/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f1db7a754ecc7eee32819d72fc097ddb848c1348 Binary files /dev/null and b/lib/python3.11/site-packages/cryptography/hazmat/asn1/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/cryptography/hazmat/asn1/__pycache__/asn1.cpython-311.pyc b/lib/python3.11/site-packages/cryptography/hazmat/asn1/__pycache__/asn1.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a1bec61498f88b37b8cab6ea52bceadf5530445f Binary files /dev/null and b/lib/python3.11/site-packages/cryptography/hazmat/asn1/__pycache__/asn1.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/cryptography/hazmat/asn1/asn1.py b/lib/python3.11/site-packages/cryptography/hazmat/asn1/asn1.py new file mode 100644 index 0000000000000000000000000000000000000000..aa18d7e5aa5206a78745ba549ed2a7b2fc8563fe --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/asn1/asn1.py @@ -0,0 +1,419 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +import builtins +import dataclasses +import sys +import types +import typing + +if sys.version_info < (3, 11): + import typing_extensions + + LiteralString = typing_extensions.LiteralString +else: + LiteralString = typing.LiteralString + +from cryptography.hazmat.bindings._rust import declarative_asn1 + +if sys.version_info < (3, 10): + NoneType = type(None) +else: + NoneType = types.NoneType # type: ignore[nonetype-type] + +T = typing.TypeVar("T", covariant=True) +U = typing.TypeVar("U") +Tag = typing.TypeVar("Tag", bound=LiteralString) + + +@dataclasses.dataclass(frozen=True) +class Variant(typing.Generic[U, Tag]): + """ + A tagged variant for CHOICE fields with the same underlying type. + + Use this when you have multiple CHOICE alternatives with the same type + and need to distinguish between them: + + foo: ( + Annotated[Variant[int, typing.Literal["IntA"]], Implicit(0)] + | Annotated[Variant[int, typing.Literal["IntB"]], Implicit(1)] + ) + + Usage: + example = Example(foo=Variant(5, "IntA")) + decoded.foo.value # The int value + decoded.foo.tag # "IntA" or "IntB" + """ + + value: U + tag: str + + +decode_der = declarative_asn1.decode_der +encode_der = declarative_asn1.encode_der + + +def _is_union(field_type: type) -> bool: + # NOTE: types.UnionType for `T | U`, typing.Union for `Union[T, U]`. + # TODO: Drop the `hasattr()` once the minimum supported Python version + # is >= 3.10. + union_types = ( + (types.UnionType, typing.Union) + if hasattr(types, "UnionType") + else (typing.Union,) + ) + return typing.get_origin(field_type) in union_types + + +def _extract_annotation( + metadata: tuple, field_name: str +) -> declarative_asn1.Annotation: + default = None + encoding = None + size = None + for raw_annotation in metadata: + if isinstance(raw_annotation, Default): + if default is not None: + raise TypeError( + f"multiple DEFAULT annotations found in field " + f"'{field_name}'" + ) + default = raw_annotation.value + elif isinstance(raw_annotation, declarative_asn1.Encoding): + if encoding is not None: + raise TypeError( + f"multiple IMPLICIT/EXPLICIT annotations found in field " + f"'{field_name}'" + ) + encoding = raw_annotation + elif isinstance(raw_annotation, declarative_asn1.Size): + if size is not None: + raise TypeError( + f"multiple SIZE annotations found in field '{field_name}'" + ) + size = raw_annotation + else: + raise TypeError(f"unsupported annotation: {raw_annotation}") + + return declarative_asn1.Annotation( + default=default, encoding=encoding, size=size + ) + + +def _normalize_field_type( + field_type: typing.Any, field_name: str +) -> declarative_asn1.AnnotatedType: + # Strip the `Annotated[...]` off, and populate the annotation + # from it if it exists. + if typing.get_origin(field_type) is typing.Annotated: + annotation = _extract_annotation(field_type.__metadata__, field_name) + field_type, *_ = typing.get_args(field_type) + else: + annotation = declarative_asn1.Annotation() + + if annotation.size is not None and ( + typing.get_origin(field_type) not in (builtins.list, SetOf) + and field_type + not in ( + builtins.bytes, + builtins.str, + BitString, + IA5String, + PrintableString, + ) + ): + raise TypeError( + f"field '{field_name}' has a SIZE annotation, but SIZE " + "annotations are only supported for fields of types: " + "[SEQUENCE OF, SET OF, BIT STRING, OCTET STRING, UTF8String, " + "PrintableString, IA5String]" + ) + + if field_type is TLV: + if isinstance(annotation.encoding, Implicit): + raise TypeError( + f"field '{field_name}' has an IMPLICIT annotation, but " + "IMPLICIT annotations are not supported for TLV types." + ) + elif annotation.default is not None: + raise TypeError( + f"field '{field_name}' has a DEFAULT annotation, but " + "DEFAULT annotations are not supported for TLV types." + ) + + if hasattr(field_type, "__asn1_root__"): + root_type = field_type.__asn1_root__ + if not isinstance( + root_type, + (declarative_asn1.Type.Sequence, declarative_asn1.Type.Set), + ): + raise TypeError(f"unsupported root type: {root_type}") + return declarative_asn1.AnnotatedType( + typing.cast(declarative_asn1.Type, root_type), annotation + ) + elif _is_union(field_type): + union_args = typing.get_args(field_type) + if len(union_args) == 2 and NoneType in union_args: + # A Union between a type and None is an OPTIONAL + optional_type = ( + union_args[0] if union_args[1] is type(None) else union_args[1] + ) + if optional_type is TLV: + raise TypeError( + "optional TLV types (`TLV | None`) are not " + "currently supported" + ) + annotated_type = _normalize_field_type(optional_type, field_name) + + if not annotated_type.annotation.is_empty(): + raise TypeError( + "optional (`X | None`) types cannot have `X` " + "annotated: annotations must apply to the union " + "(i.e: `Annotated[X | None, annotation]`)" + ) + + if annotation.default is not None: + raise TypeError( + "optional (`X | None`) types should not have a DEFAULT " + "annotation" + ) + + rust_field_type = declarative_asn1.Type.Option(annotated_type) + + else: + # Otherwise, the Union is a CHOICE + if isinstance(annotation.encoding, Implicit): + # CHOICEs cannot be IMPLICIT. See X.680 section 31.2.9. + raise TypeError( + "CHOICE (`X | Y | ...`) types should not have an IMPLICIT " + "annotation" + ) + variants = [ + _type_to_variant(arg, field_name) + for arg in union_args + if arg is not type(None) + ] + + # Union types should either be all Variants + # (`Variant[..] | Variant[..] | etc`) or all non Variants + are_union_types_tagged = variants[0].tag_name is not None + if any( + (v.tag_name is not None) != are_union_types_tagged + for v in variants + ): + raise TypeError( + "When using `asn1.Variant` in a union, all the other " + "types in the union must also be `asn1.Variant`" + ) + + if are_union_types_tagged: + tags = {v.tag_name for v in variants} + if len(variants) != len(tags): + raise TypeError( + "When using `asn1.Variant` in a union, the tags used " + "must be unique" + ) + + rust_choice_type = declarative_asn1.Type.Choice(variants) + # If None is part of the union types, this is an OPTIONAL CHOICE + rust_field_type = ( + declarative_asn1.Type.Option( + declarative_asn1.AnnotatedType( + rust_choice_type, declarative_asn1.Annotation() + ) + ) + if NoneType in union_args + else rust_choice_type + ) + + elif typing.get_origin(field_type) is builtins.list: + inner_type = _normalize_field_type( + typing.get_args(field_type)[0], field_name + ) + rust_field_type = declarative_asn1.Type.SequenceOf(inner_type) + elif typing.get_origin(field_type) is SetOf: + inner_type = _normalize_field_type( + typing.get_args(field_type)[0], field_name + ) + rust_field_type = declarative_asn1.Type.SetOf(inner_type) + else: + rust_field_type = declarative_asn1.non_root_python_to_rust(field_type) + + return declarative_asn1.AnnotatedType(rust_field_type, annotation) + + +# Convert a type to a Variant. Used with types inside Union +# annotations (T1, T2, etc in `Union[T1, T2, ...]`). +def _type_to_variant( + t: typing.Any, field_name: str +) -> declarative_asn1.Variant: + is_annotated = typing.get_origin(t) is typing.Annotated + inner_type = typing.get_args(t)[0] if is_annotated else t + + # Check if this is a Variant[T, Tag] type + if typing.get_origin(inner_type) is Variant: + value_type, tag_literal = typing.get_args(inner_type) + if typing.get_origin(tag_literal) is not typing.Literal: + raise TypeError( + "When using `asn1.Variant` in a type annotation, the second " + "type parameter must be a `typing.Literal` type. E.g: " + '`Variant[int, typing.Literal["MyInt"]]`.' + ) + tag_name = typing.get_args(tag_literal)[0] + + if hasattr(value_type, "__asn1_root__"): + rust_type = value_type.__asn1_root__ + else: + rust_type = declarative_asn1.non_root_python_to_rust(value_type) + + if is_annotated: + ann_type = declarative_asn1.AnnotatedType( + rust_type, + _extract_annotation(t.__metadata__, field_name), + ) + else: + ann_type = declarative_asn1.AnnotatedType( + rust_type, + declarative_asn1.Annotation(), + ) + + return declarative_asn1.Variant(Variant, ann_type, tag_name) + else: + # Plain type (not a tagged Variant) + return declarative_asn1.Variant( + inner_type, + _normalize_field_type(t, field_name), + None, + ) + + +def _annotate_fields( + raw_fields: dict[str, type], +) -> dict[str, declarative_asn1.AnnotatedType]: + fields = {} + for field_name, field_type in raw_fields.items(): + # Recursively normalize the field type into something that the + # Rust code can understand. + annotated_field_type = _normalize_field_type(field_type, field_name) + fields[field_name] = annotated_field_type + + return fields + + +def _register_asn1_sequence(cls: type[U]) -> None: + raw_fields = typing.get_type_hints(cls, include_extras=True) + root = declarative_asn1.Type.Sequence(cls, _annotate_fields(raw_fields)) + + setattr(cls, "__asn1_root__", root) + + +def _register_asn1_set(cls: type[U]) -> None: + raw_fields = typing.get_type_hints(cls, include_extras=True) + root = declarative_asn1.Type.Set(cls, _annotate_fields(raw_fields)) + + setattr(cls, "__asn1_root__", root) + + +# Due to https://github.com/python/mypy/issues/19731, we can't define an alias +# for `dataclass_transform` that conditionally points to `typing` or +# `typing_extensions` depending on the Python version. We work around it by +# making the whole decorated class conditional on the Python version. +if sys.version_info < (3, 11): + + @typing_extensions.dataclass_transform(kw_only_default=True) + def sequence(cls: type[U]) -> type[U]: + # We use `dataclasses.dataclass` to add an __init__ method + # to the class with keyword-only parameters. + if sys.version_info >= (3, 10): + dataclass_cls = dataclasses.dataclass( + repr=False, + eq=False, + # `match_args` was added in Python 3.10 and defaults + # to True + match_args=False, + # `kw_only` was added in Python 3.10 and defaults to + # False + kw_only=True, + )(cls) + else: + dataclass_cls = dataclasses.dataclass( + repr=False, + eq=False, + )(cls) + _register_asn1_sequence(dataclass_cls) + return dataclass_cls + + @typing_extensions.dataclass_transform(kw_only_default=True) + def set(cls: type[U]) -> type[U]: + # We use `dataclasses.dataclass` to add an __init__ method + # to the class with keyword-only parameters. + if sys.version_info >= (3, 10): + dataclass_cls = dataclasses.dataclass( + repr=False, + eq=False, + # `match_args` was added in Python 3.10 and defaults + # to True + match_args=False, + # `kw_only` was added in Python 3.10 and defaults to + # False + kw_only=True, + )(cls) + else: + dataclass_cls = dataclasses.dataclass( + repr=False, + eq=False, + )(cls) + _register_asn1_set(dataclass_cls) + return dataclass_cls + +else: + + @typing.dataclass_transform(kw_only_default=True) + def sequence(cls: type[U]) -> type[U]: + # Only add an __init__ method, with keyword-only + # parameters. + dataclass_cls = dataclasses.dataclass( + repr=False, + eq=False, + match_args=False, + kw_only=True, + )(cls) + _register_asn1_sequence(dataclass_cls) + return dataclass_cls + + @typing.dataclass_transform(kw_only_default=True) + def set(cls: type[U]) -> type[U]: + # Only add an __init__ method, with keyword-only + # parameters. + dataclass_cls = dataclasses.dataclass( + repr=False, + eq=False, + match_args=False, + kw_only=True, + )(cls) + _register_asn1_set(dataclass_cls) + return dataclass_cls + + +# TODO: replace with `Default[U]` once the min Python version is >= 3.12 +@dataclasses.dataclass(frozen=True) +class Default(typing.Generic[U]): + value: U + + +SetOf = declarative_asn1.SetOf + +Explicit = declarative_asn1.Encoding.Explicit +Implicit = declarative_asn1.Encoding.Implicit +Size = declarative_asn1.Size + +PrintableString = declarative_asn1.PrintableString +IA5String = declarative_asn1.IA5String +UTCTime = declarative_asn1.UTCTime +GeneralizedTime = declarative_asn1.GeneralizedTime +BitString = declarative_asn1.BitString +TLV = declarative_asn1.Tlv +Null = declarative_asn1.Null diff --git a/lib/python3.11/site-packages/cryptography/hazmat/backends/__init__.py b/lib/python3.11/site-packages/cryptography/hazmat/backends/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b4400aa037451d10d6bcadd455dcf98f9b72e221 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/backends/__init__.py @@ -0,0 +1,13 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +from typing import Any + + +def default_backend() -> Any: + from cryptography.hazmat.backends.openssl.backend import backend + + return backend diff --git a/lib/python3.11/site-packages/cryptography/hazmat/backends/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/cryptography/hazmat/backends/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b06e0622507d53b256e0cd6f524346dd1332e854 Binary files /dev/null and b/lib/python3.11/site-packages/cryptography/hazmat/backends/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/__init__.py b/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..51b04476cbb7f4a98051f2fc55bcc1aa35e22d05 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/__init__.py @@ -0,0 +1,9 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +from cryptography.hazmat.backends.openssl.backend import backend + +__all__ = ["backend"] diff --git a/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a4f697456e865c98c4c80533d4538082eb30e8b6 Binary files /dev/null and b/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/__pycache__/backend.cpython-311.pyc b/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/__pycache__/backend.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eec44d5af45365ffd9f1c70ef616b67f4ba4a470 Binary files /dev/null and b/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/__pycache__/backend.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/backend.py b/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/backend.py new file mode 100644 index 0000000000000000000000000000000000000000..ad64a2afd3688dcb6de74c939137a311e978aecc --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/backends/openssl/backend.py @@ -0,0 +1,312 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +from cryptography.hazmat.bindings._rust import openssl as rust_openssl +from cryptography.hazmat.bindings.openssl import binding +from cryptography.hazmat.primitives import hashes +from cryptography.hazmat.primitives._asymmetric import AsymmetricPadding +from cryptography.hazmat.primitives.asymmetric import ec +from cryptography.hazmat.primitives.asymmetric import utils as asym_utils +from cryptography.hazmat.primitives.asymmetric.padding import ( + MGF1, + OAEP, + PSS, + PKCS1v15, +) +from cryptography.hazmat.primitives.ciphers import ( + CipherAlgorithm, +) +from cryptography.hazmat.primitives.ciphers.algorithms import ( + AES, +) +from cryptography.hazmat.primitives.ciphers.modes import ( + CBC, + Mode, +) + + +class Backend: + """ + OpenSSL API binding interfaces. + """ + + name = "openssl" + + # TripleDES encryption is disallowed/deprecated throughout 2023 in + # FIPS 140-3. To keep it simple we denylist any use of TripleDES (TDEA). + _fips_ciphers = (AES,) + # Sometimes SHA1 is still permissible. That logic is contained + # within the various *_supported methods. + _fips_hashes = ( + hashes.SHA224, + hashes.SHA256, + hashes.SHA384, + hashes.SHA512, + hashes.SHA512_224, + hashes.SHA512_256, + hashes.SHA3_224, + hashes.SHA3_256, + hashes.SHA3_384, + hashes.SHA3_512, + hashes.SHAKE128, + hashes.SHAKE256, + ) + _fips_ecdh_curves = ( + ec.SECP224R1, + ec.SECP256R1, + ec.SECP384R1, + ec.SECP521R1, + ) + _fips_rsa_min_key_size = 2048 + _fips_rsa_min_public_exponent = 65537 + _fips_dsa_min_modulus = 1 << 2048 + _fips_dh_min_key_size = 2048 + _fips_dh_min_modulus = 1 << _fips_dh_min_key_size + + def __init__(self) -> None: + self._binding = binding.Binding() + self._ffi = self._binding.ffi + self._lib = self._binding.lib + self._fips_enabled = rust_openssl.is_fips_enabled() + + def __repr__(self) -> str: + return ( + f"" + ) + + def openssl_assert(self, ok: bool) -> None: + return binding._openssl_assert(ok) + + def _enable_fips(self) -> None: + # This function enables FIPS mode for OpenSSL 3.0.0 on installs that + # have the FIPS provider installed properly. + rust_openssl.enable_fips(rust_openssl._providers) + assert rust_openssl.is_fips_enabled() + self._fips_enabled = rust_openssl.is_fips_enabled() + + def openssl_version_text(self) -> str: + """ + Friendly string name of the loaded OpenSSL library. This is not + necessarily the same version as it was compiled against. + + Example: OpenSSL 3.2.1 30 Jan 2024 + """ + return rust_openssl.openssl_version_text() + + def openssl_version_number(self) -> int: + return rust_openssl.openssl_version() + + def hash_supported(self, algorithm: hashes.HashAlgorithm) -> bool: + if self._fips_enabled and not isinstance(algorithm, self._fips_hashes): + return False + + return rust_openssl.hashes.hash_supported(algorithm) + + def signature_hash_supported( + self, algorithm: hashes.HashAlgorithm + ) -> bool: + # Dedicated check for hashing algorithm use in message digest for + # signatures, e.g. RSA PKCS#1 v1.5 SHA1 (sha1WithRSAEncryption). + if self._fips_enabled and isinstance(algorithm, hashes.SHA1): + return False + return self.hash_supported(algorithm) + + def scrypt_supported(self) -> bool: + if self._fips_enabled: + return False + else: + return hasattr(rust_openssl.kdf.Scrypt, "derive") + + def argon2_supported(self) -> bool: + if self._fips_enabled: + return False + else: + return hasattr(rust_openssl.kdf.Argon2id, "derive") + + def hmac_supported(self, algorithm: hashes.HashAlgorithm) -> bool: + # FIPS mode still allows SHA1 for HMAC + if self._fips_enabled and isinstance(algorithm, hashes.SHA1): + return True + if rust_openssl.CRYPTOGRAPHY_IS_AWSLC: + return isinstance( + algorithm, + ( + hashes.MD5, + hashes.SHA1, + hashes.SHA224, + hashes.SHA256, + hashes.SHA384, + hashes.SHA512, + hashes.SHA512_224, + hashes.SHA512_256, + ), + ) + return self.hash_supported(algorithm) + + def cipher_supported(self, cipher: CipherAlgorithm, mode: Mode) -> bool: + if self._fips_enabled: + # FIPS mode requires AES. TripleDES is disallowed/deprecated in + # FIPS 140-3. + if not isinstance(cipher, self._fips_ciphers): + return False + + return rust_openssl.ciphers.cipher_supported(cipher, mode) + + def pbkdf2_hmac_supported(self, algorithm: hashes.HashAlgorithm) -> bool: + return self.hmac_supported(algorithm) + + def _consume_errors(self) -> list[rust_openssl.OpenSSLError]: + return rust_openssl.capture_error_stack() + + def _oaep_hash_supported(self, algorithm: hashes.HashAlgorithm) -> bool: + if self._fips_enabled and isinstance(algorithm, hashes.SHA1): + return False + + return isinstance( + algorithm, + ( + hashes.SHA1, + hashes.SHA224, + hashes.SHA256, + hashes.SHA384, + hashes.SHA512, + ), + ) + + def rsa_padding_supported(self, padding: AsymmetricPadding) -> bool: + if isinstance(padding, PKCS1v15): + return True + elif isinstance(padding, PSS) and isinstance(padding._mgf, MGF1): + # FIPS 186-4 only allows salt length == digest length for PSS + # It is technically acceptable to set an explicit salt length + # equal to the digest length and this will incorrectly fail, but + # since we don't do that in the tests and this method is + # private, we'll ignore that until we need to do otherwise. + if ( + self._fips_enabled + and padding._salt_length != PSS.DIGEST_LENGTH + ): + return False + return self.hash_supported(padding._mgf._algorithm) + elif isinstance(padding, OAEP) and isinstance(padding._mgf, MGF1): + return self._oaep_hash_supported( + padding._mgf._algorithm + ) and self._oaep_hash_supported(padding._algorithm) + else: + return False + + def rsa_encryption_supported(self, padding: AsymmetricPadding) -> bool: + if self._fips_enabled and isinstance(padding, PKCS1v15): + return False + else: + return self.rsa_padding_supported(padding) + + def dsa_supported(self) -> bool: + return ( + not rust_openssl.CRYPTOGRAPHY_IS_BORINGSSL + and not self._fips_enabled + ) + + def dsa_hash_supported(self, algorithm: hashes.HashAlgorithm) -> bool: + if not self.dsa_supported(): + return False + return self.signature_hash_supported(algorithm) + + def cmac_algorithm_supported(self, algorithm) -> bool: + return self.cipher_supported( + algorithm, CBC(b"\x00" * algorithm.block_size) + ) + + def elliptic_curve_supported(self, curve: ec.EllipticCurve) -> bool: + if self._fips_enabled and not isinstance( + curve, self._fips_ecdh_curves + ): + return False + + return rust_openssl.ec.curve_supported(curve) + + def elliptic_curve_signature_algorithm_supported( + self, + signature_algorithm: ec.EllipticCurveSignatureAlgorithm, + curve: ec.EllipticCurve, + ) -> bool: + # We only support ECDSA right now. + if not isinstance(signature_algorithm, ec.ECDSA): + return False + + return self.elliptic_curve_supported(curve) and ( + isinstance(signature_algorithm.algorithm, asym_utils.Prehashed) + or self.hash_supported(signature_algorithm.algorithm) + ) + + def elliptic_curve_exchange_algorithm_supported( + self, algorithm: ec.ECDH, curve: ec.EllipticCurve + ) -> bool: + return self.elliptic_curve_supported(curve) and isinstance( + algorithm, ec.ECDH + ) + + def dh_supported(self) -> bool: + return ( + not rust_openssl.CRYPTOGRAPHY_IS_BORINGSSL + and not rust_openssl.CRYPTOGRAPHY_IS_AWSLC + ) + + def dh_x942_serialization_supported(self) -> bool: + return self._lib.Cryptography_HAS_EVP_PKEY_DHX == 1 + + def x25519_supported(self) -> bool: + return not self._fips_enabled + + def x448_supported(self) -> bool: + if self._fips_enabled: + return False + return ( + not rust_openssl.CRYPTOGRAPHY_IS_LIBRESSL + and not rust_openssl.CRYPTOGRAPHY_IS_BORINGSSL + and not rust_openssl.CRYPTOGRAPHY_IS_AWSLC + ) + + def mlkem_supported(self) -> bool: + return ( + rust_openssl.CRYPTOGRAPHY_IS_AWSLC + or rust_openssl.CRYPTOGRAPHY_IS_BORINGSSL + or rust_openssl.CRYPTOGRAPHY_OPENSSL_350_OR_GREATER + ) + + def mldsa_supported(self) -> bool: + return ( + rust_openssl.CRYPTOGRAPHY_IS_AWSLC + or rust_openssl.CRYPTOGRAPHY_IS_BORINGSSL + or rust_openssl.CRYPTOGRAPHY_OPENSSL_350_OR_GREATER + ) + + def ed25519_supported(self) -> bool: + return True + + def ed448_supported(self) -> bool: + return ( + not rust_openssl.CRYPTOGRAPHY_IS_LIBRESSL + and not rust_openssl.CRYPTOGRAPHY_IS_BORINGSSL + and not rust_openssl.CRYPTOGRAPHY_IS_AWSLC + ) + + def ecdsa_deterministic_supported(self) -> bool: + return ( + rust_openssl.CRYPTOGRAPHY_OPENSSL_320_OR_GREATER + and not self._fips_enabled + ) + + def poly1305_supported(self) -> bool: + return not self._fips_enabled + + def pkcs7_supported(self) -> bool: + return True + + +backend = Backend() diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/__init__.py b/lib/python3.11/site-packages/cryptography/hazmat/bindings/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..b509336233c2fafe4185a49da5909c8bbb38dfd7 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/__init__.py @@ -0,0 +1,3 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/__init__.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c3148f12bd0422e88b6abddc98016534d512a14c --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/__init__.pyi @@ -0,0 +1,67 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import typing + +from cryptography.hazmat.primitives import padding +from cryptography.hazmat.primitives._serialization import ( + KeySerializationEncryptionBuilder, +) +from cryptography.utils import Buffer + +class PKCS7PaddingContext(padding.PaddingContext): + def __init__(self, block_size: int) -> None: ... + def update(self, data: Buffer) -> bytes: ... + def finalize(self) -> bytes: ... + +class ANSIX923PaddingContext(padding.PaddingContext): + def __init__(self, block_size: int) -> None: ... + def update(self, data: Buffer) -> bytes: ... + def finalize(self) -> bytes: ... + +class PKCS7UnpaddingContext(padding.PaddingContext): + def __init__(self, block_size: int) -> None: ... + def update(self, data: Buffer) -> bytes: ... + def finalize(self) -> bytes: ... + +class ANSIX923UnpaddingContext(padding.PaddingContext): + def __init__(self, block_size: int) -> None: ... + def update(self, data: Buffer) -> bytes: ... + def finalize(self) -> bytes: ... + +class Encoding: + PEM: typing.ClassVar[Encoding] + DER: typing.ClassVar[Encoding] + OpenSSH: typing.ClassVar[Encoding] + Raw: typing.ClassVar[Encoding] + X962: typing.ClassVar[Encoding] + SMIME: typing.ClassVar[Encoding] + +class PrivateFormat: + PKCS8: typing.ClassVar[PrivateFormat] + TraditionalOpenSSL: typing.ClassVar[PrivateFormat] + Raw: typing.ClassVar[PrivateFormat] + OpenSSH: typing.ClassVar[PrivateFormat] + PKCS12: typing.ClassVar[PrivateFormat] + def encryption_builder(self) -> KeySerializationEncryptionBuilder: ... + +class PublicFormat: + SubjectPublicKeyInfo: typing.ClassVar[PublicFormat] + PKCS1: typing.ClassVar[PublicFormat] + OpenSSH: typing.ClassVar[PublicFormat] + Raw: typing.ClassVar[PublicFormat] + CompressedPoint: typing.ClassVar[PublicFormat] + UncompressedPoint: typing.ClassVar[PublicFormat] + +class ParameterFormat: + PKCS3: typing.ClassVar[ParameterFormat] + +class ObjectIdentifier: + def __init__(self, value: str) -> None: ... + @property + def dotted_string(self) -> str: ... + @property + def _name(self) -> str: ... + +T = typing.TypeVar("T") diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/_openssl.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/_openssl.pyi new file mode 100644 index 0000000000000000000000000000000000000000..3d4ea4eb2061c7c16a2e31af216367133e55c790 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/_openssl.pyi @@ -0,0 +1,8 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import typing + +lib: typing.Any +ffi: typing.Any diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/asn1.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/asn1.pyi new file mode 100644 index 0000000000000000000000000000000000000000..3b5f208ecf09aa661854e6983507b0e7b1ef15cb --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/asn1.pyi @@ -0,0 +1,7 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +def decode_dss_signature(signature: bytes) -> tuple[int, int]: ... +def encode_dss_signature(r: int, s: int) -> bytes: ... +def parse_spki_for_data(data: bytes) -> bytes: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/declarative_asn1.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/declarative_asn1.pyi new file mode 100644 index 0000000000000000000000000000000000000000..a4c0cb6d66cd2c62f44965134399405468a63c0e --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/declarative_asn1.pyi @@ -0,0 +1,127 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import datetime +import typing + +def decode_der(cls: type, value: bytes) -> typing.Any: ... +def encode_der(value: typing.Any) -> bytes: ... +def non_root_python_to_rust(cls: type) -> Type: ... + +# Type is a Rust enum with tuple variants. For now, we express the type +# annotations like this: +class Type: + Sequence: typing.ClassVar[type] + SequenceOf: typing.ClassVar[type] + Set: typing.ClassVar[type] + SetOf: typing.ClassVar[type] + Option: typing.ClassVar[type] + Choice: typing.ClassVar[type] + PyBool: typing.ClassVar[type] + PyInt: typing.ClassVar[type] + PyBytes: typing.ClassVar[type] + PyStr: typing.ClassVar[type] + +class Annotation: + default: typing.Any | None + encoding: Encoding | None + size: Size | None + def __new__( + cls, + default: typing.Any | None = None, + encoding: Encoding | None = None, + size: Size | None = None, + ) -> Annotation: ... + def is_empty(self) -> bool: ... + +# Encoding is a Rust enum with tuple variants. For now, we express the type +# annotations like this: +class Encoding: + Implicit: typing.ClassVar[type] + Explicit: typing.ClassVar[type] + +class Size: + min: int + max: int | None + + def __new__(cls, min: int, max: int | None) -> Size: ... + @staticmethod + def exact(n: int) -> Size: ... + +class AnnotatedType: + inner: Type + annotation: Annotation + + def __new__(cls, inner: Type, annotation: Annotation) -> AnnotatedType: ... + +class AnnotatedTypeObject: + annotated_type: AnnotatedType + value: typing.Any + + def __new__( + cls, annotated_type: AnnotatedType, value: typing.Any + ) -> AnnotatedTypeObject: ... + +class Variant: + python_class: type + ann_type: AnnotatedType + tag_name: str | None + + def __new__( + cls, + python_class: type, + ann_type: AnnotatedType, + tag_name: str | None, + ) -> Variant: ... + +class PrintableString: + def __new__(cls, inner: str) -> PrintableString: ... + def __repr__(self) -> str: ... + def __eq__(self, other: object) -> bool: ... + def as_str(self) -> str: ... + +class IA5String: + def __new__(cls, inner: str) -> IA5String: ... + def __repr__(self) -> str: ... + def __eq__(self, other: object) -> bool: ... + def as_str(self) -> str: ... + +class UTCTime: + def __new__(cls, inner: datetime.datetime) -> UTCTime: ... + def __repr__(self) -> str: ... + def __eq__(self, other: object) -> bool: ... + def as_datetime(self) -> datetime.datetime: ... + +class GeneralizedTime: + def __new__(cls, inner: datetime.datetime) -> GeneralizedTime: ... + def __repr__(self) -> str: ... + def __eq__(self, other: object) -> bool: ... + def as_datetime(self) -> datetime.datetime: ... + +class BitString: + def __new__(cls, data: bytes, padding_bits: int) -> BitString: ... + def __repr__(self) -> str: ... + def __eq__(self, other: object) -> bool: ... + def as_bytes(self) -> bytes: ... + def padding_bits(self) -> int: ... + +class Tlv: + @property + def tag_bytes(self) -> bytes: ... + @property + def data(self) -> memoryview: ... + def parse(self, cls: type): ... + +T = typing.TypeVar("T") + +class SetOf(typing.Generic[T]): + def __new__(cls, inner: list[T]) -> SetOf[T]: ... + def as_list(self) -> list[T]: ... + def __eq__(self, other: object) -> bool: ... + def __repr__(self) -> str: ... + +class Null: + def __new__(cls) -> Null: ... + def __repr__(self) -> str: ... + def __eq__(self, other: object) -> bool: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/exceptions.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/exceptions.pyi new file mode 100644 index 0000000000000000000000000000000000000000..09f46b1e817f806873c4a4bdd7b8466046b5f64d --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/exceptions.pyi @@ -0,0 +1,17 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +class _Reasons: + BACKEND_MISSING_INTERFACE: _Reasons + UNSUPPORTED_HASH: _Reasons + UNSUPPORTED_CIPHER: _Reasons + UNSUPPORTED_PADDING: _Reasons + UNSUPPORTED_MGF: _Reasons + UNSUPPORTED_PUBLIC_KEY_ALGORITHM: _Reasons + UNSUPPORTED_ELLIPTIC_CURVE: _Reasons + UNSUPPORTED_SERIALIZATION: _Reasons + UNSUPPORTED_X509: _Reasons + UNSUPPORTED_EXCHANGE_ALGORITHM: _Reasons + UNSUPPORTED_DIFFIE_HELLMAN: _Reasons + UNSUPPORTED_MAC: _Reasons diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/ocsp.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/ocsp.pyi new file mode 100644 index 0000000000000000000000000000000000000000..103e96c1f117ed06924569fadb6a2b680fbd5456 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/ocsp.pyi @@ -0,0 +1,117 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import datetime +from collections.abc import Iterator + +from cryptography import x509 +from cryptography.hazmat.primitives import hashes, serialization +from cryptography.hazmat.primitives.asymmetric.types import PrivateKeyTypes +from cryptography.x509 import ocsp + +class OCSPRequest: + @property + def issuer_key_hash(self) -> bytes: ... + @property + def issuer_name_hash(self) -> bytes: ... + @property + def hash_algorithm(self) -> hashes.HashAlgorithm: ... + @property + def serial_number(self) -> int: ... + def public_bytes(self, encoding: serialization.Encoding) -> bytes: ... + @property + def extensions(self) -> x509.Extensions: ... + +class OCSPResponse: + @property + def responses(self) -> Iterator[OCSPSingleResponse]: ... + @property + def response_status(self) -> ocsp.OCSPResponseStatus: ... + @property + def signature_algorithm_oid(self) -> x509.ObjectIdentifier: ... + @property + def signature_hash_algorithm( + self, + ) -> hashes.HashAlgorithm | None: ... + @property + def signature(self) -> bytes: ... + @property + def tbs_response_bytes(self) -> bytes: ... + @property + def certificates(self) -> list[x509.Certificate]: ... + @property + def responder_key_hash(self) -> bytes | None: ... + @property + def responder_name(self) -> x509.Name | None: ... + @property + def produced_at(self) -> datetime.datetime: ... + @property + def produced_at_utc(self) -> datetime.datetime: ... + @property + def certificate_status(self) -> ocsp.OCSPCertStatus: ... + @property + def revocation_time(self) -> datetime.datetime | None: ... + @property + def revocation_time_utc(self) -> datetime.datetime | None: ... + @property + def revocation_reason(self) -> x509.ReasonFlags | None: ... + @property + def this_update(self) -> datetime.datetime: ... + @property + def this_update_utc(self) -> datetime.datetime: ... + @property + def next_update(self) -> datetime.datetime | None: ... + @property + def next_update_utc(self) -> datetime.datetime | None: ... + @property + def issuer_key_hash(self) -> bytes: ... + @property + def issuer_name_hash(self) -> bytes: ... + @property + def hash_algorithm(self) -> hashes.HashAlgorithm: ... + @property + def serial_number(self) -> int: ... + @property + def extensions(self) -> x509.Extensions: ... + @property + def single_extensions(self) -> x509.Extensions: ... + def public_bytes(self, encoding: serialization.Encoding) -> bytes: ... + +class OCSPSingleResponse: + @property + def certificate_status(self) -> ocsp.OCSPCertStatus: ... + @property + def revocation_time(self) -> datetime.datetime | None: ... + @property + def revocation_time_utc(self) -> datetime.datetime | None: ... + @property + def revocation_reason(self) -> x509.ReasonFlags | None: ... + @property + def this_update(self) -> datetime.datetime: ... + @property + def this_update_utc(self) -> datetime.datetime: ... + @property + def next_update(self) -> datetime.datetime | None: ... + @property + def next_update_utc(self) -> datetime.datetime | None: ... + @property + def issuer_key_hash(self) -> bytes: ... + @property + def issuer_name_hash(self) -> bytes: ... + @property + def hash_algorithm(self) -> hashes.HashAlgorithm: ... + @property + def serial_number(self) -> int: ... + +def load_der_ocsp_request(data: bytes) -> ocsp.OCSPRequest: ... +def load_der_ocsp_response(data: bytes) -> ocsp.OCSPResponse: ... +def create_ocsp_request( + builder: ocsp.OCSPRequestBuilder, +) -> ocsp.OCSPRequest: ... +def create_ocsp_response( + status: ocsp.OCSPResponseStatus, + builder: ocsp.OCSPResponseBuilder | None, + private_key: PrivateKeyTypes | None, + hash_algorithm: hashes.HashAlgorithm | None, +) -> ocsp.OCSPResponse: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/__init__.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/__init__.pyi new file mode 100644 index 0000000000000000000000000000000000000000..404a300a52e176d6b0e9ca123894775b27371eaa --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/__init__.pyi @@ -0,0 +1,80 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import typing + +from cryptography.hazmat.bindings._rust.openssl import ( + aead, + ciphers, + cmac, + dh, + dsa, + ec, + ed448, + ed25519, + hashes, + hmac, + hpke, + kdf, + keys, + mldsa, + mlkem, + poly1305, + rsa, + x448, + x25519, +) + +__all__ = [ + "aead", + "ciphers", + "cmac", + "dh", + "dsa", + "ec", + "ed448", + "ed25519", + "hashes", + "hmac", + "hpke", + "kdf", + "keys", + "mldsa", + "mlkem", + "openssl_version", + "openssl_version_text", + "poly1305", + "raise_openssl_error", + "rsa", + "x448", + "x25519", +] + +CRYPTOGRAPHY_IS_LIBRESSL: bool +CRYPTOGRAPHY_IS_BORINGSSL: bool +CRYPTOGRAPHY_IS_AWSLC: bool +CRYPTOGRAPHY_OPENSSL_309_OR_GREATER: bool +CRYPTOGRAPHY_OPENSSL_320_OR_GREATER: bool +CRYPTOGRAPHY_OPENSSL_330_OR_GREATER: bool +CRYPTOGRAPHY_OPENSSL_350_OR_GREATER: bool + +class Providers: ... + +_legacy_provider_loaded: bool +_providers: Providers + +def openssl_version() -> int: ... +def openssl_version_text() -> str: ... +def raise_openssl_error() -> typing.NoReturn: ... +def capture_error_stack() -> list[OpenSSLError]: ... +def is_fips_enabled() -> bool: ... +def enable_fips(providers: Providers) -> None: ... + +class OpenSSLError: + @property + def lib(self) -> int: ... + @property + def reason(self) -> int: ... + @property + def reason_text(self) -> bytes: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/aead.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/aead.pyi new file mode 100644 index 0000000000000000000000000000000000000000..eb44608a78d8e53be8f44e2789bcc417a27916fc --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/aead.pyi @@ -0,0 +1,189 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from collections.abc import Sequence + +from cryptography.utils import Buffer + +class AESGCM: + def __init__(self, key: Buffer) -> None: ... + @staticmethod + def generate_key(bit_length: int) -> bytes: ... + def encrypt( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + ) -> bytes: ... + def decrypt( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + ) -> bytes: ... + def encrypt_into( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + buf: Buffer, + ) -> int: ... + def decrypt_into( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + buf: Buffer, + ) -> int: ... + +class ChaCha20Poly1305: + def __init__(self, key: Buffer) -> None: ... + @staticmethod + def generate_key() -> bytes: ... + def encrypt( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + ) -> bytes: ... + def encrypt_into( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + buf: Buffer, + ) -> int: ... + def decrypt( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + ) -> bytes: ... + def decrypt_into( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + buf: Buffer, + ) -> int: ... + +class AESCCM: + def __init__(self, key: Buffer, tag_length: int = 16) -> None: ... + @staticmethod + def generate_key(bit_length: int) -> bytes: ... + def encrypt( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + ) -> bytes: ... + def encrypt_into( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + buf: Buffer, + ) -> int: ... + def decrypt( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + ) -> bytes: ... + def decrypt_into( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + buf: Buffer, + ) -> int: ... + +class AESSIV: + def __init__(self, key: Buffer) -> None: ... + @staticmethod + def generate_key(bit_length: int) -> bytes: ... + def encrypt( + self, + data: Buffer, + associated_data: Sequence[Buffer] | None, + ) -> bytes: ... + def encrypt_into( + self, + data: Buffer, + associated_data: Sequence[Buffer] | None, + buf: Buffer, + ) -> int: ... + def decrypt( + self, + data: Buffer, + associated_data: Sequence[Buffer] | None, + ) -> bytes: ... + def decrypt_into( + self, + data: Buffer, + associated_data: Sequence[Buffer] | None, + buf: Buffer, + ) -> int: ... + +class AESOCB3: + def __init__(self, key: Buffer) -> None: ... + @staticmethod + def generate_key(bit_length: int) -> bytes: ... + def encrypt( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + ) -> bytes: ... + def encrypt_into( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + buf: Buffer, + ) -> int: ... + def decrypt( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + ) -> bytes: ... + def decrypt_into( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + buf: Buffer, + ) -> int: ... + +class AESGCMSIV: + def __init__(self, key: Buffer) -> None: ... + @staticmethod + def generate_key(bit_length: int) -> bytes: ... + def encrypt( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + ) -> bytes: ... + def encrypt_into( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + buf: Buffer, + ) -> int: ... + def decrypt( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + ) -> bytes: ... + def decrypt_into( + self, + nonce: Buffer, + data: Buffer, + associated_data: Buffer | None, + buf: Buffer, + ) -> int: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ciphers.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ciphers.pyi new file mode 100644 index 0000000000000000000000000000000000000000..a48fb017ff562b3d26fa849ebf0c0f9dafb36aaf --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ciphers.pyi @@ -0,0 +1,38 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import typing + +from cryptography.hazmat.primitives import ciphers +from cryptography.hazmat.primitives.ciphers import modes + +@typing.overload +def create_encryption_ctx( + algorithm: ciphers.CipherAlgorithm, mode: modes.ModeWithAuthenticationTag +) -> ciphers.AEADEncryptionContext: ... +@typing.overload +def create_encryption_ctx( + algorithm: ciphers.CipherAlgorithm, mode: modes.Mode | None +) -> ciphers.CipherContext: ... +@typing.overload +def create_decryption_ctx( + algorithm: ciphers.CipherAlgorithm, mode: modes.ModeWithAuthenticationTag +) -> ciphers.AEADDecryptionContext: ... +@typing.overload +def create_decryption_ctx( + algorithm: ciphers.CipherAlgorithm, mode: modes.Mode | None +) -> ciphers.CipherContext: ... +def cipher_supported( + algorithm: ciphers.CipherAlgorithm, mode: modes.Mode +) -> bool: ... +def _advance( + ctx: ciphers.AEADEncryptionContext | ciphers.AEADDecryptionContext, n: int +) -> None: ... +def _advance_aad( + ctx: ciphers.AEADEncryptionContext | ciphers.AEADDecryptionContext, n: int +) -> None: ... + +class CipherContext: ... +class AEADEncryptionContext: ... +class AEADDecryptionContext: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/cmac.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/cmac.pyi new file mode 100644 index 0000000000000000000000000000000000000000..9c03508bc89bf087e24c684802eeec344d76bb9e --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/cmac.pyi @@ -0,0 +1,18 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import typing + +from cryptography.hazmat.primitives import ciphers + +class CMAC: + def __init__( + self, + algorithm: ciphers.BlockCipherAlgorithm, + backend: typing.Any = None, + ) -> None: ... + def update(self, data: bytes) -> None: ... + def finalize(self) -> bytes: ... + def verify(self, signature: bytes) -> None: ... + def copy(self) -> CMAC: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/dh.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/dh.pyi new file mode 100644 index 0000000000000000000000000000000000000000..08733d745c3ddc994f358193dd4b79ce359583d8 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/dh.pyi @@ -0,0 +1,51 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import typing + +from cryptography.hazmat.primitives.asymmetric import dh + +MIN_MODULUS_SIZE: int + +class DHPrivateKey: ... +class DHPublicKey: ... +class DHParameters: ... + +class DHPrivateNumbers: + def __init__(self, x: int, public_numbers: DHPublicNumbers) -> None: ... + def private_key(self, backend: typing.Any = None) -> dh.DHPrivateKey: ... + @property + def x(self) -> int: ... + @property + def public_numbers(self) -> DHPublicNumbers: ... + +class DHPublicNumbers: + def __init__( + self, y: int, parameter_numbers: DHParameterNumbers + ) -> None: ... + def public_key(self, backend: typing.Any = None) -> dh.DHPublicKey: ... + @property + def y(self) -> int: ... + @property + def parameter_numbers(self) -> DHParameterNumbers: ... + +class DHParameterNumbers: + def __init__(self, p: int, g: int, q: int | None = None) -> None: ... + def parameters(self, backend: typing.Any = None) -> dh.DHParameters: ... + @property + def p(self) -> int: ... + @property + def g(self) -> int: ... + @property + def q(self) -> int | None: ... + +def generate_parameters( + generator: int, key_size: int, backend: typing.Any = None +) -> dh.DHParameters: ... +def from_pem_parameters( + data: bytes, backend: typing.Any = None +) -> dh.DHParameters: ... +def from_der_parameters( + data: bytes, backend: typing.Any = None +) -> dh.DHParameters: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/dsa.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/dsa.pyi new file mode 100644 index 0000000000000000000000000000000000000000..0922a4c4041a90b1e884f56522f82bced398c80e --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/dsa.pyi @@ -0,0 +1,41 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import typing + +from cryptography.hazmat.primitives.asymmetric import dsa + +class DSAPrivateKey: ... +class DSAPublicKey: ... +class DSAParameters: ... + +class DSAPrivateNumbers: + def __init__(self, x: int, public_numbers: DSAPublicNumbers) -> None: ... + @property + def x(self) -> int: ... + @property + def public_numbers(self) -> DSAPublicNumbers: ... + def private_key(self, backend: typing.Any = None) -> dsa.DSAPrivateKey: ... + +class DSAPublicNumbers: + def __init__( + self, y: int, parameter_numbers: DSAParameterNumbers + ) -> None: ... + @property + def y(self) -> int: ... + @property + def parameter_numbers(self) -> DSAParameterNumbers: ... + def public_key(self, backend: typing.Any = None) -> dsa.DSAPublicKey: ... + +class DSAParameterNumbers: + def __init__(self, p: int, q: int, g: int) -> None: ... + @property + def p(self) -> int: ... + @property + def q(self) -> int: ... + @property + def g(self) -> int: ... + def parameters(self, backend: typing.Any = None) -> dsa.DSAParameters: ... + +def generate_parameters(key_size: int) -> dsa.DSAParameters: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ec.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ec.pyi new file mode 100644 index 0000000000000000000000000000000000000000..5c3b7bf6e4a9bafac1423bd3936ebc347885a950 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ec.pyi @@ -0,0 +1,52 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import typing + +from cryptography.hazmat.primitives.asymmetric import ec + +class ECPrivateKey: ... +class ECPublicKey: ... + +class EllipticCurvePrivateNumbers: + def __init__( + self, private_value: int, public_numbers: EllipticCurvePublicNumbers + ) -> None: ... + def private_key( + self, backend: typing.Any = None + ) -> ec.EllipticCurvePrivateKey: ... + @property + def private_value(self) -> int: ... + @property + def public_numbers(self) -> EllipticCurvePublicNumbers: ... + +class EllipticCurvePublicNumbers: + def __init__(self, x: int, y: int, curve: ec.EllipticCurve) -> None: ... + def public_key( + self, backend: typing.Any = None + ) -> ec.EllipticCurvePublicKey: ... + @property + def x(self) -> int: ... + @property + def y(self) -> int: ... + @property + def curve(self) -> ec.EllipticCurve: ... + def __eq__(self, other: object) -> bool: ... + +def curve_supported(curve: ec.EllipticCurve) -> bool: ... +def generate_private_key( + curve: ec.EllipticCurve, backend: typing.Any = None +) -> ec.EllipticCurvePrivateKey: ... +def from_private_numbers( + numbers: ec.EllipticCurvePrivateNumbers, +) -> ec.EllipticCurvePrivateKey: ... +def from_public_numbers( + numbers: ec.EllipticCurvePublicNumbers, +) -> ec.EllipticCurvePublicKey: ... +def from_public_bytes( + curve: ec.EllipticCurve, data: bytes +) -> ec.EllipticCurvePublicKey: ... +def derive_private_key( + private_value: int, curve: ec.EllipticCurve +) -> ec.EllipticCurvePrivateKey: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ed25519.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ed25519.pyi new file mode 100644 index 0000000000000000000000000000000000000000..f85b3d1b2361832095a00ba9b67675ad365339dd --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ed25519.pyi @@ -0,0 +1,13 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from cryptography.hazmat.primitives.asymmetric import ed25519 +from cryptography.utils import Buffer + +class Ed25519PrivateKey: ... +class Ed25519PublicKey: ... + +def generate_key() -> ed25519.Ed25519PrivateKey: ... +def from_private_bytes(data: Buffer) -> ed25519.Ed25519PrivateKey: ... +def from_public_bytes(data: bytes) -> ed25519.Ed25519PublicKey: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ed448.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ed448.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c8ca0ecb156b9d9a7e69cf10bf71f187362f3315 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/ed448.pyi @@ -0,0 +1,13 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from cryptography.hazmat.primitives.asymmetric import ed448 +from cryptography.utils import Buffer + +class Ed448PrivateKey: ... +class Ed448PublicKey: ... + +def generate_key() -> ed448.Ed448PrivateKey: ... +def from_private_bytes(data: Buffer) -> ed448.Ed448PrivateKey: ... +def from_public_bytes(data: bytes) -> ed448.Ed448PublicKey: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/hashes.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/hashes.pyi new file mode 100644 index 0000000000000000000000000000000000000000..106b531c66a17cc2ceb3effc9863b8cc614a1351 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/openssl/hashes.pyi @@ -0,0 +1,30 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import typing + +from cryptography.hazmat.primitives import hashes +from cryptography.utils import Buffer + +class Hash(hashes.HashContext): + def __init__( + self, algorithm: hashes.HashAlgorithm, backend: typing.Any = None + ) -> None: ... + @property + def algorithm(self) -> hashes.HashAlgorithm: ... + def update(self, data: Buffer) -> None: ... + def finalize(self) -> bytes: ... + def copy(self) -> Hash: ... + @staticmethod + def hash(algorithm: hashes.HashAlgorithm, data: Buffer) -> bytes: ... + +def hash_supported(algorithm: hashes.HashAlgorithm) -> bool: ... + +class XOFHash: + def __init__(self, algorithm: hashes.ExtendableOutputFunction) -> None: ... + @property + def algorithm(self) -> hashes.ExtendableOutputFunction: ... + def update(self, data: Buffer) -> None: ... + def squeeze(self, length: int) -> bytes: ... + def copy(self) -> XOFHash: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/pkcs12.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/pkcs12.pyi new file mode 100644 index 0000000000000000000000000000000000000000..b25becb6bdef7ce259dcb40f34aa9386774437ca --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/pkcs12.pyi @@ -0,0 +1,52 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import typing +from collections.abc import Iterable + +from cryptography import x509 +from cryptography.hazmat.primitives.asymmetric.types import PrivateKeyTypes +from cryptography.hazmat.primitives.serialization import ( + KeySerializationEncryption, +) +from cryptography.hazmat.primitives.serialization.pkcs12 import ( + PKCS12KeyAndCertificates, + PKCS12PrivateKeyTypes, +) +from cryptography.utils import Buffer + +class PKCS12Certificate: + def __init__( + self, cert: x509.Certificate, friendly_name: bytes | None + ) -> None: ... + @property + def friendly_name(self) -> bytes | None: ... + @property + def certificate(self) -> x509.Certificate: ... + +def load_key_and_certificates( + data: Buffer, + password: Buffer | None, + backend: typing.Any = None, +) -> tuple[ + PrivateKeyTypes | None, + x509.Certificate | None, + list[x509.Certificate], +]: ... +def load_pkcs12( + data: bytes, + password: bytes | None, + backend: typing.Any = None, +) -> PKCS12KeyAndCertificates: ... +def serialize_java_truststore( + certs: Iterable[PKCS12Certificate], + encryption_algorithm: KeySerializationEncryption, +) -> bytes: ... +def serialize_key_and_certificates( + name: bytes | None, + key: PKCS12PrivateKeyTypes | None, + cert: x509.Certificate | None, + cas: Iterable[x509.Certificate | PKCS12Certificate] | None, + encryption_algorithm: KeySerializationEncryption, +) -> bytes: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/pkcs7.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/pkcs7.pyi new file mode 100644 index 0000000000000000000000000000000000000000..358b135865a89a6675706e0fd880d3eabdb081b5 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/pkcs7.pyi @@ -0,0 +1,50 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from collections.abc import Iterable + +from cryptography import x509 +from cryptography.hazmat.primitives import serialization +from cryptography.hazmat.primitives.asymmetric import rsa +from cryptography.hazmat.primitives.serialization import pkcs7 + +def serialize_certificates( + certs: list[x509.Certificate], + encoding: serialization.Encoding, +) -> bytes: ... +def encrypt_and_serialize( + builder: pkcs7.PKCS7EnvelopeBuilder, + content_encryption_algorithm: pkcs7.ContentEncryptionAlgorithm, + encoding: serialization.Encoding, + options: Iterable[pkcs7.PKCS7Options], +) -> bytes: ... +def sign_and_serialize( + builder: pkcs7.PKCS7SignatureBuilder, + encoding: serialization.Encoding, + options: Iterable[pkcs7.PKCS7Options], +) -> bytes: ... +def decrypt_der( + data: bytes, + certificate: x509.Certificate, + private_key: rsa.RSAPrivateKey, + options: Iterable[pkcs7.PKCS7Options], +) -> bytes: ... +def decrypt_pem( + data: bytes, + certificate: x509.Certificate, + private_key: rsa.RSAPrivateKey, + options: Iterable[pkcs7.PKCS7Options], +) -> bytes: ... +def decrypt_smime( + data: bytes, + certificate: x509.Certificate, + private_key: rsa.RSAPrivateKey, + options: Iterable[pkcs7.PKCS7Options], +) -> bytes: ... +def load_pem_pkcs7_certificates( + data: bytes, +) -> list[x509.Certificate]: ... +def load_der_pkcs7_certificates( + data: bytes, +) -> list[x509.Certificate]: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/test_support.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/test_support.pyi new file mode 100644 index 0000000000000000000000000000000000000000..c6c6d0bbe12916b4299d08398493c5da8f762ae7 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/test_support.pyi @@ -0,0 +1,23 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from cryptography import x509 +from cryptography.hazmat.primitives import serialization +from cryptography.hazmat.primitives.serialization import pkcs7 +from cryptography.utils import Buffer + +class TestCertificate: + not_after_tag: int + not_before_tag: int + issuer_value_tags: list[int] + subject_value_tags: list[int] + +def test_parse_certificate(data: bytes) -> TestCertificate: ... +def pkcs7_verify( + encoding: serialization.Encoding, + sig: bytes, + msg: Buffer | None, + certs: list[x509.Certificate], + options: list[pkcs7.PKCS7Options], +) -> None: ... diff --git a/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/x509.pyi b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/x509.pyi new file mode 100644 index 0000000000000000000000000000000000000000..196a3c60f7edf6887a75ed45f7eabd330f4efc1a --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/hazmat/bindings/_rust/x509.pyi @@ -0,0 +1,312 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +import datetime +import typing +from collections.abc import Iterator + +from cryptography import x509 +from cryptography.hazmat.primitives import hashes, serialization +from cryptography.hazmat.primitives.asymmetric.ec import ECDSA +from cryptography.hazmat.primitives.asymmetric.padding import PSS, PKCS1v15 +from cryptography.hazmat.primitives.asymmetric.types import ( + CertificateIssuerPublicKeyTypes, + CertificatePublicKeyTypes, + PrivateKeyTypes, +) +from cryptography.x509 import certificate_transparency + +def load_pem_x509_certificate( + data: bytes, backend: typing.Any = None +) -> x509.Certificate: ... +def load_der_x509_certificate( + data: bytes, backend: typing.Any = None +) -> x509.Certificate: ... +def load_pem_x509_certificates( + data: bytes, +) -> list[x509.Certificate]: ... +def load_pem_x509_crl( + data: bytes, backend: typing.Any = None +) -> x509.CertificateRevocationList: ... +def load_der_x509_crl( + data: bytes, backend: typing.Any = None +) -> x509.CertificateRevocationList: ... +def load_pem_x509_csr( + data: bytes, backend: typing.Any = None +) -> x509.CertificateSigningRequest: ... +def load_der_x509_csr( + data: bytes, backend: typing.Any = None +) -> x509.CertificateSigningRequest: ... +def encode_name_bytes(name: x509.Name) -> bytes: ... +def encode_extension_value(extension: x509.ExtensionType) -> bytes: ... +def create_x509_certificate( + builder: x509.CertificateBuilder, + private_key: PrivateKeyTypes, + hash_algorithm: hashes.HashAlgorithm | None, + rsa_padding: PKCS1v15 | PSS | None, + ecdsa_deterministic: bool | None, +) -> x509.Certificate: ... +def create_x509_csr( + builder: x509.CertificateSigningRequestBuilder, + private_key: PrivateKeyTypes, + hash_algorithm: hashes.HashAlgorithm | None, + rsa_padding: PKCS1v15 | PSS | None, + ecdsa_deterministic: bool | None, +) -> x509.CertificateSigningRequest: ... +def create_revoked_certificate( + builder: x509.RevokedCertificateBuilder, +) -> x509.RevokedCertificate: ... +def create_x509_crl( + builder: x509.CertificateRevocationListBuilder, + private_key: PrivateKeyTypes, + hash_algorithm: hashes.HashAlgorithm | None, + rsa_padding: PKCS1v15 | PSS | None, + ecdsa_deterministic: bool | None, +) -> x509.CertificateRevocationList: ... + +class Sct: + @property + def version(self) -> certificate_transparency.Version: ... + @property + def log_id(self) -> bytes: ... + @property + def timestamp(self) -> datetime.datetime: ... + @property + def entry_type(self) -> certificate_transparency.LogEntryType: ... + @property + def signature_hash_algorithm(self) -> hashes.HashAlgorithm: ... + @property + def signature_algorithm( + self, + ) -> certificate_transparency.SignatureAlgorithm: ... + @property + def signature(self) -> bytes: ... + @property + def extension_bytes(self) -> bytes: ... + +class Certificate: + def fingerprint(self, algorithm: hashes.HashAlgorithm) -> bytes: ... + @property + def serial_number(self) -> int: ... + @property + def version(self) -> x509.Version: ... + def public_key(self) -> CertificatePublicKeyTypes: ... + @property + def public_key_algorithm_oid(self) -> x509.ObjectIdentifier: ... + @property + def not_valid_before(self) -> datetime.datetime: ... + @property + def not_valid_before_utc(self) -> datetime.datetime: ... + @property + def not_valid_after(self) -> datetime.datetime: ... + @property + def not_valid_after_utc(self) -> datetime.datetime: ... + @property + def issuer(self) -> x509.Name: ... + @property + def subject(self) -> x509.Name: ... + @property + def signature_hash_algorithm( + self, + ) -> hashes.HashAlgorithm | None: ... + @property + def signature_algorithm_oid(self) -> x509.ObjectIdentifier: ... + @property + def signature_algorithm_parameters( + self, + ) -> PSS | PKCS1v15 | ECDSA | None: ... + @property + def extensions(self) -> x509.Extensions: ... + @property + def signature(self) -> bytes: ... + @property + def tbs_certificate_bytes(self) -> bytes: ... + @property + def tbs_precertificate_bytes(self) -> bytes: ... + def __eq__(self, other: object) -> bool: ... + def __hash__(self) -> int: ... + def public_bytes(self, encoding: serialization.Encoding) -> bytes: ... + def verify_directly_issued_by(self, issuer: Certificate) -> None: ... + +class RevokedCertificate: + @property + def serial_number(self) -> int: ... + @property + def revocation_date(self) -> datetime.datetime: ... + @property + def revocation_date_utc(self) -> datetime.datetime: ... + @property + def extensions(self) -> x509.Extensions: ... + +class CertificateRevocationList: + def public_bytes(self, encoding: serialization.Encoding) -> bytes: ... + def fingerprint(self, algorithm: hashes.HashAlgorithm) -> bytes: ... + def get_revoked_certificate_by_serial_number( + self, serial_number: int + ) -> x509.RevokedCertificate | None: ... + @property + def signature_hash_algorithm( + self, + ) -> hashes.HashAlgorithm | None: ... + @property + def signature_algorithm_oid(self) -> x509.ObjectIdentifier: ... + @property + def signature_algorithm_parameters( + self, + ) -> PSS | PKCS1v15 | ECDSA | None: ... + @property + def issuer(self) -> x509.Name: ... + @property + def next_update(self) -> datetime.datetime | None: ... + @property + def next_update_utc(self) -> datetime.datetime | None: ... + @property + def last_update(self) -> datetime.datetime: ... + @property + def last_update_utc(self) -> datetime.datetime: ... + @property + def extensions(self) -> x509.Extensions: ... + @property + def signature(self) -> bytes: ... + @property + def tbs_certlist_bytes(self) -> bytes: ... + def __eq__(self, other: object) -> bool: ... + def __len__(self) -> int: ... + @typing.overload + def __getitem__(self, idx: int) -> x509.RevokedCertificate: ... + @typing.overload + def __getitem__(self, idx: slice) -> list[x509.RevokedCertificate]: ... + def __iter__(self) -> Iterator[x509.RevokedCertificate]: ... + def is_signature_valid( + self, public_key: CertificateIssuerPublicKeyTypes + ) -> bool: ... + +class CertificateSigningRequest: + def __eq__(self, other: object) -> bool: ... + def __hash__(self) -> int: ... + def public_key(self) -> CertificatePublicKeyTypes: ... + @property + def subject(self) -> x509.Name: ... + @property + def signature_hash_algorithm( + self, + ) -> hashes.HashAlgorithm | None: ... + @property + def signature_algorithm_oid(self) -> x509.ObjectIdentifier: ... + @property + def signature_algorithm_parameters( + self, + ) -> PSS | PKCS1v15 | ECDSA | None: ... + @property + def extensions(self) -> x509.Extensions: ... + @property + def attributes(self) -> x509.Attributes: ... + def public_bytes(self, encoding: serialization.Encoding) -> bytes: ... + @property + def signature(self) -> bytes: ... + @property + def tbs_certrequest_bytes(self) -> bytes: ... + @property + def is_signature_valid(self) -> bool: ... + +class PolicyBuilder: + def time(self, time: datetime.datetime) -> PolicyBuilder: ... + def store(self, store: Store) -> PolicyBuilder: ... + def max_chain_depth(self, max_chain_depth: int) -> PolicyBuilder: ... + def extension_policies( + self, *, ca_policy: ExtensionPolicy, ee_policy: ExtensionPolicy + ) -> PolicyBuilder: ... + def build_client_verifier(self) -> ClientVerifier: ... + def build_server_verifier( + self, subject: x509.verification.Subject + ) -> ServerVerifier: ... + +class Policy: + @property + def max_chain_depth(self) -> int: ... + @property + def subject(self) -> x509.verification.Subject | None: ... + @property + def validation_time(self) -> datetime.datetime: ... + @property + def extended_key_usage(self) -> x509.ObjectIdentifier: ... + @property + def minimum_rsa_modulus(self) -> int: ... + +class Criticality: + CRITICAL: Criticality + AGNOSTIC: Criticality + NON_CRITICAL: Criticality + +T = typing.TypeVar("T", contravariant=True, bound=x509.ExtensionType) + +MaybeExtensionValidatorCallback = typing.Callable[ + [ + Policy, + x509.Certificate, + T | None, + ], + None, +] + +PresentExtensionValidatorCallback = typing.Callable[ + [Policy, x509.Certificate, T], + None, +] + +class ExtensionPolicy: + @staticmethod + def permit_all() -> ExtensionPolicy: ... + @staticmethod + def webpki_defaults_ca() -> ExtensionPolicy: ... + @staticmethod + def webpki_defaults_ee() -> ExtensionPolicy: ... + def require_not_present( + self, extension_type: type[x509.ExtensionType] + ) -> ExtensionPolicy: ... + def may_be_present( + self, + extension_type: type[T], + criticality: Criticality, + validator: MaybeExtensionValidatorCallback[T] | None, + ) -> ExtensionPolicy: ... + def require_present( + self, + extension_type: type[T], + criticality: Criticality, + validator: PresentExtensionValidatorCallback[T] | None, + ) -> ExtensionPolicy: ... + +class VerifiedClient: + @property + def subjects(self) -> list[x509.GeneralName] | None: ... + @property + def chain(self) -> list[x509.Certificate]: ... + +class ClientVerifier: + @property + def policy(self) -> Policy: ... + @property + def store(self) -> Store: ... + def verify( + self, + leaf: x509.Certificate, + intermediates: list[x509.Certificate], + ) -> VerifiedClient: ... + +class ServerVerifier: + @property + def policy(self) -> Policy: ... + @property + def store(self) -> Store: ... + def verify( + self, + leaf: x509.Certificate, + intermediates: list[x509.Certificate], + ) -> list[x509.Certificate]: ... + +class Store: + def __init__(self, certs: list[x509.Certificate]) -> None: ... + +class VerificationError(Exception): ... diff --git a/lib/python3.11/site-packages/cryptography/py.typed b/lib/python3.11/site-packages/cryptography/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/cryptography/utils.py b/lib/python3.11/site-packages/cryptography/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..9cfc9927362668d654a09bae232c27811ca44842 --- /dev/null +++ b/lib/python3.11/site-packages/cryptography/utils.py @@ -0,0 +1,135 @@ +# This file is dual licensed under the terms of the Apache License, Version +# 2.0, and the BSD License. See the LICENSE file in the root of this repository +# for complete details. + +from __future__ import annotations + +import enum +import sys +import types +import typing +import warnings +from collections.abc import Callable, Sequence + + +# We use a UserWarning subclass, instead of DeprecationWarning, because CPython +# decided deprecation warnings should be invisible by default. +class CryptographyDeprecationWarning(UserWarning): + pass + + +# Several APIs were deprecated with no specific end-of-life date because of the +# ubiquity of their use. They should not be removed until we agree on when that +# cycle ends. +DeprecatedIn36 = CryptographyDeprecationWarning +DeprecatedIn40 = CryptographyDeprecationWarning +DeprecatedIn41 = CryptographyDeprecationWarning +DeprecatedIn42 = CryptographyDeprecationWarning +DeprecatedIn43 = CryptographyDeprecationWarning +DeprecatedIn47 = CryptographyDeprecationWarning + + +# If you're wondering why we don't use `Buffer`, it's because `Buffer` would +# be more accurately named: Bufferable. It means something which has an +# `__buffer__`. Which means you can't actually treat the result as a buffer +# (and do things like take a `len()`). +Buffer = typing.Union[bytes, bytearray, memoryview] + + +def _check_bytes(name: str, value: bytes) -> None: + if not isinstance(value, bytes): + raise TypeError(f"{name} must be bytes") + + +def _check_byteslike(name: str, value: Buffer) -> None: + try: + memoryview(value) + except TypeError: + raise TypeError(f"{name} must be bytes-like") + + +def int_to_bytes(integer: int, length: int | None = None) -> bytes: + if length == 0: + raise ValueError("length argument can't be 0") + return integer.to_bytes( + length or (integer.bit_length() + 7) // 8 or 1, "big" + ) + + +class InterfaceNotImplemented(Exception): + pass + + +class _DeprecatedValue: + def __init__(self, value: object, message: str, warning_class): + self.value = value + self.message = message + self.warning_class = warning_class + + +class _ModuleWithDeprecations(types.ModuleType): + def __init__(self, module: types.ModuleType): + super().__init__(module.__name__) + self.__dict__["_module"] = module + + def __getattr__(self, name: str) -> typing.Any: + obj = getattr(self._module, name) + if isinstance(obj, _DeprecatedValue): + warnings.warn(obj.message, obj.warning_class, stacklevel=2) + obj = obj.value + return obj + + def __setattr__(self, attr: str, value: object) -> None: + setattr(self._module, attr, value) + + def __delattr__(self, attr: str) -> None: + obj = getattr(self._module, attr) + if isinstance(obj, _DeprecatedValue): + warnings.warn(obj.message, obj.warning_class, stacklevel=2) + + delattr(self._module, attr) + + def __dir__(self) -> Sequence[str]: + return ["_module", *dir(self._module)] + + +def deprecated( + value: object, + module_name: str, + message: str, + warning_class: type[Warning], + name: str | None = None, +) -> _DeprecatedValue: + module = sys.modules[module_name] + if not isinstance(module, _ModuleWithDeprecations): + sys.modules[module_name] = module = _ModuleWithDeprecations(module) + dv = _DeprecatedValue(value, message, warning_class) + # Maintain backwards compatibility with `name is None` for pyOpenSSL. + if name is not None: + setattr(module, name, dv) + return dv + + +def cached_property(func: Callable) -> property: + cached_name = f"_cached_{func}" + sentinel = object() + + def inner(instance: object): + cache = getattr(instance, cached_name, sentinel) + if cache is not sentinel: + return cache + result = func(instance) + setattr(instance, cached_name, result) + return result + + return property(inner) + + +# Python 3.10 changed representation of enums. We use well-defined object +# representation and string representation from Python 3.9. +class Enum(enum.Enum): + def __repr__(self) -> str: + return f"<{self.__class__.__name__}.{self._name_}: {self._value_!r}>" + + def __str__(self) -> str: + return f"{self.__class__.__name__}.{self._name_}" diff --git a/lib/python3.11/site-packages/httpx-0.28.1.dist-info/INSTALLER b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/lib/python3.11/site-packages/httpx-0.28.1.dist-info/METADATA b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..b0d2b196385e98259971519793447c1fd7a9a643 --- /dev/null +++ b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/METADATA @@ -0,0 +1,203 @@ +Metadata-Version: 2.3 +Name: httpx +Version: 0.28.1 +Summary: The next generation HTTP client. +Project-URL: Changelog, https://github.com/encode/httpx/blob/master/CHANGELOG.md +Project-URL: Documentation, https://www.python-httpx.org +Project-URL: Homepage, https://github.com/encode/httpx +Project-URL: Source, https://github.com/encode/httpx +Author-email: Tom Christie +License: BSD-3-Clause +Classifier: Development Status :: 4 - Beta +Classifier: Environment :: Web Environment +Classifier: Framework :: AsyncIO +Classifier: Framework :: Trio +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: BSD License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Topic :: Internet :: WWW/HTTP +Requires-Python: >=3.8 +Requires-Dist: anyio +Requires-Dist: certifi +Requires-Dist: httpcore==1.* +Requires-Dist: idna +Provides-Extra: brotli +Requires-Dist: brotli; (platform_python_implementation == 'CPython') and extra == 'brotli' +Requires-Dist: brotlicffi; (platform_python_implementation != 'CPython') and extra == 'brotli' +Provides-Extra: cli +Requires-Dist: click==8.*; extra == 'cli' +Requires-Dist: pygments==2.*; extra == 'cli' +Requires-Dist: rich<14,>=10; extra == 'cli' +Provides-Extra: http2 +Requires-Dist: h2<5,>=3; extra == 'http2' +Provides-Extra: socks +Requires-Dist: socksio==1.*; extra == 'socks' +Provides-Extra: zstd +Requires-Dist: zstandard>=0.18.0; extra == 'zstd' +Description-Content-Type: text/markdown + +

+ HTTPX +

+ +

HTTPX - A next-generation HTTP client for Python.

+ +

+ + Test Suite + + + Package version + +

+ +HTTPX is a fully featured HTTP client library for Python 3. It includes **an integrated command line client**, has support for both **HTTP/1.1 and HTTP/2**, and provides both **sync and async APIs**. + +--- + +Install HTTPX using pip: + +```shell +$ pip install httpx +``` + +Now, let's get started: + +```pycon +>>> import httpx +>>> r = httpx.get('https://www.example.org/') +>>> r + +>>> r.status_code +200 +>>> r.headers['content-type'] +'text/html; charset=UTF-8' +>>> r.text +'\n\n\nExample Domain...' +``` + +Or, using the command-line client. + +```shell +$ pip install 'httpx[cli]' # The command line client is an optional dependency. +``` + +Which now allows us to use HTTPX directly from the command-line... + +

+ httpx --help +

+ +Sending a request... + +

+ httpx http://httpbin.org/json +

+ +## Features + +HTTPX builds on the well-established usability of `requests`, and gives you: + +* A broadly [requests-compatible API](https://www.python-httpx.org/compatibility/). +* An integrated command-line client. +* HTTP/1.1 [and HTTP/2 support](https://www.python-httpx.org/http2/). +* Standard synchronous interface, but with [async support if you need it](https://www.python-httpx.org/async/). +* Ability to make requests directly to [WSGI applications](https://www.python-httpx.org/advanced/transports/#wsgi-transport) or [ASGI applications](https://www.python-httpx.org/advanced/transports/#asgi-transport). +* Strict timeouts everywhere. +* Fully type annotated. +* 100% test coverage. + +Plus all the standard features of `requests`... + +* International Domains and URLs +* Keep-Alive & Connection Pooling +* Sessions with Cookie Persistence +* Browser-style SSL Verification +* Basic/Digest Authentication +* Elegant Key/Value Cookies +* Automatic Decompression +* Automatic Content Decoding +* Unicode Response Bodies +* Multipart File Uploads +* HTTP(S) Proxy Support +* Connection Timeouts +* Streaming Downloads +* .netrc Support +* Chunked Requests + +## Installation + +Install with pip: + +```shell +$ pip install httpx +``` + +Or, to include the optional HTTP/2 support, use: + +```shell +$ pip install httpx[http2] +``` + +HTTPX requires Python 3.8+. + +## Documentation + +Project documentation is available at [https://www.python-httpx.org/](https://www.python-httpx.org/). + +For a run-through of all the basics, head over to the [QuickStart](https://www.python-httpx.org/quickstart/). + +For more advanced topics, see the [Advanced Usage](https://www.python-httpx.org/advanced/) section, the [async support](https://www.python-httpx.org/async/) section, or the [HTTP/2](https://www.python-httpx.org/http2/) section. + +The [Developer Interface](https://www.python-httpx.org/api/) provides a comprehensive API reference. + +To find out about tools that integrate with HTTPX, see [Third Party Packages](https://www.python-httpx.org/third_party_packages/). + +## Contribute + +If you want to contribute with HTTPX check out the [Contributing Guide](https://www.python-httpx.org/contributing/) to learn how to start. + +## Dependencies + +The HTTPX project relies on these excellent libraries: + +* `httpcore` - The underlying transport implementation for `httpx`. + * `h11` - HTTP/1.1 support. +* `certifi` - SSL certificates. +* `idna` - Internationalized domain name support. +* `sniffio` - Async library autodetection. + +As well as these optional installs: + +* `h2` - HTTP/2 support. *(Optional, with `httpx[http2]`)* +* `socksio` - SOCKS proxy support. *(Optional, with `httpx[socks]`)* +* `rich` - Rich terminal support. *(Optional, with `httpx[cli]`)* +* `click` - Command line client support. *(Optional, with `httpx[cli]`)* +* `brotli` or `brotlicffi` - Decoding for "brotli" compressed responses. *(Optional, with `httpx[brotli]`)* +* `zstandard` - Decoding for "zstd" compressed responses. *(Optional, with `httpx[zstd]`)* + +A huge amount of credit is due to `requests` for the API layout that +much of this work follows, as well as to `urllib3` for plenty of design +inspiration around the lower-level networking details. + +--- + +

HTTPX is BSD licensed code.
Designed & crafted with care.
— 🦋 —

+ +## Release Information + +### Fixed + +* Reintroduced supposedly-private `URLTypes` shortcut. (#2673) + + +--- + +[Full changelog](https://github.com/encode/httpx/blob/master/CHANGELOG.md) diff --git a/lib/python3.11/site-packages/httpx-0.28.1.dist-info/RECORD b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..654897068c0cdd1bb35773f1dd8bbd55779bd94d --- /dev/null +++ b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/RECORD @@ -0,0 +1,54 @@ +../../../bin/httpx,sha256=TnVk8oixUBVqGxN4q73HkJFvq5BegCZTOx4YFN1Ez_w,185 +httpx-0.28.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +httpx-0.28.1.dist-info/METADATA,sha256=_rubD48-gNV8gZnDBPNcQzboWB0dGNeYPJJ2a4J5OyU,7052 +httpx-0.28.1.dist-info/RECORD,, +httpx-0.28.1.dist-info/WHEEL,sha256=C2FUgwZgiLbznR-k0b_5k3Ai_1aASOXDss3lzCUsUug,87 +httpx-0.28.1.dist-info/entry_points.txt,sha256=2lVkdQmxLA1pNMgSN2eV89o90HCZezhmNwsy6ryKDSA,37 +httpx-0.28.1.dist-info/licenses/LICENSE.md,sha256=TsWdVE8StfU5o6cW_TIaxYzNgDC0ZSIfLIgCAM3yjY0,1508 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+httpx/_auth.py,sha256=Yr3QwaUSK17rGYx-7j-FdicFIzz4Y9FFV-1F4-7RXX4,11891 +httpx/_client.py,sha256=xD-UG67-WMkeltAAOeGGj-cZ2RRTAm19sWRxlFY7_40,65714 +httpx/_config.py,sha256=pPp2U-wicfcKsF-KYRE1LYdt3e6ERGeIoXZ8Gjo3LWc,8547 +httpx/_content.py,sha256=LGGzrJTR3OvN4Mb1GVVNLXkXJH-6oKlwAttO9p5w_yg,8161 +httpx/_decoders.py,sha256=p0dX8I0NEHexs3UGp4SsZutiMhsXrrWl6-GnqVb0iKM,12041 +httpx/_exceptions.py,sha256=bxW7fxzgVMAdNTbwT0Vnq04gJDW1_gI_GFiQPuMyjL0,8527 +httpx/_main.py,sha256=Cg9GMabiTT_swaDfUgIRitSwxLRMSwUDOm7LdSGqlA4,15626 +httpx/_models.py,sha256=4__Guyv1gLxuZChwim8kfQNiIOcJ9acreFOSurvZfms,44700 +httpx/_multipart.py,sha256=KOHEZZl6oohg9mPaKyyu345qq1rJLg35TUG3YAzXB3Y,9843 +httpx/_status_codes.py,sha256=DYn-2ufBgMeXy5s8x3_TB7wjAuAAMewTakPrm5rXEsc,5639 +httpx/_transports/__init__.py,sha256=GbUoBSAOp7z-l-9j5YhMhR3DMIcn6FVLhj072O3Nnno,275 +httpx/_transports/__pycache__/__init__.cpython-311.pyc,, +httpx/_transports/__pycache__/asgi.cpython-311.pyc,, +httpx/_transports/__pycache__/base.cpython-311.pyc,, +httpx/_transports/__pycache__/default.cpython-311.pyc,, +httpx/_transports/__pycache__/mock.cpython-311.pyc,, +httpx/_transports/__pycache__/wsgi.cpython-311.pyc,, +httpx/_transports/asgi.py,sha256=HRfiDYMPt4wQH2gFgHZg4c-i3sblo6bL5GTqcET-xz8,5501 +httpx/_transports/base.py,sha256=kZS_VMbViYfF570pogUCJ1bulz-ybfL51Pqs9yktebU,2523 +httpx/_transports/default.py,sha256=AzeaRUyVwCccTyyNJexDf0n1dFfzzydpdIQgvw7PLnk,13983 +httpx/_transports/mock.py,sha256=PTo0d567RITXxGrki6kN7_67wwAxfwiMDcuXJiZCjEo,1232 +httpx/_transports/wsgi.py,sha256=NcPX3Xap_EwCFZWO_OaSyQNuInCYx1QMNbO8GAei6jY,4825 +httpx/_types.py,sha256=Jyh41GQq7AOev8IOWKDAg7zCbvHAfufmW5g_PiTtErY,2965 +httpx/_urlparse.py,sha256=ZAmH47ONfkxrrj-PPYhGeiHjb6AjKCS-ANWIN4OL_KY,18546 +httpx/_urls.py,sha256=dX99VR1DSOHpgo9Aq7PzYO4FKdxqKjwyNp8grf8dHN0,21550 +httpx/_utils.py,sha256=_TVeqAKvxJkKHdz7dFeb4s0LZqQXgeFkXSgfiHBK_1o,8285 +httpx/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 diff --git a/lib/python3.11/site-packages/httpx-0.28.1.dist-info/WHEEL b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..21aaa72961a8af71c17d2cb3b76d5f7f567100e4 --- /dev/null +++ b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/WHEEL @@ -0,0 +1,4 @@ +Wheel-Version: 1.0 +Generator: hatchling 1.26.3 +Root-Is-Purelib: true +Tag: py3-none-any diff --git a/lib/python3.11/site-packages/httpx-0.28.1.dist-info/entry_points.txt b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/entry_points.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ae96007f7d725813fd02dc1d06d3834ee1939e4 --- /dev/null +++ b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/entry_points.txt @@ -0,0 +1,2 @@ +[console_scripts] +httpx = httpx:main diff --git a/lib/python3.11/site-packages/httpx-0.28.1.dist-info/licenses/LICENSE.md b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/licenses/LICENSE.md new file mode 100644 index 0000000000000000000000000000000000000000..ab79d16a3f4c6c894c028d1f7431811e8711b42b --- /dev/null +++ b/lib/python3.11/site-packages/httpx-0.28.1.dist-info/licenses/LICENSE.md @@ -0,0 +1,12 @@ +Copyright © 2019, [Encode OSS Ltd](https://www.encode.io/). +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/lib/python3.11/site-packages/httpx/__init__.py b/lib/python3.11/site-packages/httpx/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e9addde071f81758baf350c4ab6bde2556340131 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/__init__.py @@ -0,0 +1,105 @@ +from .__version__ import __description__, __title__, __version__ +from ._api import * +from ._auth import * +from ._client import * +from ._config import * +from ._content import * +from ._exceptions import * +from ._models import * +from ._status_codes import * +from ._transports import * +from ._types import * +from ._urls import * + +try: + from ._main import main +except ImportError: # pragma: no cover + + def main() -> None: # type: ignore + import sys + + print( + "The httpx command line client could not run because the required " + "dependencies were not installed.\nMake sure you've installed " + "everything with: pip install 'httpx[cli]'" + ) + sys.exit(1) + + +__all__ = [ + "__description__", + "__title__", + "__version__", + "ASGITransport", + "AsyncBaseTransport", + "AsyncByteStream", + "AsyncClient", + "AsyncHTTPTransport", + "Auth", + "BaseTransport", + "BasicAuth", + "ByteStream", + "Client", + "CloseError", + "codes", + "ConnectError", + "ConnectTimeout", + "CookieConflict", + "Cookies", + "create_ssl_context", + "DecodingError", + "delete", + "DigestAuth", + "get", + "head", + "Headers", + "HTTPError", + "HTTPStatusError", + "HTTPTransport", + "InvalidURL", + "Limits", + "LocalProtocolError", + "main", + "MockTransport", + "NetRCAuth", + "NetworkError", + "options", + "patch", + "PoolTimeout", + "post", + "ProtocolError", + "Proxy", + "ProxyError", + "put", + "QueryParams", + "ReadError", + "ReadTimeout", + "RemoteProtocolError", + "request", + "Request", + "RequestError", + "RequestNotRead", + "Response", + "ResponseNotRead", + "stream", + "StreamClosed", + "StreamConsumed", + "StreamError", + "SyncByteStream", + "Timeout", + "TimeoutException", + "TooManyRedirects", + "TransportError", + "UnsupportedProtocol", + "URL", + "USE_CLIENT_DEFAULT", + "WriteError", + "WriteTimeout", + "WSGITransport", +] + + +__locals = locals() +for __name in __all__: + if not __name.startswith("__"): + setattr(__locals[__name], "__module__", "httpx") # noqa diff --git a/lib/python3.11/site-packages/httpx/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/httpx/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 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0000000000000000000000000000000000000000..c3cda1ecda8629edbdca2e3bc04bc51dba5e1430 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_api.py @@ -0,0 +1,438 @@ +from __future__ import annotations + +import typing +from contextlib import contextmanager + +from ._client import Client +from ._config import DEFAULT_TIMEOUT_CONFIG +from ._models import Response +from ._types import ( + AuthTypes, + CookieTypes, + HeaderTypes, + ProxyTypes, + QueryParamTypes, + RequestContent, + RequestData, + RequestFiles, + TimeoutTypes, +) +from ._urls import URL + +if typing.TYPE_CHECKING: + import ssl # pragma: no cover + + +__all__ = [ + "delete", + "get", + "head", + "options", + "patch", + "post", + "put", + "request", + "stream", +] + + +def request( + method: str, + url: URL | str, + *, + params: QueryParamTypes | None = None, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | None = None, + proxy: ProxyTypes | None = None, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + follow_redirects: bool = False, + verify: ssl.SSLContext | str | bool = True, + trust_env: bool = True, +) -> Response: + """ + Sends an HTTP request. + + **Parameters:** + + * **method** - HTTP method for the new `Request` object: `GET`, `OPTIONS`, + `HEAD`, `POST`, `PUT`, `PATCH`, or `DELETE`. + * **url** - URL for the new `Request` object. + * **params** - *(optional)* Query parameters to include in the URL, as a + string, dictionary, or sequence of two-tuples. + * **content** - *(optional)* Binary content to include in the body of the + request, as bytes or a byte iterator. + * **data** - *(optional)* Form data to include in the body of the request, + as a dictionary. + * **files** - *(optional)* A dictionary of upload files to include in the + body of the request. + * **json** - *(optional)* A JSON serializable object to include in the body + of the request. + * **headers** - *(optional)* Dictionary of HTTP headers to include in the + request. + * **cookies** - *(optional)* Dictionary of Cookie items to include in the + request. + * **auth** - *(optional)* An authentication class to use when sending the + request. + * **proxy** - *(optional)* A proxy URL where all the traffic should be routed. + * **timeout** - *(optional)* The timeout configuration to use when sending + the request. + * **follow_redirects** - *(optional)* Enables or disables HTTP redirects. + * **verify** - *(optional)* Either `True` to use an SSL context with the + default CA bundle, `False` to disable verification, or an instance of + `ssl.SSLContext` to use a custom context. + * **trust_env** - *(optional)* Enables or disables usage of environment + variables for configuration. + + **Returns:** `Response` + + Usage: + + ``` + >>> import httpx + >>> response = httpx.request('GET', 'https://httpbin.org/get') + >>> response + + ``` + """ + with Client( + cookies=cookies, + proxy=proxy, + verify=verify, + timeout=timeout, + trust_env=trust_env, + ) as client: + return client.request( + method=method, + url=url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + auth=auth, + follow_redirects=follow_redirects, + ) + + +@contextmanager +def stream( + method: str, + url: URL | str, + *, + params: QueryParamTypes | None = None, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | None = None, + proxy: ProxyTypes | None = None, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + follow_redirects: bool = False, + verify: ssl.SSLContext | str | bool = True, + trust_env: bool = True, +) -> typing.Iterator[Response]: + """ + Alternative to `httpx.request()` that streams the response body + instead of loading it into memory at once. + + **Parameters**: See `httpx.request`. + + See also: [Streaming Responses][0] + + [0]: /quickstart#streaming-responses + """ + with Client( + cookies=cookies, + proxy=proxy, + verify=verify, + timeout=timeout, + trust_env=trust_env, + ) as client: + with client.stream( + method=method, + url=url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + auth=auth, + follow_redirects=follow_redirects, + ) as response: + yield response + + +def get( + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | None = None, + proxy: ProxyTypes | None = None, + follow_redirects: bool = False, + verify: ssl.SSLContext | str | bool = True, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + trust_env: bool = True, +) -> Response: + """ + Sends a `GET` request. + + **Parameters**: See `httpx.request`. + + Note that the `data`, `files`, `json` and `content` parameters are not available + on this function, as `GET` requests should not include a request body. + """ + return request( + "GET", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + proxy=proxy, + follow_redirects=follow_redirects, + verify=verify, + timeout=timeout, + trust_env=trust_env, + ) + + +def options( + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | None = None, + proxy: ProxyTypes | None = None, + follow_redirects: bool = False, + verify: ssl.SSLContext | str | bool = True, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + trust_env: bool = True, +) -> Response: + """ + Sends an `OPTIONS` request. + + **Parameters**: See `httpx.request`. + + Note that the `data`, `files`, `json` and `content` parameters are not available + on this function, as `OPTIONS` requests should not include a request body. + """ + return request( + "OPTIONS", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + proxy=proxy, + follow_redirects=follow_redirects, + verify=verify, + timeout=timeout, + trust_env=trust_env, + ) + + +def head( + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | None = None, + proxy: ProxyTypes | None = None, + follow_redirects: bool = False, + verify: ssl.SSLContext | str | bool = True, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + trust_env: bool = True, +) -> Response: + """ + Sends a `HEAD` request. + + **Parameters**: See `httpx.request`. + + Note that the `data`, `files`, `json` and `content` parameters are not available + on this function, as `HEAD` requests should not include a request body. + """ + return request( + "HEAD", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + proxy=proxy, + follow_redirects=follow_redirects, + verify=verify, + timeout=timeout, + trust_env=trust_env, + ) + + +def post( + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | None = None, + proxy: ProxyTypes | None = None, + follow_redirects: bool = False, + verify: ssl.SSLContext | str | bool = True, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + trust_env: bool = True, +) -> Response: + """ + Sends a `POST` request. + + **Parameters**: See `httpx.request`. + """ + return request( + "POST", + url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + proxy=proxy, + follow_redirects=follow_redirects, + verify=verify, + timeout=timeout, + trust_env=trust_env, + ) + + +def put( + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | None = None, + proxy: ProxyTypes | None = None, + follow_redirects: bool = False, + verify: ssl.SSLContext | str | bool = True, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + trust_env: bool = True, +) -> Response: + """ + Sends a `PUT` request. + + **Parameters**: See `httpx.request`. + """ + return request( + "PUT", + url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + proxy=proxy, + follow_redirects=follow_redirects, + verify=verify, + timeout=timeout, + trust_env=trust_env, + ) + + +def patch( + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | None = None, + proxy: ProxyTypes | None = None, + follow_redirects: bool = False, + verify: ssl.SSLContext | str | bool = True, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + trust_env: bool = True, +) -> Response: + """ + Sends a `PATCH` request. + + **Parameters**: See `httpx.request`. + """ + return request( + "PATCH", + url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + proxy=proxy, + follow_redirects=follow_redirects, + verify=verify, + timeout=timeout, + trust_env=trust_env, + ) + + +def delete( + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | None = None, + proxy: ProxyTypes | None = None, + follow_redirects: bool = False, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + verify: ssl.SSLContext | str | bool = True, + trust_env: bool = True, +) -> Response: + """ + Sends a `DELETE` request. + + **Parameters**: See `httpx.request`. + + Note that the `data`, `files`, `json` and `content` parameters are not available + on this function, as `DELETE` requests should not include a request body. + """ + return request( + "DELETE", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + proxy=proxy, + follow_redirects=follow_redirects, + verify=verify, + timeout=timeout, + trust_env=trust_env, + ) diff --git a/lib/python3.11/site-packages/httpx/_auth.py b/lib/python3.11/site-packages/httpx/_auth.py new file mode 100644 index 0000000000000000000000000000000000000000..b03971ab4b311d60790dc22ca24d9966426ec0a4 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_auth.py @@ -0,0 +1,348 @@ +from __future__ import annotations + +import hashlib +import os +import re +import time +import typing +from base64 import b64encode +from urllib.request import parse_http_list + +from ._exceptions import ProtocolError +from ._models import Cookies, Request, Response +from ._utils import to_bytes, to_str, unquote + +if typing.TYPE_CHECKING: # pragma: no cover + from hashlib import _Hash + + +__all__ = ["Auth", "BasicAuth", "DigestAuth", "NetRCAuth"] + + +class Auth: + """ + Base class for all authentication schemes. + + To implement a custom authentication scheme, subclass `Auth` and override + the `.auth_flow()` method. + + If the authentication scheme does I/O such as disk access or network calls, or uses + synchronization primitives such as locks, you should override `.sync_auth_flow()` + and/or `.async_auth_flow()` instead of `.auth_flow()` to provide specialized + implementations that will be used by `Client` and `AsyncClient` respectively. + """ + + requires_request_body = False + requires_response_body = False + + def auth_flow(self, request: Request) -> typing.Generator[Request, Response, None]: + """ + Execute the authentication flow. + + To dispatch a request, `yield` it: + + ``` + yield request + ``` + + The client will `.send()` the response back into the flow generator. You can + access it like so: + + ``` + response = yield request + ``` + + A `return` (or reaching the end of the generator) will result in the + client returning the last response obtained from the server. + + You can dispatch as many requests as is necessary. + """ + yield request + + def sync_auth_flow( + self, request: Request + ) -> typing.Generator[Request, Response, None]: + """ + Execute the authentication flow synchronously. + + By default, this defers to `.auth_flow()`. You should override this method + when the authentication scheme does I/O and/or uses concurrency primitives. + """ + if self.requires_request_body: + request.read() + + flow = self.auth_flow(request) + request = next(flow) + + while True: + response = yield request + if self.requires_response_body: + response.read() + + try: + request = flow.send(response) + except StopIteration: + break + + async def async_auth_flow( + self, request: Request + ) -> typing.AsyncGenerator[Request, Response]: + """ + Execute the authentication flow asynchronously. + + By default, this defers to `.auth_flow()`. You should override this method + when the authentication scheme does I/O and/or uses concurrency primitives. + """ + if self.requires_request_body: + await request.aread() + + flow = self.auth_flow(request) + request = next(flow) + + while True: + response = yield request + if self.requires_response_body: + await response.aread() + + try: + request = flow.send(response) + except StopIteration: + break + + +class FunctionAuth(Auth): + """ + Allows the 'auth' argument to be passed as a simple callable function, + that takes the request, and returns a new, modified request. + """ + + def __init__(self, func: typing.Callable[[Request], Request]) -> None: + self._func = func + + def auth_flow(self, request: Request) -> typing.Generator[Request, Response, None]: + yield self._func(request) + + +class BasicAuth(Auth): + """ + Allows the 'auth' argument to be passed as a (username, password) pair, + and uses HTTP Basic authentication. + """ + + def __init__(self, username: str | bytes, password: str | bytes) -> None: + self._auth_header = self._build_auth_header(username, password) + + def auth_flow(self, request: Request) -> typing.Generator[Request, Response, None]: + request.headers["Authorization"] = self._auth_header + yield request + + def _build_auth_header(self, username: str | bytes, password: str | bytes) -> str: + userpass = b":".join((to_bytes(username), to_bytes(password))) + token = b64encode(userpass).decode() + return f"Basic {token}" + + +class NetRCAuth(Auth): + """ + Use a 'netrc' file to lookup basic auth credentials based on the url host. + """ + + def __init__(self, file: str | None = None) -> None: + # Lazily import 'netrc'. + # There's no need for us to load this module unless 'NetRCAuth' is being used. + import netrc + + self._netrc_info = netrc.netrc(file) + + def auth_flow(self, request: Request) -> typing.Generator[Request, Response, None]: + auth_info = self._netrc_info.authenticators(request.url.host) + if auth_info is None or not auth_info[2]: + # The netrc file did not have authentication credentials for this host. + yield request + else: + # Build a basic auth header with credentials from the netrc file. + request.headers["Authorization"] = self._build_auth_header( + username=auth_info[0], password=auth_info[2] + ) + yield request + + def _build_auth_header(self, username: str | bytes, password: str | bytes) -> str: + userpass = b":".join((to_bytes(username), to_bytes(password))) + token = b64encode(userpass).decode() + return f"Basic {token}" + + +class DigestAuth(Auth): + _ALGORITHM_TO_HASH_FUNCTION: dict[str, typing.Callable[[bytes], _Hash]] = { + "MD5": hashlib.md5, + "MD5-SESS": hashlib.md5, + "SHA": hashlib.sha1, + "SHA-SESS": hashlib.sha1, + "SHA-256": hashlib.sha256, + "SHA-256-SESS": hashlib.sha256, + "SHA-512": hashlib.sha512, + "SHA-512-SESS": hashlib.sha512, + } + + def __init__(self, username: str | bytes, password: str | bytes) -> None: + self._username = to_bytes(username) + self._password = to_bytes(password) + self._last_challenge: _DigestAuthChallenge | None = None + self._nonce_count = 1 + + def auth_flow(self, request: Request) -> typing.Generator[Request, Response, None]: + if self._last_challenge: + request.headers["Authorization"] = self._build_auth_header( + request, self._last_challenge + ) + + response = yield request + + if response.status_code != 401 or "www-authenticate" not in response.headers: + # If the response is not a 401 then we don't + # need to build an authenticated request. + return + + for auth_header in response.headers.get_list("www-authenticate"): + if auth_header.lower().startswith("digest "): + break + else: + # If the response does not include a 'WWW-Authenticate: Digest ...' + # header, then we don't need to build an authenticated request. + return + + self._last_challenge = self._parse_challenge(request, response, auth_header) + self._nonce_count = 1 + + request.headers["Authorization"] = self._build_auth_header( + request, self._last_challenge + ) + if response.cookies: + Cookies(response.cookies).set_cookie_header(request=request) + yield request + + def _parse_challenge( + self, request: Request, response: Response, auth_header: str + ) -> _DigestAuthChallenge: + """ + Returns a challenge from a Digest WWW-Authenticate header. + These take the form of: + `Digest realm="realm@host.com",qop="auth,auth-int",nonce="abc",opaque="xyz"` + """ + scheme, _, fields = auth_header.partition(" ") + + # This method should only ever have been called with a Digest auth header. + assert scheme.lower() == "digest" + + header_dict: dict[str, str] = {} + for field in parse_http_list(fields): + key, value = field.strip().split("=", 1) + header_dict[key] = unquote(value) + + try: + realm = header_dict["realm"].encode() + nonce = header_dict["nonce"].encode() + algorithm = header_dict.get("algorithm", "MD5") + opaque = header_dict["opaque"].encode() if "opaque" in header_dict else None + qop = header_dict["qop"].encode() if "qop" in header_dict else None + return _DigestAuthChallenge( + realm=realm, nonce=nonce, algorithm=algorithm, opaque=opaque, qop=qop + ) + except KeyError as exc: + message = "Malformed Digest WWW-Authenticate header" + raise ProtocolError(message, request=request) from exc + + def _build_auth_header( + self, request: Request, challenge: _DigestAuthChallenge + ) -> str: + hash_func = self._ALGORITHM_TO_HASH_FUNCTION[challenge.algorithm.upper()] + + def digest(data: bytes) -> bytes: + return hash_func(data).hexdigest().encode() + + A1 = b":".join((self._username, challenge.realm, self._password)) + + path = request.url.raw_path + A2 = b":".join((request.method.encode(), path)) + # TODO: implement auth-int + HA2 = digest(A2) + + nc_value = b"%08x" % self._nonce_count + cnonce = self._get_client_nonce(self._nonce_count, challenge.nonce) + self._nonce_count += 1 + + HA1 = digest(A1) + if challenge.algorithm.lower().endswith("-sess"): + HA1 = digest(b":".join((HA1, challenge.nonce, cnonce))) + + qop = self._resolve_qop(challenge.qop, request=request) + if qop is None: + # Following RFC 2069 + digest_data = [HA1, challenge.nonce, HA2] + else: + # Following RFC 2617/7616 + digest_data = [HA1, challenge.nonce, nc_value, cnonce, qop, HA2] + + format_args = { + "username": self._username, + "realm": challenge.realm, + "nonce": challenge.nonce, + "uri": path, + "response": digest(b":".join(digest_data)), + "algorithm": challenge.algorithm.encode(), + } + if challenge.opaque: + format_args["opaque"] = challenge.opaque + if qop: + format_args["qop"] = b"auth" + format_args["nc"] = nc_value + format_args["cnonce"] = cnonce + + return "Digest " + self._get_header_value(format_args) + + def _get_client_nonce(self, nonce_count: int, nonce: bytes) -> bytes: + s = str(nonce_count).encode() + s += nonce + s += time.ctime().encode() + s += os.urandom(8) + + return hashlib.sha1(s).hexdigest()[:16].encode() + + def _get_header_value(self, header_fields: dict[str, bytes]) -> str: + NON_QUOTED_FIELDS = ("algorithm", "qop", "nc") + QUOTED_TEMPLATE = '{}="{}"' + NON_QUOTED_TEMPLATE = "{}={}" + + header_value = "" + for i, (field, value) in enumerate(header_fields.items()): + if i > 0: + header_value += ", " + template = ( + QUOTED_TEMPLATE + if field not in NON_QUOTED_FIELDS + else NON_QUOTED_TEMPLATE + ) + header_value += template.format(field, to_str(value)) + + return header_value + + def _resolve_qop(self, qop: bytes | None, request: Request) -> bytes | None: + if qop is None: + return None + qops = re.split(b", ?", qop) + if b"auth" in qops: + return b"auth" + + if qops == [b"auth-int"]: + raise NotImplementedError("Digest auth-int support is not yet implemented") + + message = f'Unexpected qop value "{qop!r}" in digest auth' + raise ProtocolError(message, request=request) + + +class _DigestAuthChallenge(typing.NamedTuple): + realm: bytes + nonce: bytes + algorithm: str + opaque: bytes | None + qop: bytes | None diff --git a/lib/python3.11/site-packages/httpx/_client.py b/lib/python3.11/site-packages/httpx/_client.py new file mode 100644 index 0000000000000000000000000000000000000000..2249231f8c3b912c731ff160344d3672e2f11738 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_client.py @@ -0,0 +1,2019 @@ +from __future__ import annotations + +import datetime +import enum +import logging +import time +import typing +import warnings +from contextlib import asynccontextmanager, contextmanager +from types import TracebackType + +from .__version__ import __version__ +from ._auth import Auth, BasicAuth, FunctionAuth +from ._config import ( + DEFAULT_LIMITS, + DEFAULT_MAX_REDIRECTS, + DEFAULT_TIMEOUT_CONFIG, + Limits, + Proxy, + Timeout, +) +from ._decoders import SUPPORTED_DECODERS +from ._exceptions import ( + InvalidURL, + RemoteProtocolError, + TooManyRedirects, + request_context, +) +from ._models import Cookies, Headers, Request, Response +from ._status_codes import codes +from ._transports.base import AsyncBaseTransport, BaseTransport +from ._transports.default import AsyncHTTPTransport, HTTPTransport +from ._types import ( + AsyncByteStream, + AuthTypes, + CertTypes, + CookieTypes, + HeaderTypes, + ProxyTypes, + QueryParamTypes, + RequestContent, + RequestData, + RequestExtensions, + RequestFiles, + SyncByteStream, + TimeoutTypes, +) +from ._urls import URL, QueryParams +from ._utils import URLPattern, get_environment_proxies + +if typing.TYPE_CHECKING: + import ssl # pragma: no cover + +__all__ = ["USE_CLIENT_DEFAULT", "AsyncClient", "Client"] + +# The type annotation for @classmethod and context managers here follows PEP 484 +# https://www.python.org/dev/peps/pep-0484/#annotating-instance-and-class-methods +T = typing.TypeVar("T", bound="Client") +U = typing.TypeVar("U", bound="AsyncClient") + + +def _is_https_redirect(url: URL, location: URL) -> bool: + """ + Return 'True' if 'location' is a HTTPS upgrade of 'url' + """ + if url.host != location.host: + return False + + return ( + url.scheme == "http" + and _port_or_default(url) == 80 + and location.scheme == "https" + and _port_or_default(location) == 443 + ) + + +def _port_or_default(url: URL) -> int | None: + if url.port is not None: + return url.port + return {"http": 80, "https": 443}.get(url.scheme) + + +def _same_origin(url: URL, other: URL) -> bool: + """ + Return 'True' if the given URLs share the same origin. + """ + return ( + url.scheme == other.scheme + and url.host == other.host + and _port_or_default(url) == _port_or_default(other) + ) + + +class UseClientDefault: + """ + For some parameters such as `auth=...` and `timeout=...` we need to be able + to indicate the default "unset" state, in a way that is distinctly different + to using `None`. + + The default "unset" state indicates that whatever default is set on the + client should be used. This is different to setting `None`, which + explicitly disables the parameter, possibly overriding a client default. + + For example we use `timeout=USE_CLIENT_DEFAULT` in the `request()` signature. + Omitting the `timeout` parameter will send a request using whatever default + timeout has been configured on the client. Including `timeout=None` will + ensure no timeout is used. + + Note that user code shouldn't need to use the `USE_CLIENT_DEFAULT` constant, + but it is used internally when a parameter is not included. + """ + + +USE_CLIENT_DEFAULT = UseClientDefault() + + +logger = logging.getLogger("httpx") + +USER_AGENT = f"python-httpx/{__version__}" +ACCEPT_ENCODING = ", ".join( + [key for key in SUPPORTED_DECODERS.keys() if key != "identity"] +) + + +class ClientState(enum.Enum): + # UNOPENED: + # The client has been instantiated, but has not been used to send a request, + # or been opened by entering the context of a `with` block. + UNOPENED = 1 + # OPENED: + # The client has either sent a request, or is within a `with` block. + OPENED = 2 + # CLOSED: + # The client has either exited the `with` block, or `close()` has + # been called explicitly. + CLOSED = 3 + + +class BoundSyncStream(SyncByteStream): + """ + A byte stream that is bound to a given response instance, and that + ensures the `response.elapsed` is set once the response is closed. + """ + + def __init__( + self, stream: SyncByteStream, response: Response, start: float + ) -> None: + self._stream = stream + self._response = response + self._start = start + + def __iter__(self) -> typing.Iterator[bytes]: + for chunk in self._stream: + yield chunk + + def close(self) -> None: + elapsed = time.perf_counter() - self._start + self._response.elapsed = datetime.timedelta(seconds=elapsed) + self._stream.close() + + +class BoundAsyncStream(AsyncByteStream): + """ + An async byte stream that is bound to a given response instance, and that + ensures the `response.elapsed` is set once the response is closed. + """ + + def __init__( + self, stream: AsyncByteStream, response: Response, start: float + ) -> None: + self._stream = stream + self._response = response + self._start = start + + async def __aiter__(self) -> typing.AsyncIterator[bytes]: + async for chunk in self._stream: + yield chunk + + async def aclose(self) -> None: + elapsed = time.perf_counter() - self._start + self._response.elapsed = datetime.timedelta(seconds=elapsed) + await self._stream.aclose() + + +EventHook = typing.Callable[..., typing.Any] + + +class BaseClient: + def __init__( + self, + *, + auth: AuthTypes | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + follow_redirects: bool = False, + max_redirects: int = DEFAULT_MAX_REDIRECTS, + event_hooks: None | (typing.Mapping[str, list[EventHook]]) = None, + base_url: URL | str = "", + trust_env: bool = True, + default_encoding: str | typing.Callable[[bytes], str] = "utf-8", + ) -> None: + event_hooks = {} if event_hooks is None else event_hooks + + self._base_url = self._enforce_trailing_slash(URL(base_url)) + + self._auth = self._build_auth(auth) + self._params = QueryParams(params) + self.headers = Headers(headers) + self._cookies = Cookies(cookies) + self._timeout = Timeout(timeout) + self.follow_redirects = follow_redirects + self.max_redirects = max_redirects + self._event_hooks = { + "request": list(event_hooks.get("request", [])), + "response": list(event_hooks.get("response", [])), + } + self._trust_env = trust_env + self._default_encoding = default_encoding + self._state = ClientState.UNOPENED + + @property + def is_closed(self) -> bool: + """ + Check if the client being closed + """ + return self._state == ClientState.CLOSED + + @property + def trust_env(self) -> bool: + return self._trust_env + + def _enforce_trailing_slash(self, url: URL) -> URL: + if url.raw_path.endswith(b"/"): + return url + return url.copy_with(raw_path=url.raw_path + b"/") + + def _get_proxy_map( + self, proxy: ProxyTypes | None, allow_env_proxies: bool + ) -> dict[str, Proxy | None]: + if proxy is None: + if allow_env_proxies: + return { + key: None if url is None else Proxy(url=url) + for key, url in get_environment_proxies().items() + } + return {} + else: + proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy + return {"all://": proxy} + + @property + def timeout(self) -> Timeout: + return self._timeout + + @timeout.setter + def timeout(self, timeout: TimeoutTypes) -> None: + self._timeout = Timeout(timeout) + + @property + def event_hooks(self) -> dict[str, list[EventHook]]: + return self._event_hooks + + @event_hooks.setter + def event_hooks(self, event_hooks: dict[str, list[EventHook]]) -> None: + self._event_hooks = { + "request": list(event_hooks.get("request", [])), + "response": list(event_hooks.get("response", [])), + } + + @property + def auth(self) -> Auth | None: + """ + Authentication class used when none is passed at the request-level. + + See also [Authentication][0]. + + [0]: /quickstart/#authentication + """ + return self._auth + + @auth.setter + def auth(self, auth: AuthTypes) -> None: + self._auth = self._build_auth(auth) + + @property + def base_url(self) -> URL: + """ + Base URL to use when sending requests with relative URLs. + """ + return self._base_url + + @base_url.setter + def base_url(self, url: URL | str) -> None: + self._base_url = self._enforce_trailing_slash(URL(url)) + + @property + def headers(self) -> Headers: + """ + HTTP headers to include when sending requests. + """ + return self._headers + + @headers.setter + def headers(self, headers: HeaderTypes) -> None: + client_headers = Headers( + { + b"Accept": b"*/*", + b"Accept-Encoding": ACCEPT_ENCODING.encode("ascii"), + b"Connection": b"keep-alive", + b"User-Agent": USER_AGENT.encode("ascii"), + } + ) + client_headers.update(headers) + self._headers = client_headers + + @property + def cookies(self) -> Cookies: + """ + Cookie values to include when sending requests. + """ + return self._cookies + + @cookies.setter + def cookies(self, cookies: CookieTypes) -> None: + self._cookies = Cookies(cookies) + + @property + def params(self) -> QueryParams: + """ + Query parameters to include in the URL when sending requests. + """ + return self._params + + @params.setter + def params(self, params: QueryParamTypes) -> None: + self._params = QueryParams(params) + + def build_request( + self, + method: str, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Request: + """ + Build and return a request instance. + + * The `params`, `headers` and `cookies` arguments + are merged with any values set on the client. + * The `url` argument is merged with any `base_url` set on the client. + + See also: [Request instances][0] + + [0]: /advanced/clients/#request-instances + """ + url = self._merge_url(url) + headers = self._merge_headers(headers) + cookies = self._merge_cookies(cookies) + params = self._merge_queryparams(params) + extensions = {} if extensions is None else extensions + if "timeout" not in extensions: + timeout = ( + self.timeout + if isinstance(timeout, UseClientDefault) + else Timeout(timeout) + ) + extensions = dict(**extensions, timeout=timeout.as_dict()) + return Request( + method, + url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + extensions=extensions, + ) + + def _merge_url(self, url: URL | str) -> URL: + """ + Merge a URL argument together with any 'base_url' on the client, + to create the URL used for the outgoing request. + """ + merge_url = URL(url) + if merge_url.is_relative_url: + # To merge URLs we always append to the base URL. To get this + # behaviour correct we always ensure the base URL ends in a '/' + # separator, and strip any leading '/' from the merge URL. + # + # So, eg... + # + # >>> client = Client(base_url="https://www.example.com/subpath") + # >>> client.base_url + # URL('https://www.example.com/subpath/') + # >>> client.build_request("GET", "/path").url + # URL('https://www.example.com/subpath/path') + merge_raw_path = self.base_url.raw_path + merge_url.raw_path.lstrip(b"/") + return self.base_url.copy_with(raw_path=merge_raw_path) + return merge_url + + def _merge_cookies(self, cookies: CookieTypes | None = None) -> CookieTypes | None: + """ + Merge a cookies argument together with any cookies on the client, + to create the cookies used for the outgoing request. + """ + if cookies or self.cookies: + merged_cookies = Cookies(self.cookies) + merged_cookies.update(cookies) + return merged_cookies + return cookies + + def _merge_headers(self, headers: HeaderTypes | None = None) -> HeaderTypes | None: + """ + Merge a headers argument together with any headers on the client, + to create the headers used for the outgoing request. + """ + merged_headers = Headers(self.headers) + merged_headers.update(headers) + return merged_headers + + def _merge_queryparams( + self, params: QueryParamTypes | None = None + ) -> QueryParamTypes | None: + """ + Merge a queryparams argument together with any queryparams on the client, + to create the queryparams used for the outgoing request. + """ + if params or self.params: + merged_queryparams = QueryParams(self.params) + return merged_queryparams.merge(params) + return params + + def _build_auth(self, auth: AuthTypes | None) -> Auth | None: + if auth is None: + return None + elif isinstance(auth, tuple): + return BasicAuth(username=auth[0], password=auth[1]) + elif isinstance(auth, Auth): + return auth + elif callable(auth): + return FunctionAuth(func=auth) + else: + raise TypeError(f'Invalid "auth" argument: {auth!r}') + + def _build_request_auth( + self, + request: Request, + auth: AuthTypes | UseClientDefault | None = USE_CLIENT_DEFAULT, + ) -> Auth: + auth = ( + self._auth if isinstance(auth, UseClientDefault) else self._build_auth(auth) + ) + + if auth is not None: + return auth + + username, password = request.url.username, request.url.password + if username or password: + return BasicAuth(username=username, password=password) + + return Auth() + + def _build_redirect_request(self, request: Request, response: Response) -> Request: + """ + Given a request and a redirect response, return a new request that + should be used to effect the redirect. + """ + method = self._redirect_method(request, response) + url = self._redirect_url(request, response) + headers = self._redirect_headers(request, url, method) + stream = self._redirect_stream(request, method) + cookies = Cookies(self.cookies) + return Request( + method=method, + url=url, + headers=headers, + cookies=cookies, + stream=stream, + extensions=request.extensions, + ) + + def _redirect_method(self, request: Request, response: Response) -> str: + """ + When being redirected we may want to change the method of the request + based on certain specs or browser behavior. + """ + method = request.method + + # https://tools.ietf.org/html/rfc7231#section-6.4.4 + if response.status_code == codes.SEE_OTHER and method != "HEAD": + method = "GET" + + # Do what the browsers do, despite standards... + # Turn 302s into GETs. + if response.status_code == codes.FOUND and method != "HEAD": + method = "GET" + + # If a POST is responded to with a 301, turn it into a GET. + # This bizarre behaviour is explained in 'requests' issue 1704. + if response.status_code == codes.MOVED_PERMANENTLY and method == "POST": + method = "GET" + + return method + + def _redirect_url(self, request: Request, response: Response) -> URL: + """ + Return the URL for the redirect to follow. + """ + location = response.headers["Location"] + + try: + url = URL(location) + except InvalidURL as exc: + raise RemoteProtocolError( + f"Invalid URL in location header: {exc}.", request=request + ) from None + + # Handle malformed 'Location' headers that are "absolute" form, have no host. + # See: https://github.com/encode/httpx/issues/771 + if url.scheme and not url.host: + url = url.copy_with(host=request.url.host) + + # Facilitate relative 'Location' headers, as allowed by RFC 7231. + # (e.g. '/path/to/resource' instead of 'http://domain.tld/path/to/resource') + if url.is_relative_url: + url = request.url.join(url) + + # Attach previous fragment if needed (RFC 7231 7.1.2) + if request.url.fragment and not url.fragment: + url = url.copy_with(fragment=request.url.fragment) + + return url + + def _redirect_headers(self, request: Request, url: URL, method: str) -> Headers: + """ + Return the headers that should be used for the redirect request. + """ + headers = Headers(request.headers) + + if not _same_origin(url, request.url): + if not _is_https_redirect(request.url, url): + # Strip Authorization headers when responses are redirected + # away from the origin. (Except for direct HTTP to HTTPS redirects.) + headers.pop("Authorization", None) + + # Update the Host header. + headers["Host"] = url.netloc.decode("ascii") + + if method != request.method and method == "GET": + # If we've switch to a 'GET' request, then strip any headers which + # are only relevant to the request body. + headers.pop("Content-Length", None) + headers.pop("Transfer-Encoding", None) + + # We should use the client cookie store to determine any cookie header, + # rather than whatever was on the original outgoing request. + headers.pop("Cookie", None) + + return headers + + def _redirect_stream( + self, request: Request, method: str + ) -> SyncByteStream | AsyncByteStream | None: + """ + Return the body that should be used for the redirect request. + """ + if method != request.method and method == "GET": + return None + + return request.stream + + def _set_timeout(self, request: Request) -> None: + if "timeout" not in request.extensions: + timeout = ( + self.timeout + if isinstance(self.timeout, UseClientDefault) + else Timeout(self.timeout) + ) + request.extensions = dict(**request.extensions, timeout=timeout.as_dict()) + + +class Client(BaseClient): + """ + An HTTP client, with connection pooling, HTTP/2, redirects, cookie persistence, etc. + + It can be shared between threads. + + Usage: + + ```python + >>> client = httpx.Client() + >>> response = client.get('https://example.org') + ``` + + **Parameters:** + + * **auth** - *(optional)* An authentication class to use when sending + requests. + * **params** - *(optional)* Query parameters to include in request URLs, as + a string, dictionary, or sequence of two-tuples. + * **headers** - *(optional)* Dictionary of HTTP headers to include when + sending requests. + * **cookies** - *(optional)* Dictionary of Cookie items to include when + sending requests. + * **verify** - *(optional)* Either `True` to use an SSL context with the + default CA bundle, `False` to disable verification, or an instance of + `ssl.SSLContext` to use a custom context. + * **http2** - *(optional)* A boolean indicating if HTTP/2 support should be + enabled. Defaults to `False`. + * **proxy** - *(optional)* A proxy URL where all the traffic should be routed. + * **timeout** - *(optional)* The timeout configuration to use when sending + requests. + * **limits** - *(optional)* The limits configuration to use. + * **max_redirects** - *(optional)* The maximum number of redirect responses + that should be followed. + * **base_url** - *(optional)* A URL to use as the base when building + request URLs. + * **transport** - *(optional)* A transport class to use for sending requests + over the network. + * **trust_env** - *(optional)* Enables or disables usage of environment + variables for configuration. + * **default_encoding** - *(optional)* The default encoding to use for decoding + response text, if no charset information is included in a response Content-Type + header. Set to a callable for automatic character set detection. Default: "utf-8". + """ + + def __init__( + self, + *, + auth: AuthTypes | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + verify: ssl.SSLContext | str | bool = True, + cert: CertTypes | None = None, + trust_env: bool = True, + http1: bool = True, + http2: bool = False, + proxy: ProxyTypes | None = None, + mounts: None | (typing.Mapping[str, BaseTransport | None]) = None, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + follow_redirects: bool = False, + limits: Limits = DEFAULT_LIMITS, + max_redirects: int = DEFAULT_MAX_REDIRECTS, + event_hooks: None | (typing.Mapping[str, list[EventHook]]) = None, + base_url: URL | str = "", + transport: BaseTransport | None = None, + default_encoding: str | typing.Callable[[bytes], str] = "utf-8", + ) -> None: + super().__init__( + auth=auth, + params=params, + headers=headers, + cookies=cookies, + timeout=timeout, + follow_redirects=follow_redirects, + max_redirects=max_redirects, + event_hooks=event_hooks, + base_url=base_url, + trust_env=trust_env, + default_encoding=default_encoding, + ) + + if http2: + try: + import h2 # noqa + except ImportError: # pragma: no cover + raise ImportError( + "Using http2=True, but the 'h2' package is not installed. " + "Make sure to install httpx using `pip install httpx[http2]`." + ) from None + + allow_env_proxies = trust_env and transport is None + proxy_map = self._get_proxy_map(proxy, allow_env_proxies) + + self._transport = self._init_transport( + verify=verify, + cert=cert, + trust_env=trust_env, + http1=http1, + http2=http2, + limits=limits, + transport=transport, + ) + self._mounts: dict[URLPattern, BaseTransport | None] = { + URLPattern(key): None + if proxy is None + else self._init_proxy_transport( + proxy, + verify=verify, + cert=cert, + trust_env=trust_env, + http1=http1, + http2=http2, + limits=limits, + ) + for key, proxy in proxy_map.items() + } + if mounts is not None: + self._mounts.update( + {URLPattern(key): transport for key, transport in mounts.items()} + ) + + self._mounts = dict(sorted(self._mounts.items())) + + def _init_transport( + self, + verify: ssl.SSLContext | str | bool = True, + cert: CertTypes | None = None, + trust_env: bool = True, + http1: bool = True, + http2: bool = False, + limits: Limits = DEFAULT_LIMITS, + transport: BaseTransport | None = None, + ) -> BaseTransport: + if transport is not None: + return transport + + return HTTPTransport( + verify=verify, + cert=cert, + trust_env=trust_env, + http1=http1, + http2=http2, + limits=limits, + ) + + def _init_proxy_transport( + self, + proxy: Proxy, + verify: ssl.SSLContext | str | bool = True, + cert: CertTypes | None = None, + trust_env: bool = True, + http1: bool = True, + http2: bool = False, + limits: Limits = DEFAULT_LIMITS, + ) -> BaseTransport: + return HTTPTransport( + verify=verify, + cert=cert, + trust_env=trust_env, + http1=http1, + http2=http2, + limits=limits, + proxy=proxy, + ) + + def _transport_for_url(self, url: URL) -> BaseTransport: + """ + Returns the transport instance that should be used for a given URL. + This will either be the standard connection pool, or a proxy. + """ + for pattern, transport in self._mounts.items(): + if pattern.matches(url): + return self._transport if transport is None else transport + + return self._transport + + def request( + self, + method: str, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault | None = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Build and send a request. + + Equivalent to: + + ```python + request = client.build_request(...) + response = client.send(request, ...) + ``` + + See `Client.build_request()`, `Client.send()` and + [Merging of configuration][0] for how the various parameters + are merged with client-level configuration. + + [0]: /advanced/clients/#merging-of-configuration + """ + if cookies is not None: + message = ( + "Setting per-request cookies=<...> is being deprecated, because " + "the expected behaviour on cookie persistence is ambiguous. Set " + "cookies directly on the client instance instead." + ) + warnings.warn(message, DeprecationWarning, stacklevel=2) + + request = self.build_request( + method=method, + url=url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + timeout=timeout, + extensions=extensions, + ) + return self.send(request, auth=auth, follow_redirects=follow_redirects) + + @contextmanager + def stream( + self, + method: str, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault | None = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> typing.Iterator[Response]: + """ + Alternative to `httpx.request()` that streams the response body + instead of loading it into memory at once. + + **Parameters**: See `httpx.request`. + + See also: [Streaming Responses][0] + + [0]: /quickstart#streaming-responses + """ + request = self.build_request( + method=method, + url=url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + timeout=timeout, + extensions=extensions, + ) + response = self.send( + request=request, + auth=auth, + follow_redirects=follow_redirects, + stream=True, + ) + try: + yield response + finally: + response.close() + + def send( + self, + request: Request, + *, + stream: bool = False, + auth: AuthTypes | UseClientDefault | None = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + ) -> Response: + """ + Send a request. + + The request is sent as-is, unmodified. + + Typically you'll want to build one with `Client.build_request()` + so that any client-level configuration is merged into the request, + but passing an explicit `httpx.Request()` is supported as well. + + See also: [Request instances][0] + + [0]: /advanced/clients/#request-instances + """ + if self._state == ClientState.CLOSED: + raise RuntimeError("Cannot send a request, as the client has been closed.") + + self._state = ClientState.OPENED + follow_redirects = ( + self.follow_redirects + if isinstance(follow_redirects, UseClientDefault) + else follow_redirects + ) + + self._set_timeout(request) + + auth = self._build_request_auth(request, auth) + + response = self._send_handling_auth( + request, + auth=auth, + follow_redirects=follow_redirects, + history=[], + ) + try: + if not stream: + response.read() + + return response + + except BaseException as exc: + response.close() + raise exc + + def _send_handling_auth( + self, + request: Request, + auth: Auth, + follow_redirects: bool, + history: list[Response], + ) -> Response: + auth_flow = auth.sync_auth_flow(request) + try: + request = next(auth_flow) + + while True: + response = self._send_handling_redirects( + request, + follow_redirects=follow_redirects, + history=history, + ) + try: + try: + next_request = auth_flow.send(response) + except StopIteration: + return response + + response.history = list(history) + response.read() + request = next_request + history.append(response) + + except BaseException as exc: + response.close() + raise exc + finally: + auth_flow.close() + + def _send_handling_redirects( + self, + request: Request, + follow_redirects: bool, + history: list[Response], + ) -> Response: + while True: + if len(history) > self.max_redirects: + raise TooManyRedirects( + "Exceeded maximum allowed redirects.", request=request + ) + + for hook in self._event_hooks["request"]: + hook(request) + + response = self._send_single_request(request) + try: + for hook in self._event_hooks["response"]: + hook(response) + response.history = list(history) + + if not response.has_redirect_location: + return response + + request = self._build_redirect_request(request, response) + history = history + [response] + + if follow_redirects: + response.read() + else: + response.next_request = request + return response + + except BaseException as exc: + response.close() + raise exc + + def _send_single_request(self, request: Request) -> Response: + """ + Sends a single request, without handling any redirections. + """ + transport = self._transport_for_url(request.url) + start = time.perf_counter() + + if not isinstance(request.stream, SyncByteStream): + raise RuntimeError( + "Attempted to send an async request with a sync Client instance." + ) + + with request_context(request=request): + response = transport.handle_request(request) + + assert isinstance(response.stream, SyncByteStream) + + response.request = request + response.stream = BoundSyncStream( + response.stream, response=response, start=start + ) + self.cookies.extract_cookies(response) + response.default_encoding = self._default_encoding + + logger.info( + 'HTTP Request: %s %s "%s %d %s"', + request.method, + request.url, + response.http_version, + response.status_code, + response.reason_phrase, + ) + + return response + + def get( + self, + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault | None = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `GET` request. + + **Parameters**: See `httpx.request`. + """ + return self.request( + "GET", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + def options( + self, + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send an `OPTIONS` request. + + **Parameters**: See `httpx.request`. + """ + return self.request( + "OPTIONS", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + def head( + self, + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `HEAD` request. + + **Parameters**: See `httpx.request`. + """ + return self.request( + "HEAD", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + def post( + self, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `POST` request. + + **Parameters**: See `httpx.request`. + """ + return self.request( + "POST", + url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + def put( + self, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `PUT` request. + + **Parameters**: See `httpx.request`. + """ + return self.request( + "PUT", + url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + def patch( + self, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `PATCH` request. + + **Parameters**: See `httpx.request`. + """ + return self.request( + "PATCH", + url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + def delete( + self, + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `DELETE` request. + + **Parameters**: See `httpx.request`. + """ + return self.request( + "DELETE", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + def close(self) -> None: + """ + Close transport and proxies. + """ + if self._state != ClientState.CLOSED: + self._state = ClientState.CLOSED + + self._transport.close() + for transport in self._mounts.values(): + if transport is not None: + transport.close() + + def __enter__(self: T) -> T: + if self._state != ClientState.UNOPENED: + msg = { + ClientState.OPENED: "Cannot open a client instance more than once.", + ClientState.CLOSED: ( + "Cannot reopen a client instance, once it has been closed." + ), + }[self._state] + raise RuntimeError(msg) + + self._state = ClientState.OPENED + + self._transport.__enter__() + for transport in self._mounts.values(): + if transport is not None: + transport.__enter__() + return self + + def __exit__( + self, + exc_type: type[BaseException] | None = None, + exc_value: BaseException | None = None, + traceback: TracebackType | None = None, + ) -> None: + self._state = ClientState.CLOSED + + self._transport.__exit__(exc_type, exc_value, traceback) + for transport in self._mounts.values(): + if transport is not None: + transport.__exit__(exc_type, exc_value, traceback) + + +class AsyncClient(BaseClient): + """ + An asynchronous HTTP client, with connection pooling, HTTP/2, redirects, + cookie persistence, etc. + + It can be shared between tasks. + + Usage: + + ```python + >>> async with httpx.AsyncClient() as client: + >>> response = await client.get('https://example.org') + ``` + + **Parameters:** + + * **auth** - *(optional)* An authentication class to use when sending + requests. + * **params** - *(optional)* Query parameters to include in request URLs, as + a string, dictionary, or sequence of two-tuples. + * **headers** - *(optional)* Dictionary of HTTP headers to include when + sending requests. + * **cookies** - *(optional)* Dictionary of Cookie items to include when + sending requests. + * **verify** - *(optional)* Either `True` to use an SSL context with the + default CA bundle, `False` to disable verification, or an instance of + `ssl.SSLContext` to use a custom context. + * **http2** - *(optional)* A boolean indicating if HTTP/2 support should be + enabled. Defaults to `False`. + * **proxy** - *(optional)* A proxy URL where all the traffic should be routed. + * **timeout** - *(optional)* The timeout configuration to use when sending + requests. + * **limits** - *(optional)* The limits configuration to use. + * **max_redirects** - *(optional)* The maximum number of redirect responses + that should be followed. + * **base_url** - *(optional)* A URL to use as the base when building + request URLs. + * **transport** - *(optional)* A transport class to use for sending requests + over the network. + * **trust_env** - *(optional)* Enables or disables usage of environment + variables for configuration. + * **default_encoding** - *(optional)* The default encoding to use for decoding + response text, if no charset information is included in a response Content-Type + header. Set to a callable for automatic character set detection. Default: "utf-8". + """ + + def __init__( + self, + *, + auth: AuthTypes | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + verify: ssl.SSLContext | str | bool = True, + cert: CertTypes | None = None, + http1: bool = True, + http2: bool = False, + proxy: ProxyTypes | None = None, + mounts: None | (typing.Mapping[str, AsyncBaseTransport | None]) = None, + timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, + follow_redirects: bool = False, + limits: Limits = DEFAULT_LIMITS, + max_redirects: int = DEFAULT_MAX_REDIRECTS, + event_hooks: None | (typing.Mapping[str, list[EventHook]]) = None, + base_url: URL | str = "", + transport: AsyncBaseTransport | None = None, + trust_env: bool = True, + default_encoding: str | typing.Callable[[bytes], str] = "utf-8", + ) -> None: + super().__init__( + auth=auth, + params=params, + headers=headers, + cookies=cookies, + timeout=timeout, + follow_redirects=follow_redirects, + max_redirects=max_redirects, + event_hooks=event_hooks, + base_url=base_url, + trust_env=trust_env, + default_encoding=default_encoding, + ) + + if http2: + try: + import h2 # noqa + except ImportError: # pragma: no cover + raise ImportError( + "Using http2=True, but the 'h2' package is not installed. " + "Make sure to install httpx using `pip install httpx[http2]`." + ) from None + + allow_env_proxies = trust_env and transport is None + proxy_map = self._get_proxy_map(proxy, allow_env_proxies) + + self._transport = self._init_transport( + verify=verify, + cert=cert, + trust_env=trust_env, + http1=http1, + http2=http2, + limits=limits, + transport=transport, + ) + + self._mounts: dict[URLPattern, AsyncBaseTransport | None] = { + URLPattern(key): None + if proxy is None + else self._init_proxy_transport( + proxy, + verify=verify, + cert=cert, + trust_env=trust_env, + http1=http1, + http2=http2, + limits=limits, + ) + for key, proxy in proxy_map.items() + } + if mounts is not None: + self._mounts.update( + {URLPattern(key): transport for key, transport in mounts.items()} + ) + self._mounts = dict(sorted(self._mounts.items())) + + def _init_transport( + self, + verify: ssl.SSLContext | str | bool = True, + cert: CertTypes | None = None, + trust_env: bool = True, + http1: bool = True, + http2: bool = False, + limits: Limits = DEFAULT_LIMITS, + transport: AsyncBaseTransport | None = None, + ) -> AsyncBaseTransport: + if transport is not None: + return transport + + return AsyncHTTPTransport( + verify=verify, + cert=cert, + trust_env=trust_env, + http1=http1, + http2=http2, + limits=limits, + ) + + def _init_proxy_transport( + self, + proxy: Proxy, + verify: ssl.SSLContext | str | bool = True, + cert: CertTypes | None = None, + trust_env: bool = True, + http1: bool = True, + http2: bool = False, + limits: Limits = DEFAULT_LIMITS, + ) -> AsyncBaseTransport: + return AsyncHTTPTransport( + verify=verify, + cert=cert, + trust_env=trust_env, + http1=http1, + http2=http2, + limits=limits, + proxy=proxy, + ) + + def _transport_for_url(self, url: URL) -> AsyncBaseTransport: + """ + Returns the transport instance that should be used for a given URL. + This will either be the standard connection pool, or a proxy. + """ + for pattern, transport in self._mounts.items(): + if pattern.matches(url): + return self._transport if transport is None else transport + + return self._transport + + async def request( + self, + method: str, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault | None = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Build and send a request. + + Equivalent to: + + ```python + request = client.build_request(...) + response = await client.send(request, ...) + ``` + + See `AsyncClient.build_request()`, `AsyncClient.send()` + and [Merging of configuration][0] for how the various parameters + are merged with client-level configuration. + + [0]: /advanced/clients/#merging-of-configuration + """ + + if cookies is not None: # pragma: no cover + message = ( + "Setting per-request cookies=<...> is being deprecated, because " + "the expected behaviour on cookie persistence is ambiguous. Set " + "cookies directly on the client instance instead." + ) + warnings.warn(message, DeprecationWarning, stacklevel=2) + + request = self.build_request( + method=method, + url=url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + timeout=timeout, + extensions=extensions, + ) + return await self.send(request, auth=auth, follow_redirects=follow_redirects) + + @asynccontextmanager + async def stream( + self, + method: str, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault | None = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> typing.AsyncIterator[Response]: + """ + Alternative to `httpx.request()` that streams the response body + instead of loading it into memory at once. + + **Parameters**: See `httpx.request`. + + See also: [Streaming Responses][0] + + [0]: /quickstart#streaming-responses + """ + request = self.build_request( + method=method, + url=url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + timeout=timeout, + extensions=extensions, + ) + response = await self.send( + request=request, + auth=auth, + follow_redirects=follow_redirects, + stream=True, + ) + try: + yield response + finally: + await response.aclose() + + async def send( + self, + request: Request, + *, + stream: bool = False, + auth: AuthTypes | UseClientDefault | None = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + ) -> Response: + """ + Send a request. + + The request is sent as-is, unmodified. + + Typically you'll want to build one with `AsyncClient.build_request()` + so that any client-level configuration is merged into the request, + but passing an explicit `httpx.Request()` is supported as well. + + See also: [Request instances][0] + + [0]: /advanced/clients/#request-instances + """ + if self._state == ClientState.CLOSED: + raise RuntimeError("Cannot send a request, as the client has been closed.") + + self._state = ClientState.OPENED + follow_redirects = ( + self.follow_redirects + if isinstance(follow_redirects, UseClientDefault) + else follow_redirects + ) + + self._set_timeout(request) + + auth = self._build_request_auth(request, auth) + + response = await self._send_handling_auth( + request, + auth=auth, + follow_redirects=follow_redirects, + history=[], + ) + try: + if not stream: + await response.aread() + + return response + + except BaseException as exc: + await response.aclose() + raise exc + + async def _send_handling_auth( + self, + request: Request, + auth: Auth, + follow_redirects: bool, + history: list[Response], + ) -> Response: + auth_flow = auth.async_auth_flow(request) + try: + request = await auth_flow.__anext__() + + while True: + response = await self._send_handling_redirects( + request, + follow_redirects=follow_redirects, + history=history, + ) + try: + try: + next_request = await auth_flow.asend(response) + except StopAsyncIteration: + return response + + response.history = list(history) + await response.aread() + request = next_request + history.append(response) + + except BaseException as exc: + await response.aclose() + raise exc + finally: + await auth_flow.aclose() + + async def _send_handling_redirects( + self, + request: Request, + follow_redirects: bool, + history: list[Response], + ) -> Response: + while True: + if len(history) > self.max_redirects: + raise TooManyRedirects( + "Exceeded maximum allowed redirects.", request=request + ) + + for hook in self._event_hooks["request"]: + await hook(request) + + response = await self._send_single_request(request) + try: + for hook in self._event_hooks["response"]: + await hook(response) + + response.history = list(history) + + if not response.has_redirect_location: + return response + + request = self._build_redirect_request(request, response) + history = history + [response] + + if follow_redirects: + await response.aread() + else: + response.next_request = request + return response + + except BaseException as exc: + await response.aclose() + raise exc + + async def _send_single_request(self, request: Request) -> Response: + """ + Sends a single request, without handling any redirections. + """ + transport = self._transport_for_url(request.url) + start = time.perf_counter() + + if not isinstance(request.stream, AsyncByteStream): + raise RuntimeError( + "Attempted to send an sync request with an AsyncClient instance." + ) + + with request_context(request=request): + response = await transport.handle_async_request(request) + + assert isinstance(response.stream, AsyncByteStream) + response.request = request + response.stream = BoundAsyncStream( + response.stream, response=response, start=start + ) + self.cookies.extract_cookies(response) + response.default_encoding = self._default_encoding + + logger.info( + 'HTTP Request: %s %s "%s %d %s"', + request.method, + request.url, + response.http_version, + response.status_code, + response.reason_phrase, + ) + + return response + + async def get( + self, + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault | None = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `GET` request. + + **Parameters**: See `httpx.request`. + """ + return await self.request( + "GET", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + async def options( + self, + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send an `OPTIONS` request. + + **Parameters**: See `httpx.request`. + """ + return await self.request( + "OPTIONS", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + async def head( + self, + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `HEAD` request. + + **Parameters**: See `httpx.request`. + """ + return await self.request( + "HEAD", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + async def post( + self, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `POST` request. + + **Parameters**: See `httpx.request`. + """ + return await self.request( + "POST", + url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + async def put( + self, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `PUT` request. + + **Parameters**: See `httpx.request`. + """ + return await self.request( + "PUT", + url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + async def patch( + self, + url: URL | str, + *, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `PATCH` request. + + **Parameters**: See `httpx.request`. + """ + return await self.request( + "PATCH", + url, + content=content, + data=data, + files=files, + json=json, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + async def delete( + self, + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + auth: AuthTypes | UseClientDefault = USE_CLIENT_DEFAULT, + follow_redirects: bool | UseClientDefault = USE_CLIENT_DEFAULT, + timeout: TimeoutTypes | UseClientDefault = USE_CLIENT_DEFAULT, + extensions: RequestExtensions | None = None, + ) -> Response: + """ + Send a `DELETE` request. + + **Parameters**: See `httpx.request`. + """ + return await self.request( + "DELETE", + url, + params=params, + headers=headers, + cookies=cookies, + auth=auth, + follow_redirects=follow_redirects, + timeout=timeout, + extensions=extensions, + ) + + async def aclose(self) -> None: + """ + Close transport and proxies. + """ + if self._state != ClientState.CLOSED: + self._state = ClientState.CLOSED + + await self._transport.aclose() + for proxy in self._mounts.values(): + if proxy is not None: + await proxy.aclose() + + async def __aenter__(self: U) -> U: + if self._state != ClientState.UNOPENED: + msg = { + ClientState.OPENED: "Cannot open a client instance more than once.", + ClientState.CLOSED: ( + "Cannot reopen a client instance, once it has been closed." + ), + }[self._state] + raise RuntimeError(msg) + + self._state = ClientState.OPENED + + await self._transport.__aenter__() + for proxy in self._mounts.values(): + if proxy is not None: + await proxy.__aenter__() + return self + + async def __aexit__( + self, + exc_type: type[BaseException] | None = None, + exc_value: BaseException | None = None, + traceback: TracebackType | None = None, + ) -> None: + self._state = ClientState.CLOSED + + await self._transport.__aexit__(exc_type, exc_value, traceback) + for proxy in self._mounts.values(): + if proxy is not None: + await proxy.__aexit__(exc_type, exc_value, traceback) diff --git a/lib/python3.11/site-packages/httpx/_config.py b/lib/python3.11/site-packages/httpx/_config.py new file mode 100644 index 0000000000000000000000000000000000000000..467a6c90ae269babe3af7963d9d7c78b9f012268 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_config.py @@ -0,0 +1,248 @@ +from __future__ import annotations + +import os +import typing + +from ._models import Headers +from ._types import CertTypes, HeaderTypes, TimeoutTypes +from ._urls import URL + +if typing.TYPE_CHECKING: + import ssl # pragma: no cover + +__all__ = ["Limits", "Proxy", "Timeout", "create_ssl_context"] + + +class UnsetType: + pass # pragma: no cover + + +UNSET = UnsetType() + + +def create_ssl_context( + verify: ssl.SSLContext | str | bool = True, + cert: CertTypes | None = None, + trust_env: bool = True, +) -> ssl.SSLContext: + import ssl + import warnings + + import certifi + + if verify is True: + if trust_env and os.environ.get("SSL_CERT_FILE"): # pragma: nocover + ctx = ssl.create_default_context(cafile=os.environ["SSL_CERT_FILE"]) + elif trust_env and os.environ.get("SSL_CERT_DIR"): # pragma: nocover + ctx = ssl.create_default_context(capath=os.environ["SSL_CERT_DIR"]) + else: + # Default case... + ctx = ssl.create_default_context(cafile=certifi.where()) + elif verify is False: + ctx = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT) + ctx.check_hostname = False + ctx.verify_mode = ssl.CERT_NONE + elif isinstance(verify, str): # pragma: nocover + message = ( + "`verify=` is deprecated. " + "Use `verify=ssl.create_default_context(cafile=...)` " + "or `verify=ssl.create_default_context(capath=...)` instead." + ) + warnings.warn(message, DeprecationWarning) + if os.path.isdir(verify): + return ssl.create_default_context(capath=verify) + return ssl.create_default_context(cafile=verify) + else: + ctx = verify + + if cert: # pragma: nocover + message = ( + "`cert=...` is deprecated. Use `verify=` instead," + "with `.load_cert_chain()` to configure the certificate chain." + ) + warnings.warn(message, DeprecationWarning) + if isinstance(cert, str): + ctx.load_cert_chain(cert) + else: + ctx.load_cert_chain(*cert) + + return ctx + + +class Timeout: + """ + Timeout configuration. + + **Usage**: + + Timeout(None) # No timeouts. + Timeout(5.0) # 5s timeout on all operations. + Timeout(None, connect=5.0) # 5s timeout on connect, no other timeouts. + Timeout(5.0, connect=10.0) # 10s timeout on connect. 5s timeout elsewhere. + Timeout(5.0, pool=None) # No timeout on acquiring connection from pool. + # 5s timeout elsewhere. + """ + + def __init__( + self, + timeout: TimeoutTypes | UnsetType = UNSET, + *, + connect: None | float | UnsetType = UNSET, + read: None | float | UnsetType = UNSET, + write: None | float | UnsetType = UNSET, + pool: None | float | UnsetType = UNSET, + ) -> None: + if isinstance(timeout, Timeout): + # Passed as a single explicit Timeout. + assert connect is UNSET + assert read is UNSET + assert write is UNSET + assert pool is UNSET + self.connect = timeout.connect # type: typing.Optional[float] + self.read = timeout.read # type: typing.Optional[float] + self.write = timeout.write # type: typing.Optional[float] + self.pool = timeout.pool # type: typing.Optional[float] + elif isinstance(timeout, tuple): + # Passed as a tuple. + self.connect = timeout[0] + self.read = timeout[1] + self.write = None if len(timeout) < 3 else timeout[2] + self.pool = None if len(timeout) < 4 else timeout[3] + elif not ( + isinstance(connect, UnsetType) + or isinstance(read, UnsetType) + or isinstance(write, UnsetType) + or isinstance(pool, UnsetType) + ): + self.connect = connect + self.read = read + self.write = write + self.pool = pool + else: + if isinstance(timeout, UnsetType): + raise ValueError( + "httpx.Timeout must either include a default, or set all " + "four parameters explicitly." + ) + self.connect = timeout if isinstance(connect, UnsetType) else connect + self.read = timeout if isinstance(read, UnsetType) else read + self.write = timeout if isinstance(write, UnsetType) else write + self.pool = timeout if isinstance(pool, UnsetType) else pool + + def as_dict(self) -> dict[str, float | None]: + return { + "connect": self.connect, + "read": self.read, + "write": self.write, + "pool": self.pool, + } + + def __eq__(self, other: typing.Any) -> bool: + return ( + isinstance(other, self.__class__) + and self.connect == other.connect + and self.read == other.read + and self.write == other.write + and self.pool == other.pool + ) + + def __repr__(self) -> str: + class_name = self.__class__.__name__ + if len({self.connect, self.read, self.write, self.pool}) == 1: + return f"{class_name}(timeout={self.connect})" + return ( + f"{class_name}(connect={self.connect}, " + f"read={self.read}, write={self.write}, pool={self.pool})" + ) + + +class Limits: + """ + Configuration for limits to various client behaviors. + + **Parameters:** + + * **max_connections** - The maximum number of concurrent connections that may be + established. + * **max_keepalive_connections** - Allow the connection pool to maintain + keep-alive connections below this point. Should be less than or equal + to `max_connections`. + * **keepalive_expiry** - Time limit on idle keep-alive connections in seconds. + """ + + def __init__( + self, + *, + max_connections: int | None = None, + max_keepalive_connections: int | None = None, + keepalive_expiry: float | None = 5.0, + ) -> None: + self.max_connections = max_connections + self.max_keepalive_connections = max_keepalive_connections + self.keepalive_expiry = keepalive_expiry + + def __eq__(self, other: typing.Any) -> bool: + return ( + isinstance(other, self.__class__) + and self.max_connections == other.max_connections + and self.max_keepalive_connections == other.max_keepalive_connections + and self.keepalive_expiry == other.keepalive_expiry + ) + + def __repr__(self) -> str: + class_name = self.__class__.__name__ + return ( + f"{class_name}(max_connections={self.max_connections}, " + f"max_keepalive_connections={self.max_keepalive_connections}, " + f"keepalive_expiry={self.keepalive_expiry})" + ) + + +class Proxy: + def __init__( + self, + url: URL | str, + *, + ssl_context: ssl.SSLContext | None = None, + auth: tuple[str, str] | None = None, + headers: HeaderTypes | None = None, + ) -> None: + url = URL(url) + headers = Headers(headers) + + if url.scheme not in ("http", "https", "socks5", "socks5h"): + raise ValueError(f"Unknown scheme for proxy URL {url!r}") + + if url.username or url.password: + # Remove any auth credentials from the URL. + auth = (url.username, url.password) + url = url.copy_with(username=None, password=None) + + self.url = url + self.auth = auth + self.headers = headers + self.ssl_context = ssl_context + + @property + def raw_auth(self) -> tuple[bytes, bytes] | None: + # The proxy authentication as raw bytes. + return ( + None + if self.auth is None + else (self.auth[0].encode("utf-8"), self.auth[1].encode("utf-8")) + ) + + def __repr__(self) -> str: + # The authentication is represented with the password component masked. + auth = (self.auth[0], "********") if self.auth else None + + # Build a nice concise representation. + url_str = f"{str(self.url)!r}" + auth_str = f", auth={auth!r}" if auth else "" + headers_str = f", headers={dict(self.headers)!r}" if self.headers else "" + return f"Proxy({url_str}{auth_str}{headers_str})" + + +DEFAULT_TIMEOUT_CONFIG = Timeout(timeout=5.0) +DEFAULT_LIMITS = Limits(max_connections=100, max_keepalive_connections=20) +DEFAULT_MAX_REDIRECTS = 20 diff --git a/lib/python3.11/site-packages/httpx/_content.py b/lib/python3.11/site-packages/httpx/_content.py new file mode 100644 index 0000000000000000000000000000000000000000..6f479a0885f723b7395843d41164a87041820776 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_content.py @@ -0,0 +1,240 @@ +from __future__ import annotations + +import inspect +import warnings +from json import dumps as json_dumps +from typing import ( + Any, + AsyncIterable, + AsyncIterator, + Iterable, + Iterator, + Mapping, +) +from urllib.parse import urlencode + +from ._exceptions import StreamClosed, StreamConsumed +from ._multipart import MultipartStream +from ._types import ( + AsyncByteStream, + RequestContent, + RequestData, + RequestFiles, + ResponseContent, + SyncByteStream, +) +from ._utils import peek_filelike_length, primitive_value_to_str + +__all__ = ["ByteStream"] + + +class ByteStream(AsyncByteStream, SyncByteStream): + def __init__(self, stream: bytes) -> None: + self._stream = stream + + def __iter__(self) -> Iterator[bytes]: + yield self._stream + + async def __aiter__(self) -> AsyncIterator[bytes]: + yield self._stream + + +class IteratorByteStream(SyncByteStream): + CHUNK_SIZE = 65_536 + + def __init__(self, stream: Iterable[bytes]) -> None: + self._stream = stream + self._is_stream_consumed = False + self._is_generator = inspect.isgenerator(stream) + + def __iter__(self) -> Iterator[bytes]: + if self._is_stream_consumed and self._is_generator: + raise StreamConsumed() + + self._is_stream_consumed = True + if hasattr(self._stream, "read"): + # File-like interfaces should use 'read' directly. + chunk = self._stream.read(self.CHUNK_SIZE) + while chunk: + yield chunk + chunk = self._stream.read(self.CHUNK_SIZE) + else: + # Otherwise iterate. + for part in self._stream: + yield part + + +class AsyncIteratorByteStream(AsyncByteStream): + CHUNK_SIZE = 65_536 + + def __init__(self, stream: AsyncIterable[bytes]) -> None: + self._stream = stream + self._is_stream_consumed = False + self._is_generator = inspect.isasyncgen(stream) + + async def __aiter__(self) -> AsyncIterator[bytes]: + if self._is_stream_consumed and self._is_generator: + raise StreamConsumed() + + self._is_stream_consumed = True + if hasattr(self._stream, "aread"): + # File-like interfaces should use 'aread' directly. + chunk = await self._stream.aread(self.CHUNK_SIZE) + while chunk: + yield chunk + chunk = await self._stream.aread(self.CHUNK_SIZE) + else: + # Otherwise iterate. + async for part in self._stream: + yield part + + +class UnattachedStream(AsyncByteStream, SyncByteStream): + """ + If a request or response is serialized using pickle, then it is no longer + attached to a stream for I/O purposes. Any stream operations should result + in `httpx.StreamClosed`. + """ + + def __iter__(self) -> Iterator[bytes]: + raise StreamClosed() + + async def __aiter__(self) -> AsyncIterator[bytes]: + raise StreamClosed() + yield b"" # pragma: no cover + + +def encode_content( + content: str | bytes | Iterable[bytes] | AsyncIterable[bytes], +) -> tuple[dict[str, str], SyncByteStream | AsyncByteStream]: + if isinstance(content, (bytes, str)): + body = content.encode("utf-8") if isinstance(content, str) else content + content_length = len(body) + headers = {"Content-Length": str(content_length)} if body else {} + return headers, ByteStream(body) + + elif isinstance(content, Iterable) and not isinstance(content, dict): + # `not isinstance(content, dict)` is a bit oddly specific, but it + # catches a case that's easy for users to make in error, and would + # otherwise pass through here, like any other bytes-iterable, + # because `dict` happens to be iterable. See issue #2491. + content_length_or_none = peek_filelike_length(content) + + if content_length_or_none is None: + headers = {"Transfer-Encoding": "chunked"} + else: + headers = {"Content-Length": str(content_length_or_none)} + return headers, IteratorByteStream(content) # type: ignore + + elif isinstance(content, AsyncIterable): + headers = {"Transfer-Encoding": "chunked"} + return headers, AsyncIteratorByteStream(content) + + raise TypeError(f"Unexpected type for 'content', {type(content)!r}") + + +def encode_urlencoded_data( + data: RequestData, +) -> tuple[dict[str, str], ByteStream]: + plain_data = [] + for key, value in data.items(): + if isinstance(value, (list, tuple)): + plain_data.extend([(key, primitive_value_to_str(item)) for item in value]) + else: + plain_data.append((key, primitive_value_to_str(value))) + body = urlencode(plain_data, doseq=True).encode("utf-8") + content_length = str(len(body)) + content_type = "application/x-www-form-urlencoded" + headers = {"Content-Length": content_length, "Content-Type": content_type} + return headers, ByteStream(body) + + +def encode_multipart_data( + data: RequestData, files: RequestFiles, boundary: bytes | None +) -> tuple[dict[str, str], MultipartStream]: + multipart = MultipartStream(data=data, files=files, boundary=boundary) + headers = multipart.get_headers() + return headers, multipart + + +def encode_text(text: str) -> tuple[dict[str, str], ByteStream]: + body = text.encode("utf-8") + content_length = str(len(body)) + content_type = "text/plain; charset=utf-8" + headers = {"Content-Length": content_length, "Content-Type": content_type} + return headers, ByteStream(body) + + +def encode_html(html: str) -> tuple[dict[str, str], ByteStream]: + body = html.encode("utf-8") + content_length = str(len(body)) + content_type = "text/html; charset=utf-8" + headers = {"Content-Length": content_length, "Content-Type": content_type} + return headers, ByteStream(body) + + +def encode_json(json: Any) -> tuple[dict[str, str], ByteStream]: + body = json_dumps( + json, ensure_ascii=False, separators=(",", ":"), allow_nan=False + ).encode("utf-8") + content_length = str(len(body)) + content_type = "application/json" + headers = {"Content-Length": content_length, "Content-Type": content_type} + return headers, ByteStream(body) + + +def encode_request( + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: Any | None = None, + boundary: bytes | None = None, +) -> tuple[dict[str, str], SyncByteStream | AsyncByteStream]: + """ + Handles encoding the given `content`, `data`, `files`, and `json`, + returning a two-tuple of (, ). + """ + if data is not None and not isinstance(data, Mapping): + # We prefer to separate `content=` + # for raw request content, and `data=
` for url encoded or + # multipart form content. + # + # However for compat with requests, we *do* still support + # `data=` usages. We deal with that case here, treating it + # as if `content=<...>` had been supplied instead. + message = "Use 'content=<...>' to upload raw bytes/text content." + warnings.warn(message, DeprecationWarning, stacklevel=2) + return encode_content(data) + + if content is not None: + return encode_content(content) + elif files: + return encode_multipart_data(data or {}, files, boundary) + elif data: + return encode_urlencoded_data(data) + elif json is not None: + return encode_json(json) + + return {}, ByteStream(b"") + + +def encode_response( + content: ResponseContent | None = None, + text: str | None = None, + html: str | None = None, + json: Any | None = None, +) -> tuple[dict[str, str], SyncByteStream | AsyncByteStream]: + """ + Handles encoding the given `content`, returning a two-tuple of + (, ). + """ + if content is not None: + return encode_content(content) + elif text is not None: + return encode_text(text) + elif html is not None: + return encode_html(html) + elif json is not None: + return encode_json(json) + + return {}, ByteStream(b"") diff --git a/lib/python3.11/site-packages/httpx/_decoders.py b/lib/python3.11/site-packages/httpx/_decoders.py new file mode 100644 index 0000000000000000000000000000000000000000..899dfada878e1181fca6d3c75a79526a076abb9e --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_decoders.py @@ -0,0 +1,393 @@ +""" +Handlers for Content-Encoding. + +See: https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Encoding +""" + +from __future__ import annotations + +import codecs +import io +import typing +import zlib + +from ._exceptions import DecodingError + +# Brotli support is optional +try: + # The C bindings in `brotli` are recommended for CPython. + import brotli +except ImportError: # pragma: no cover + try: + # The CFFI bindings in `brotlicffi` are recommended for PyPy + # and other environments. + import brotlicffi as brotli + except ImportError: + brotli = None + + +# Zstandard support is optional +try: + import zstandard +except ImportError: # pragma: no cover + zstandard = None # type: ignore + + +class ContentDecoder: + def decode(self, data: bytes) -> bytes: + raise NotImplementedError() # pragma: no cover + + def flush(self) -> bytes: + raise NotImplementedError() # pragma: no cover + + +class IdentityDecoder(ContentDecoder): + """ + Handle unencoded data. + """ + + def decode(self, data: bytes) -> bytes: + return data + + def flush(self) -> bytes: + return b"" + + +class DeflateDecoder(ContentDecoder): + """ + Handle 'deflate' decoding. + + See: https://stackoverflow.com/questions/1838699 + """ + + def __init__(self) -> None: + self.first_attempt = True + self.decompressor = zlib.decompressobj() + + def decode(self, data: bytes) -> bytes: + was_first_attempt = self.first_attempt + self.first_attempt = False + try: + return self.decompressor.decompress(data) + except zlib.error as exc: + if was_first_attempt: + self.decompressor = zlib.decompressobj(-zlib.MAX_WBITS) + return self.decode(data) + raise DecodingError(str(exc)) from exc + + def flush(self) -> bytes: + try: + return self.decompressor.flush() + except zlib.error as exc: # pragma: no cover + raise DecodingError(str(exc)) from exc + + +class GZipDecoder(ContentDecoder): + """ + Handle 'gzip' decoding. + + See: https://stackoverflow.com/questions/1838699 + """ + + def __init__(self) -> None: + self.decompressor = zlib.decompressobj(zlib.MAX_WBITS | 16) + + def decode(self, data: bytes) -> bytes: + try: + return self.decompressor.decompress(data) + except zlib.error as exc: + raise DecodingError(str(exc)) from exc + + def flush(self) -> bytes: + try: + return self.decompressor.flush() + except zlib.error as exc: # pragma: no cover + raise DecodingError(str(exc)) from exc + + +class BrotliDecoder(ContentDecoder): + """ + Handle 'brotli' decoding. + + Requires `pip install brotlipy`. See: https://brotlipy.readthedocs.io/ + or `pip install brotli`. See https://github.com/google/brotli + Supports both 'brotlipy' and 'Brotli' packages since they share an import + name. The top branches are for 'brotlipy' and bottom branches for 'Brotli' + """ + + def __init__(self) -> None: + if brotli is None: # pragma: no cover + raise ImportError( + "Using 'BrotliDecoder', but neither of the 'brotlicffi' or 'brotli' " + "packages have been installed. " + "Make sure to install httpx using `pip install httpx[brotli]`." + ) from None + + self.decompressor = brotli.Decompressor() + self.seen_data = False + self._decompress: typing.Callable[[bytes], bytes] + if hasattr(self.decompressor, "decompress"): + # The 'brotlicffi' package. + self._decompress = self.decompressor.decompress # pragma: no cover + else: + # The 'brotli' package. + self._decompress = self.decompressor.process # pragma: no cover + + def decode(self, data: bytes) -> bytes: + if not data: + return b"" + self.seen_data = True + try: + return self._decompress(data) + except brotli.error as exc: + raise DecodingError(str(exc)) from exc + + def flush(self) -> bytes: + if not self.seen_data: + return b"" + try: + if hasattr(self.decompressor, "finish"): + # Only available in the 'brotlicffi' package. + + # As the decompressor decompresses eagerly, this + # will never actually emit any data. However, it will potentially throw + # errors if a truncated or damaged data stream has been used. + self.decompressor.finish() # pragma: no cover + return b"" + except brotli.error as exc: # pragma: no cover + raise DecodingError(str(exc)) from exc + + +class ZStandardDecoder(ContentDecoder): + """ + Handle 'zstd' RFC 8878 decoding. + + Requires `pip install zstandard`. + Can be installed as a dependency of httpx using `pip install httpx[zstd]`. + """ + + # inspired by the ZstdDecoder implementation in urllib3 + def __init__(self) -> None: + if zstandard is None: # pragma: no cover + raise ImportError( + "Using 'ZStandardDecoder', ..." + "Make sure to install httpx using `pip install httpx[zstd]`." + ) from None + + self.decompressor = zstandard.ZstdDecompressor().decompressobj() + self.seen_data = False + + def decode(self, data: bytes) -> bytes: + assert zstandard is not None + self.seen_data = True + output = io.BytesIO() + try: + output.write(self.decompressor.decompress(data)) + while self.decompressor.eof and self.decompressor.unused_data: + unused_data = self.decompressor.unused_data + self.decompressor = zstandard.ZstdDecompressor().decompressobj() + output.write(self.decompressor.decompress(unused_data)) + except zstandard.ZstdError as exc: + raise DecodingError(str(exc)) from exc + return output.getvalue() + + def flush(self) -> bytes: + if not self.seen_data: + return b"" + ret = self.decompressor.flush() # note: this is a no-op + if not self.decompressor.eof: + raise DecodingError("Zstandard data is incomplete") # pragma: no cover + return bytes(ret) + + +class MultiDecoder(ContentDecoder): + """ + Handle the case where multiple encodings have been applied. + """ + + def __init__(self, children: typing.Sequence[ContentDecoder]) -> None: + """ + 'children' should be a sequence of decoders in the order in which + each was applied. + """ + # Note that we reverse the order for decoding. + self.children = list(reversed(children)) + + def decode(self, data: bytes) -> bytes: + for child in self.children: + data = child.decode(data) + return data + + def flush(self) -> bytes: + data = b"" + for child in self.children: + data = child.decode(data) + child.flush() + return data + + +class ByteChunker: + """ + Handles returning byte content in fixed-size chunks. + """ + + def __init__(self, chunk_size: int | None = None) -> None: + self._buffer = io.BytesIO() + self._chunk_size = chunk_size + + def decode(self, content: bytes) -> list[bytes]: + if self._chunk_size is None: + return [content] if content else [] + + self._buffer.write(content) + if self._buffer.tell() >= self._chunk_size: + value = self._buffer.getvalue() + chunks = [ + value[i : i + self._chunk_size] + for i in range(0, len(value), self._chunk_size) + ] + if len(chunks[-1]) == self._chunk_size: + self._buffer.seek(0) + self._buffer.truncate() + return chunks + else: + self._buffer.seek(0) + self._buffer.write(chunks[-1]) + self._buffer.truncate() + return chunks[:-1] + else: + return [] + + def flush(self) -> list[bytes]: + value = self._buffer.getvalue() + self._buffer.seek(0) + self._buffer.truncate() + return [value] if value else [] + + +class TextChunker: + """ + Handles returning text content in fixed-size chunks. + """ + + def __init__(self, chunk_size: int | None = None) -> None: + self._buffer = io.StringIO() + self._chunk_size = chunk_size + + def decode(self, content: str) -> list[str]: + if self._chunk_size is None: + return [content] if content else [] + + self._buffer.write(content) + if self._buffer.tell() >= self._chunk_size: + value = self._buffer.getvalue() + chunks = [ + value[i : i + self._chunk_size] + for i in range(0, len(value), self._chunk_size) + ] + if len(chunks[-1]) == self._chunk_size: + self._buffer.seek(0) + self._buffer.truncate() + return chunks + else: + self._buffer.seek(0) + self._buffer.write(chunks[-1]) + self._buffer.truncate() + return chunks[:-1] + else: + return [] + + def flush(self) -> list[str]: + value = self._buffer.getvalue() + self._buffer.seek(0) + self._buffer.truncate() + return [value] if value else [] + + +class TextDecoder: + """ + Handles incrementally decoding bytes into text + """ + + def __init__(self, encoding: str = "utf-8") -> None: + self.decoder = codecs.getincrementaldecoder(encoding)(errors="replace") + + def decode(self, data: bytes) -> str: + return self.decoder.decode(data) + + def flush(self) -> str: + return self.decoder.decode(b"", True) + + +class LineDecoder: + """ + Handles incrementally reading lines from text. + + Has the same behaviour as the stdllib splitlines, + but handling the input iteratively. + """ + + def __init__(self) -> None: + self.buffer: list[str] = [] + self.trailing_cr: bool = False + + def decode(self, text: str) -> list[str]: + # See https://docs.python.org/3/library/stdtypes.html#str.splitlines + NEWLINE_CHARS = "\n\r\x0b\x0c\x1c\x1d\x1e\x85\u2028\u2029" + + # We always push a trailing `\r` into the next decode iteration. + if self.trailing_cr: + text = "\r" + text + self.trailing_cr = False + if text.endswith("\r"): + self.trailing_cr = True + text = text[:-1] + + if not text: + # NOTE: the edge case input of empty text doesn't occur in practice, + # because other httpx internals filter out this value + return [] # pragma: no cover + + trailing_newline = text[-1] in NEWLINE_CHARS + lines = text.splitlines() + + if len(lines) == 1 and not trailing_newline: + # No new lines, buffer the input and continue. + self.buffer.append(lines[0]) + return [] + + if self.buffer: + # Include any existing buffer in the first portion of the + # splitlines result. + lines = ["".join(self.buffer) + lines[0]] + lines[1:] + self.buffer = [] + + if not trailing_newline: + # If the last segment of splitlines is not newline terminated, + # then drop it from our output and start a new buffer. + self.buffer = [lines.pop()] + + return lines + + def flush(self) -> list[str]: + if not self.buffer and not self.trailing_cr: + return [] + + lines = ["".join(self.buffer)] + self.buffer = [] + self.trailing_cr = False + return lines + + +SUPPORTED_DECODERS = { + "identity": IdentityDecoder, + "gzip": GZipDecoder, + "deflate": DeflateDecoder, + "br": BrotliDecoder, + "zstd": ZStandardDecoder, +} + + +if brotli is None: + SUPPORTED_DECODERS.pop("br") # pragma: no cover +if zstandard is None: + SUPPORTED_DECODERS.pop("zstd") # pragma: no cover diff --git a/lib/python3.11/site-packages/httpx/_exceptions.py b/lib/python3.11/site-packages/httpx/_exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..77f45a6d3986d15626fc8a5fd459d6a3e0fbe466 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_exceptions.py @@ -0,0 +1,379 @@ +""" +Our exception hierarchy: + +* HTTPError + x RequestError + + TransportError + - TimeoutException + · ConnectTimeout + · ReadTimeout + · WriteTimeout + · PoolTimeout + - NetworkError + · ConnectError + · ReadError + · WriteError + · CloseError + - ProtocolError + · LocalProtocolError + · RemoteProtocolError + - ProxyError + - UnsupportedProtocol + + DecodingError + + TooManyRedirects + x HTTPStatusError +* InvalidURL +* CookieConflict +* StreamError + x StreamConsumed + x StreamClosed + x ResponseNotRead + x RequestNotRead +""" + +from __future__ import annotations + +import contextlib +import typing + +if typing.TYPE_CHECKING: + from ._models import Request, Response # pragma: no cover + +__all__ = [ + "CloseError", + "ConnectError", + "ConnectTimeout", + "CookieConflict", + "DecodingError", + "HTTPError", + "HTTPStatusError", + "InvalidURL", + "LocalProtocolError", + "NetworkError", + "PoolTimeout", + "ProtocolError", + "ProxyError", + "ReadError", + "ReadTimeout", + "RemoteProtocolError", + "RequestError", + "RequestNotRead", + "ResponseNotRead", + "StreamClosed", + "StreamConsumed", + "StreamError", + "TimeoutException", + "TooManyRedirects", + "TransportError", + "UnsupportedProtocol", + "WriteError", + "WriteTimeout", +] + + +class HTTPError(Exception): + """ + Base class for `RequestError` and `HTTPStatusError`. + + Useful for `try...except` blocks when issuing a request, + and then calling `.raise_for_status()`. + + For example: + + ``` + try: + response = httpx.get("https://www.example.com") + response.raise_for_status() + except httpx.HTTPError as exc: + print(f"HTTP Exception for {exc.request.url} - {exc}") + ``` + """ + + def __init__(self, message: str) -> None: + super().__init__(message) + self._request: Request | None = None + + @property + def request(self) -> Request: + if self._request is None: + raise RuntimeError("The .request property has not been set.") + return self._request + + @request.setter + def request(self, request: Request) -> None: + self._request = request + + +class RequestError(HTTPError): + """ + Base class for all exceptions that may occur when issuing a `.request()`. + """ + + def __init__(self, message: str, *, request: Request | None = None) -> None: + super().__init__(message) + # At the point an exception is raised we won't typically have a request + # instance to associate it with. + # + # The 'request_context' context manager is used within the Client and + # Response methods in order to ensure that any raised exceptions + # have a `.request` property set on them. + self._request = request + + +class TransportError(RequestError): + """ + Base class for all exceptions that occur at the level of the Transport API. + """ + + +# Timeout exceptions... + + +class TimeoutException(TransportError): + """ + The base class for timeout errors. + + An operation has timed out. + """ + + +class ConnectTimeout(TimeoutException): + """ + Timed out while connecting to the host. + """ + + +class ReadTimeout(TimeoutException): + """ + Timed out while receiving data from the host. + """ + + +class WriteTimeout(TimeoutException): + """ + Timed out while sending data to the host. + """ + + +class PoolTimeout(TimeoutException): + """ + Timed out waiting to acquire a connection from the pool. + """ + + +# Core networking exceptions... + + +class NetworkError(TransportError): + """ + The base class for network-related errors. + + An error occurred while interacting with the network. + """ + + +class ReadError(NetworkError): + """ + Failed to receive data from the network. + """ + + +class WriteError(NetworkError): + """ + Failed to send data through the network. + """ + + +class ConnectError(NetworkError): + """ + Failed to establish a connection. + """ + + +class CloseError(NetworkError): + """ + Failed to close a connection. + """ + + +# Other transport exceptions... + + +class ProxyError(TransportError): + """ + An error occurred while establishing a proxy connection. + """ + + +class UnsupportedProtocol(TransportError): + """ + Attempted to make a request to an unsupported protocol. + + For example issuing a request to `ftp://www.example.com`. + """ + + +class ProtocolError(TransportError): + """ + The protocol was violated. + """ + + +class LocalProtocolError(ProtocolError): + """ + A protocol was violated by the client. + + For example if the user instantiated a `Request` instance explicitly, + failed to include the mandatory `Host:` header, and then issued it directly + using `client.send()`. + """ + + +class RemoteProtocolError(ProtocolError): + """ + The protocol was violated by the server. + + For example, returning malformed HTTP. + """ + + +# Other request exceptions... + + +class DecodingError(RequestError): + """ + Decoding of the response failed, due to a malformed encoding. + """ + + +class TooManyRedirects(RequestError): + """ + Too many redirects. + """ + + +# Client errors + + +class HTTPStatusError(HTTPError): + """ + The response had an error HTTP status of 4xx or 5xx. + + May be raised when calling `response.raise_for_status()` + """ + + def __init__(self, message: str, *, request: Request, response: Response) -> None: + super().__init__(message) + self.request = request + self.response = response + + +class InvalidURL(Exception): + """ + URL is improperly formed or cannot be parsed. + """ + + def __init__(self, message: str) -> None: + super().__init__(message) + + +class CookieConflict(Exception): + """ + Attempted to lookup a cookie by name, but multiple cookies existed. + + Can occur when calling `response.cookies.get(...)`. + """ + + def __init__(self, message: str) -> None: + super().__init__(message) + + +# Stream exceptions... + +# These may occur as the result of a programming error, by accessing +# the request/response stream in an invalid manner. + + +class StreamError(RuntimeError): + """ + The base class for stream exceptions. + + The developer made an error in accessing the request stream in + an invalid way. + """ + + def __init__(self, message: str) -> None: + super().__init__(message) + + +class StreamConsumed(StreamError): + """ + Attempted to read or stream content, but the content has already + been streamed. + """ + + def __init__(self) -> None: + message = ( + "Attempted to read or stream some content, but the content has " + "already been streamed. For requests, this could be due to passing " + "a generator as request content, and then receiving a redirect " + "response or a secondary request as part of an authentication flow." + "For responses, this could be due to attempting to stream the response " + "content more than once." + ) + super().__init__(message) + + +class StreamClosed(StreamError): + """ + Attempted to read or stream response content, but the request has been + closed. + """ + + def __init__(self) -> None: + message = ( + "Attempted to read or stream content, but the stream has " "been closed." + ) + super().__init__(message) + + +class ResponseNotRead(StreamError): + """ + Attempted to access streaming response content, without having called `read()`. + """ + + def __init__(self) -> None: + message = ( + "Attempted to access streaming response content," + " without having called `read()`." + ) + super().__init__(message) + + +class RequestNotRead(StreamError): + """ + Attempted to access streaming request content, without having called `read()`. + """ + + def __init__(self) -> None: + message = ( + "Attempted to access streaming request content," + " without having called `read()`." + ) + super().__init__(message) + + +@contextlib.contextmanager +def request_context( + request: Request | None = None, +) -> typing.Iterator[None]: + """ + A context manager that can be used to attach the given request context + to any `RequestError` exceptions that are raised within the block. + """ + try: + yield + except RequestError as exc: + if request is not None: + exc.request = request + raise exc diff --git a/lib/python3.11/site-packages/httpx/_main.py b/lib/python3.11/site-packages/httpx/_main.py new file mode 100644 index 0000000000000000000000000000000000000000..cffa4bb7db0f930f4db56653a061c4d7400ba4e6 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_main.py @@ -0,0 +1,506 @@ +from __future__ import annotations + +import functools +import json +import sys +import typing + +import click +import pygments.lexers +import pygments.util +import rich.console +import rich.markup +import rich.progress +import rich.syntax +import rich.table + +from ._client import Client +from ._exceptions import RequestError +from ._models import Response +from ._status_codes import codes + +if typing.TYPE_CHECKING: + import httpcore # pragma: no cover + + +def print_help() -> None: + console = rich.console.Console() + + console.print("[bold]HTTPX :butterfly:", justify="center") + console.print() + console.print("A next generation HTTP client.", justify="center") + console.print() + console.print( + "Usage: [bold]httpx[/bold] [cyan] [OPTIONS][/cyan] ", justify="left" + ) + console.print() + + table = rich.table.Table.grid(padding=1, pad_edge=True) + table.add_column("Parameter", no_wrap=True, justify="left", style="bold") + table.add_column("Description") + table.add_row( + "-m, --method [cyan]METHOD", + "Request method, such as GET, POST, PUT, PATCH, DELETE, OPTIONS, HEAD.\n" + "[Default: GET, or POST if a request body is included]", + ) + table.add_row( + "-p, --params [cyan] ...", + "Query parameters to include in the request URL.", + ) + table.add_row( + "-c, --content [cyan]TEXT", "Byte content to include in the request body." + ) + table.add_row( + "-d, --data [cyan] ...", "Form data to include in the request body." + ) + table.add_row( + "-f, --files [cyan] ...", + "Form files to include in the request body.", + ) + table.add_row("-j, --json [cyan]TEXT", "JSON data to include in the request body.") + table.add_row( + "-h, --headers [cyan] ...", + "Include additional HTTP headers in the request.", + ) + table.add_row( + "--cookies [cyan] ...", "Cookies to include in the request." + ) + table.add_row( + "--auth [cyan]", + "Username and password to include in the request. Specify '-' for the password" + " to use a password prompt. Note that using --verbose/-v will expose" + " the Authorization header, including the password encoding" + " in a trivially reversible format.", + ) + + table.add_row( + "--proxy [cyan]URL", + "Send the request via a proxy. Should be the URL giving the proxy address.", + ) + + table.add_row( + "--timeout [cyan]FLOAT", + "Timeout value to use for network operations, such as establishing the" + " connection, reading some data, etc... [Default: 5.0]", + ) + + table.add_row("--follow-redirects", "Automatically follow redirects.") + table.add_row("--no-verify", "Disable SSL verification.") + table.add_row( + "--http2", "Send the request using HTTP/2, if the remote server supports it." + ) + + table.add_row( + "--download [cyan]FILE", + "Save the response content as a file, rather than displaying it.", + ) + + table.add_row("-v, --verbose", "Verbose output. Show request as well as response.") + table.add_row("--help", "Show this message and exit.") + console.print(table) + + +def get_lexer_for_response(response: Response) -> str: + content_type = response.headers.get("Content-Type") + if content_type is not None: + mime_type, _, _ = content_type.partition(";") + try: + return typing.cast( + str, pygments.lexers.get_lexer_for_mimetype(mime_type.strip()).name + ) + except pygments.util.ClassNotFound: # pragma: no cover + pass + return "" # pragma: no cover + + +def format_request_headers(request: httpcore.Request, http2: bool = False) -> str: + version = "HTTP/2" if http2 else "HTTP/1.1" + headers = [ + (name.lower() if http2 else name, value) for name, value in request.headers + ] + method = request.method.decode("ascii") + target = request.url.target.decode("ascii") + lines = [f"{method} {target} {version}"] + [ + f"{name.decode('ascii')}: {value.decode('ascii')}" for name, value in headers + ] + return "\n".join(lines) + + +def format_response_headers( + http_version: bytes, + status: int, + reason_phrase: bytes | None, + headers: list[tuple[bytes, bytes]], +) -> str: + version = http_version.decode("ascii") + reason = ( + codes.get_reason_phrase(status) + if reason_phrase is None + else reason_phrase.decode("ascii") + ) + lines = [f"{version} {status} {reason}"] + [ + f"{name.decode('ascii')}: {value.decode('ascii')}" for name, value in headers + ] + return "\n".join(lines) + + +def print_request_headers(request: httpcore.Request, http2: bool = False) -> None: + console = rich.console.Console() + http_text = format_request_headers(request, http2=http2) + syntax = rich.syntax.Syntax(http_text, "http", theme="ansi_dark", word_wrap=True) + console.print(syntax) + syntax = rich.syntax.Syntax("", "http", theme="ansi_dark", word_wrap=True) + console.print(syntax) + + +def print_response_headers( + http_version: bytes, + status: int, + reason_phrase: bytes | None, + headers: list[tuple[bytes, bytes]], +) -> None: + console = rich.console.Console() + http_text = format_response_headers(http_version, status, reason_phrase, headers) + syntax = rich.syntax.Syntax(http_text, "http", theme="ansi_dark", word_wrap=True) + console.print(syntax) + syntax = rich.syntax.Syntax("", "http", theme="ansi_dark", word_wrap=True) + console.print(syntax) + + +def print_response(response: Response) -> None: + console = rich.console.Console() + lexer_name = get_lexer_for_response(response) + if lexer_name: + if lexer_name.lower() == "json": + try: + data = response.json() + text = json.dumps(data, indent=4) + except ValueError: # pragma: no cover + text = response.text + else: + text = response.text + + syntax = rich.syntax.Syntax(text, lexer_name, theme="ansi_dark", word_wrap=True) + console.print(syntax) + else: + console.print(f"<{len(response.content)} bytes of binary data>") + + +_PCTRTT = typing.Tuple[typing.Tuple[str, str], ...] +_PCTRTTT = typing.Tuple[_PCTRTT, ...] +_PeerCertRetDictType = typing.Dict[str, typing.Union[str, _PCTRTTT, _PCTRTT]] + + +def format_certificate(cert: _PeerCertRetDictType) -> str: # pragma: no cover + lines = [] + for key, value in cert.items(): + if isinstance(value, (list, tuple)): + lines.append(f"* {key}:") + for item in value: + if key in ("subject", "issuer"): + for sub_item in item: + lines.append(f"* {sub_item[0]}: {sub_item[1]!r}") + elif isinstance(item, tuple) and len(item) == 2: + lines.append(f"* {item[0]}: {item[1]!r}") + else: + lines.append(f"* {item!r}") + else: + lines.append(f"* {key}: {value!r}") + return "\n".join(lines) + + +def trace( + name: str, info: typing.Mapping[str, typing.Any], verbose: bool = False +) -> None: + console = rich.console.Console() + if name == "connection.connect_tcp.started" and verbose: + host = info["host"] + console.print(f"* Connecting to {host!r}") + elif name == "connection.connect_tcp.complete" and verbose: + stream = info["return_value"] + server_addr = stream.get_extra_info("server_addr") + console.print(f"* Connected to {server_addr[0]!r} on port {server_addr[1]}") + elif name == "connection.start_tls.complete" and verbose: # pragma: no cover + stream = info["return_value"] + ssl_object = stream.get_extra_info("ssl_object") + version = ssl_object.version() + cipher = ssl_object.cipher() + server_cert = ssl_object.getpeercert() + alpn = ssl_object.selected_alpn_protocol() + console.print(f"* SSL established using {version!r} / {cipher[0]!r}") + console.print(f"* Selected ALPN protocol: {alpn!r}") + if server_cert: + console.print("* Server certificate:") + console.print(format_certificate(server_cert)) + elif name == "http11.send_request_headers.started" and verbose: + request = info["request"] + print_request_headers(request, http2=False) + elif name == "http2.send_request_headers.started" and verbose: # pragma: no cover + request = info["request"] + print_request_headers(request, http2=True) + elif name == "http11.receive_response_headers.complete": + http_version, status, reason_phrase, headers = info["return_value"] + print_response_headers(http_version, status, reason_phrase, headers) + elif name == "http2.receive_response_headers.complete": # pragma: no cover + status, headers = info["return_value"] + http_version = b"HTTP/2" + reason_phrase = None + print_response_headers(http_version, status, reason_phrase, headers) + + +def download_response(response: Response, download: typing.BinaryIO) -> None: + console = rich.console.Console() + console.print() + content_length = response.headers.get("Content-Length") + with rich.progress.Progress( + "[progress.description]{task.description}", + "[progress.percentage]{task.percentage:>3.0f}%", + rich.progress.BarColumn(bar_width=None), + rich.progress.DownloadColumn(), + rich.progress.TransferSpeedColumn(), + ) as progress: + description = f"Downloading [bold]{rich.markup.escape(download.name)}" + download_task = progress.add_task( + description, + total=int(content_length or 0), + start=content_length is not None, + ) + for chunk in response.iter_bytes(): + download.write(chunk) + progress.update(download_task, completed=response.num_bytes_downloaded) + + +def validate_json( + ctx: click.Context, + param: click.Option | click.Parameter, + value: typing.Any, +) -> typing.Any: + if value is None: + return None + + try: + return json.loads(value) + except json.JSONDecodeError: # pragma: no cover + raise click.BadParameter("Not valid JSON") + + +def validate_auth( + ctx: click.Context, + param: click.Option | click.Parameter, + value: typing.Any, +) -> typing.Any: + if value == (None, None): + return None + + username, password = value + if password == "-": # pragma: no cover + password = click.prompt("Password", hide_input=True) + return (username, password) + + +def handle_help( + ctx: click.Context, + param: click.Option | click.Parameter, + value: typing.Any, +) -> None: + if not value or ctx.resilient_parsing: + return + + print_help() + ctx.exit() + + +@click.command(add_help_option=False) +@click.argument("url", type=str) +@click.option( + "--method", + "-m", + "method", + type=str, + help=( + "Request method, such as GET, POST, PUT, PATCH, DELETE, OPTIONS, HEAD. " + "[Default: GET, or POST if a request body is included]" + ), +) +@click.option( + "--params", + "-p", + "params", + type=(str, str), + multiple=True, + help="Query parameters to include in the request URL.", +) +@click.option( + "--content", + "-c", + "content", + type=str, + help="Byte content to include in the request body.", +) +@click.option( + "--data", + "-d", + "data", + type=(str, str), + multiple=True, + help="Form data to include in the request body.", +) +@click.option( + "--files", + "-f", + "files", + type=(str, click.File(mode="rb")), + multiple=True, + help="Form files to include in the request body.", +) +@click.option( + "--json", + "-j", + "json", + type=str, + callback=validate_json, + help="JSON data to include in the request body.", +) +@click.option( + "--headers", + "-h", + "headers", + type=(str, str), + multiple=True, + help="Include additional HTTP headers in the request.", +) +@click.option( + "--cookies", + "cookies", + type=(str, str), + multiple=True, + help="Cookies to include in the request.", +) +@click.option( + "--auth", + "auth", + type=(str, str), + default=(None, None), + callback=validate_auth, + help=( + "Username and password to include in the request. " + "Specify '-' for the password to use a password prompt. " + "Note that using --verbose/-v will expose the Authorization header, " + "including the password encoding in a trivially reversible format." + ), +) +@click.option( + "--proxy", + "proxy", + type=str, + default=None, + help="Send the request via a proxy. Should be the URL giving the proxy address.", +) +@click.option( + "--timeout", + "timeout", + type=float, + default=5.0, + help=( + "Timeout value to use for network operations, such as establishing the " + "connection, reading some data, etc... [Default: 5.0]" + ), +) +@click.option( + "--follow-redirects", + "follow_redirects", + is_flag=True, + default=False, + help="Automatically follow redirects.", +) +@click.option( + "--no-verify", + "verify", + is_flag=True, + default=True, + help="Disable SSL verification.", +) +@click.option( + "--http2", + "http2", + type=bool, + is_flag=True, + default=False, + help="Send the request using HTTP/2, if the remote server supports it.", +) +@click.option( + "--download", + type=click.File("wb"), + help="Save the response content as a file, rather than displaying it.", +) +@click.option( + "--verbose", + "-v", + type=bool, + is_flag=True, + default=False, + help="Verbose. Show request as well as response.", +) +@click.option( + "--help", + is_flag=True, + is_eager=True, + expose_value=False, + callback=handle_help, + help="Show this message and exit.", +) +def main( + url: str, + method: str, + params: list[tuple[str, str]], + content: str, + data: list[tuple[str, str]], + files: list[tuple[str, click.File]], + json: str, + headers: list[tuple[str, str]], + cookies: list[tuple[str, str]], + auth: tuple[str, str] | None, + proxy: str, + timeout: float, + follow_redirects: bool, + verify: bool, + http2: bool, + download: typing.BinaryIO | None, + verbose: bool, +) -> None: + """ + An HTTP command line client. + Sends a request and displays the response. + """ + if not method: + method = "POST" if content or data or files or json else "GET" + + try: + with Client(proxy=proxy, timeout=timeout, http2=http2, verify=verify) as client: + with client.stream( + method, + url, + params=list(params), + content=content, + data=dict(data), + files=files, # type: ignore + json=json, + headers=headers, + cookies=dict(cookies), + auth=auth, + follow_redirects=follow_redirects, + extensions={"trace": functools.partial(trace, verbose=verbose)}, + ) as response: + if download is not None: + download_response(response, download) + else: + response.read() + if response.content: + print_response(response) + + except RequestError as exc: + console = rich.console.Console() + console.print(f"[red]{type(exc).__name__}[/red]: {exc}") + sys.exit(1) + + sys.exit(0 if response.is_success else 1) diff --git a/lib/python3.11/site-packages/httpx/_models.py b/lib/python3.11/site-packages/httpx/_models.py new file mode 100644 index 0000000000000000000000000000000000000000..67d74bf86bfc80e22d9a4a3153572845accd9039 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_models.py @@ -0,0 +1,1277 @@ +from __future__ import annotations + +import codecs +import datetime +import email.message +import json as jsonlib +import re +import typing +import urllib.request +from collections.abc import Mapping +from http.cookiejar import Cookie, CookieJar + +from ._content import ByteStream, UnattachedStream, encode_request, encode_response +from ._decoders import ( + SUPPORTED_DECODERS, + ByteChunker, + ContentDecoder, + IdentityDecoder, + LineDecoder, + MultiDecoder, + TextChunker, + TextDecoder, +) +from ._exceptions import ( + CookieConflict, + HTTPStatusError, + RequestNotRead, + ResponseNotRead, + StreamClosed, + StreamConsumed, + request_context, +) +from ._multipart import get_multipart_boundary_from_content_type +from ._status_codes import codes +from ._types import ( + AsyncByteStream, + CookieTypes, + HeaderTypes, + QueryParamTypes, + RequestContent, + RequestData, + RequestExtensions, + RequestFiles, + ResponseContent, + ResponseExtensions, + SyncByteStream, +) +from ._urls import URL +from ._utils import to_bytes_or_str, to_str + +__all__ = ["Cookies", "Headers", "Request", "Response"] + +SENSITIVE_HEADERS = {"authorization", "proxy-authorization"} + + +def _is_known_encoding(encoding: str) -> bool: + """ + Return `True` if `encoding` is a known codec. + """ + try: + codecs.lookup(encoding) + except LookupError: + return False + return True + + +def _normalize_header_key(key: str | bytes, encoding: str | None = None) -> bytes: + """ + Coerce str/bytes into a strictly byte-wise HTTP header key. + """ + return key if isinstance(key, bytes) else key.encode(encoding or "ascii") + + +def _normalize_header_value(value: str | bytes, encoding: str | None = None) -> bytes: + """ + Coerce str/bytes into a strictly byte-wise HTTP header value. + """ + if isinstance(value, bytes): + return value + if not isinstance(value, str): + raise TypeError(f"Header value must be str or bytes, not {type(value)}") + return value.encode(encoding or "ascii") + + +def _parse_content_type_charset(content_type: str) -> str | None: + # We used to use `cgi.parse_header()` here, but `cgi` became a dead battery. + # See: https://peps.python.org/pep-0594/#cgi + msg = email.message.Message() + msg["content-type"] = content_type + return msg.get_content_charset(failobj=None) + + +def _parse_header_links(value: str) -> list[dict[str, str]]: + """ + Returns a list of parsed link headers, for more info see: + https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Link + The generic syntax of those is: + Link: < uri-reference >; param1=value1; param2="value2" + So for instance: + Link; '; type="image/jpeg",;' + would return + [ + {"url": "http:/.../front.jpeg", "type": "image/jpeg"}, + {"url": "http://.../back.jpeg"}, + ] + :param value: HTTP Link entity-header field + :return: list of parsed link headers + """ + links: list[dict[str, str]] = [] + replace_chars = " '\"" + value = value.strip(replace_chars) + if not value: + return links + for val in re.split(", *<", value): + try: + url, params = val.split(";", 1) + except ValueError: + url, params = val, "" + link = {"url": url.strip("<> '\"")} + for param in params.split(";"): + try: + key, value = param.split("=") + except ValueError: + break + link[key.strip(replace_chars)] = value.strip(replace_chars) + links.append(link) + return links + + +def _obfuscate_sensitive_headers( + items: typing.Iterable[tuple[typing.AnyStr, typing.AnyStr]], +) -> typing.Iterator[tuple[typing.AnyStr, typing.AnyStr]]: + for k, v in items: + if to_str(k.lower()) in SENSITIVE_HEADERS: + v = to_bytes_or_str("[secure]", match_type_of=v) + yield k, v + + +class Headers(typing.MutableMapping[str, str]): + """ + HTTP headers, as a case-insensitive multi-dict. + """ + + def __init__( + self, + headers: HeaderTypes | None = None, + encoding: str | None = None, + ) -> None: + self._list = [] # type: typing.List[typing.Tuple[bytes, bytes, bytes]] + + if isinstance(headers, Headers): + self._list = list(headers._list) + elif isinstance(headers, Mapping): + for k, v in headers.items(): + bytes_key = _normalize_header_key(k, encoding) + bytes_value = _normalize_header_value(v, encoding) + self._list.append((bytes_key, bytes_key.lower(), bytes_value)) + elif headers is not None: + for k, v in headers: + bytes_key = _normalize_header_key(k, encoding) + bytes_value = _normalize_header_value(v, encoding) + self._list.append((bytes_key, bytes_key.lower(), bytes_value)) + + self._encoding = encoding + + @property + def encoding(self) -> str: + """ + Header encoding is mandated as ascii, but we allow fallbacks to utf-8 + or iso-8859-1. + """ + if self._encoding is None: + for encoding in ["ascii", "utf-8"]: + for key, value in self.raw: + try: + key.decode(encoding) + value.decode(encoding) + except UnicodeDecodeError: + break + else: + # The else block runs if 'break' did not occur, meaning + # all values fitted the encoding. + self._encoding = encoding + break + else: + # The ISO-8859-1 encoding covers all 256 code points in a byte, + # so will never raise decode errors. + self._encoding = "iso-8859-1" + return self._encoding + + @encoding.setter + def encoding(self, value: str) -> None: + self._encoding = value + + @property + def raw(self) -> list[tuple[bytes, bytes]]: + """ + Returns a list of the raw header items, as byte pairs. + """ + return [(raw_key, value) for raw_key, _, value in self._list] + + def keys(self) -> typing.KeysView[str]: + return {key.decode(self.encoding): None for _, key, value in self._list}.keys() + + def values(self) -> typing.ValuesView[str]: + values_dict: dict[str, str] = {} + for _, key, value in self._list: + str_key = key.decode(self.encoding) + str_value = value.decode(self.encoding) + if str_key in values_dict: + values_dict[str_key] += f", {str_value}" + else: + values_dict[str_key] = str_value + return values_dict.values() + + def items(self) -> typing.ItemsView[str, str]: + """ + Return `(key, value)` items of headers. Concatenate headers + into a single comma separated value when a key occurs multiple times. + """ + values_dict: dict[str, str] = {} + for _, key, value in self._list: + str_key = key.decode(self.encoding) + str_value = value.decode(self.encoding) + if str_key in values_dict: + values_dict[str_key] += f", {str_value}" + else: + values_dict[str_key] = str_value + return values_dict.items() + + def multi_items(self) -> list[tuple[str, str]]: + """ + Return a list of `(key, value)` pairs of headers. Allow multiple + occurrences of the same key without concatenating into a single + comma separated value. + """ + return [ + (key.decode(self.encoding), value.decode(self.encoding)) + for _, key, value in self._list + ] + + def get(self, key: str, default: typing.Any = None) -> typing.Any: + """ + Return a header value. If multiple occurrences of the header occur + then concatenate them together with commas. + """ + try: + return self[key] + except KeyError: + return default + + def get_list(self, key: str, split_commas: bool = False) -> list[str]: + """ + Return a list of all header values for a given key. + If `split_commas=True` is passed, then any comma separated header + values are split into multiple return strings. + """ + get_header_key = key.lower().encode(self.encoding) + + values = [ + item_value.decode(self.encoding) + for _, item_key, item_value in self._list + if item_key.lower() == get_header_key + ] + + if not split_commas: + return values + + split_values = [] + for value in values: + split_values.extend([item.strip() for item in value.split(",")]) + return split_values + + def update(self, headers: HeaderTypes | None = None) -> None: # type: ignore + headers = Headers(headers) + for key in headers.keys(): + if key in self: + self.pop(key) + self._list.extend(headers._list) + + def copy(self) -> Headers: + return Headers(self, encoding=self.encoding) + + def __getitem__(self, key: str) -> str: + """ + Return a single header value. + + If there are multiple headers with the same key, then we concatenate + them with commas. See: https://tools.ietf.org/html/rfc7230#section-3.2.2 + """ + normalized_key = key.lower().encode(self.encoding) + + items = [ + header_value.decode(self.encoding) + for _, header_key, header_value in self._list + if header_key == normalized_key + ] + + if items: + return ", ".join(items) + + raise KeyError(key) + + def __setitem__(self, key: str, value: str) -> None: + """ + Set the header `key` to `value`, removing any duplicate entries. + Retains insertion order. + """ + set_key = key.encode(self._encoding or "utf-8") + set_value = value.encode(self._encoding or "utf-8") + lookup_key = set_key.lower() + + found_indexes = [ + idx + for idx, (_, item_key, _) in enumerate(self._list) + if item_key == lookup_key + ] + + for idx in reversed(found_indexes[1:]): + del self._list[idx] + + if found_indexes: + idx = found_indexes[0] + self._list[idx] = (set_key, lookup_key, set_value) + else: + self._list.append((set_key, lookup_key, set_value)) + + def __delitem__(self, key: str) -> None: + """ + Remove the header `key`. + """ + del_key = key.lower().encode(self.encoding) + + pop_indexes = [ + idx + for idx, (_, item_key, _) in enumerate(self._list) + if item_key.lower() == del_key + ] + + if not pop_indexes: + raise KeyError(key) + + for idx in reversed(pop_indexes): + del self._list[idx] + + def __contains__(self, key: typing.Any) -> bool: + header_key = key.lower().encode(self.encoding) + return header_key in [key for _, key, _ in self._list] + + def __iter__(self) -> typing.Iterator[typing.Any]: + return iter(self.keys()) + + def __len__(self) -> int: + return len(self._list) + + def __eq__(self, other: typing.Any) -> bool: + try: + other_headers = Headers(other) + except ValueError: + return False + + self_list = [(key, value) for _, key, value in self._list] + other_list = [(key, value) for _, key, value in other_headers._list] + return sorted(self_list) == sorted(other_list) + + def __repr__(self) -> str: + class_name = self.__class__.__name__ + + encoding_str = "" + if self.encoding != "ascii": + encoding_str = f", encoding={self.encoding!r}" + + as_list = list(_obfuscate_sensitive_headers(self.multi_items())) + as_dict = dict(as_list) + + no_duplicate_keys = len(as_dict) == len(as_list) + if no_duplicate_keys: + return f"{class_name}({as_dict!r}{encoding_str})" + return f"{class_name}({as_list!r}{encoding_str})" + + +class Request: + def __init__( + self, + method: str, + url: URL | str, + *, + params: QueryParamTypes | None = None, + headers: HeaderTypes | None = None, + cookies: CookieTypes | None = None, + content: RequestContent | None = None, + data: RequestData | None = None, + files: RequestFiles | None = None, + json: typing.Any | None = None, + stream: SyncByteStream | AsyncByteStream | None = None, + extensions: RequestExtensions | None = None, + ) -> None: + self.method = method.upper() + self.url = URL(url) if params is None else URL(url, params=params) + self.headers = Headers(headers) + self.extensions = {} if extensions is None else dict(extensions) + + if cookies: + Cookies(cookies).set_cookie_header(self) + + if stream is None: + content_type: str | None = self.headers.get("content-type") + headers, stream = encode_request( + content=content, + data=data, + files=files, + json=json, + boundary=get_multipart_boundary_from_content_type( + content_type=content_type.encode(self.headers.encoding) + if content_type + else None + ), + ) + self._prepare(headers) + self.stream = stream + # Load the request body, except for streaming content. + if isinstance(stream, ByteStream): + self.read() + else: + # There's an important distinction between `Request(content=...)`, + # and `Request(stream=...)`. + # + # Using `content=...` implies automatically populated `Host` and content + # headers, of either `Content-Length: ...` or `Transfer-Encoding: chunked`. + # + # Using `stream=...` will not automatically include *any* + # auto-populated headers. + # + # As an end-user you don't really need `stream=...`. It's only + # useful when: + # + # * Preserving the request stream when copying requests, eg for redirects. + # * Creating request instances on the *server-side* of the transport API. + self.stream = stream + + def _prepare(self, default_headers: dict[str, str]) -> None: + for key, value in default_headers.items(): + # Ignore Transfer-Encoding if the Content-Length has been set explicitly. + if key.lower() == "transfer-encoding" and "Content-Length" in self.headers: + continue + self.headers.setdefault(key, value) + + auto_headers: list[tuple[bytes, bytes]] = [] + + has_host = "Host" in self.headers + has_content_length = ( + "Content-Length" in self.headers or "Transfer-Encoding" in self.headers + ) + + if not has_host and self.url.host: + auto_headers.append((b"Host", self.url.netloc)) + if not has_content_length and self.method in ("POST", "PUT", "PATCH"): + auto_headers.append((b"Content-Length", b"0")) + + self.headers = Headers(auto_headers + self.headers.raw) + + @property + def content(self) -> bytes: + if not hasattr(self, "_content"): + raise RequestNotRead() + return self._content + + def read(self) -> bytes: + """ + Read and return the request content. + """ + if not hasattr(self, "_content"): + assert isinstance(self.stream, typing.Iterable) + self._content = b"".join(self.stream) + if not isinstance(self.stream, ByteStream): + # If a streaming request has been read entirely into memory, then + # we can replace the stream with a raw bytes implementation, + # to ensure that any non-replayable streams can still be used. + self.stream = ByteStream(self._content) + return self._content + + async def aread(self) -> bytes: + """ + Read and return the request content. + """ + if not hasattr(self, "_content"): + assert isinstance(self.stream, typing.AsyncIterable) + self._content = b"".join([part async for part in self.stream]) + if not isinstance(self.stream, ByteStream): + # If a streaming request has been read entirely into memory, then + # we can replace the stream with a raw bytes implementation, + # to ensure that any non-replayable streams can still be used. + self.stream = ByteStream(self._content) + return self._content + + def __repr__(self) -> str: + class_name = self.__class__.__name__ + url = str(self.url) + return f"<{class_name}({self.method!r}, {url!r})>" + + def __getstate__(self) -> dict[str, typing.Any]: + return { + name: value + for name, value in self.__dict__.items() + if name not in ["extensions", "stream"] + } + + def __setstate__(self, state: dict[str, typing.Any]) -> None: + for name, value in state.items(): + setattr(self, name, value) + self.extensions = {} + self.stream = UnattachedStream() + + +class Response: + def __init__( + self, + status_code: int, + *, + headers: HeaderTypes | None = None, + content: ResponseContent | None = None, + text: str | None = None, + html: str | None = None, + json: typing.Any = None, + stream: SyncByteStream | AsyncByteStream | None = None, + request: Request | None = None, + extensions: ResponseExtensions | None = None, + history: list[Response] | None = None, + default_encoding: str | typing.Callable[[bytes], str] = "utf-8", + ) -> None: + self.status_code = status_code + self.headers = Headers(headers) + + self._request: Request | None = request + + # When follow_redirects=False and a redirect is received, + # the client will set `response.next_request`. + self.next_request: Request | None = None + + self.extensions = {} if extensions is None else dict(extensions) + self.history = [] if history is None else list(history) + + self.is_closed = False + self.is_stream_consumed = False + + self.default_encoding = default_encoding + + if stream is None: + headers, stream = encode_response(content, text, html, json) + self._prepare(headers) + self.stream = stream + if isinstance(stream, ByteStream): + # Load the response body, except for streaming content. + self.read() + else: + # There's an important distinction between `Response(content=...)`, + # and `Response(stream=...)`. + # + # Using `content=...` implies automatically populated content headers, + # of either `Content-Length: ...` or `Transfer-Encoding: chunked`. + # + # Using `stream=...` will not automatically include any content headers. + # + # As an end-user you don't really need `stream=...`. It's only + # useful when creating response instances having received a stream + # from the transport API. + self.stream = stream + + self._num_bytes_downloaded = 0 + + def _prepare(self, default_headers: dict[str, str]) -> None: + for key, value in default_headers.items(): + # Ignore Transfer-Encoding if the Content-Length has been set explicitly. + if key.lower() == "transfer-encoding" and "content-length" in self.headers: + continue + self.headers.setdefault(key, value) + + @property + def elapsed(self) -> datetime.timedelta: + """ + Returns the time taken for the complete request/response + cycle to complete. + """ + if not hasattr(self, "_elapsed"): + raise RuntimeError( + "'.elapsed' may only be accessed after the response " + "has been read or closed." + ) + return self._elapsed + + @elapsed.setter + def elapsed(self, elapsed: datetime.timedelta) -> None: + self._elapsed = elapsed + + @property + def request(self) -> Request: + """ + Returns the request instance associated to the current response. + """ + if self._request is None: + raise RuntimeError( + "The request instance has not been set on this response." + ) + return self._request + + @request.setter + def request(self, value: Request) -> None: + self._request = value + + @property + def http_version(self) -> str: + try: + http_version: bytes = self.extensions["http_version"] + except KeyError: + return "HTTP/1.1" + else: + return http_version.decode("ascii", errors="ignore") + + @property + def reason_phrase(self) -> str: + try: + reason_phrase: bytes = self.extensions["reason_phrase"] + except KeyError: + return codes.get_reason_phrase(self.status_code) + else: + return reason_phrase.decode("ascii", errors="ignore") + + @property + def url(self) -> URL: + """ + Returns the URL for which the request was made. + """ + return self.request.url + + @property + def content(self) -> bytes: + if not hasattr(self, "_content"): + raise ResponseNotRead() + return self._content + + @property + def text(self) -> str: + if not hasattr(self, "_text"): + content = self.content + if not content: + self._text = "" + else: + decoder = TextDecoder(encoding=self.encoding or "utf-8") + self._text = "".join([decoder.decode(self.content), decoder.flush()]) + return self._text + + @property + def encoding(self) -> str | None: + """ + Return an encoding to use for decoding the byte content into text. + The priority for determining this is given by... + + * `.encoding = <>` has been set explicitly. + * The encoding as specified by the charset parameter in the Content-Type header. + * The encoding as determined by `default_encoding`, which may either be + a string like "utf-8" indicating the encoding to use, or may be a callable + which enables charset autodetection. + """ + if not hasattr(self, "_encoding"): + encoding = self.charset_encoding + if encoding is None or not _is_known_encoding(encoding): + if isinstance(self.default_encoding, str): + encoding = self.default_encoding + elif hasattr(self, "_content"): + encoding = self.default_encoding(self._content) + self._encoding = encoding or "utf-8" + return self._encoding + + @encoding.setter + def encoding(self, value: str) -> None: + """ + Set the encoding to use for decoding the byte content into text. + + If the `text` attribute has been accessed, attempting to set the + encoding will throw a ValueError. + """ + if hasattr(self, "_text"): + raise ValueError( + "Setting encoding after `text` has been accessed is not allowed." + ) + self._encoding = value + + @property + def charset_encoding(self) -> str | None: + """ + Return the encoding, as specified by the Content-Type header. + """ + content_type = self.headers.get("Content-Type") + if content_type is None: + return None + + return _parse_content_type_charset(content_type) + + def _get_content_decoder(self) -> ContentDecoder: + """ + Returns a decoder instance which can be used to decode the raw byte + content, depending on the Content-Encoding used in the response. + """ + if not hasattr(self, "_decoder"): + decoders: list[ContentDecoder] = [] + values = self.headers.get_list("content-encoding", split_commas=True) + for value in values: + value = value.strip().lower() + try: + decoder_cls = SUPPORTED_DECODERS[value] + decoders.append(decoder_cls()) + except KeyError: + continue + + if len(decoders) == 1: + self._decoder = decoders[0] + elif len(decoders) > 1: + self._decoder = MultiDecoder(children=decoders) + else: + self._decoder = IdentityDecoder() + + return self._decoder + + @property + def is_informational(self) -> bool: + """ + A property which is `True` for 1xx status codes, `False` otherwise. + """ + return codes.is_informational(self.status_code) + + @property + def is_success(self) -> bool: + """ + A property which is `True` for 2xx status codes, `False` otherwise. + """ + return codes.is_success(self.status_code) + + @property + def is_redirect(self) -> bool: + """ + A property which is `True` for 3xx status codes, `False` otherwise. + + Note that not all responses with a 3xx status code indicate a URL redirect. + + Use `response.has_redirect_location` to determine responses with a properly + formed URL redirection. + """ + return codes.is_redirect(self.status_code) + + @property + def is_client_error(self) -> bool: + """ + A property which is `True` for 4xx status codes, `False` otherwise. + """ + return codes.is_client_error(self.status_code) + + @property + def is_server_error(self) -> bool: + """ + A property which is `True` for 5xx status codes, `False` otherwise. + """ + return codes.is_server_error(self.status_code) + + @property + def is_error(self) -> bool: + """ + A property which is `True` for 4xx and 5xx status codes, `False` otherwise. + """ + return codes.is_error(self.status_code) + + @property + def has_redirect_location(self) -> bool: + """ + Returns True for 3xx responses with a properly formed URL redirection, + `False` otherwise. + """ + return ( + self.status_code + in ( + # 301 (Cacheable redirect. Method may change to GET.) + codes.MOVED_PERMANENTLY, + # 302 (Uncacheable redirect. Method may change to GET.) + codes.FOUND, + # 303 (Client should make a GET or HEAD request.) + codes.SEE_OTHER, + # 307 (Equiv. 302, but retain method) + codes.TEMPORARY_REDIRECT, + # 308 (Equiv. 301, but retain method) + codes.PERMANENT_REDIRECT, + ) + and "Location" in self.headers + ) + + def raise_for_status(self) -> Response: + """ + Raise the `HTTPStatusError` if one occurred. + """ + request = self._request + if request is None: + raise RuntimeError( + "Cannot call `raise_for_status` as the request " + "instance has not been set on this response." + ) + + if self.is_success: + return self + + if self.has_redirect_location: + message = ( + "{error_type} '{0.status_code} {0.reason_phrase}' for url '{0.url}'\n" + "Redirect location: '{0.headers[location]}'\n" + "For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/{0.status_code}" + ) + else: + message = ( + "{error_type} '{0.status_code} {0.reason_phrase}' for url '{0.url}'\n" + "For more information check: https://developer.mozilla.org/en-US/docs/Web/HTTP/Status/{0.status_code}" + ) + + status_class = self.status_code // 100 + error_types = { + 1: "Informational response", + 3: "Redirect response", + 4: "Client error", + 5: "Server error", + } + error_type = error_types.get(status_class, "Invalid status code") + message = message.format(self, error_type=error_type) + raise HTTPStatusError(message, request=request, response=self) + + def json(self, **kwargs: typing.Any) -> typing.Any: + return jsonlib.loads(self.content, **kwargs) + + @property + def cookies(self) -> Cookies: + if not hasattr(self, "_cookies"): + self._cookies = Cookies() + self._cookies.extract_cookies(self) + return self._cookies + + @property + def links(self) -> dict[str | None, dict[str, str]]: + """ + Returns the parsed header links of the response, if any + """ + header = self.headers.get("link") + if header is None: + return {} + + return { + (link.get("rel") or link.get("url")): link + for link in _parse_header_links(header) + } + + @property + def num_bytes_downloaded(self) -> int: + return self._num_bytes_downloaded + + def __repr__(self) -> str: + return f"" + + def __getstate__(self) -> dict[str, typing.Any]: + return { + name: value + for name, value in self.__dict__.items() + if name not in ["extensions", "stream", "is_closed", "_decoder"] + } + + def __setstate__(self, state: dict[str, typing.Any]) -> None: + for name, value in state.items(): + setattr(self, name, value) + self.is_closed = True + self.extensions = {} + self.stream = UnattachedStream() + + def read(self) -> bytes: + """ + Read and return the response content. + """ + if not hasattr(self, "_content"): + self._content = b"".join(self.iter_bytes()) + return self._content + + def iter_bytes(self, chunk_size: int | None = None) -> typing.Iterator[bytes]: + """ + A byte-iterator over the decoded response content. + This allows us to handle gzip, deflate, brotli, and zstd encoded responses. + """ + if hasattr(self, "_content"): + chunk_size = len(self._content) if chunk_size is None else chunk_size + for i in range(0, len(self._content), max(chunk_size, 1)): + yield self._content[i : i + chunk_size] + else: + decoder = self._get_content_decoder() + chunker = ByteChunker(chunk_size=chunk_size) + with request_context(request=self._request): + for raw_bytes in self.iter_raw(): + decoded = decoder.decode(raw_bytes) + for chunk in chunker.decode(decoded): + yield chunk + decoded = decoder.flush() + for chunk in chunker.decode(decoded): + yield chunk # pragma: no cover + for chunk in chunker.flush(): + yield chunk + + def iter_text(self, chunk_size: int | None = None) -> typing.Iterator[str]: + """ + A str-iterator over the decoded response content + that handles both gzip, deflate, etc but also detects the content's + string encoding. + """ + decoder = TextDecoder(encoding=self.encoding or "utf-8") + chunker = TextChunker(chunk_size=chunk_size) + with request_context(request=self._request): + for byte_content in self.iter_bytes(): + text_content = decoder.decode(byte_content) + for chunk in chunker.decode(text_content): + yield chunk + text_content = decoder.flush() + for chunk in chunker.decode(text_content): + yield chunk # pragma: no cover + for chunk in chunker.flush(): + yield chunk + + def iter_lines(self) -> typing.Iterator[str]: + decoder = LineDecoder() + with request_context(request=self._request): + for text in self.iter_text(): + for line in decoder.decode(text): + yield line + for line in decoder.flush(): + yield line + + def iter_raw(self, chunk_size: int | None = None) -> typing.Iterator[bytes]: + """ + A byte-iterator over the raw response content. + """ + if self.is_stream_consumed: + raise StreamConsumed() + if self.is_closed: + raise StreamClosed() + if not isinstance(self.stream, SyncByteStream): + raise RuntimeError("Attempted to call a sync iterator on an async stream.") + + self.is_stream_consumed = True + self._num_bytes_downloaded = 0 + chunker = ByteChunker(chunk_size=chunk_size) + + with request_context(request=self._request): + for raw_stream_bytes in self.stream: + self._num_bytes_downloaded += len(raw_stream_bytes) + for chunk in chunker.decode(raw_stream_bytes): + yield chunk + + for chunk in chunker.flush(): + yield chunk + + self.close() + + def close(self) -> None: + """ + Close the response and release the connection. + Automatically called if the response body is read to completion. + """ + if not isinstance(self.stream, SyncByteStream): + raise RuntimeError("Attempted to call an sync close on an async stream.") + + if not self.is_closed: + self.is_closed = True + with request_context(request=self._request): + self.stream.close() + + async def aread(self) -> bytes: + """ + Read and return the response content. + """ + if not hasattr(self, "_content"): + self._content = b"".join([part async for part in self.aiter_bytes()]) + return self._content + + async def aiter_bytes( + self, chunk_size: int | None = None + ) -> typing.AsyncIterator[bytes]: + """ + A byte-iterator over the decoded response content. + This allows us to handle gzip, deflate, brotli, and zstd encoded responses. + """ + if hasattr(self, "_content"): + chunk_size = len(self._content) if chunk_size is None else chunk_size + for i in range(0, len(self._content), max(chunk_size, 1)): + yield self._content[i : i + chunk_size] + else: + decoder = self._get_content_decoder() + chunker = ByteChunker(chunk_size=chunk_size) + with request_context(request=self._request): + async for raw_bytes in self.aiter_raw(): + decoded = decoder.decode(raw_bytes) + for chunk in chunker.decode(decoded): + yield chunk + decoded = decoder.flush() + for chunk in chunker.decode(decoded): + yield chunk # pragma: no cover + for chunk in chunker.flush(): + yield chunk + + async def aiter_text( + self, chunk_size: int | None = None + ) -> typing.AsyncIterator[str]: + """ + A str-iterator over the decoded response content + that handles both gzip, deflate, etc but also detects the content's + string encoding. + """ + decoder = TextDecoder(encoding=self.encoding or "utf-8") + chunker = TextChunker(chunk_size=chunk_size) + with request_context(request=self._request): + async for byte_content in self.aiter_bytes(): + text_content = decoder.decode(byte_content) + for chunk in chunker.decode(text_content): + yield chunk + text_content = decoder.flush() + for chunk in chunker.decode(text_content): + yield chunk # pragma: no cover + for chunk in chunker.flush(): + yield chunk + + async def aiter_lines(self) -> typing.AsyncIterator[str]: + decoder = LineDecoder() + with request_context(request=self._request): + async for text in self.aiter_text(): + for line in decoder.decode(text): + yield line + for line in decoder.flush(): + yield line + + async def aiter_raw( + self, chunk_size: int | None = None + ) -> typing.AsyncIterator[bytes]: + """ + A byte-iterator over the raw response content. + """ + if self.is_stream_consumed: + raise StreamConsumed() + if self.is_closed: + raise StreamClosed() + if not isinstance(self.stream, AsyncByteStream): + raise RuntimeError("Attempted to call an async iterator on an sync stream.") + + self.is_stream_consumed = True + self._num_bytes_downloaded = 0 + chunker = ByteChunker(chunk_size=chunk_size) + + with request_context(request=self._request): + async for raw_stream_bytes in self.stream: + self._num_bytes_downloaded += len(raw_stream_bytes) + for chunk in chunker.decode(raw_stream_bytes): + yield chunk + + for chunk in chunker.flush(): + yield chunk + + await self.aclose() + + async def aclose(self) -> None: + """ + Close the response and release the connection. + Automatically called if the response body is read to completion. + """ + if not isinstance(self.stream, AsyncByteStream): + raise RuntimeError("Attempted to call an async close on an sync stream.") + + if not self.is_closed: + self.is_closed = True + with request_context(request=self._request): + await self.stream.aclose() + + +class Cookies(typing.MutableMapping[str, str]): + """ + HTTP Cookies, as a mutable mapping. + """ + + def __init__(self, cookies: CookieTypes | None = None) -> None: + if cookies is None or isinstance(cookies, dict): + self.jar = CookieJar() + if isinstance(cookies, dict): + for key, value in cookies.items(): + self.set(key, value) + elif isinstance(cookies, list): + self.jar = CookieJar() + for key, value in cookies: + self.set(key, value) + elif isinstance(cookies, Cookies): + self.jar = CookieJar() + for cookie in cookies.jar: + self.jar.set_cookie(cookie) + else: + self.jar = cookies + + def extract_cookies(self, response: Response) -> None: + """ + Loads any cookies based on the response `Set-Cookie` headers. + """ + urllib_response = self._CookieCompatResponse(response) + urllib_request = self._CookieCompatRequest(response.request) + + self.jar.extract_cookies(urllib_response, urllib_request) # type: ignore + + def set_cookie_header(self, request: Request) -> None: + """ + Sets an appropriate 'Cookie:' HTTP header on the `Request`. + """ + urllib_request = self._CookieCompatRequest(request) + self.jar.add_cookie_header(urllib_request) + + def set(self, name: str, value: str, domain: str = "", path: str = "/") -> None: + """ + Set a cookie value by name. May optionally include domain and path. + """ + kwargs = { + "version": 0, + "name": name, + "value": value, + "port": None, + "port_specified": False, + "domain": domain, + "domain_specified": bool(domain), + "domain_initial_dot": domain.startswith("."), + "path": path, + "path_specified": bool(path), + "secure": False, + "expires": None, + "discard": True, + "comment": None, + "comment_url": None, + "rest": {"HttpOnly": None}, + "rfc2109": False, + } + cookie = Cookie(**kwargs) # type: ignore + self.jar.set_cookie(cookie) + + def get( # type: ignore + self, + name: str, + default: str | None = None, + domain: str | None = None, + path: str | None = None, + ) -> str | None: + """ + Get a cookie by name. May optionally include domain and path + in order to specify exactly which cookie to retrieve. + """ + value = None + for cookie in self.jar: + if cookie.name == name: + if domain is None or cookie.domain == domain: + if path is None or cookie.path == path: + if value is not None: + message = f"Multiple cookies exist with name={name}" + raise CookieConflict(message) + value = cookie.value + + if value is None: + return default + return value + + def delete( + self, + name: str, + domain: str | None = None, + path: str | None = None, + ) -> None: + """ + Delete a cookie by name. May optionally include domain and path + in order to specify exactly which cookie to delete. + """ + if domain is not None and path is not None: + return self.jar.clear(domain, path, name) + + remove = [ + cookie + for cookie in self.jar + if cookie.name == name + and (domain is None or cookie.domain == domain) + and (path is None or cookie.path == path) + ] + + for cookie in remove: + self.jar.clear(cookie.domain, cookie.path, cookie.name) + + def clear(self, domain: str | None = None, path: str | None = None) -> None: + """ + Delete all cookies. Optionally include a domain and path in + order to only delete a subset of all the cookies. + """ + args = [] + if domain is not None: + args.append(domain) + if path is not None: + assert domain is not None + args.append(path) + self.jar.clear(*args) + + def update(self, cookies: CookieTypes | None = None) -> None: # type: ignore + cookies = Cookies(cookies) + for cookie in cookies.jar: + self.jar.set_cookie(cookie) + + def __setitem__(self, name: str, value: str) -> None: + return self.set(name, value) + + def __getitem__(self, name: str) -> str: + value = self.get(name) + if value is None: + raise KeyError(name) + return value + + def __delitem__(self, name: str) -> None: + return self.delete(name) + + def __len__(self) -> int: + return len(self.jar) + + def __iter__(self) -> typing.Iterator[str]: + return (cookie.name for cookie in self.jar) + + def __bool__(self) -> bool: + for _ in self.jar: + return True + return False + + def __repr__(self) -> str: + cookies_repr = ", ".join( + [ + f"" + for cookie in self.jar + ] + ) + + return f"" + + class _CookieCompatRequest(urllib.request.Request): + """ + Wraps a `Request` instance up in a compatibility interface suitable + for use with `CookieJar` operations. + """ + + def __init__(self, request: Request) -> None: + super().__init__( + url=str(request.url), + headers=dict(request.headers), + method=request.method, + ) + self.request = request + + def add_unredirected_header(self, key: str, value: str) -> None: + super().add_unredirected_header(key, value) + self.request.headers[key] = value + + class _CookieCompatResponse: + """ + Wraps a `Request` instance up in a compatibility interface suitable + for use with `CookieJar` operations. + """ + + def __init__(self, response: Response) -> None: + self.response = response + + def info(self) -> email.message.Message: + info = email.message.Message() + for key, value in self.response.headers.multi_items(): + # Note that setting `info[key]` here is an "append" operation, + # not a "replace" operation. + # https://docs.python.org/3/library/email.compat32-message.html#email.message.Message.__setitem__ + info[key] = value + return info diff --git a/lib/python3.11/site-packages/httpx/_multipart.py b/lib/python3.11/site-packages/httpx/_multipart.py new file mode 100644 index 0000000000000000000000000000000000000000..b4761af9b2cf384de5189269927d781a700dbe46 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_multipart.py @@ -0,0 +1,300 @@ +from __future__ import annotations + +import io +import mimetypes +import os +import re +import typing +from pathlib import Path + +from ._types import ( + AsyncByteStream, + FileContent, + FileTypes, + RequestData, + RequestFiles, + SyncByteStream, +) +from ._utils import ( + peek_filelike_length, + primitive_value_to_str, + to_bytes, +) + +_HTML5_FORM_ENCODING_REPLACEMENTS = {'"': "%22", "\\": "\\\\"} +_HTML5_FORM_ENCODING_REPLACEMENTS.update( + {chr(c): "%{:02X}".format(c) for c in range(0x1F + 1) if c != 0x1B} +) +_HTML5_FORM_ENCODING_RE = re.compile( + r"|".join([re.escape(c) for c in _HTML5_FORM_ENCODING_REPLACEMENTS.keys()]) +) + + +def _format_form_param(name: str, value: str) -> bytes: + """ + Encode a name/value pair within a multipart form. + """ + + def replacer(match: typing.Match[str]) -> str: + return _HTML5_FORM_ENCODING_REPLACEMENTS[match.group(0)] + + value = _HTML5_FORM_ENCODING_RE.sub(replacer, value) + return f'{name}="{value}"'.encode() + + +def _guess_content_type(filename: str | None) -> str | None: + """ + Guesses the mimetype based on a filename. Defaults to `application/octet-stream`. + + Returns `None` if `filename` is `None` or empty. + """ + if filename: + return mimetypes.guess_type(filename)[0] or "application/octet-stream" + return None + + +def get_multipart_boundary_from_content_type( + content_type: bytes | None, +) -> bytes | None: + if not content_type or not content_type.startswith(b"multipart/form-data"): + return None + # parse boundary according to + # https://www.rfc-editor.org/rfc/rfc2046#section-5.1.1 + if b";" in content_type: + for section in content_type.split(b";"): + if section.strip().lower().startswith(b"boundary="): + return section.strip()[len(b"boundary=") :].strip(b'"') + return None + + +class DataField: + """ + A single form field item, within a multipart form field. + """ + + def __init__(self, name: str, value: str | bytes | int | float | None) -> None: + if not isinstance(name, str): + raise TypeError( + f"Invalid type for name. Expected str, got {type(name)}: {name!r}" + ) + if value is not None and not isinstance(value, (str, bytes, int, float)): + raise TypeError( + "Invalid type for value. Expected primitive type," + f" got {type(value)}: {value!r}" + ) + self.name = name + self.value: str | bytes = ( + value if isinstance(value, bytes) else primitive_value_to_str(value) + ) + + def render_headers(self) -> bytes: + if not hasattr(self, "_headers"): + name = _format_form_param("name", self.name) + self._headers = b"".join( + [b"Content-Disposition: form-data; ", name, b"\r\n\r\n"] + ) + + return self._headers + + def render_data(self) -> bytes: + if not hasattr(self, "_data"): + self._data = to_bytes(self.value) + + return self._data + + def get_length(self) -> int: + headers = self.render_headers() + data = self.render_data() + return len(headers) + len(data) + + def render(self) -> typing.Iterator[bytes]: + yield self.render_headers() + yield self.render_data() + + +class FileField: + """ + A single file field item, within a multipart form field. + """ + + CHUNK_SIZE = 64 * 1024 + + def __init__(self, name: str, value: FileTypes) -> None: + self.name = name + + fileobj: FileContent + + headers: dict[str, str] = {} + content_type: str | None = None + + # This large tuple based API largely mirror's requests' API + # It would be good to think of better APIs for this that we could + # include in httpx 2.0 since variable length tuples(especially of 4 elements) + # are quite unwieldly + if isinstance(value, tuple): + if len(value) == 2: + # neither the 3rd parameter (content_type) nor the 4th (headers) + # was included + filename, fileobj = value + elif len(value) == 3: + filename, fileobj, content_type = value + else: + # all 4 parameters included + filename, fileobj, content_type, headers = value # type: ignore + else: + filename = Path(str(getattr(value, "name", "upload"))).name + fileobj = value + + if content_type is None: + content_type = _guess_content_type(filename) + + has_content_type_header = any("content-type" in key.lower() for key in headers) + if content_type is not None and not has_content_type_header: + # note that unlike requests, we ignore the content_type provided in the 3rd + # tuple element if it is also included in the headers requests does + # the opposite (it overwrites the headerwith the 3rd tuple element) + headers["Content-Type"] = content_type + + if isinstance(fileobj, io.StringIO): + raise TypeError( + "Multipart file uploads require 'io.BytesIO', not 'io.StringIO'." + ) + if isinstance(fileobj, io.TextIOBase): + raise TypeError( + "Multipart file uploads must be opened in binary mode, not text mode." + ) + + self.filename = filename + self.file = fileobj + self.headers = headers + + def get_length(self) -> int | None: + headers = self.render_headers() + + if isinstance(self.file, (str, bytes)): + return len(headers) + len(to_bytes(self.file)) + + file_length = peek_filelike_length(self.file) + + # If we can't determine the filesize without reading it into memory, + # then return `None` here, to indicate an unknown file length. + if file_length is None: + return None + + return len(headers) + file_length + + def render_headers(self) -> bytes: + if not hasattr(self, "_headers"): + parts = [ + b"Content-Disposition: form-data; ", + _format_form_param("name", self.name), + ] + if self.filename: + filename = _format_form_param("filename", self.filename) + parts.extend([b"; ", filename]) + for header_name, header_value in self.headers.items(): + key, val = f"\r\n{header_name}: ".encode(), header_value.encode() + parts.extend([key, val]) + parts.append(b"\r\n\r\n") + self._headers = b"".join(parts) + + return self._headers + + def render_data(self) -> typing.Iterator[bytes]: + if isinstance(self.file, (str, bytes)): + yield to_bytes(self.file) + return + + if hasattr(self.file, "seek"): + try: + self.file.seek(0) + except io.UnsupportedOperation: + pass + + chunk = self.file.read(self.CHUNK_SIZE) + while chunk: + yield to_bytes(chunk) + chunk = self.file.read(self.CHUNK_SIZE) + + def render(self) -> typing.Iterator[bytes]: + yield self.render_headers() + yield from self.render_data() + + +class MultipartStream(SyncByteStream, AsyncByteStream): + """ + Request content as streaming multipart encoded form data. + """ + + def __init__( + self, + data: RequestData, + files: RequestFiles, + boundary: bytes | None = None, + ) -> None: + if boundary is None: + boundary = os.urandom(16).hex().encode("ascii") + + self.boundary = boundary + self.content_type = "multipart/form-data; boundary=%s" % boundary.decode( + "ascii" + ) + self.fields = list(self._iter_fields(data, files)) + + def _iter_fields( + self, data: RequestData, files: RequestFiles + ) -> typing.Iterator[FileField | DataField]: + for name, value in data.items(): + if isinstance(value, (tuple, list)): + for item in value: + yield DataField(name=name, value=item) + else: + yield DataField(name=name, value=value) + + file_items = files.items() if isinstance(files, typing.Mapping) else files + for name, value in file_items: + yield FileField(name=name, value=value) + + def iter_chunks(self) -> typing.Iterator[bytes]: + for field in self.fields: + yield b"--%s\r\n" % self.boundary + yield from field.render() + yield b"\r\n" + yield b"--%s--\r\n" % self.boundary + + def get_content_length(self) -> int | None: + """ + Return the length of the multipart encoded content, or `None` if + any of the files have a length that cannot be determined upfront. + """ + boundary_length = len(self.boundary) + length = 0 + + for field in self.fields: + field_length = field.get_length() + if field_length is None: + return None + + length += 2 + boundary_length + 2 # b"--{boundary}\r\n" + length += field_length + length += 2 # b"\r\n" + + length += 2 + boundary_length + 4 # b"--{boundary}--\r\n" + return length + + # Content stream interface. + + def get_headers(self) -> dict[str, str]: + content_length = self.get_content_length() + content_type = self.content_type + if content_length is None: + return {"Transfer-Encoding": "chunked", "Content-Type": content_type} + return {"Content-Length": str(content_length), "Content-Type": content_type} + + def __iter__(self) -> typing.Iterator[bytes]: + for chunk in self.iter_chunks(): + yield chunk + + async def __aiter__(self) -> typing.AsyncIterator[bytes]: + for chunk in self.iter_chunks(): + yield chunk diff --git a/lib/python3.11/site-packages/httpx/_status_codes.py b/lib/python3.11/site-packages/httpx/_status_codes.py new file mode 100644 index 0000000000000000000000000000000000000000..133a6231a5b53fd2f073799ca1bd07c50abe40ae --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_status_codes.py @@ -0,0 +1,162 @@ +from __future__ import annotations + +from enum import IntEnum + +__all__ = ["codes"] + + +class codes(IntEnum): + """HTTP status codes and reason phrases + + Status codes from the following RFCs are all observed: + + * RFC 7231: Hypertext Transfer Protocol (HTTP/1.1), obsoletes 2616 + * RFC 6585: Additional HTTP Status Codes + * RFC 3229: Delta encoding in HTTP + * RFC 4918: HTTP Extensions for WebDAV, obsoletes 2518 + * RFC 5842: Binding Extensions to WebDAV + * RFC 7238: Permanent Redirect + * RFC 2295: Transparent Content Negotiation in HTTP + * RFC 2774: An HTTP Extension Framework + * RFC 7540: Hypertext Transfer Protocol Version 2 (HTTP/2) + * RFC 2324: Hyper Text Coffee Pot Control Protocol (HTCPCP/1.0) + * RFC 7725: An HTTP Status Code to Report Legal Obstacles + * RFC 8297: An HTTP Status Code for Indicating Hints + * RFC 8470: Using Early Data in HTTP + """ + + def __new__(cls, value: int, phrase: str = "") -> codes: + obj = int.__new__(cls, value) + obj._value_ = value + + obj.phrase = phrase # type: ignore[attr-defined] + return obj + + def __str__(self) -> str: + return str(self.value) + + @classmethod + def get_reason_phrase(cls, value: int) -> str: + try: + return codes(value).phrase # type: ignore + except ValueError: + return "" + + @classmethod + def is_informational(cls, value: int) -> bool: + """ + Returns `True` for 1xx status codes, `False` otherwise. + """ + return 100 <= value <= 199 + + @classmethod + def is_success(cls, value: int) -> bool: + """ + Returns `True` for 2xx status codes, `False` otherwise. + """ + return 200 <= value <= 299 + + @classmethod + def is_redirect(cls, value: int) -> bool: + """ + Returns `True` for 3xx status codes, `False` otherwise. + """ + return 300 <= value <= 399 + + @classmethod + def is_client_error(cls, value: int) -> bool: + """ + Returns `True` for 4xx status codes, `False` otherwise. + """ + return 400 <= value <= 499 + + @classmethod + def is_server_error(cls, value: int) -> bool: + """ + Returns `True` for 5xx status codes, `False` otherwise. + """ + return 500 <= value <= 599 + + @classmethod + def is_error(cls, value: int) -> bool: + """ + Returns `True` for 4xx or 5xx status codes, `False` otherwise. + """ + return 400 <= value <= 599 + + # informational + CONTINUE = 100, "Continue" + SWITCHING_PROTOCOLS = 101, "Switching Protocols" + PROCESSING = 102, "Processing" + EARLY_HINTS = 103, "Early Hints" + + # success + OK = 200, "OK" + CREATED = 201, "Created" + ACCEPTED = 202, "Accepted" + NON_AUTHORITATIVE_INFORMATION = 203, "Non-Authoritative Information" + NO_CONTENT = 204, "No Content" + RESET_CONTENT = 205, "Reset Content" + PARTIAL_CONTENT = 206, "Partial Content" + MULTI_STATUS = 207, "Multi-Status" + ALREADY_REPORTED = 208, "Already Reported" + IM_USED = 226, "IM Used" + + # redirection + MULTIPLE_CHOICES = 300, "Multiple Choices" + MOVED_PERMANENTLY = 301, "Moved Permanently" + FOUND = 302, "Found" + SEE_OTHER = 303, "See Other" + NOT_MODIFIED = 304, "Not Modified" + USE_PROXY = 305, "Use Proxy" + TEMPORARY_REDIRECT = 307, "Temporary Redirect" + PERMANENT_REDIRECT = 308, "Permanent Redirect" + + # client error + BAD_REQUEST = 400, "Bad Request" + UNAUTHORIZED = 401, "Unauthorized" + PAYMENT_REQUIRED = 402, "Payment Required" + FORBIDDEN = 403, "Forbidden" + NOT_FOUND = 404, "Not Found" + METHOD_NOT_ALLOWED = 405, "Method Not Allowed" + NOT_ACCEPTABLE = 406, "Not Acceptable" + PROXY_AUTHENTICATION_REQUIRED = 407, "Proxy Authentication Required" + REQUEST_TIMEOUT = 408, "Request Timeout" + CONFLICT = 409, "Conflict" + GONE = 410, "Gone" + LENGTH_REQUIRED = 411, "Length Required" + PRECONDITION_FAILED = 412, "Precondition Failed" + REQUEST_ENTITY_TOO_LARGE = 413, "Request Entity Too Large" + REQUEST_URI_TOO_LONG = 414, "Request-URI Too Long" + UNSUPPORTED_MEDIA_TYPE = 415, "Unsupported Media Type" + REQUESTED_RANGE_NOT_SATISFIABLE = 416, "Requested Range Not Satisfiable" + EXPECTATION_FAILED = 417, "Expectation Failed" + IM_A_TEAPOT = 418, "I'm a teapot" + MISDIRECTED_REQUEST = 421, "Misdirected Request" + UNPROCESSABLE_ENTITY = 422, "Unprocessable Entity" + LOCKED = 423, "Locked" + FAILED_DEPENDENCY = 424, "Failed Dependency" + TOO_EARLY = 425, "Too Early" + UPGRADE_REQUIRED = 426, "Upgrade Required" + PRECONDITION_REQUIRED = 428, "Precondition Required" + TOO_MANY_REQUESTS = 429, "Too Many Requests" + REQUEST_HEADER_FIELDS_TOO_LARGE = 431, "Request Header Fields Too Large" + UNAVAILABLE_FOR_LEGAL_REASONS = 451, "Unavailable For Legal Reasons" + + # server errors + INTERNAL_SERVER_ERROR = 500, "Internal Server Error" + NOT_IMPLEMENTED = 501, "Not Implemented" + BAD_GATEWAY = 502, "Bad Gateway" + SERVICE_UNAVAILABLE = 503, "Service Unavailable" + GATEWAY_TIMEOUT = 504, "Gateway Timeout" + HTTP_VERSION_NOT_SUPPORTED = 505, "HTTP Version Not Supported" + VARIANT_ALSO_NEGOTIATES = 506, "Variant Also Negotiates" + INSUFFICIENT_STORAGE = 507, "Insufficient Storage" + LOOP_DETECTED = 508, "Loop Detected" + NOT_EXTENDED = 510, "Not Extended" + NETWORK_AUTHENTICATION_REQUIRED = 511, "Network Authentication Required" + + +# Include lower-case styles for `requests` compatibility. +for code in codes: + setattr(codes, code._name_.lower(), int(code)) diff --git a/lib/python3.11/site-packages/httpx/_transports/__init__.py b/lib/python3.11/site-packages/httpx/_transports/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7a321053b29bcd48698cf2bd74a1d19c8556aefb --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_transports/__init__.py @@ -0,0 +1,15 @@ +from .asgi import * +from .base import * +from .default import * +from .mock import * +from .wsgi import * + +__all__ = [ + "ASGITransport", + "AsyncBaseTransport", + "BaseTransport", + "AsyncHTTPTransport", + "HTTPTransport", + "MockTransport", + "WSGITransport", +] diff --git a/lib/python3.11/site-packages/httpx/_transports/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/httpx/_transports/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..752df8867b453f37be75ddf2e2a298e96d960374 Binary files /dev/null and b/lib/python3.11/site-packages/httpx/_transports/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/httpx/_transports/__pycache__/asgi.cpython-311.pyc b/lib/python3.11/site-packages/httpx/_transports/__pycache__/asgi.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0e186b7f87b2eb5bb38219445ff471382ca694af Binary files /dev/null and b/lib/python3.11/site-packages/httpx/_transports/__pycache__/asgi.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/httpx/_transports/__pycache__/base.cpython-311.pyc b/lib/python3.11/site-packages/httpx/_transports/__pycache__/base.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..05f8d09e729bd7e17af1acff1d8d8bf612234a89 Binary files /dev/null and b/lib/python3.11/site-packages/httpx/_transports/__pycache__/base.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/httpx/_transports/__pycache__/default.cpython-311.pyc b/lib/python3.11/site-packages/httpx/_transports/__pycache__/default.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6d7e4ea6ff2a54ce0c4e122631143c61d5ca93c8 Binary files /dev/null and b/lib/python3.11/site-packages/httpx/_transports/__pycache__/default.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/httpx/_transports/__pycache__/mock.cpython-311.pyc b/lib/python3.11/site-packages/httpx/_transports/__pycache__/mock.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a5c3110bb78b0547a09830aaabddf55c71268bbe Binary files /dev/null and b/lib/python3.11/site-packages/httpx/_transports/__pycache__/mock.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/httpx/_transports/__pycache__/wsgi.cpython-311.pyc b/lib/python3.11/site-packages/httpx/_transports/__pycache__/wsgi.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..72b91b7ced97a9baf01ee6655868fd1d2278c234 Binary files /dev/null and b/lib/python3.11/site-packages/httpx/_transports/__pycache__/wsgi.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/httpx/_transports/asgi.py b/lib/python3.11/site-packages/httpx/_transports/asgi.py new file mode 100644 index 0000000000000000000000000000000000000000..2bc4efae0e1b14620f75f712eb15ecf500d14eef --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_transports/asgi.py @@ -0,0 +1,187 @@ +from __future__ import annotations + +import typing + +from .._models import Request, Response +from .._types import AsyncByteStream +from .base import AsyncBaseTransport + +if typing.TYPE_CHECKING: # pragma: no cover + import asyncio + + import trio + + Event = typing.Union[asyncio.Event, trio.Event] + + +_Message = typing.MutableMapping[str, typing.Any] +_Receive = typing.Callable[[], typing.Awaitable[_Message]] +_Send = typing.Callable[ + [typing.MutableMapping[str, typing.Any]], typing.Awaitable[None] +] +_ASGIApp = typing.Callable[ + [typing.MutableMapping[str, typing.Any], _Receive, _Send], typing.Awaitable[None] +] + +__all__ = ["ASGITransport"] + + +def is_running_trio() -> bool: + try: + # sniffio is a dependency of trio. + + # See https://github.com/python-trio/trio/issues/2802 + import sniffio + + if sniffio.current_async_library() == "trio": + return True + except ImportError: # pragma: nocover + pass + + return False + + +def create_event() -> Event: + if is_running_trio(): + import trio + + return trio.Event() + + import asyncio + + return asyncio.Event() + + +class ASGIResponseStream(AsyncByteStream): + def __init__(self, body: list[bytes]) -> None: + self._body = body + + async def __aiter__(self) -> typing.AsyncIterator[bytes]: + yield b"".join(self._body) + + +class ASGITransport(AsyncBaseTransport): + """ + A custom AsyncTransport that handles sending requests directly to an ASGI app. + + ```python + transport = httpx.ASGITransport( + app=app, + root_path="/submount", + client=("1.2.3.4", 123) + ) + client = httpx.AsyncClient(transport=transport) + ``` + + Arguments: + + * `app` - The ASGI application. + * `raise_app_exceptions` - Boolean indicating if exceptions in the application + should be raised. Default to `True`. Can be set to `False` for use cases + such as testing the content of a client 500 response. + * `root_path` - The root path on which the ASGI application should be mounted. + * `client` - A two-tuple indicating the client IP and port of incoming requests. + ``` + """ + + def __init__( + self, + app: _ASGIApp, + raise_app_exceptions: bool = True, + root_path: str = "", + client: tuple[str, int] = ("127.0.0.1", 123), + ) -> None: + self.app = app + self.raise_app_exceptions = raise_app_exceptions + self.root_path = root_path + self.client = client + + async def handle_async_request( + self, + request: Request, + ) -> Response: + assert isinstance(request.stream, AsyncByteStream) + + # ASGI scope. + scope = { + "type": "http", + "asgi": {"version": "3.0"}, + "http_version": "1.1", + "method": request.method, + "headers": [(k.lower(), v) for (k, v) in request.headers.raw], + "scheme": request.url.scheme, + "path": request.url.path, + "raw_path": request.url.raw_path.split(b"?")[0], + "query_string": request.url.query, + "server": (request.url.host, request.url.port), + "client": self.client, + "root_path": self.root_path, + } + + # Request. + request_body_chunks = request.stream.__aiter__() + request_complete = False + + # Response. + status_code = None + response_headers = None + body_parts = [] + response_started = False + response_complete = create_event() + + # ASGI callables. + + async def receive() -> dict[str, typing.Any]: + nonlocal request_complete + + if request_complete: + await response_complete.wait() + return {"type": "http.disconnect"} + + try: + body = await request_body_chunks.__anext__() + except StopAsyncIteration: + request_complete = True + return {"type": "http.request", "body": b"", "more_body": False} + return {"type": "http.request", "body": body, "more_body": True} + + async def send(message: typing.MutableMapping[str, typing.Any]) -> None: + nonlocal status_code, response_headers, response_started + + if message["type"] == "http.response.start": + assert not response_started + + status_code = message["status"] + response_headers = message.get("headers", []) + response_started = True + + elif message["type"] == "http.response.body": + assert not response_complete.is_set() + body = message.get("body", b"") + more_body = message.get("more_body", False) + + if body and request.method != "HEAD": + body_parts.append(body) + + if not more_body: + response_complete.set() + + try: + await self.app(scope, receive, send) + except Exception: # noqa: PIE-786 + if self.raise_app_exceptions: + raise + + response_complete.set() + if status_code is None: + status_code = 500 + if response_headers is None: + response_headers = {} + + assert response_complete.is_set() + assert status_code is not None + assert response_headers is not None + + stream = ASGIResponseStream(body_parts) + + return Response(status_code, headers=response_headers, stream=stream) diff --git a/lib/python3.11/site-packages/httpx/_transports/base.py b/lib/python3.11/site-packages/httpx/_transports/base.py new file mode 100644 index 0000000000000000000000000000000000000000..66fd99d702480b555c06694fe14715ea6df3dfc3 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_transports/base.py @@ -0,0 +1,86 @@ +from __future__ import annotations + +import typing +from types import TracebackType + +from .._models import Request, Response + +T = typing.TypeVar("T", bound="BaseTransport") +A = typing.TypeVar("A", bound="AsyncBaseTransport") + +__all__ = ["AsyncBaseTransport", "BaseTransport"] + + +class BaseTransport: + def __enter__(self: T) -> T: + return self + + def __exit__( + self, + exc_type: type[BaseException] | None = None, + exc_value: BaseException | None = None, + traceback: TracebackType | None = None, + ) -> None: + self.close() + + def handle_request(self, request: Request) -> Response: + """ + Send a single HTTP request and return a response. + + Developers shouldn't typically ever need to call into this API directly, + since the Client class provides all the higher level user-facing API + niceties. + + In order to properly release any network resources, the response + stream should *either* be consumed immediately, with a call to + `response.stream.read()`, or else the `handle_request` call should + be followed with a try/finally block to ensuring the stream is + always closed. + + Example usage: + + with httpx.HTTPTransport() as transport: + req = httpx.Request( + method=b"GET", + url=(b"https", b"www.example.com", 443, b"/"), + headers=[(b"Host", b"www.example.com")], + ) + resp = transport.handle_request(req) + body = resp.stream.read() + print(resp.status_code, resp.headers, body) + + + Takes a `Request` instance as the only argument. + + Returns a `Response` instance. + """ + raise NotImplementedError( + "The 'handle_request' method must be implemented." + ) # pragma: no cover + + def close(self) -> None: + pass + + +class AsyncBaseTransport: + async def __aenter__(self: A) -> A: + return self + + async def __aexit__( + self, + exc_type: type[BaseException] | None = None, + exc_value: BaseException | None = None, + traceback: TracebackType | None = None, + ) -> None: + await self.aclose() + + async def handle_async_request( + self, + request: Request, + ) -> Response: + raise NotImplementedError( + "The 'handle_async_request' method must be implemented." + ) # pragma: no cover + + async def aclose(self) -> None: + pass diff --git a/lib/python3.11/site-packages/httpx/_transports/default.py b/lib/python3.11/site-packages/httpx/_transports/default.py new file mode 100644 index 0000000000000000000000000000000000000000..d5aa05ff234fd3fbf4fee88c4a7d3e3c151a538f --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_transports/default.py @@ -0,0 +1,406 @@ +""" +Custom transports, with nicely configured defaults. + +The following additional keyword arguments are currently supported by httpcore... + +* uds: str +* local_address: str +* retries: int + +Example usages... + +# Disable HTTP/2 on a single specific domain. +mounts = { + "all://": httpx.HTTPTransport(http2=True), + "all://*example.org": httpx.HTTPTransport() +} + +# Using advanced httpcore configuration, with connection retries. +transport = httpx.HTTPTransport(retries=1) +client = httpx.Client(transport=transport) + +# Using advanced httpcore configuration, with unix domain sockets. +transport = httpx.HTTPTransport(uds="socket.uds") +client = httpx.Client(transport=transport) +""" + +from __future__ import annotations + +import contextlib +import typing +from types import TracebackType + +if typing.TYPE_CHECKING: + import ssl # pragma: no cover + + import httpx # pragma: no cover + +from .._config import DEFAULT_LIMITS, Limits, Proxy, create_ssl_context +from .._exceptions import ( + ConnectError, + ConnectTimeout, + LocalProtocolError, + NetworkError, + PoolTimeout, + ProtocolError, + ProxyError, + ReadError, + ReadTimeout, + RemoteProtocolError, + TimeoutException, + UnsupportedProtocol, + WriteError, + WriteTimeout, +) +from .._models import Request, Response +from .._types import AsyncByteStream, CertTypes, ProxyTypes, SyncByteStream +from .._urls import URL +from .base import AsyncBaseTransport, BaseTransport + +T = typing.TypeVar("T", bound="HTTPTransport") +A = typing.TypeVar("A", bound="AsyncHTTPTransport") + +SOCKET_OPTION = typing.Union[ + typing.Tuple[int, int, int], + typing.Tuple[int, int, typing.Union[bytes, bytearray]], + typing.Tuple[int, int, None, int], +] + +__all__ = ["AsyncHTTPTransport", "HTTPTransport"] + +HTTPCORE_EXC_MAP: dict[type[Exception], type[httpx.HTTPError]] = {} + + +def _load_httpcore_exceptions() -> dict[type[Exception], type[httpx.HTTPError]]: + import httpcore + + return { + httpcore.TimeoutException: TimeoutException, + httpcore.ConnectTimeout: ConnectTimeout, + httpcore.ReadTimeout: ReadTimeout, + httpcore.WriteTimeout: WriteTimeout, + httpcore.PoolTimeout: PoolTimeout, + httpcore.NetworkError: NetworkError, + httpcore.ConnectError: ConnectError, + httpcore.ReadError: ReadError, + httpcore.WriteError: WriteError, + httpcore.ProxyError: ProxyError, + httpcore.UnsupportedProtocol: UnsupportedProtocol, + httpcore.ProtocolError: ProtocolError, + httpcore.LocalProtocolError: LocalProtocolError, + httpcore.RemoteProtocolError: RemoteProtocolError, + } + + +@contextlib.contextmanager +def map_httpcore_exceptions() -> typing.Iterator[None]: + global HTTPCORE_EXC_MAP + if len(HTTPCORE_EXC_MAP) == 0: + HTTPCORE_EXC_MAP = _load_httpcore_exceptions() + try: + yield + except Exception as exc: + mapped_exc = None + + for from_exc, to_exc in HTTPCORE_EXC_MAP.items(): + if not isinstance(exc, from_exc): + continue + # We want to map to the most specific exception we can find. + # Eg if `exc` is an `httpcore.ReadTimeout`, we want to map to + # `httpx.ReadTimeout`, not just `httpx.TimeoutException`. + if mapped_exc is None or issubclass(to_exc, mapped_exc): + mapped_exc = to_exc + + if mapped_exc is None: # pragma: no cover + raise + + message = str(exc) + raise mapped_exc(message) from exc + + +class ResponseStream(SyncByteStream): + def __init__(self, httpcore_stream: typing.Iterable[bytes]) -> None: + self._httpcore_stream = httpcore_stream + + def __iter__(self) -> typing.Iterator[bytes]: + with map_httpcore_exceptions(): + for part in self._httpcore_stream: + yield part + + def close(self) -> None: + if hasattr(self._httpcore_stream, "close"): + self._httpcore_stream.close() + + +class HTTPTransport(BaseTransport): + def __init__( + self, + verify: ssl.SSLContext | str | bool = True, + cert: CertTypes | None = None, + trust_env: bool = True, + http1: bool = True, + http2: bool = False, + limits: Limits = DEFAULT_LIMITS, + proxy: ProxyTypes | None = None, + uds: str | None = None, + local_address: str | None = None, + retries: int = 0, + socket_options: typing.Iterable[SOCKET_OPTION] | None = None, + ) -> None: + import httpcore + + proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy + ssl_context = create_ssl_context(verify=verify, cert=cert, trust_env=trust_env) + + if proxy is None: + self._pool = httpcore.ConnectionPool( + ssl_context=ssl_context, + max_connections=limits.max_connections, + max_keepalive_connections=limits.max_keepalive_connections, + keepalive_expiry=limits.keepalive_expiry, + http1=http1, + http2=http2, + uds=uds, + local_address=local_address, + retries=retries, + socket_options=socket_options, + ) + elif proxy.url.scheme in ("http", "https"): + self._pool = httpcore.HTTPProxy( + proxy_url=httpcore.URL( + scheme=proxy.url.raw_scheme, + host=proxy.url.raw_host, + port=proxy.url.port, + target=proxy.url.raw_path, + ), + proxy_auth=proxy.raw_auth, + proxy_headers=proxy.headers.raw, + ssl_context=ssl_context, + proxy_ssl_context=proxy.ssl_context, + max_connections=limits.max_connections, + max_keepalive_connections=limits.max_keepalive_connections, + keepalive_expiry=limits.keepalive_expiry, + http1=http1, + http2=http2, + socket_options=socket_options, + ) + elif proxy.url.scheme in ("socks5", "socks5h"): + try: + import socksio # noqa + except ImportError: # pragma: no cover + raise ImportError( + "Using SOCKS proxy, but the 'socksio' package is not installed. " + "Make sure to install httpx using `pip install httpx[socks]`." + ) from None + + self._pool = httpcore.SOCKSProxy( + proxy_url=httpcore.URL( + scheme=proxy.url.raw_scheme, + host=proxy.url.raw_host, + port=proxy.url.port, + target=proxy.url.raw_path, + ), + proxy_auth=proxy.raw_auth, + ssl_context=ssl_context, + max_connections=limits.max_connections, + max_keepalive_connections=limits.max_keepalive_connections, + keepalive_expiry=limits.keepalive_expiry, + http1=http1, + http2=http2, + ) + else: # pragma: no cover + raise ValueError( + "Proxy protocol must be either 'http', 'https', 'socks5', or 'socks5h'," + f" but got {proxy.url.scheme!r}." + ) + + def __enter__(self: T) -> T: # Use generics for subclass support. + self._pool.__enter__() + return self + + def __exit__( + self, + exc_type: type[BaseException] | None = None, + exc_value: BaseException | None = None, + traceback: TracebackType | None = None, + ) -> None: + with map_httpcore_exceptions(): + self._pool.__exit__(exc_type, exc_value, traceback) + + def handle_request( + self, + request: Request, + ) -> Response: + assert isinstance(request.stream, SyncByteStream) + import httpcore + + req = httpcore.Request( + method=request.method, + url=httpcore.URL( + scheme=request.url.raw_scheme, + host=request.url.raw_host, + port=request.url.port, + target=request.url.raw_path, + ), + headers=request.headers.raw, + content=request.stream, + extensions=request.extensions, + ) + with map_httpcore_exceptions(): + resp = self._pool.handle_request(req) + + assert isinstance(resp.stream, typing.Iterable) + + return Response( + status_code=resp.status, + headers=resp.headers, + stream=ResponseStream(resp.stream), + extensions=resp.extensions, + ) + + def close(self) -> None: + self._pool.close() + + +class AsyncResponseStream(AsyncByteStream): + def __init__(self, httpcore_stream: typing.AsyncIterable[bytes]) -> None: + self._httpcore_stream = httpcore_stream + + async def __aiter__(self) -> typing.AsyncIterator[bytes]: + with map_httpcore_exceptions(): + async for part in self._httpcore_stream: + yield part + + async def aclose(self) -> None: + if hasattr(self._httpcore_stream, "aclose"): + await self._httpcore_stream.aclose() + + +class AsyncHTTPTransport(AsyncBaseTransport): + def __init__( + self, + verify: ssl.SSLContext | str | bool = True, + cert: CertTypes | None = None, + trust_env: bool = True, + http1: bool = True, + http2: bool = False, + limits: Limits = DEFAULT_LIMITS, + proxy: ProxyTypes | None = None, + uds: str | None = None, + local_address: str | None = None, + retries: int = 0, + socket_options: typing.Iterable[SOCKET_OPTION] | None = None, + ) -> None: + import httpcore + + proxy = Proxy(url=proxy) if isinstance(proxy, (str, URL)) else proxy + ssl_context = create_ssl_context(verify=verify, cert=cert, trust_env=trust_env) + + if proxy is None: + self._pool = httpcore.AsyncConnectionPool( + ssl_context=ssl_context, + max_connections=limits.max_connections, + max_keepalive_connections=limits.max_keepalive_connections, + keepalive_expiry=limits.keepalive_expiry, + http1=http1, + http2=http2, + uds=uds, + local_address=local_address, + retries=retries, + socket_options=socket_options, + ) + elif proxy.url.scheme in ("http", "https"): + self._pool = httpcore.AsyncHTTPProxy( + proxy_url=httpcore.URL( + scheme=proxy.url.raw_scheme, + host=proxy.url.raw_host, + port=proxy.url.port, + target=proxy.url.raw_path, + ), + proxy_auth=proxy.raw_auth, + proxy_headers=proxy.headers.raw, + proxy_ssl_context=proxy.ssl_context, + ssl_context=ssl_context, + max_connections=limits.max_connections, + max_keepalive_connections=limits.max_keepalive_connections, + keepalive_expiry=limits.keepalive_expiry, + http1=http1, + http2=http2, + socket_options=socket_options, + ) + elif proxy.url.scheme in ("socks5", "socks5h"): + try: + import socksio # noqa + except ImportError: # pragma: no cover + raise ImportError( + "Using SOCKS proxy, but the 'socksio' package is not installed. " + "Make sure to install httpx using `pip install httpx[socks]`." + ) from None + + self._pool = httpcore.AsyncSOCKSProxy( + proxy_url=httpcore.URL( + scheme=proxy.url.raw_scheme, + host=proxy.url.raw_host, + port=proxy.url.port, + target=proxy.url.raw_path, + ), + proxy_auth=proxy.raw_auth, + ssl_context=ssl_context, + max_connections=limits.max_connections, + max_keepalive_connections=limits.max_keepalive_connections, + keepalive_expiry=limits.keepalive_expiry, + http1=http1, + http2=http2, + ) + else: # pragma: no cover + raise ValueError( + "Proxy protocol must be either 'http', 'https', 'socks5', or 'socks5h'," + " but got {proxy.url.scheme!r}." + ) + + async def __aenter__(self: A) -> A: # Use generics for subclass support. + await self._pool.__aenter__() + return self + + async def __aexit__( + self, + exc_type: type[BaseException] | None = None, + exc_value: BaseException | None = None, + traceback: TracebackType | None = None, + ) -> None: + with map_httpcore_exceptions(): + await self._pool.__aexit__(exc_type, exc_value, traceback) + + async def handle_async_request( + self, + request: Request, + ) -> Response: + assert isinstance(request.stream, AsyncByteStream) + import httpcore + + req = httpcore.Request( + method=request.method, + url=httpcore.URL( + scheme=request.url.raw_scheme, + host=request.url.raw_host, + port=request.url.port, + target=request.url.raw_path, + ), + headers=request.headers.raw, + content=request.stream, + extensions=request.extensions, + ) + with map_httpcore_exceptions(): + resp = await self._pool.handle_async_request(req) + + assert isinstance(resp.stream, typing.AsyncIterable) + + return Response( + status_code=resp.status, + headers=resp.headers, + stream=AsyncResponseStream(resp.stream), + extensions=resp.extensions, + ) + + async def aclose(self) -> None: + await self._pool.aclose() diff --git a/lib/python3.11/site-packages/httpx/_transports/mock.py b/lib/python3.11/site-packages/httpx/_transports/mock.py new file mode 100644 index 0000000000000000000000000000000000000000..8c418f59e06cae43abdbb626ec21cafc7e8c6277 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_transports/mock.py @@ -0,0 +1,43 @@ +from __future__ import annotations + +import typing + +from .._models import Request, Response +from .base import AsyncBaseTransport, BaseTransport + +SyncHandler = typing.Callable[[Request], Response] +AsyncHandler = typing.Callable[[Request], typing.Coroutine[None, None, Response]] + + +__all__ = ["MockTransport"] + + +class MockTransport(AsyncBaseTransport, BaseTransport): + def __init__(self, handler: SyncHandler | AsyncHandler) -> None: + self.handler = handler + + def handle_request( + self, + request: Request, + ) -> Response: + request.read() + response = self.handler(request) + if not isinstance(response, Response): # pragma: no cover + raise TypeError("Cannot use an async handler in a sync Client") + return response + + async def handle_async_request( + self, + request: Request, + ) -> Response: + await request.aread() + response = self.handler(request) + + # Allow handler to *optionally* be an `async` function. + # If it is, then the `response` variable need to be awaited to actually + # return the result. + + if not isinstance(response, Response): + response = await response + + return response diff --git a/lib/python3.11/site-packages/httpx/_transports/wsgi.py b/lib/python3.11/site-packages/httpx/_transports/wsgi.py new file mode 100644 index 0000000000000000000000000000000000000000..8592ffe017a87367cc7578184540096a9682908d --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_transports/wsgi.py @@ -0,0 +1,149 @@ +from __future__ import annotations + +import io +import itertools +import sys +import typing + +from .._models import Request, Response +from .._types import SyncByteStream +from .base import BaseTransport + +if typing.TYPE_CHECKING: + from _typeshed import OptExcInfo # pragma: no cover + from _typeshed.wsgi import WSGIApplication # pragma: no cover + +_T = typing.TypeVar("_T") + + +__all__ = ["WSGITransport"] + + +def _skip_leading_empty_chunks(body: typing.Iterable[_T]) -> typing.Iterable[_T]: + body = iter(body) + for chunk in body: + if chunk: + return itertools.chain([chunk], body) + return [] + + +class WSGIByteStream(SyncByteStream): + def __init__(self, result: typing.Iterable[bytes]) -> None: + self._close = getattr(result, "close", None) + self._result = _skip_leading_empty_chunks(result) + + def __iter__(self) -> typing.Iterator[bytes]: + for part in self._result: + yield part + + def close(self) -> None: + if self._close is not None: + self._close() + + +class WSGITransport(BaseTransport): + """ + A custom transport that handles sending requests directly to an WSGI app. + The simplest way to use this functionality is to use the `app` argument. + + ``` + client = httpx.Client(app=app) + ``` + + Alternatively, you can setup the transport instance explicitly. + This allows you to include any additional configuration arguments specific + to the WSGITransport class: + + ``` + transport = httpx.WSGITransport( + app=app, + script_name="/submount", + remote_addr="1.2.3.4" + ) + client = httpx.Client(transport=transport) + ``` + + Arguments: + + * `app` - The WSGI application. + * `raise_app_exceptions` - Boolean indicating if exceptions in the application + should be raised. Default to `True`. Can be set to `False` for use cases + such as testing the content of a client 500 response. + * `script_name` - The root path on which the WSGI application should be mounted. + * `remote_addr` - A string indicating the client IP of incoming requests. + ``` + """ + + def __init__( + self, + app: WSGIApplication, + raise_app_exceptions: bool = True, + script_name: str = "", + remote_addr: str = "127.0.0.1", + wsgi_errors: typing.TextIO | None = None, + ) -> None: + self.app = app + self.raise_app_exceptions = raise_app_exceptions + self.script_name = script_name + self.remote_addr = remote_addr + self.wsgi_errors = wsgi_errors + + def handle_request(self, request: Request) -> Response: + request.read() + wsgi_input = io.BytesIO(request.content) + + port = request.url.port or {"http": 80, "https": 443}[request.url.scheme] + environ = { + "wsgi.version": (1, 0), + "wsgi.url_scheme": request.url.scheme, + "wsgi.input": wsgi_input, + "wsgi.errors": self.wsgi_errors or sys.stderr, + "wsgi.multithread": True, + "wsgi.multiprocess": False, + "wsgi.run_once": False, + "REQUEST_METHOD": request.method, + "SCRIPT_NAME": self.script_name, + "PATH_INFO": request.url.path, + "QUERY_STRING": request.url.query.decode("ascii"), + "SERVER_NAME": request.url.host, + "SERVER_PORT": str(port), + "SERVER_PROTOCOL": "HTTP/1.1", + "REMOTE_ADDR": self.remote_addr, + } + for header_key, header_value in request.headers.raw: + key = header_key.decode("ascii").upper().replace("-", "_") + if key not in ("CONTENT_TYPE", "CONTENT_LENGTH"): + key = "HTTP_" + key + environ[key] = header_value.decode("ascii") + + seen_status = None + seen_response_headers = None + seen_exc_info = None + + def start_response( + status: str, + response_headers: list[tuple[str, str]], + exc_info: OptExcInfo | None = None, + ) -> typing.Callable[[bytes], typing.Any]: + nonlocal seen_status, seen_response_headers, seen_exc_info + seen_status = status + seen_response_headers = response_headers + seen_exc_info = exc_info + return lambda _: None + + result = self.app(environ, start_response) + + stream = WSGIByteStream(result) + + assert seen_status is not None + assert seen_response_headers is not None + if seen_exc_info and seen_exc_info[0] and self.raise_app_exceptions: + raise seen_exc_info[1] + + status_code = int(seen_status.split()[0]) + headers = [ + (key.encode("ascii"), value.encode("ascii")) + for key, value in seen_response_headers + ] + + return Response(status_code, headers=headers, stream=stream) diff --git a/lib/python3.11/site-packages/httpx/_types.py b/lib/python3.11/site-packages/httpx/_types.py new file mode 100644 index 0000000000000000000000000000000000000000..704dfdffc8ba61eb913fa918072381e410b23c00 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_types.py @@ -0,0 +1,114 @@ +""" +Type definitions for type checking purposes. +""" + +from http.cookiejar import CookieJar +from typing import ( + IO, + TYPE_CHECKING, + Any, + AsyncIterable, + AsyncIterator, + Callable, + Dict, + Iterable, + Iterator, + List, + Mapping, + Optional, + Sequence, + Tuple, + Union, +) + +if TYPE_CHECKING: # pragma: no cover + from ._auth import Auth # noqa: F401 + from ._config import Proxy, Timeout # noqa: F401 + from ._models import Cookies, Headers, Request # noqa: F401 + from ._urls import URL, QueryParams # noqa: F401 + + +PrimitiveData = Optional[Union[str, int, float, bool]] + +URLTypes = Union["URL", str] + +QueryParamTypes = Union[ + "QueryParams", + Mapping[str, Union[PrimitiveData, Sequence[PrimitiveData]]], + List[Tuple[str, PrimitiveData]], + Tuple[Tuple[str, PrimitiveData], ...], + str, + bytes, +] + +HeaderTypes = Union[ + "Headers", + Mapping[str, str], + Mapping[bytes, bytes], + Sequence[Tuple[str, str]], + Sequence[Tuple[bytes, bytes]], +] + +CookieTypes = Union["Cookies", CookieJar, Dict[str, str], List[Tuple[str, str]]] + +TimeoutTypes = Union[ + Optional[float], + Tuple[Optional[float], Optional[float], Optional[float], Optional[float]], + "Timeout", +] +ProxyTypes = Union["URL", str, "Proxy"] +CertTypes = Union[str, Tuple[str, str], Tuple[str, str, str]] + +AuthTypes = Union[ + Tuple[Union[str, bytes], Union[str, bytes]], + Callable[["Request"], "Request"], + "Auth", +] + +RequestContent = Union[str, bytes, Iterable[bytes], AsyncIterable[bytes]] +ResponseContent = Union[str, bytes, Iterable[bytes], AsyncIterable[bytes]] +ResponseExtensions = Mapping[str, Any] + +RequestData = Mapping[str, Any] + +FileContent = Union[IO[bytes], bytes, str] +FileTypes = Union[ + # file (or bytes) + FileContent, + # (filename, file (or bytes)) + Tuple[Optional[str], FileContent], + # (filename, file (or bytes), content_type) + Tuple[Optional[str], FileContent, Optional[str]], + # (filename, file (or bytes), content_type, headers) + Tuple[Optional[str], FileContent, Optional[str], Mapping[str, str]], +] +RequestFiles = Union[Mapping[str, FileTypes], Sequence[Tuple[str, FileTypes]]] + +RequestExtensions = Mapping[str, Any] + +__all__ = ["AsyncByteStream", "SyncByteStream"] + + +class SyncByteStream: + def __iter__(self) -> Iterator[bytes]: + raise NotImplementedError( + "The '__iter__' method must be implemented." + ) # pragma: no cover + yield b"" # pragma: no cover + + def close(self) -> None: + """ + Subclasses can override this method to release any network resources + after a request/response cycle is complete. + """ + + +class AsyncByteStream: + async def __aiter__(self) -> AsyncIterator[bytes]: + raise NotImplementedError( + "The '__aiter__' method must be implemented." + ) # pragma: no cover + yield b"" # pragma: no cover + + async def aclose(self) -> None: + pass diff --git a/lib/python3.11/site-packages/httpx/_urlparse.py b/lib/python3.11/site-packages/httpx/_urlparse.py new file mode 100644 index 0000000000000000000000000000000000000000..bf190fd560ee4fc8a11af371a15fc5f1dc284d34 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_urlparse.py @@ -0,0 +1,527 @@ +""" +An implementation of `urlparse` that provides URL validation and normalization +as described by RFC3986. + +We rely on this implementation rather than the one in Python's stdlib, because: + +* It provides more complete URL validation. +* It properly differentiates between an empty querystring and an absent querystring, + to distinguish URLs with a trailing '?'. +* It handles scheme, hostname, port, and path normalization. +* It supports IDNA hostnames, normalizing them to their encoded form. +* The API supports passing individual components, as well as the complete URL string. + +Previously we relied on the excellent `rfc3986` package to handle URL parsing and +validation, but this module provides a simpler alternative, with less indirection +required. +""" + +from __future__ import annotations + +import ipaddress +import re +import typing + +import idna + +from ._exceptions import InvalidURL + +MAX_URL_LENGTH = 65536 + +# https://datatracker.ietf.org/doc/html/rfc3986.html#section-2.3 +UNRESERVED_CHARACTERS = ( + "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-._~" +) +SUB_DELIMS = "!$&'()*+,;=" + +PERCENT_ENCODED_REGEX = re.compile("%[A-Fa-f0-9]{2}") + +# https://url.spec.whatwg.org/#percent-encoded-bytes + +# The fragment percent-encode set is the C0 control percent-encode set +# and U+0020 SPACE, U+0022 ("), U+003C (<), U+003E (>), and U+0060 (`). +FRAG_SAFE = "".join( + [chr(i) for i in range(0x20, 0x7F) if i not in (0x20, 0x22, 0x3C, 0x3E, 0x60)] +) + +# The query percent-encode set is the C0 control percent-encode set +# and U+0020 SPACE, U+0022 ("), U+0023 (#), U+003C (<), and U+003E (>). +QUERY_SAFE = "".join( + [chr(i) for i in range(0x20, 0x7F) if i not in (0x20, 0x22, 0x23, 0x3C, 0x3E)] +) + +# The path percent-encode set is the query percent-encode set +# and U+003F (?), U+0060 (`), U+007B ({), and U+007D (}). +PATH_SAFE = "".join( + [ + chr(i) + for i in range(0x20, 0x7F) + if i not in (0x20, 0x22, 0x23, 0x3C, 0x3E) + (0x3F, 0x60, 0x7B, 0x7D) + ] +) + +# The userinfo percent-encode set is the path percent-encode set +# and U+002F (/), U+003A (:), U+003B (;), U+003D (=), U+0040 (@), +# U+005B ([) to U+005E (^), inclusive, and U+007C (|). +USERNAME_SAFE = "".join( + [ + chr(i) + for i in range(0x20, 0x7F) + if i + not in (0x20, 0x22, 0x23, 0x3C, 0x3E) + + (0x3F, 0x60, 0x7B, 0x7D) + + (0x2F, 0x3A, 0x3B, 0x3D, 0x40, 0x5B, 0x5C, 0x5D, 0x5E, 0x7C) + ] +) +PASSWORD_SAFE = "".join( + [ + chr(i) + for i in range(0x20, 0x7F) + if i + not in (0x20, 0x22, 0x23, 0x3C, 0x3E) + + (0x3F, 0x60, 0x7B, 0x7D) + + (0x2F, 0x3A, 0x3B, 0x3D, 0x40, 0x5B, 0x5C, 0x5D, 0x5E, 0x7C) + ] +) +# Note... The terminology 'userinfo' percent-encode set in the WHATWG document +# is used for the username and password quoting. For the joint userinfo component +# we remove U+003A (:) from the safe set. +USERINFO_SAFE = "".join( + [ + chr(i) + for i in range(0x20, 0x7F) + if i + not in (0x20, 0x22, 0x23, 0x3C, 0x3E) + + (0x3F, 0x60, 0x7B, 0x7D) + + (0x2F, 0x3B, 0x3D, 0x40, 0x5B, 0x5C, 0x5D, 0x5E, 0x7C) + ] +) + + +# {scheme}: (optional) +# //{authority} (optional) +# {path} +# ?{query} (optional) +# #{fragment} (optional) +URL_REGEX = re.compile( + ( + r"(?:(?P{scheme}):)?" + r"(?://(?P{authority}))?" + r"(?P{path})" + r"(?:\?(?P{query}))?" + r"(?:#(?P{fragment}))?" + ).format( + scheme="([a-zA-Z][a-zA-Z0-9+.-]*)?", + authority="[^/?#]*", + path="[^?#]*", + query="[^#]*", + fragment=".*", + ) +) + +# {userinfo}@ (optional) +# {host} +# :{port} (optional) +AUTHORITY_REGEX = re.compile( + ( + r"(?:(?P{userinfo})@)?" r"(?P{host})" r":?(?P{port})?" + ).format( + userinfo=".*", # Any character sequence. + host="(\\[.*\\]|[^:@]*)", # Either any character sequence excluding ':' or '@', + # or an IPv6 address enclosed within square brackets. + port=".*", # Any character sequence. + ) +) + + +# If we call urlparse with an individual component, then we need to regex +# validate that component individually. +# Note that we're duplicating the same strings as above. Shock! Horror!! +COMPONENT_REGEX = { + "scheme": re.compile("([a-zA-Z][a-zA-Z0-9+.-]*)?"), + "authority": re.compile("[^/?#]*"), + "path": re.compile("[^?#]*"), + "query": re.compile("[^#]*"), + "fragment": re.compile(".*"), + "userinfo": re.compile("[^@]*"), + "host": re.compile("(\\[.*\\]|[^:]*)"), + "port": re.compile(".*"), +} + + +# We use these simple regexs as a first pass before handing off to +# the stdlib 'ipaddress' module for IP address validation. +IPv4_STYLE_HOSTNAME = re.compile(r"^[0-9]+\.[0-9]+\.[0-9]+\.[0-9]+$") +IPv6_STYLE_HOSTNAME = re.compile(r"^\[.*\]$") + + +class ParseResult(typing.NamedTuple): + scheme: str + userinfo: str + host: str + port: int | None + path: str + query: str | None + fragment: str | None + + @property + def authority(self) -> str: + return "".join( + [ + f"{self.userinfo}@" if self.userinfo else "", + f"[{self.host}]" if ":" in self.host else self.host, + f":{self.port}" if self.port is not None else "", + ] + ) + + @property + def netloc(self) -> str: + return "".join( + [ + f"[{self.host}]" if ":" in self.host else self.host, + f":{self.port}" if self.port is not None else "", + ] + ) + + def copy_with(self, **kwargs: str | None) -> ParseResult: + if not kwargs: + return self + + defaults = { + "scheme": self.scheme, + "authority": self.authority, + "path": self.path, + "query": self.query, + "fragment": self.fragment, + } + defaults.update(kwargs) + return urlparse("", **defaults) + + def __str__(self) -> str: + authority = self.authority + return "".join( + [ + f"{self.scheme}:" if self.scheme else "", + f"//{authority}" if authority else "", + self.path, + f"?{self.query}" if self.query is not None else "", + f"#{self.fragment}" if self.fragment is not None else "", + ] + ) + + +def urlparse(url: str = "", **kwargs: str | None) -> ParseResult: + # Initial basic checks on allowable URLs. + # --------------------------------------- + + # Hard limit the maximum allowable URL length. + if len(url) > MAX_URL_LENGTH: + raise InvalidURL("URL too long") + + # If a URL includes any ASCII control characters including \t, \r, \n, + # then treat it as invalid. + if any(char.isascii() and not char.isprintable() for char in url): + char = next(char for char in url if char.isascii() and not char.isprintable()) + idx = url.find(char) + error = ( + f"Invalid non-printable ASCII character in URL, {char!r} at position {idx}." + ) + raise InvalidURL(error) + + # Some keyword arguments require special handling. + # ------------------------------------------------ + + # Coerce "port" to a string, if it is provided as an integer. + if "port" in kwargs: + port = kwargs["port"] + kwargs["port"] = str(port) if isinstance(port, int) else port + + # Replace "netloc" with "host and "port". + if "netloc" in kwargs: + netloc = kwargs.pop("netloc") or "" + kwargs["host"], _, kwargs["port"] = netloc.partition(":") + + # Replace "username" and/or "password" with "userinfo". + if "username" in kwargs or "password" in kwargs: + username = quote(kwargs.pop("username", "") or "", safe=USERNAME_SAFE) + password = quote(kwargs.pop("password", "") or "", safe=PASSWORD_SAFE) + kwargs["userinfo"] = f"{username}:{password}" if password else username + + # Replace "raw_path" with "path" and "query". + if "raw_path" in kwargs: + raw_path = kwargs.pop("raw_path") or "" + kwargs["path"], seperator, kwargs["query"] = raw_path.partition("?") + if not seperator: + kwargs["query"] = None + + # Ensure that IPv6 "host" addresses are always escaped with "[...]". + if "host" in kwargs: + host = kwargs.get("host") or "" + if ":" in host and not (host.startswith("[") and host.endswith("]")): + kwargs["host"] = f"[{host}]" + + # If any keyword arguments are provided, ensure they are valid. + # ------------------------------------------------------------- + + for key, value in kwargs.items(): + if value is not None: + if len(value) > MAX_URL_LENGTH: + raise InvalidURL(f"URL component '{key}' too long") + + # If a component includes any ASCII control characters including \t, \r, \n, + # then treat it as invalid. + if any(char.isascii() and not char.isprintable() for char in value): + char = next( + char for char in value if char.isascii() and not char.isprintable() + ) + idx = value.find(char) + error = ( + f"Invalid non-printable ASCII character in URL {key} component, " + f"{char!r} at position {idx}." + ) + raise InvalidURL(error) + + # Ensure that keyword arguments match as a valid regex. + if not COMPONENT_REGEX[key].fullmatch(value): + raise InvalidURL(f"Invalid URL component '{key}'") + + # The URL_REGEX will always match, but may have empty components. + url_match = URL_REGEX.match(url) + assert url_match is not None + url_dict = url_match.groupdict() + + # * 'scheme', 'authority', and 'path' may be empty strings. + # * 'query' may be 'None', indicating no trailing "?" portion. + # Any string including the empty string, indicates a trailing "?". + # * 'fragment' may be 'None', indicating no trailing "#" portion. + # Any string including the empty string, indicates a trailing "#". + scheme = kwargs.get("scheme", url_dict["scheme"]) or "" + authority = kwargs.get("authority", url_dict["authority"]) or "" + path = kwargs.get("path", url_dict["path"]) or "" + query = kwargs.get("query", url_dict["query"]) + frag = kwargs.get("fragment", url_dict["fragment"]) + + # The AUTHORITY_REGEX will always match, but may have empty components. + authority_match = AUTHORITY_REGEX.match(authority) + assert authority_match is not None + authority_dict = authority_match.groupdict() + + # * 'userinfo' and 'host' may be empty strings. + # * 'port' may be 'None'. + userinfo = kwargs.get("userinfo", authority_dict["userinfo"]) or "" + host = kwargs.get("host", authority_dict["host"]) or "" + port = kwargs.get("port", authority_dict["port"]) + + # Normalize and validate each component. + # We end up with a parsed representation of the URL, + # with components that are plain ASCII bytestrings. + parsed_scheme: str = scheme.lower() + parsed_userinfo: str = quote(userinfo, safe=USERINFO_SAFE) + parsed_host: str = encode_host(host) + parsed_port: int | None = normalize_port(port, scheme) + + has_scheme = parsed_scheme != "" + has_authority = ( + parsed_userinfo != "" or parsed_host != "" or parsed_port is not None + ) + validate_path(path, has_scheme=has_scheme, has_authority=has_authority) + if has_scheme or has_authority: + path = normalize_path(path) + + parsed_path: str = quote(path, safe=PATH_SAFE) + parsed_query: str | None = None if query is None else quote(query, safe=QUERY_SAFE) + parsed_frag: str | None = None if frag is None else quote(frag, safe=FRAG_SAFE) + + # The parsed ASCII bytestrings are our canonical form. + # All properties of the URL are derived from these. + return ParseResult( + parsed_scheme, + parsed_userinfo, + parsed_host, + parsed_port, + parsed_path, + parsed_query, + parsed_frag, + ) + + +def encode_host(host: str) -> str: + if not host: + return "" + + elif IPv4_STYLE_HOSTNAME.match(host): + # Validate IPv4 hostnames like #.#.#.# + # + # From https://datatracker.ietf.org/doc/html/rfc3986/#section-3.2.2 + # + # IPv4address = dec-octet "." dec-octet "." dec-octet "." dec-octet + try: + ipaddress.IPv4Address(host) + except ipaddress.AddressValueError: + raise InvalidURL(f"Invalid IPv4 address: {host!r}") + return host + + elif IPv6_STYLE_HOSTNAME.match(host): + # Validate IPv6 hostnames like [...] + # + # From https://datatracker.ietf.org/doc/html/rfc3986/#section-3.2.2 + # + # "A host identified by an Internet Protocol literal address, version 6 + # [RFC3513] or later, is distinguished by enclosing the IP literal + # within square brackets ("[" and "]"). This is the only place where + # square bracket characters are allowed in the URI syntax." + try: + ipaddress.IPv6Address(host[1:-1]) + except ipaddress.AddressValueError: + raise InvalidURL(f"Invalid IPv6 address: {host!r}") + return host[1:-1] + + elif host.isascii(): + # Regular ASCII hostnames + # + # From https://datatracker.ietf.org/doc/html/rfc3986/#section-3.2.2 + # + # reg-name = *( unreserved / pct-encoded / sub-delims ) + WHATWG_SAFE = '"`{}%|\\' + return quote(host.lower(), safe=SUB_DELIMS + WHATWG_SAFE) + + # IDNA hostnames + try: + return idna.encode(host.lower()).decode("ascii") + except idna.IDNAError: + raise InvalidURL(f"Invalid IDNA hostname: {host!r}") + + +def normalize_port(port: str | int | None, scheme: str) -> int | None: + # From https://tools.ietf.org/html/rfc3986#section-3.2.3 + # + # "A scheme may define a default port. For example, the "http" scheme + # defines a default port of "80", corresponding to its reserved TCP + # port number. The type of port designated by the port number (e.g., + # TCP, UDP, SCTP) is defined by the URI scheme. URI producers and + # normalizers should omit the port component and its ":" delimiter if + # port is empty or if its value would be the same as that of the + # scheme's default." + if port is None or port == "": + return None + + try: + port_as_int = int(port) + except ValueError: + raise InvalidURL(f"Invalid port: {port!r}") + + # See https://url.spec.whatwg.org/#url-miscellaneous + default_port = {"ftp": 21, "http": 80, "https": 443, "ws": 80, "wss": 443}.get( + scheme + ) + if port_as_int == default_port: + return None + return port_as_int + + +def validate_path(path: str, has_scheme: bool, has_authority: bool) -> None: + """ + Path validation rules that depend on if the URL contains + a scheme or authority component. + + See https://datatracker.ietf.org/doc/html/rfc3986.html#section-3.3 + """ + if has_authority: + # If a URI contains an authority component, then the path component + # must either be empty or begin with a slash ("/") character." + if path and not path.startswith("/"): + raise InvalidURL("For absolute URLs, path must be empty or begin with '/'") + + if not has_scheme and not has_authority: + # If a URI does not contain an authority component, then the path cannot begin + # with two slash characters ("//"). + if path.startswith("//"): + raise InvalidURL("Relative URLs cannot have a path starting with '//'") + + # In addition, a URI reference (Section 4.1) may be a relative-path reference, + # in which case the first path segment cannot contain a colon (":") character. + if path.startswith(":"): + raise InvalidURL("Relative URLs cannot have a path starting with ':'") + + +def normalize_path(path: str) -> str: + """ + Drop "." and ".." segments from a URL path. + + For example: + + normalize_path("/path/./to/somewhere/..") == "/path/to" + """ + # Fast return when no '.' characters in the path. + if "." not in path: + return path + + components = path.split("/") + + # Fast return when no '.' or '..' components in the path. + if "." not in components and ".." not in components: + return path + + # https://datatracker.ietf.org/doc/html/rfc3986#section-5.2.4 + output: list[str] = [] + for component in components: + if component == ".": + pass + elif component == "..": + if output and output != [""]: + output.pop() + else: + output.append(component) + return "/".join(output) + + +def PERCENT(string: str) -> str: + return "".join([f"%{byte:02X}" for byte in string.encode("utf-8")]) + + +def percent_encoded(string: str, safe: str) -> str: + """ + Use percent-encoding to quote a string. + """ + NON_ESCAPED_CHARS = UNRESERVED_CHARACTERS + safe + + # Fast path for strings that don't need escaping. + if not string.rstrip(NON_ESCAPED_CHARS): + return string + + return "".join( + [char if char in NON_ESCAPED_CHARS else PERCENT(char) for char in string] + ) + + +def quote(string: str, safe: str) -> str: + """ + Use percent-encoding to quote a string, omitting existing '%xx' escape sequences. + + See: https://www.rfc-editor.org/rfc/rfc3986#section-2.1 + + * `string`: The string to be percent-escaped. + * `safe`: A string containing characters that may be treated as safe, and do not + need to be escaped. Unreserved characters are always treated as safe. + See: https://www.rfc-editor.org/rfc/rfc3986#section-2.3 + """ + parts = [] + current_position = 0 + for match in re.finditer(PERCENT_ENCODED_REGEX, string): + start_position, end_position = match.start(), match.end() + matched_text = match.group(0) + # Add any text up to the '%xx' escape sequence. + if start_position != current_position: + leading_text = string[current_position:start_position] + parts.append(percent_encoded(leading_text, safe=safe)) + + # Add the '%xx' escape sequence. + parts.append(matched_text) + current_position = end_position + + # Add any text after the final '%xx' escape sequence. + if current_position != len(string): + trailing_text = string[current_position:] + parts.append(percent_encoded(trailing_text, safe=safe)) + + return "".join(parts) diff --git a/lib/python3.11/site-packages/httpx/_urls.py b/lib/python3.11/site-packages/httpx/_urls.py new file mode 100644 index 0000000000000000000000000000000000000000..147a8fa333acaf31618d37ba2896e3a5bf5e4d02 --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_urls.py @@ -0,0 +1,641 @@ +from __future__ import annotations + +import typing +from urllib.parse import parse_qs, unquote, urlencode + +import idna + +from ._types import QueryParamTypes +from ._urlparse import urlparse +from ._utils import primitive_value_to_str + +__all__ = ["URL", "QueryParams"] + + +class URL: + """ + url = httpx.URL("HTTPS://jo%40email.com:a%20secret@müller.de:1234/pa%20th?search=ab#anchorlink") + + assert url.scheme == "https" + assert url.username == "jo@email.com" + assert url.password == "a secret" + assert url.userinfo == b"jo%40email.com:a%20secret" + assert url.host == "müller.de" + assert url.raw_host == b"xn--mller-kva.de" + assert url.port == 1234 + assert url.netloc == b"xn--mller-kva.de:1234" + assert url.path == "/pa th" + assert url.query == b"?search=ab" + assert url.raw_path == b"/pa%20th?search=ab" + assert url.fragment == "anchorlink" + + The components of a URL are broken down like this: + + https://jo%40email.com:a%20secret@müller.de:1234/pa%20th?search=ab#anchorlink + [scheme] [ username ] [password] [ host ][port][ path ] [ query ] [fragment] + [ userinfo ] [ netloc ][ raw_path ] + + Note that: + + * `url.scheme` is normalized to always be lowercased. + + * `url.host` is normalized to always be lowercased. Internationalized domain + names are represented in unicode, without IDNA encoding applied. For instance: + + url = httpx.URL("http://中国.icom.museum") + assert url.host == "中国.icom.museum" + url = httpx.URL("http://xn--fiqs8s.icom.museum") + assert url.host == "中国.icom.museum" + + * `url.raw_host` is normalized to always be lowercased, and is IDNA encoded. + + url = httpx.URL("http://中国.icom.museum") + assert url.raw_host == b"xn--fiqs8s.icom.museum" + url = httpx.URL("http://xn--fiqs8s.icom.museum") + assert url.raw_host == b"xn--fiqs8s.icom.museum" + + * `url.port` is either None or an integer. URLs that include the default port for + "http", "https", "ws", "wss", and "ftp" schemes have their port + normalized to `None`. + + assert httpx.URL("http://example.com") == httpx.URL("http://example.com:80") + assert httpx.URL("http://example.com").port is None + assert httpx.URL("http://example.com:80").port is None + + * `url.userinfo` is raw bytes, without URL escaping. Usually you'll want to work + with `url.username` and `url.password` instead, which handle the URL escaping. + + * `url.raw_path` is raw bytes of both the path and query, without URL escaping. + This portion is used as the target when constructing HTTP requests. Usually you'll + want to work with `url.path` instead. + + * `url.query` is raw bytes, without URL escaping. A URL query string portion can + only be properly URL escaped when decoding the parameter names and values + themselves. + """ + + def __init__(self, url: URL | str = "", **kwargs: typing.Any) -> None: + if kwargs: + allowed = { + "scheme": str, + "username": str, + "password": str, + "userinfo": bytes, + "host": str, + "port": int, + "netloc": bytes, + "path": str, + "query": bytes, + "raw_path": bytes, + "fragment": str, + "params": object, + } + + # Perform type checking for all supported keyword arguments. + for key, value in kwargs.items(): + if key not in allowed: + message = f"{key!r} is an invalid keyword argument for URL()" + raise TypeError(message) + if value is not None and not isinstance(value, allowed[key]): + expected = allowed[key].__name__ + seen = type(value).__name__ + message = f"Argument {key!r} must be {expected} but got {seen}" + raise TypeError(message) + if isinstance(value, bytes): + kwargs[key] = value.decode("ascii") + + if "params" in kwargs: + # Replace any "params" keyword with the raw "query" instead. + # + # Ensure that empty params use `kwargs["query"] = None` rather + # than `kwargs["query"] = ""`, so that generated URLs do not + # include an empty trailing "?". + params = kwargs.pop("params") + kwargs["query"] = None if not params else str(QueryParams(params)) + + if isinstance(url, str): + self._uri_reference = urlparse(url, **kwargs) + elif isinstance(url, URL): + self._uri_reference = url._uri_reference.copy_with(**kwargs) + else: + raise TypeError( + "Invalid type for url. Expected str or httpx.URL," + f" got {type(url)}: {url!r}" + ) + + @property + def scheme(self) -> str: + """ + The URL scheme, such as "http", "https". + Always normalised to lowercase. + """ + return self._uri_reference.scheme + + @property + def raw_scheme(self) -> bytes: + """ + The raw bytes representation of the URL scheme, such as b"http", b"https". + Always normalised to lowercase. + """ + return self._uri_reference.scheme.encode("ascii") + + @property + def userinfo(self) -> bytes: + """ + The URL userinfo as a raw bytestring. + For example: b"jo%40email.com:a%20secret". + """ + return self._uri_reference.userinfo.encode("ascii") + + @property + def username(self) -> str: + """ + The URL username as a string, with URL decoding applied. + For example: "jo@email.com" + """ + userinfo = self._uri_reference.userinfo + return unquote(userinfo.partition(":")[0]) + + @property + def password(self) -> str: + """ + The URL password as a string, with URL decoding applied. + For example: "a secret" + """ + userinfo = self._uri_reference.userinfo + return unquote(userinfo.partition(":")[2]) + + @property + def host(self) -> str: + """ + The URL host as a string. + Always normalized to lowercase, with IDNA hosts decoded into unicode. + + Examples: + + url = httpx.URL("http://www.EXAMPLE.org") + assert url.host == "www.example.org" + + url = httpx.URL("http://中国.icom.museum") + assert url.host == "中国.icom.museum" + + url = httpx.URL("http://xn--fiqs8s.icom.museum") + assert url.host == "中国.icom.museum" + + url = httpx.URL("https://[::ffff:192.168.0.1]") + assert url.host == "::ffff:192.168.0.1" + """ + host: str = self._uri_reference.host + + if host.startswith("xn--"): + host = idna.decode(host) + + return host + + @property + def raw_host(self) -> bytes: + """ + The raw bytes representation of the URL host. + Always normalized to lowercase, and IDNA encoded. + + Examples: + + url = httpx.URL("http://www.EXAMPLE.org") + assert url.raw_host == b"www.example.org" + + url = httpx.URL("http://中国.icom.museum") + assert url.raw_host == b"xn--fiqs8s.icom.museum" + + url = httpx.URL("http://xn--fiqs8s.icom.museum") + assert url.raw_host == b"xn--fiqs8s.icom.museum" + + url = httpx.URL("https://[::ffff:192.168.0.1]") + assert url.raw_host == b"::ffff:192.168.0.1" + """ + return self._uri_reference.host.encode("ascii") + + @property + def port(self) -> int | None: + """ + The URL port as an integer. + + Note that the URL class performs port normalization as per the WHATWG spec. + Default ports for "http", "https", "ws", "wss", and "ftp" schemes are always + treated as `None`. + + For example: + + assert httpx.URL("http://www.example.com") == httpx.URL("http://www.example.com:80") + assert httpx.URL("http://www.example.com:80").port is None + """ + return self._uri_reference.port + + @property + def netloc(self) -> bytes: + """ + Either `` or `:` as bytes. + Always normalized to lowercase, and IDNA encoded. + + This property may be used for generating the value of a request + "Host" header. + """ + return self._uri_reference.netloc.encode("ascii") + + @property + def path(self) -> str: + """ + The URL path as a string. Excluding the query string, and URL decoded. + + For example: + + url = httpx.URL("https://example.com/pa%20th") + assert url.path == "/pa th" + """ + path = self._uri_reference.path or "/" + return unquote(path) + + @property + def query(self) -> bytes: + """ + The URL query string, as raw bytes, excluding the leading b"?". + + This is necessarily a bytewise interface, because we cannot + perform URL decoding of this representation until we've parsed + the keys and values into a QueryParams instance. + + For example: + + url = httpx.URL("https://example.com/?filter=some%20search%20terms") + assert url.query == b"filter=some%20search%20terms" + """ + query = self._uri_reference.query or "" + return query.encode("ascii") + + @property + def params(self) -> QueryParams: + """ + The URL query parameters, neatly parsed and packaged into an immutable + multidict representation. + """ + return QueryParams(self._uri_reference.query) + + @property + def raw_path(self) -> bytes: + """ + The complete URL path and query string as raw bytes. + Used as the target when constructing HTTP requests. + + For example: + + GET /users?search=some%20text HTTP/1.1 + Host: www.example.org + Connection: close + """ + path = self._uri_reference.path or "/" + if self._uri_reference.query is not None: + path += "?" + self._uri_reference.query + return path.encode("ascii") + + @property + def fragment(self) -> str: + """ + The URL fragments, as used in HTML anchors. + As a string, without the leading '#'. + """ + return unquote(self._uri_reference.fragment or "") + + @property + def is_absolute_url(self) -> bool: + """ + Return `True` for absolute URLs such as 'http://example.com/path', + and `False` for relative URLs such as '/path'. + """ + # We don't use `.is_absolute` from `rfc3986` because it treats + # URLs with a fragment portion as not absolute. + # What we actually care about is if the URL provides + # a scheme and hostname to which connections should be made. + return bool(self._uri_reference.scheme and self._uri_reference.host) + + @property + def is_relative_url(self) -> bool: + """ + Return `False` for absolute URLs such as 'http://example.com/path', + and `True` for relative URLs such as '/path'. + """ + return not self.is_absolute_url + + def copy_with(self, **kwargs: typing.Any) -> URL: + """ + Copy this URL, returning a new URL with some components altered. + Accepts the same set of parameters as the components that are made + available via properties on the `URL` class. + + For example: + + url = httpx.URL("https://www.example.com").copy_with( + username="jo@gmail.com", password="a secret" + ) + assert url == "https://jo%40email.com:a%20secret@www.example.com" + """ + return URL(self, **kwargs) + + def copy_set_param(self, key: str, value: typing.Any = None) -> URL: + return self.copy_with(params=self.params.set(key, value)) + + def copy_add_param(self, key: str, value: typing.Any = None) -> URL: + return self.copy_with(params=self.params.add(key, value)) + + def copy_remove_param(self, key: str) -> URL: + return self.copy_with(params=self.params.remove(key)) + + def copy_merge_params(self, params: QueryParamTypes) -> URL: + return self.copy_with(params=self.params.merge(params)) + + def join(self, url: URL | str) -> URL: + """ + Return an absolute URL, using this URL as the base. + + Eg. + + url = httpx.URL("https://www.example.com/test") + url = url.join("/new/path") + assert url == "https://www.example.com/new/path" + """ + from urllib.parse import urljoin + + return URL(urljoin(str(self), str(URL(url)))) + + def __hash__(self) -> int: + return hash(str(self)) + + def __eq__(self, other: typing.Any) -> bool: + return isinstance(other, (URL, str)) and str(self) == str(URL(other)) + + def __str__(self) -> str: + return str(self._uri_reference) + + def __repr__(self) -> str: + scheme, userinfo, host, port, path, query, fragment = self._uri_reference + + if ":" in userinfo: + # Mask any password component. + userinfo = f'{userinfo.split(":")[0]}:[secure]' + + authority = "".join( + [ + f"{userinfo}@" if userinfo else "", + f"[{host}]" if ":" in host else host, + f":{port}" if port is not None else "", + ] + ) + url = "".join( + [ + f"{self.scheme}:" if scheme else "", + f"//{authority}" if authority else "", + path, + f"?{query}" if query is not None else "", + f"#{fragment}" if fragment is not None else "", + ] + ) + + return f"{self.__class__.__name__}({url!r})" + + @property + def raw(self) -> tuple[bytes, bytes, int, bytes]: # pragma: nocover + import collections + import warnings + + warnings.warn("URL.raw is deprecated.") + RawURL = collections.namedtuple( + "RawURL", ["raw_scheme", "raw_host", "port", "raw_path"] + ) + return RawURL( + raw_scheme=self.raw_scheme, + raw_host=self.raw_host, + port=self.port, + raw_path=self.raw_path, + ) + + +class QueryParams(typing.Mapping[str, str]): + """ + URL query parameters, as a multi-dict. + """ + + def __init__(self, *args: QueryParamTypes | None, **kwargs: typing.Any) -> None: + assert len(args) < 2, "Too many arguments." + assert not (args and kwargs), "Cannot mix named and unnamed arguments." + + value = args[0] if args else kwargs + + if value is None or isinstance(value, (str, bytes)): + value = value.decode("ascii") if isinstance(value, bytes) else value + self._dict = parse_qs(value, keep_blank_values=True) + elif isinstance(value, QueryParams): + self._dict = {k: list(v) for k, v in value._dict.items()} + else: + dict_value: dict[typing.Any, list[typing.Any]] = {} + if isinstance(value, (list, tuple)): + # Convert list inputs like: + # [("a", "123"), ("a", "456"), ("b", "789")] + # To a dict representation, like: + # {"a": ["123", "456"], "b": ["789"]} + for item in value: + dict_value.setdefault(item[0], []).append(item[1]) + else: + # Convert dict inputs like: + # {"a": "123", "b": ["456", "789"]} + # To dict inputs where values are always lists, like: + # {"a": ["123"], "b": ["456", "789"]} + dict_value = { + k: list(v) if isinstance(v, (list, tuple)) else [v] + for k, v in value.items() + } + + # Ensure that keys and values are neatly coerced to strings. + # We coerce values `True` and `False` to JSON-like "true" and "false" + # representations, and coerce `None` values to the empty string. + self._dict = { + str(k): [primitive_value_to_str(item) for item in v] + for k, v in dict_value.items() + } + + def keys(self) -> typing.KeysView[str]: + """ + Return all the keys in the query params. + + Usage: + + q = httpx.QueryParams("a=123&a=456&b=789") + assert list(q.keys()) == ["a", "b"] + """ + return self._dict.keys() + + def values(self) -> typing.ValuesView[str]: + """ + Return all the values in the query params. If a key occurs more than once + only the first item for that key is returned. + + Usage: + + q = httpx.QueryParams("a=123&a=456&b=789") + assert list(q.values()) == ["123", "789"] + """ + return {k: v[0] for k, v in self._dict.items()}.values() + + def items(self) -> typing.ItemsView[str, str]: + """ + Return all items in the query params. If a key occurs more than once + only the first item for that key is returned. + + Usage: + + q = httpx.QueryParams("a=123&a=456&b=789") + assert list(q.items()) == [("a", "123"), ("b", "789")] + """ + return {k: v[0] for k, v in self._dict.items()}.items() + + def multi_items(self) -> list[tuple[str, str]]: + """ + Return all items in the query params. Allow duplicate keys to occur. + + Usage: + + q = httpx.QueryParams("a=123&a=456&b=789") + assert list(q.multi_items()) == [("a", "123"), ("a", "456"), ("b", "789")] + """ + multi_items: list[tuple[str, str]] = [] + for k, v in self._dict.items(): + multi_items.extend([(k, i) for i in v]) + return multi_items + + def get(self, key: typing.Any, default: typing.Any = None) -> typing.Any: + """ + Get a value from the query param for a given key. If the key occurs + more than once, then only the first value is returned. + + Usage: + + q = httpx.QueryParams("a=123&a=456&b=789") + assert q.get("a") == "123" + """ + if key in self._dict: + return self._dict[str(key)][0] + return default + + def get_list(self, key: str) -> list[str]: + """ + Get all values from the query param for a given key. + + Usage: + + q = httpx.QueryParams("a=123&a=456&b=789") + assert q.get_list("a") == ["123", "456"] + """ + return list(self._dict.get(str(key), [])) + + def set(self, key: str, value: typing.Any = None) -> QueryParams: + """ + Return a new QueryParams instance, setting the value of a key. + + Usage: + + q = httpx.QueryParams("a=123") + q = q.set("a", "456") + assert q == httpx.QueryParams("a=456") + """ + q = QueryParams() + q._dict = dict(self._dict) + q._dict[str(key)] = [primitive_value_to_str(value)] + return q + + def add(self, key: str, value: typing.Any = None) -> QueryParams: + """ + Return a new QueryParams instance, setting or appending the value of a key. + + Usage: + + q = httpx.QueryParams("a=123") + q = q.add("a", "456") + assert q == httpx.QueryParams("a=123&a=456") + """ + q = QueryParams() + q._dict = dict(self._dict) + q._dict[str(key)] = q.get_list(key) + [primitive_value_to_str(value)] + return q + + def remove(self, key: str) -> QueryParams: + """ + Return a new QueryParams instance, removing the value of a key. + + Usage: + + q = httpx.QueryParams("a=123") + q = q.remove("a") + assert q == httpx.QueryParams("") + """ + q = QueryParams() + q._dict = dict(self._dict) + q._dict.pop(str(key), None) + return q + + def merge(self, params: QueryParamTypes | None = None) -> QueryParams: + """ + Return a new QueryParams instance, updated with. + + Usage: + + q = httpx.QueryParams("a=123") + q = q.merge({"b": "456"}) + assert q == httpx.QueryParams("a=123&b=456") + + q = httpx.QueryParams("a=123") + q = q.merge({"a": "456", "b": "789"}) + assert q == httpx.QueryParams("a=456&b=789") + """ + q = QueryParams(params) + q._dict = {**self._dict, **q._dict} + return q + + def __getitem__(self, key: typing.Any) -> str: + return self._dict[key][0] + + def __contains__(self, key: typing.Any) -> bool: + return key in self._dict + + def __iter__(self) -> typing.Iterator[typing.Any]: + return iter(self.keys()) + + def __len__(self) -> int: + return len(self._dict) + + def __bool__(self) -> bool: + return bool(self._dict) + + def __hash__(self) -> int: + return hash(str(self)) + + def __eq__(self, other: typing.Any) -> bool: + if not isinstance(other, self.__class__): + return False + return sorted(self.multi_items()) == sorted(other.multi_items()) + + def __str__(self) -> str: + return urlencode(self.multi_items()) + + def __repr__(self) -> str: + class_name = self.__class__.__name__ + query_string = str(self) + return f"{class_name}({query_string!r})" + + def update(self, params: QueryParamTypes | None = None) -> None: + raise RuntimeError( + "QueryParams are immutable since 0.18.0. " + "Use `q = q.merge(...)` to create an updated copy." + ) + + def __setitem__(self, key: str, value: str) -> None: + raise RuntimeError( + "QueryParams are immutable since 0.18.0. " + "Use `q = q.set(key, value)` to create an updated copy." + ) diff --git a/lib/python3.11/site-packages/httpx/_utils.py b/lib/python3.11/site-packages/httpx/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7fe827da4d071b32ea6da44328629699d6fc88ce --- /dev/null +++ b/lib/python3.11/site-packages/httpx/_utils.py @@ -0,0 +1,242 @@ +from __future__ import annotations + +import ipaddress +import os +import re +import typing +from urllib.request import getproxies + +from ._types import PrimitiveData + +if typing.TYPE_CHECKING: # pragma: no cover + from ._urls import URL + + +def primitive_value_to_str(value: PrimitiveData) -> str: + """ + Coerce a primitive data type into a string value. + + Note that we prefer JSON-style 'true'/'false' for boolean values here. + """ + if value is True: + return "true" + elif value is False: + return "false" + elif value is None: + return "" + return str(value) + + +def get_environment_proxies() -> dict[str, str | None]: + """Gets proxy information from the environment""" + + # urllib.request.getproxies() falls back on System + # Registry and Config for proxies on Windows and macOS. + # We don't want to propagate non-HTTP proxies into + # our configuration such as 'TRAVIS_APT_PROXY'. + proxy_info = getproxies() + mounts: dict[str, str | None] = {} + + for scheme in ("http", "https", "all"): + if proxy_info.get(scheme): + hostname = proxy_info[scheme] + mounts[f"{scheme}://"] = ( + hostname if "://" in hostname else f"http://{hostname}" + ) + + no_proxy_hosts = [host.strip() for host in proxy_info.get("no", "").split(",")] + for hostname in no_proxy_hosts: + # See https://curl.haxx.se/libcurl/c/CURLOPT_NOPROXY.html for details + # on how names in `NO_PROXY` are handled. + if hostname == "*": + # If NO_PROXY=* is used or if "*" occurs as any one of the comma + # separated hostnames, then we should just bypass any information + # from HTTP_PROXY, HTTPS_PROXY, ALL_PROXY, and always ignore + # proxies. + return {} + elif hostname: + # NO_PROXY=.google.com is marked as "all://*.google.com, + # which disables "www.google.com" but not "google.com" + # NO_PROXY=google.com is marked as "all://*google.com, + # which disables "www.google.com" and "google.com". + # (But not "wwwgoogle.com") + # NO_PROXY can include domains, IPv6, IPv4 addresses and "localhost" + # NO_PROXY=example.com,::1,localhost,192.168.0.0/16 + if "://" in hostname: + mounts[hostname] = None + elif is_ipv4_hostname(hostname): + mounts[f"all://{hostname}"] = None + elif is_ipv6_hostname(hostname): + mounts[f"all://[{hostname}]"] = None + elif hostname.lower() == "localhost": + mounts[f"all://{hostname}"] = None + else: + mounts[f"all://*{hostname}"] = None + + return mounts + + +def to_bytes(value: str | bytes, encoding: str = "utf-8") -> bytes: + return value.encode(encoding) if isinstance(value, str) else value + + +def to_str(value: str | bytes, encoding: str = "utf-8") -> str: + return value if isinstance(value, str) else value.decode(encoding) + + +def to_bytes_or_str(value: str, match_type_of: typing.AnyStr) -> typing.AnyStr: + return value if isinstance(match_type_of, str) else value.encode() + + +def unquote(value: str) -> str: + return value[1:-1] if value[0] == value[-1] == '"' else value + + +def peek_filelike_length(stream: typing.Any) -> int | None: + """ + Given a file-like stream object, return its length in number of bytes + without reading it into memory. + """ + try: + # Is it an actual file? + fd = stream.fileno() + # Yup, seems to be an actual file. + length = os.fstat(fd).st_size + except (AttributeError, OSError): + # No... Maybe it's something that supports random access, like `io.BytesIO`? + try: + # Assuming so, go to end of stream to figure out its length, + # then put it back in place. + offset = stream.tell() + length = stream.seek(0, os.SEEK_END) + stream.seek(offset) + except (AttributeError, OSError): + # Not even that? Sorry, we're doomed... + return None + + return length + + +class URLPattern: + """ + A utility class currently used for making lookups against proxy keys... + + # Wildcard matching... + >>> pattern = URLPattern("all://") + >>> pattern.matches(httpx.URL("http://example.com")) + True + + # Witch scheme matching... + >>> pattern = URLPattern("https://") + >>> pattern.matches(httpx.URL("https://example.com")) + True + >>> pattern.matches(httpx.URL("http://example.com")) + False + + # With domain matching... + >>> pattern = URLPattern("https://example.com") + >>> pattern.matches(httpx.URL("https://example.com")) + True + >>> pattern.matches(httpx.URL("http://example.com")) + False + >>> pattern.matches(httpx.URL("https://other.com")) + False + + # Wildcard scheme, with domain matching... + >>> pattern = URLPattern("all://example.com") + >>> pattern.matches(httpx.URL("https://example.com")) + True + >>> pattern.matches(httpx.URL("http://example.com")) + True + >>> pattern.matches(httpx.URL("https://other.com")) + False + + # With port matching... + >>> pattern = URLPattern("https://example.com:1234") + >>> pattern.matches(httpx.URL("https://example.com:1234")) + True + >>> pattern.matches(httpx.URL("https://example.com")) + False + """ + + def __init__(self, pattern: str) -> None: + from ._urls import URL + + if pattern and ":" not in pattern: + raise ValueError( + f"Proxy keys should use proper URL forms rather " + f"than plain scheme strings. " + f'Instead of "{pattern}", use "{pattern}://"' + ) + + url = URL(pattern) + self.pattern = pattern + self.scheme = "" if url.scheme == "all" else url.scheme + self.host = "" if url.host == "*" else url.host + self.port = url.port + if not url.host or url.host == "*": + self.host_regex: typing.Pattern[str] | None = None + elif url.host.startswith("*."): + # *.example.com should match "www.example.com", but not "example.com" + domain = re.escape(url.host[2:]) + self.host_regex = re.compile(f"^.+\\.{domain}$") + elif url.host.startswith("*"): + # *example.com should match "www.example.com" and "example.com" + domain = re.escape(url.host[1:]) + self.host_regex = re.compile(f"^(.+\\.)?{domain}$") + else: + # example.com should match "example.com" but not "www.example.com" + domain = re.escape(url.host) + self.host_regex = re.compile(f"^{domain}$") + + def matches(self, other: URL) -> bool: + if self.scheme and self.scheme != other.scheme: + return False + if ( + self.host + and self.host_regex is not None + and not self.host_regex.match(other.host) + ): + return False + if self.port is not None and self.port != other.port: + return False + return True + + @property + def priority(self) -> tuple[int, int, int]: + """ + The priority allows URLPattern instances to be sortable, so that + we can match from most specific to least specific. + """ + # URLs with a port should take priority over URLs without a port. + port_priority = 0 if self.port is not None else 1 + # Longer hostnames should match first. + host_priority = -len(self.host) + # Longer schemes should match first. + scheme_priority = -len(self.scheme) + return (port_priority, host_priority, scheme_priority) + + def __hash__(self) -> int: + return hash(self.pattern) + + def __lt__(self, other: URLPattern) -> bool: + return self.priority < other.priority + + def __eq__(self, other: typing.Any) -> bool: + return isinstance(other, URLPattern) and self.pattern == other.pattern + + +def is_ipv4_hostname(hostname: str) -> bool: + try: + ipaddress.IPv4Address(hostname.split("/")[0]) + except Exception: + return False + return True + + +def is_ipv6_hostname(hostname: str) -> bool: + try: + ipaddress.IPv6Address(hostname.split("/")[0]) + except Exception: + return False + return True diff --git a/lib/python3.11/site-packages/httpx/py.typed b/lib/python3.11/site-packages/httpx/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/pandas-3.0.3.dist-info/INSTALLER b/lib/python3.11/site-packages/pandas-3.0.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/lib/python3.11/site-packages/pandas-3.0.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/lib/python3.11/site-packages/pandas-3.0.3.dist-info/LICENSE b/lib/python3.11/site-packages/pandas-3.0.3.dist-info/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..7ce451a64a3481e189ff2a0b5e8613d5e0dc2a17 --- /dev/null +++ b/lib/python3.11/site-packages/pandas-3.0.3.dist-info/LICENSE @@ -0,0 +1,1060 @@ +BSD 3-Clause License + +Copyright (c) 2008-2011, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team +All rights reserved. + +Copyright (c) 2011-2026, Open source contributors. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. 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Wilcox +Ada Worcester +Alex Dowad +Alex Suykov +Alexander Monakov +Andre McCurdy +Andrew Kelley +Anthony G. Basile +Aric Belsito +Arvid Picciani +Bartosz Brachaczek +Benjamin Peterson +Bobby Bingham +Boris Brezillon +Brent Cook +Chris Spiegel +Clément Vasseur +Daniel Micay +Daniel Sabogal +Daurnimator +David Carlier +David Edelsohn +Denys Vlasenko +Dmitry Ivanov +Dmitry V. Levin +Drew DeVault +Emil Renner Berthing +Fangrui Song +Felix Fietkau +Felix Janda +Gianluca Anzolin +Hauke Mehrtens +He X +Hiltjo Posthuma +Isaac Dunham +Jaydeep Patil +Jens Gustedt +Jeremy Huntwork +Jo-Philipp Wich +Joakim Sindholt +John Spencer +Julien Ramseier +Justin Cormack +Kaarle Ritvanen +Khem Raj +Kylie McClain +Leah Neukirchen +Luca Barbato +Luka Perkov +M Farkas-Dyck (Strake) +Mahesh Bodapati +Markus Wichmann +Masanori Ogino +Michael Clark +Michael Forney +Mikhail Kremnyov +Natanael Copa +Nicholas J. Kain +orc +Pascal Cuoq +Patrick Oppenlander +Petr Hosek +Petr Skocik +Pierre Carrier +Reini Urban +Rich Felker +Richard Pennington +Ryan Fairfax +Samuel Holland +Segev Finer +Shiz +sin +Solar Designer +Stefan Kristiansson +Stefan O'Rear +Szabolcs Nagy +Timo Teräs +Trutz Behn +Valentin Ochs +Will Dietz +William Haddon +William Pitcock + +Portions of this software are derived from third-party works licensed +under terms compatible with the above MIT license: + +The TRE regular expression implementation (src/regex/reg* and +src/regex/tre*) is Copyright © 2001-2008 Ville Laurikari and licensed +under a 2-clause BSD license (license text in the source files). The +included version has been heavily modified by Rich Felker in 2012, in +the interests of size, simplicity, and namespace cleanliness. + +Much of the math library code (src/math/* and src/complex/*) is +Copyright © 1993,2004 Sun Microsystems or +Copyright © 2003-2011 David Schultz or +Copyright © 2003-2009 Steven G. Kargl or +Copyright © 2003-2009 Bruce D. Evans or +Copyright © 2008 Stephen L. Moshier or +Copyright © 2017-2018 Arm Limited +and labelled as such in comments in the individual source files. All +have been licensed under extremely permissive terms. + +The ARM memcpy code (src/string/arm/memcpy.S) is Copyright © 2008 +The Android Open Source Project and is licensed under a two-clause BSD +license. It was taken from Bionic libc, used on Android. + +The AArch64 memcpy and memset code (src/string/aarch64/*) are +Copyright © 1999-2019, Arm Limited. + +The implementation of DES for crypt (src/crypt/crypt_des.c) is +Copyright © 1994 David Burren. It is licensed under a BSD license. + +The implementation of blowfish crypt (src/crypt/crypt_blowfish.c) was +originally written by Solar Designer and placed into the public +domain. The code also comes with a fallback permissive license for use +in jurisdictions that may not recognize the public domain. + +The smoothsort implementation (src/stdlib/qsort.c) is Copyright © 2011 +Valentin Ochs and is licensed under an MIT-style license. + +The x86_64 port was written by Nicholas J. Kain and is licensed under +the standard MIT terms. + +The mips and microblaze ports were originally written by Richard +Pennington for use in the ellcc project. The original code was adapted +by Rich Felker for build system and code conventions during upstream +integration. It is licensed under the standard MIT terms. + +The mips64 port was contributed by Imagination Technologies and is +licensed under the standard MIT terms. + +The powerpc port was also originally written by Richard Pennington, +and later supplemented and integrated by John Spencer. 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HISTORY OF THE SOFTWARE +========================== + +Python was created in the early 1990s by Guido van Rossum at Stichting +Mathematisch Centrum (CWI, see https://www.cwi.nl) in the Netherlands +as a successor of a language called ABC. Guido remains Python's +principal author, although it includes many contributions from others. + +In 1995, Guido continued his work on Python at the Corporation for +National Research Initiatives (CNRI, see https://www.cnri.reston.va.us) +in Reston, Virginia where he released several versions of the +software. + +In May 2000, Guido and the Python core development team moved to +BeOpen.com to form the BeOpen PythonLabs team. In October of the same +year, the PythonLabs team moved to Digital Creations, which became +Zope Corporation. In 2001, the Python Software Foundation (PSF, see +https://www.python.org/psf/) was formed, a non-profit organization +created specifically to own Python-related Intellectual Property. +Zope Corporation was a sponsoring member of the PSF. + +All Python releases are Open Source (see https://opensource.org for +the Open Source Definition). Historically, most, but not all, Python +releases have also been GPL-compatible; the table below summarizes +the various releases. + + Release Derived Year Owner GPL- + from compatible? (1) + + 0.9.0 thru 1.2 1991-1995 CWI yes + 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes + 1.6 1.5.2 2000 CNRI no + 2.0 1.6 2000 BeOpen.com no + 1.6.1 1.6 2001 CNRI yes (2) + 2.1 2.0+1.6.1 2001 PSF no + 2.0.1 2.0+1.6.1 2001 PSF yes + 2.1.1 2.1+2.0.1 2001 PSF yes + 2.1.2 2.1.1 2002 PSF yes + 2.1.3 2.1.2 2002 PSF yes + 2.2 and above 2.1.1 2001-now PSF yes + +Footnotes: + +(1) GPL-compatible doesn't mean that we're distributing Python under + the GPL. All Python licenses, unlike the GPL, let you distribute + a modified version without making your changes open source. The + GPL-compatible licenses make it possible to combine Python with + other software that is released under the GPL; the others don't. + +(2) According to Richard Stallman, 1.6.1 is not GPL-compatible, + because its license has a choice of law clause. According to + CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1 + is "not incompatible" with the GPL. + +Thanks to the many outside volunteers who have worked under Guido's +direction to make these releases possible. + + +B. 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Wilcox + Ada Worcester + Alex Dowad + Alex Suykov + Alexander Monakov + Andre McCurdy + Andrew Kelley + Anthony G. Basile + Aric Belsito + Arvid Picciani + Bartosz Brachaczek + Benjamin Peterson + Bobby Bingham + Boris Brezillon + Brent Cook + Chris Spiegel + Clément Vasseur + Daniel Micay + Daniel Sabogal + Daurnimator + David Carlier + David Edelsohn + Denys Vlasenko + Dmitry Ivanov + Dmitry V. Levin + Drew DeVault + Emil Renner Berthing + Fangrui Song + Felix Fietkau + Felix Janda + Gianluca Anzolin + Hauke Mehrtens + He X + Hiltjo Posthuma + Isaac Dunham + Jaydeep Patil + Jens Gustedt + Jeremy Huntwork + Jo-Philipp Wich + Joakim Sindholt + John Spencer + Julien Ramseier + Justin Cormack + Kaarle Ritvanen + Khem Raj + Kylie McClain + Leah Neukirchen + Luca Barbato + Luka Perkov + M Farkas-Dyck (Strake) + Mahesh Bodapati + Markus Wichmann + Masanori Ogino + Michael Clark + Michael Forney + Mikhail Kremnyov + Natanael Copa + Nicholas J. Kain + orc + Pascal Cuoq + Patrick Oppenlander + Petr Hosek + Petr Skocik + Pierre Carrier + Reini Urban + Rich Felker + Richard Pennington + Ryan Fairfax + Samuel Holland + Segev Finer + Shiz + sin + Solar Designer + Stefan Kristiansson + Stefan O'Rear + Szabolcs Nagy + Timo Teräs + Trutz Behn + Valentin Ochs + Will Dietz + William Haddon + William Pitcock + + Portions of this software are derived from third-party works licensed + under terms compatible with the above MIT license: + + The TRE regular expression implementation (src/regex/reg* and + src/regex/tre*) is Copyright © 2001-2008 Ville Laurikari and licensed + under a 2-clause BSD license (license text in the source files). 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HISTORY OF THE SOFTWARE + ========================== + + Python was created in the early 1990s by Guido van Rossum at Stichting + Mathematisch Centrum (CWI, see https://www.cwi.nl) in the Netherlands + as a successor of a language called ABC. Guido remains Python's + principal author, although it includes many contributions from others. + + In 1995, Guido continued his work on Python at the Corporation for + National Research Initiatives (CNRI, see https://www.cnri.reston.va.us) + in Reston, Virginia where he released several versions of the + software. + + In May 2000, Guido and the Python core development team moved to + BeOpen.com to form the BeOpen PythonLabs team. In October of the same + year, the PythonLabs team moved to Digital Creations, which became + Zope Corporation. In 2001, the Python Software Foundation (PSF, see + https://www.python.org/psf/) was formed, a non-profit organization + created specifically to own Python-related Intellectual Property. + Zope Corporation was a sponsoring member of the PSF. + + All Python releases are Open Source (see https://opensource.org for + the Open Source Definition). Historically, most, but not all, Python + releases have also been GPL-compatible; the table below summarizes + the various releases. + + Release Derived Year Owner GPL- + from compatible? (1) + + 0.9.0 thru 1.2 1991-1995 CWI yes + 1.3 thru 1.5.2 1.2 1995-1999 CNRI yes + 1.6 1.5.2 2000 CNRI no + 2.0 1.6 2000 BeOpen.com no + 1.6.1 1.6 2001 CNRI yes (2) + 2.1 2.0+1.6.1 2001 PSF no + 2.0.1 2.0+1.6.1 2001 PSF yes + 2.1.1 2.1+2.0.1 2001 PSF yes + 2.1.2 2.1.1 2002 PSF yes + 2.1.3 2.1.2 2002 PSF yes + 2.2 and above 2.1.1 2001-now PSF yes + + Footnotes: + + (1) GPL-compatible doesn't mean that we're distributing Python under + the GPL. All Python licenses, unlike the GPL, let you distribute + a modified version without making your changes open source. The + GPL-compatible licenses make it possible to combine Python with + other software that is released under the GPL; the others don't. + + (2) According to Richard Stallman, 1.6.1 is not GPL-compatible, + because its license has a choice of law clause. According to + CNRI, however, Stallman's lawyer has told CNRI's lawyer that 1.6.1 + is "not incompatible" with the GPL. + + Thanks to the many outside volunteers who have worked under Guido's + direction to make these releases possible. + + + B. TERMS AND CONDITIONS FOR ACCESSING OR OTHERWISE USING PYTHON + =============================================================== + + Python software and documentation are licensed under the + Python Software Foundation License Version 2. + + Starting with Python 3.8.6, examples, recipes, and other code in + the documentation are dual licensed under the PSF License Version 2 + and the Zero-Clause BSD license. + + Some software incorporated into Python is under different licenses. + The licenses are listed with code falling under that license. + + + PYTHON SOFTWARE FOUNDATION LICENSE VERSION 2 + -------------------------------------------- + + 1. This LICENSE AGREEMENT is between the Python Software Foundation + ("PSF"), and the Individual or Organization ("Licensee") accessing and + otherwise using this software ("Python") in source or binary form and + its associated documentation. + + 2. Subject to the terms and conditions of this License Agreement, PSF hereby + grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce, + analyze, test, perform and/or display publicly, prepare derivative works, + distribute, and otherwise use Python alone or in any derivative version, + provided, however, that PSF's License Agreement and PSF's notice of copyright, + i.e., "Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, + 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023 Python Software Foundation; + All Rights Reserved" are retained in Python alone or in any derivative version + prepared by Licensee. + + 3. In the event Licensee prepares a derivative work that is based on + or incorporates Python or any part thereof, and wants to make + the derivative work available to others as provided herein, then + Licensee hereby agrees to include in any such work a brief summary of + the changes made to Python. + + 4. 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This + Agreement may also be obtained from a proxy server on the internet + using the following URL: http://hdl.handle.net/1895.22/1013". + + 3. In the event Licensee prepares a derivative work that is based on + or incorporates Python 1.6.1 or any part thereof, and wants to make + the derivative work available to others as provided herein, then + Licensee hereby agrees to include in any such work a brief summary of + the changes made to Python 1.6.1. + + 4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS" + basis. CNRI MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR + IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, CNRI MAKES NO AND + DISCLAIMS ANY REPRESENTATION OR WARRANTY OF MERCHANTABILITY OR FITNESS + FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF PYTHON 1.6.1 WILL NOT + INFRINGE ANY THIRD PARTY RIGHTS. + + 5. 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This License Agreement shall be governed by the federal + intellectual property law of the United States, including without + limitation the federal copyright law, and, to the extent such + U.S. federal law does not apply, by the law of the Commonwealth of + Virginia, excluding Virginia's conflict of law provisions. + Notwithstanding the foregoing, with regard to derivative works based + on Python 1.6.1 that incorporate non-separable material that was + previously distributed under the GNU General Public License (GPL), the + law of the Commonwealth of Virginia shall govern this License + Agreement only as to issues arising under or with respect to + Paragraphs 4, 5, and 7 of this License Agreement. Nothing in this + License Agreement shall be deemed to create any relationship of + agency, partnership, or joint venture between CNRI and Licensee. 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All rights reserved. + + Permission to use, copy, modify, and distribute this software and its + documentation for any purpose and without fee is hereby granted, + provided that the above copyright notice appear in all copies and that + both that copyright notice and this permission notice appear in + supporting documentation, and that the name of Stichting Mathematisch + Centrum or CWI not be used in advertising or publicity pertaining to + distribution of the software without specific, written prior + permission. + + STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO + THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND + FITNESS, IN NO EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE + FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES + WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN + ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT + OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. + + ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON DOCUMENTATION + ---------------------------------------------------------------------- + + Permission to use, copy, modify, and/or distribute this software for any + purpose with or without fee is hereby granted. + + THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH + REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY + AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, + INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM + LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR + OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR + PERFORMANCE OF THIS SOFTWARE. + Copyright (c) 2014, Al Sweigart + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * Neither the name of the {organization} nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE + FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Copyright (c) 2017 Anthony Sottile + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in + all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE.Developed by ESN, an Electronic Arts Inc. studio. + Copyright (c) 2014, Electronic Arts Inc. + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + * Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + * Neither the name of ESN, Electronic Arts Inc. nor the + names of its contributors may be used to endorse or promote products + derived from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND + ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED + WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + DISCLAIMED. IN NO EVENT SHALL ELECTRONIC ARTS INC. BE LIABLE + FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES + (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; + LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND + ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + ---- + + Portions of code from MODP_ASCII - Ascii transformations (upper/lower, etc) + https://github.com/client9/stringencoders + + Copyright 2005, 2006, 2007 + Nick Galbreath -- nickg [at] modp [dot] com + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + Neither the name of the modp.com nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + This is the standard "new" BSD license: + http://www.opensource.org/licenses/bsd-license.php + + https://github.com/client9/stringencoders/blob/cfd5c1507325ae497ea9bacdacba12c0ffd79d30/COPYING + + ---- + + Numeric decoder derived from from TCL library + https://opensource.apple.com/source/tcl/tcl-14/tcl/license.terms + * Copyright (c) 1988-1993 The Regents of the University of California. + * Copyright (c) 1994 Sun Microsystems, Inc. + + This software is copyrighted by the Regents of the University of + California, Sun Microsystems, Inc., Scriptics Corporation, ActiveState + Corporation and other parties. The following terms apply to all files + associated with the software unless explicitly disclaimed in + individual files. + + The authors hereby grant permission to use, copy, modify, distribute, + and license this software and its documentation for any purpose, provided + that existing copyright notices are retained in all copies and that this + notice is included verbatim in any distributions. No written agreement, + license, or royalty fee is required for any of the authorized uses. + Modifications to this software may be copyrighted by their authors + and need not follow the licensing terms described here, provided that + the new terms are clearly indicated on the first page of each file where + they apply. + + IN NO EVENT SHALL THE AUTHORS OR DISTRIBUTORS BE LIABLE TO ANY PARTY + FOR DIRECT, INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES + ARISING OUT OF THE USE OF THIS SOFTWARE, ITS DOCUMENTATION, OR ANY + DERIVATIVES THEREOF, EVEN IF THE AUTHORS HAVE BEEN ADVISED OF THE + POSSIBILITY OF SUCH DAMAGE. + + THE AUTHORS AND DISTRIBUTORS SPECIFICALLY DISCLAIM ANY WARRANTIES, + INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. THIS SOFTWARE + IS PROVIDED ON AN "AS IS" BASIS, AND THE AUTHORS AND DISTRIBUTORS HAVE + NO OBLIGATION TO PROVIDE MAINTENANCE, SUPPORT, UPDATES, ENHANCEMENTS, OR + MODIFICATIONS. + + GOVERNMENT USE: If you are acquiring this software on behalf of the + U.S. government, the Government shall have only "Restricted Rights" + in the software and related documentation as defined in the Federal + Acquisition Regulations (FARs) in Clause 52.227.19 (c) (2). If you + are acquiring the software on behalf of the Department of Defense, the + software shall be classified as "Commercial Computer Software" and the + Government shall have only "Restricted Rights" as defined in Clause + 252.227-7013 (c) (1) of DFARs. Notwithstanding the foregoing, the + authors grant the U.S. Government and others acting in its behalf + permission to use and distribute the software in accordance with the + terms specified in this license. +Classifier: Development Status :: 5 - Production/Stable +Classifier: Environment :: Console +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: BSD License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Cython +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: 3.14 +Classifier: Topic :: Scientific/Engineering +Project-URL: homepage, https://pandas.pydata.org +Project-URL: documentation, https://pandas.pydata.org/docs/ +Project-URL: repository, https://github.com/pandas-dev/pandas +Requires-Python: >=3.11 +Requires-Dist: numpy>=1.26.0; 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extra == "all" +Requires-Dist: pytest>=8.3.4; extra == "all" +Requires-Dist: pytest-xdist>=3.6.1; extra == "all" +Requires-Dist: python-calamine>=0.3.0; extra == "all" +Requires-Dist: pytz>=2020.1; extra == "all" +Requires-Dist: pyxlsb>=1.0.10; extra == "all" +Requires-Dist: qtpy>=2.4.2; extra == "all" +Requires-Dist: scipy>=1.14.1; extra == "all" +Requires-Dist: s3fs>=2024.10.0; extra == "all" +Requires-Dist: SQLAlchemy>=2.0.36; extra == "all" +Requires-Dist: tables>=3.10.1; extra == "all" +Requires-Dist: tabulate>=0.9.0; extra == "all" +Requires-Dist: xarray>=2024.10.0; extra == "all" +Requires-Dist: xlrd>=2.0.1; extra == "all" +Requires-Dist: xlsxwriter>=3.2.0; extra == "all" +Requires-Dist: zstandard>=0.23.0; extra == "all" +Description-Content-Type: text/markdown + + + + Pandas Logo + + +----------------- + +# pandas: A Powerful Python Data Analysis Toolkit + +| | | +| --- | --- | +| Testing | [![CI - Test](https://github.com/pandas-dev/pandas/actions/workflows/unit-tests.yml/badge.svg)](https://github.com/pandas-dev/pandas/actions/workflows/unit-tests.yml) [![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas) | +| Package | [![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/) [![PyPI Downloads](https://img.shields.io/pypi/dm/pandas.svg?label=PyPI%20downloads)](https://pypi.org/project/pandas/) [![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/conda-forge/pandas) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/pandas.svg?label=Conda%20downloads)](https://anaconda.org/conda-forge/pandas) | +| Meta | [![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134) [![License - BSD 3-Clause](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE) [![Slack](https://img.shields.io/badge/join_Slack-information-brightgreen.svg?logo=slack)](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) [![LFX Health Score](https://insights.linuxfoundation.org/api/badge/health-score?project=pandas-dev-pandas)](https://insights.linuxfoundation.org/project/pandas-dev-pandas) | + + +## What is it? + +**pandas** is a Python package that provides fast, flexible, and expressive data +structures designed to make working with "relational" or "labeled" data both +easy and intuitive. It aims to be the fundamental high-level building block for +doing practical, **real-world** data analysis in Python. Additionally, it has +the broader goal of becoming **the most powerful and flexible open-source data +analysis/manipulation tool available in any language**. It is already well on +its way towards this goal. + +## Table of Contents + +- [Main Features](#main-features) +- [Where to get it](#where-to-get-it) +- [Dependencies](#dependencies) +- [Installation from sources](#installation-from-sources) +- [License](#license) +- [Documentation](#documentation) +- [Background](#background) +- [Getting Help](#getting-help) +- [Discussion and Development](#discussion-and-development) +- [Contributing to pandas](#contributing-to-pandas) + +## Main Features +Here are just a few of the things that pandas does well: + + - Easy handling of [**missing data**][missing-data] (represented as + `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data + - Size mutability: columns can be [**inserted and + deleted**][insertion-deletion] from DataFrame and higher dimensional + objects + - Automatic and explicit [**data alignment**][alignment]: objects can + be explicitly aligned to a set of labels, or the user can simply + ignore the labels and let `Series`, `DataFrame`, etc. automatically + align the data for you in computations + - Powerful, flexible [**group by**][groupby] functionality to perform + split-apply-combine operations on data sets, for both aggregating + and transforming data + - Make it [**easy to convert**][conversion] ragged, + differently-indexed data in other Python and NumPy data structures + into DataFrame objects + - Intelligent label-based [**slicing**][slicing], [**fancy + indexing**][fancy-indexing], and [**subsetting**][subsetting] of + large data sets + - Intuitive [**merging**][merging] and [**joining**][joining] data + sets + - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of + data sets + - [**Hierarchical**][mi] labeling of axes (possible to have multiple + labels per tick) + - Robust I/O tools for loading data from [**flat files**][flat-files] + (CSV and delimited), [**Excel files**][excel], [**databases**][db], + and saving/loading data from the ultrafast [**HDF5 format**][hdfstore] + - [**Time series**][timeseries]-specific functionality: date range + generation and frequency conversion, moving window statistics, + date shifting and lagging + + + [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html + [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion + [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures + [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine + [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe + [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges + [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced + [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing + [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging + [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index + [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html + [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html + [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex + [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files + [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files + [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries + [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables + [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality + +## Where to get it +The source code is currently hosted on GitHub at: +https://github.com/pandas-dev/pandas + +Binary installers for the latest released version are available at the [Python +Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://anaconda.org/conda-forge/pandas). + +```sh +# conda +conda install -c conda-forge pandas +``` + +```sh +# or PyPI +pip install pandas +``` + +The list of changes to pandas between each release can be found +[here](https://pandas.pydata.org/pandas-docs/stable/whatsnew/index.html). For full +details, see the commit logs at https://github.com/pandas-dev/pandas. + +## Dependencies +- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org) +- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html) +- [tzdata - Provides an IANA time zone database](https://tzdata.readthedocs.io/en/latest/) (Only required on Windows/Emscripten) + +See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies. + +## Installation from sources +To install pandas from source you need [Cython](https://cython.org/) in addition to the normal +dependencies above. Cython can be installed from PyPI: + +```sh +pip install cython +``` + +In the `pandas` directory (same one where you found this file after +cloning the git repo), execute: + +```sh +pip install . +``` + +or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable): + + +```sh +python -m pip install -ve . --no-build-isolation --config-settings editable-verbose=true +``` + +See the full instructions for [installing from source](https://pandas.pydata.org/docs/dev/development/contributing_environment.html). + +## License +[BSD 3](LICENSE) + +## Documentation +The official documentation is hosted on [PyData.org](https://pandas.pydata.org/pandas-docs/stable/). + +## Background +Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and +has been under active development since then. + +## Getting Help + +For usage questions, the best place to go to is [Stack Overflow](https://stackoverflow.com/questions/tagged/pandas). +Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata). + +## Discussion and Development +Most development discussions take place on GitHub in this repo, via the [GitHub issue tracker](https://github.com/pandas-dev/pandas/issues). + +Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Slack channel](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) is available for quick development related questions. + +There are also frequent [community meetings](https://pandas.pydata.org/docs/dev/development/community.html#community-meeting) for project maintainers open to the community as well as monthly [new contributor meetings](https://pandas.pydata.org/docs/dev/development/community.html#new-contributor-meeting) to help support new contributors. + +Additional information on the communication channels can be found on the [contributor community](https://pandas.pydata.org/docs/development/community.html) page. + +## Contributing to pandas + +[![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas) + +All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. + +A detailed overview on how to contribute can be found in the **[contributing guide](https://pandas.pydata.org/docs/dev/development/contributing.html)**. + +If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?q=is%3Aissue%20state%3Aopen%20label%3ADocs%20sort%3Aupdated-desc) and [good first issue](https://github.com/pandas-dev/pandas/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22%20sort%3Aupdated-desc) where you could start out. + +You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https://www.codetriage.com/pandas-dev/pandas). + +Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’... you can do something about it! + +Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Slack](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack). + +As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: [Contributor Code of Conduct](https://github.com/pandas-dev/.github/blob/master/CODE_OF_CONDUCT.md) + +
+ +[Go to Top](#table-of-contents) diff --git a/lib/python3.11/site-packages/pandas-3.0.3.dist-info/RECORD b/lib/python3.11/site-packages/pandas-3.0.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..1959a7bd7139a3cbc9b37ed82b5c6577bdb702f4 --- /dev/null +++ b/lib/python3.11/site-packages/pandas-3.0.3.dist-info/RECORD @@ -0,0 +1,2943 @@ +pandas-3.0.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +pandas-3.0.3.dist-info/LICENSE,sha256=XBBS6SHmLTbM2lCmFYXpwERLgMYvObV8Tz8NX7YvUHE,52127 +pandas-3.0.3.dist-info/METADATA,sha256=UpQsrZvX5mJa1G-YOlW-9s3ZnaOCLdsZOT1taXLRxAo,79468 +pandas-3.0.3.dist-info/RECORD,, +pandas-3.0.3.dist-info/WHEEL,sha256=-PRFuKV2Mmc9_5cjuD-UNN7YLg4OyntAAjaeo6DTU3w,138 +pandas-3.0.3.dist-info/entry_points.txt,sha256=OVLKNEPs-Q7IWypWBL6fxv56_zt4sRnEI7zawo6y_0w,69 +pandas/__init__.py,sha256=dC-FwTXuZUv_EgPvzudmS_gvxzBaw5Ev8wSgY_X36jw,8223 +pandas/__pycache__/__init__.cpython-311.pyc,, 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b/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_constructors.py @@ -0,0 +1,164 @@ +from datetime import datetime + +import numpy as np +import pytest + +from pandas import ( + Index, + RangeIndex, + Series, +) +import pandas._testing as tm + + +class TestRangeIndexConstructors: + @pytest.mark.parametrize("name", [None, "foo"]) + @pytest.mark.parametrize( + "args, kwargs, start, stop, step", + [ + ((5,), {}, 0, 5, 1), + ((1, 5), {}, 1, 5, 1), + ((1, 5, 2), {}, 1, 5, 2), + ((0,), {}, 0, 0, 1), + ((0, 0), {}, 0, 0, 1), + ((), {"start": 0}, 0, 0, 1), + ((), {"stop": 0}, 0, 0, 1), + ], + ) + def test_constructor(self, args, kwargs, start, stop, step, name): + result = RangeIndex(*args, name=name, **kwargs) + expected = Index(np.arange(start, stop, step, dtype=np.int64), name=name) + assert isinstance(result, RangeIndex) + assert result.name is name + assert result._range == range(start, stop, step) + tm.assert_index_equal(result, expected, exact="equiv") + + def test_constructor_invalid_args(self): + msg = "RangeIndex\\(\\.\\.\\.\\) must be called with integers" + with pytest.raises(TypeError, match=msg): + RangeIndex() + + with pytest.raises(TypeError, match=msg): + RangeIndex(name="Foo") + + # we don't allow on a bare Index + msg = ( + r"Index\(\.\.\.\) must be called with a collection of some " + r"kind, 0 was passed" + ) + with pytest.raises(TypeError, match=msg): + Index(0) + + @pytest.mark.parametrize( + "args", + [ + Index(["a", "b"]), + Series(["a", "b"]), + np.array(["a", "b"]), + [], + np.arange(0, 10), + np.array([1]), + [1], + ], + ) + def test_constructor_additional_invalid_args(self, args): + msg = f"Value needs to be a scalar value, was type {type(args).__name__}" + with pytest.raises(TypeError, match=msg): + RangeIndex(args) + + @pytest.mark.parametrize("args", ["foo", datetime(2000, 1, 1, 0, 0)]) + def test_constructor_invalid_args_wrong_type(self, args): + msg = f"Wrong type {type(args)} for value {args}" + with pytest.raises(TypeError, match=msg): + RangeIndex(args) + + def test_constructor_same(self): + # pass thru w and w/o copy + index = RangeIndex(1, 5, 2) + result = RangeIndex(index, copy=False) + assert result.identical(index) + + result = RangeIndex(index, copy=True) + tm.assert_index_equal(result, index, exact=True) + + result = RangeIndex(index) + tm.assert_index_equal(result, index, exact=True) + + with pytest.raises( + ValueError, + match="Incorrect `dtype` passed: expected signed integer, received float64", + ): + RangeIndex(index, dtype="float64") + + def test_constructor_range_object(self): + result = RangeIndex(range(1, 5, 2)) + expected = RangeIndex(1, 5, 2) + tm.assert_index_equal(result, expected, exact=True) + + def test_constructor_range(self): + result = RangeIndex.from_range(range(1, 5, 2)) + expected = RangeIndex(1, 5, 2) + tm.assert_index_equal(result, expected, exact=True) + + result = RangeIndex.from_range(range(5, 6)) + expected = RangeIndex(5, 6, 1) + tm.assert_index_equal(result, expected, exact=True) + + # an invalid range + result = RangeIndex.from_range(range(5, 1)) + expected = RangeIndex(0, 0, 1) + tm.assert_index_equal(result, expected, exact=True) + + result = RangeIndex.from_range(range(5)) + expected = RangeIndex(0, 5, 1) + tm.assert_index_equal(result, expected, exact=True) + + result = Index(range(1, 5, 2)) + expected = RangeIndex(1, 5, 2) + tm.assert_index_equal(result, expected, exact=True) + + msg = ( + r"(RangeIndex.)?from_range\(\) got an unexpected keyword argument( 'copy')?" + ) + with pytest.raises(TypeError, match=msg): + RangeIndex.from_range(range(10), copy=True) + + def test_constructor_name(self): + # GH#12288 + orig = RangeIndex(10) + orig.name = "original" + + copy = RangeIndex(orig) + copy.name = "copy" + + assert orig.name == "original" + assert copy.name == "copy" + + new = Index(copy) + assert new.name == "copy" + + new.name = "new" + assert orig.name == "original" + assert copy.name == "copy" + assert new.name == "new" + + def test_constructor_corner(self): + arr = np.array([1, 2, 3, 4], dtype=object) + index = RangeIndex(1, 5) + assert index.values.dtype == np.int64 + expected = Index(arr).astype("int64") + + tm.assert_index_equal(index, expected, exact="equiv") + + # non-int raise Exception + with pytest.raises(TypeError, match=r"Wrong type \"): + RangeIndex("1", "10", "1") + with pytest.raises(TypeError, match=r"Wrong type \"): + RangeIndex(1.1, 10.2, 1.3) + + # invalid passed type + with pytest.raises( + ValueError, + match="Incorrect `dtype` passed: expected signed integer, received float64", + ): + RangeIndex(1, 5, dtype="float64") diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_indexing.py b/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..695be18043da97c40b76f58715dde9b327bf73ef --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_indexing.py @@ -0,0 +1,148 @@ +import numpy as np +import pytest + +from pandas import ( + Index, + RangeIndex, +) +import pandas._testing as tm + + +class TestGetIndexer: + def test_get_indexer(self): + index = RangeIndex(start=0, stop=20, step=2) + target = RangeIndex(10) + indexer = index.get_indexer(target) + expected = np.array([0, -1, 1, -1, 2, -1, 3, -1, 4, -1], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected) + + def test_get_indexer_pad(self): + index = RangeIndex(start=0, stop=20, step=2) + target = RangeIndex(10) + indexer = index.get_indexer(target, method="pad") + expected = np.array([0, 0, 1, 1, 2, 2, 3, 3, 4, 4], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected) + + def test_get_indexer_backfill(self): + index = RangeIndex(start=0, stop=20, step=2) + target = RangeIndex(10) + indexer = index.get_indexer(target, method="backfill") + expected = np.array([0, 1, 1, 2, 2, 3, 3, 4, 4, 5], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected) + + def test_get_indexer_limit(self): + # GH#28631 + idx = RangeIndex(4) + target = RangeIndex(6) + result = idx.get_indexer(target, method="pad", limit=1) + expected = np.array([0, 1, 2, 3, 3, -1], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize("stop", [0, -1, -2]) + def test_get_indexer_decreasing(self, stop): + # GH#28678 + index = RangeIndex(7, stop, -3) + result = index.get_indexer(range(9)) + expected = np.array([-1, 2, -1, -1, 1, -1, -1, 0, -1], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + def test_get_indexer_missing_value_casting_string_dtype(self): + # GH#55833 + idx = Index(["a", "b", None]) + result = idx.get_indexer([None]) + expected = np.array([2], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + result = idx.get_indexer([None, True]) + expected = np.array([2, -1], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + +class TestTake: + def test_take_preserve_name(self): + index = RangeIndex(1, 5, name="foo") + taken = index.take([3, 0, 1]) + assert index.name == taken.name + + def test_take_fill_value(self): + # GH#12631 + idx = RangeIndex(1, 4, name="xxx") + result = idx.take(np.array([1, 0, -1])) + expected = Index([2, 1, 3], dtype=np.int64, name="xxx") + tm.assert_index_equal(result, expected) + + # fill_value + msg = "Unable to fill values because RangeIndex cannot contain NA" + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -1]), fill_value=True) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) + expected = Index([2, 1, 3], dtype=np.int64, name="xxx") + tm.assert_index_equal(result, expected) + + msg = "Unable to fill values because RangeIndex cannot contain NA" + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + def test_take_raises_index_error(self): + idx = RangeIndex(1, 4, name="xxx") + + msg = "index -5 is out of bounds for (axis 0 with )?size 3" + with pytest.raises(IndexError, match=msg): + idx.take(np.array([1, -5])) + + msg = "index -4 is out of bounds for (axis 0 with )?size 3" + with pytest.raises(IndexError, match=msg): + idx.take(np.array([1, -4])) + + # no errors + result = idx.take(np.array([1, -3])) + expected = Index([2, 1], dtype=np.int64, name="xxx") + tm.assert_index_equal(result, expected) + + def test_take_accepts_empty_array(self): + idx = RangeIndex(1, 4, name="foo") + result = idx.take(np.array([])) + expected = Index([], dtype=np.int64, name="foo") + tm.assert_index_equal(result, expected) + + # empty index + idx = RangeIndex(0, name="foo") + result = idx.take(np.array([])) + expected = Index([], dtype=np.int64, name="foo") + tm.assert_index_equal(result, expected) + + def test_take_accepts_non_int64_array(self): + idx = RangeIndex(1, 4, name="foo") + result = idx.take(np.array([2, 1], dtype=np.uint32)) + expected = Index([3, 2], dtype=np.int64, name="foo") + tm.assert_index_equal(result, expected) + + def test_take_when_index_has_step(self): + idx = RangeIndex(1, 11, 3, name="foo") # [1, 4, 7, 10] + result = idx.take(np.array([1, 0, -1, -4])) + expected = Index([4, 1, 10, 1], dtype=np.int64, name="foo") + tm.assert_index_equal(result, expected) + + def test_take_when_index_has_negative_step(self): + idx = RangeIndex(11, -4, -2, name="foo") # [11, 9, 7, 5, 3, 1, -1, -3] + result = idx.take(np.array([1, 0, -1, -8])) + expected = Index([9, 11, -3, 11], dtype=np.int64, name="foo") + tm.assert_index_equal(result, expected) + + +class TestWhere: + def test_where_putmask_range_cast(self): + # GH#43240 + idx = RangeIndex(0, 5, name="test") + + mask = np.array([True, True, False, False, False]) + result = idx.putmask(mask, 10) + expected = Index([10, 10, 2, 3, 4], dtype=np.int64, name="test") + tm.assert_index_equal(result, expected) + + result = idx.where(~mask, 10) + tm.assert_index_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_join.py b/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_join.py new file mode 100644 index 0000000000000000000000000000000000000000..09db30b1d4c51f73ae26c556b56cf75a4da07fb1 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_join.py @@ -0,0 +1,235 @@ +import numpy as np +import pytest + +from pandas import ( + Index, + RangeIndex, +) +import pandas._testing as tm + + +class TestJoin: + def test_join_outer(self): + # join with Index[int64] + index = RangeIndex(start=0, stop=20, step=2) + other = Index(np.arange(25, 14, -1, dtype=np.int64)) + + res, lidx, ridx = index.join(other, how="outer", return_indexers=True) + noidx_res = index.join(other, how="outer") + tm.assert_index_equal(res, noidx_res) + + eres = Index( + [0, 2, 4, 6, 8, 10, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25] + ) + elidx = np.array( + [0, 1, 2, 3, 4, 5, 6, 7, -1, 8, -1, 9, -1, -1, -1, -1, -1, -1, -1], + dtype=np.intp, + ) + eridx = np.array( + [-1, -1, -1, -1, -1, -1, -1, -1, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0], + dtype=np.intp, + ) + + assert isinstance(res, Index) and res.dtype == np.dtype(np.int64) + assert not isinstance(res, RangeIndex) + tm.assert_index_equal(res, eres, exact=True) + tm.assert_numpy_array_equal(lidx, elidx) + tm.assert_numpy_array_equal(ridx, eridx) + + # join with RangeIndex + other = RangeIndex(25, 14, -1) + + res, lidx, ridx = index.join(other, how="outer", return_indexers=True) + noidx_res = index.join(other, how="outer") + tm.assert_index_equal(res, noidx_res) + + assert isinstance(res, Index) and res.dtype == np.int64 + assert not isinstance(res, RangeIndex) + tm.assert_index_equal(res, eres) + tm.assert_numpy_array_equal(lidx, elidx) + tm.assert_numpy_array_equal(ridx, eridx) + + def test_join_inner(self): + # Join with non-RangeIndex + index = RangeIndex(start=0, stop=20, step=2) + other = Index(np.arange(25, 14, -1, dtype=np.int64)) + + res, lidx, ridx = index.join(other, how="inner", return_indexers=True) + + # no guarantee of sortedness, so sort for comparison purposes + ind = res.argsort() + res = res.take(ind) + lidx = lidx.take(ind) + ridx = ridx.take(ind) + + eres = Index([16, 18]) + elidx = np.array([8, 9], dtype=np.intp) + eridx = np.array([9, 7], dtype=np.intp) + + assert isinstance(res, Index) and res.dtype == np.int64 + tm.assert_index_equal(res, eres) + tm.assert_numpy_array_equal(lidx, elidx) + tm.assert_numpy_array_equal(ridx, eridx) + + # Join two RangeIndex + other = RangeIndex(25, 14, -1) + + res, lidx, ridx = index.join(other, how="inner", return_indexers=True) + + assert isinstance(res, RangeIndex) + tm.assert_index_equal(res, eres, exact="equiv") + tm.assert_numpy_array_equal(lidx, elidx) + tm.assert_numpy_array_equal(ridx, eridx) + + def test_join_left(self): + # Join with Index[int64] + index = RangeIndex(start=0, stop=20, step=2) + other = Index(np.arange(25, 14, -1, dtype=np.int64)) + + res, lidx, ridx = index.join(other, how="left", return_indexers=True) + eres = index + eridx = np.array([-1, -1, -1, -1, -1, -1, -1, -1, 9, 7], dtype=np.intp) + + assert isinstance(res, RangeIndex) + tm.assert_index_equal(res, eres) + assert lidx is None + tm.assert_numpy_array_equal(ridx, eridx) + + # Join withRangeIndex + other = Index(np.arange(25, 14, -1, dtype=np.int64)) + + res, lidx, ridx = index.join(other, how="left", return_indexers=True) + + assert isinstance(res, RangeIndex) + tm.assert_index_equal(res, eres) + assert lidx is None + tm.assert_numpy_array_equal(ridx, eridx) + + def test_join_right(self): + # Join with Index[int64] + index = RangeIndex(start=0, stop=20, step=2) + other = Index(np.arange(25, 14, -1, dtype=np.int64)) + + res, lidx, ridx = index.join(other, how="right", return_indexers=True) + eres = other + elidx = np.array([-1, -1, -1, -1, -1, -1, -1, 9, -1, 8, -1], dtype=np.intp) + + assert isinstance(other, Index) and other.dtype == np.int64 + tm.assert_index_equal(res, eres) + tm.assert_numpy_array_equal(lidx, elidx) + assert ridx is None + + # Join withRangeIndex + other = RangeIndex(25, 14, -1) + + res, lidx, ridx = index.join(other, how="right", return_indexers=True) + eres = other + + assert isinstance(other, RangeIndex) + tm.assert_index_equal(res, eres) + tm.assert_numpy_array_equal(lidx, elidx) + assert ridx is None + + def test_join_non_int_index(self): + index = RangeIndex(start=0, stop=20, step=2) + other = Index([3, 6, 7, 8, 10], dtype=object) + + outer = index.join(other, how="outer") + outer2 = other.join(index, how="outer") + expected = Index([0, 2, 3, 4, 6, 7, 8, 10, 12, 14, 16, 18]) + tm.assert_index_equal(outer, outer2) + tm.assert_index_equal(outer, expected) + + inner = index.join(other, how="inner") + inner2 = other.join(index, how="inner") + expected = Index([6, 8, 10]) + tm.assert_index_equal(inner, inner2) + tm.assert_index_equal(inner, expected) + + left = index.join(other, how="left") + tm.assert_index_equal(left, index.astype(object)) + + left2 = other.join(index, how="left") + tm.assert_index_equal(left2, other) + + right = index.join(other, how="right") + tm.assert_index_equal(right, other) + + right2 = other.join(index, how="right") + tm.assert_index_equal(right2, index.astype(object)) + + def test_join_non_unique(self): + index = RangeIndex(start=0, stop=20, step=2) + other = Index([4, 4, 3, 3]) + + res, lidx, ridx = index.join(other, return_indexers=True) + + eres = Index([0, 2, 4, 4, 6, 8, 10, 12, 14, 16, 18]) + elidx = np.array([0, 1, 2, 2, 3, 4, 5, 6, 7, 8, 9], dtype=np.intp) + eridx = np.array([-1, -1, 0, 1, -1, -1, -1, -1, -1, -1, -1], dtype=np.intp) + + tm.assert_index_equal(res, eres) + tm.assert_numpy_array_equal(lidx, elidx) + tm.assert_numpy_array_equal(ridx, eridx) + + def test_join_self(self, join_type): + index = RangeIndex(start=0, stop=20, step=2) + joined = index.join(index, how=join_type) + assert index is joined + + +@pytest.mark.parametrize( + "left, right, expected, expected_lidx, expected_ridx, how", + [ + [RangeIndex(2), RangeIndex(3), RangeIndex(2), None, [0, 1], "left"], + [RangeIndex(2), RangeIndex(2), RangeIndex(2), None, None, "left"], + [RangeIndex(2), RangeIndex(20, 22), RangeIndex(2), None, [-1, -1], "left"], + [RangeIndex(2), RangeIndex(3), RangeIndex(3), [0, 1, -1], None, "right"], + [RangeIndex(2), RangeIndex(2), RangeIndex(2), None, None, "right"], + [ + RangeIndex(2), + RangeIndex(20, 22), + RangeIndex(20, 22), + [-1, -1], + None, + "right", + ], + [RangeIndex(2), RangeIndex(3), RangeIndex(2), [0, 1], [0, 1], "inner"], + [RangeIndex(2), RangeIndex(2), RangeIndex(2), None, None, "inner"], + [RangeIndex(2), RangeIndex(1, 3), RangeIndex(1, 2), [1], [0], "inner"], + [RangeIndex(2), RangeIndex(3), RangeIndex(3), [0, 1, -1], [0, 1, 2], "outer"], + [RangeIndex(2), RangeIndex(2), RangeIndex(2), None, None, "outer"], + [ + RangeIndex(2), + RangeIndex(2, 4), + RangeIndex(4), + [0, 1, -1, -1], + [-1, -1, 0, 1], + "outer", + ], + [RangeIndex(2), RangeIndex(0), RangeIndex(2), None, [-1, -1], "left"], + [RangeIndex(2), RangeIndex(0), RangeIndex(0), [], None, "right"], + [RangeIndex(2), RangeIndex(0), RangeIndex(0), [], None, "inner"], + [RangeIndex(2), RangeIndex(0), RangeIndex(2), None, [-1, -1], "outer"], + ], +) +@pytest.mark.parametrize( + "right_type", [RangeIndex, lambda x: Index(list(x), dtype=x.dtype)] +) +def test_join_preserves_rangeindex( + left, right, expected, expected_lidx, expected_ridx, how, right_type +): + result, lidx, ridx = left.join(right_type(right), how=how, return_indexers=True) + tm.assert_index_equal(result, expected, exact=True) + + if expected_lidx is None: + assert lidx is expected_lidx + else: + exp_lidx = np.array(expected_lidx, dtype=np.intp) + tm.assert_numpy_array_equal(lidx, exp_lidx) + + if expected_ridx is None: + assert ridx is expected_ridx + else: + exp_ridx = np.array(expected_ridx, dtype=np.intp) + tm.assert_numpy_array_equal(ridx, exp_ridx) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_range.py b/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_range.py new file mode 100644 index 0000000000000000000000000000000000000000..2cf8febc416712b7c027b71028ca3916fa6e5018 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_range.py @@ -0,0 +1,927 @@ +import numpy as np +import pytest + +from pandas.core.dtypes.common import ensure_platform_int + +import pandas as pd +from pandas import ( + Index, + RangeIndex, +) +import pandas._testing as tm +from pandas.core.indexes.range import min_fitting_element + + +class TestRangeIndex: + @pytest.fixture + def simple_index(self): + return RangeIndex(start=0, stop=20, step=2) + + def test_constructor_unwraps_index(self): + result = RangeIndex(1, 3) + expected = np.array([1, 2], dtype=np.int64) + tm.assert_numpy_array_equal(result._data, expected) + + def test_can_hold_identifiers(self, simple_index): + idx = simple_index + key = idx[0] + assert idx._can_hold_identifiers_and_holds_name(key) is False + + def test_too_many_names(self, simple_index): + index = simple_index + with pytest.raises(ValueError, match="^Length"): + index.names = ["roger", "harold"] + + @pytest.mark.parametrize( + "index, start, stop, step", + [ + (RangeIndex(5), 0, 5, 1), + (RangeIndex(0, 5), 0, 5, 1), + (RangeIndex(5, step=2), 0, 5, 2), + (RangeIndex(1, 5, 2), 1, 5, 2), + ], + ) + def test_start_stop_step_attrs(self, index, start, stop, step): + # GH 25710 + assert index.start == start + assert index.stop == stop + assert index.step == step + + def test_copy(self): + i = RangeIndex(5, name="Foo") + i_copy = i.copy() + assert i_copy is not i + assert i_copy.identical(i) + assert i_copy._range == range(0, 5, 1) + assert i_copy.name == "Foo" + + def test_repr(self): + i = RangeIndex(5, name="Foo") + result = repr(i) + expected = "RangeIndex(start=0, stop=5, step=1, name='Foo')" + assert result == expected + + result = eval(result) + tm.assert_index_equal(result, i, exact=True) + + i = RangeIndex(5, 0, -1) + result = repr(i) + expected = "RangeIndex(start=5, stop=0, step=-1)" + assert result == expected + + result = eval(result) + tm.assert_index_equal(result, i, exact=True) + + def test_insert(self): + idx = RangeIndex(5, name="Foo") + result = idx[1:4] + + # test 0th element + tm.assert_index_equal(idx[0:4], result.insert(0, idx[0]), exact="equiv") + + # GH 18295 (test missing) + expected = Index([0, np.nan, 1, 2, 3, 4], dtype=np.float64) + for na in [np.nan, None, pd.NA]: + result = RangeIndex(5).insert(1, na) + tm.assert_index_equal(result, expected) + + result = RangeIndex(5).insert(1, pd.NaT) + expected = Index([0, pd.NaT, 1, 2, 3, 4], dtype=object) + tm.assert_index_equal(result, expected) + + def test_insert_edges_preserves_rangeindex(self): + idx = Index(range(4, 9, 2)) + + result = idx.insert(0, 2) + expected = Index(range(2, 9, 2)) + tm.assert_index_equal(result, expected, exact=True) + + result = idx.insert(3, 10) + expected = Index(range(4, 11, 2)) + tm.assert_index_equal(result, expected, exact=True) + + def test_insert_middle_preserves_rangeindex(self): + # insert in the middle + idx = Index(range(0, 3, 2)) + result = idx.insert(1, 1) + expected = Index(range(3)) + tm.assert_index_equal(result, expected, exact=True) + + idx = idx * 2 + result = idx.insert(1, 2) + expected = expected * 2 + tm.assert_index_equal(result, expected, exact=True) + + def test_delete(self): + idx = RangeIndex(5, name="Foo") + expected = idx[1:] + result = idx.delete(0) + tm.assert_index_equal(result, expected, exact=True) + assert result.name == expected.name + + expected = idx[:-1] + result = idx.delete(-1) + tm.assert_index_equal(result, expected, exact=True) + assert result.name == expected.name + + msg = "index 5 is out of bounds for axis 0 with size 5" + with pytest.raises((IndexError, ValueError), match=msg): + # either depending on numpy version + result = idx.delete(len(idx)) + + def test_delete_preserves_rangeindex(self): + idx = Index(range(2), name="foo") + + result = idx.delete([1]) + expected = Index(range(1), name="foo") + tm.assert_index_equal(result, expected, exact=True) + + result = idx.delete(1) + tm.assert_index_equal(result, expected, exact=True) + + def test_delete_preserves_rangeindex_middle(self): + idx = Index(range(3), name="foo") + result = idx.delete(1) + expected = idx[::2] + tm.assert_index_equal(result, expected, exact=True) + + result = idx.delete(-2) + tm.assert_index_equal(result, expected, exact=True) + + def test_delete_preserves_rangeindex_list_at_end(self): + idx = RangeIndex(0, 6, 1) + + loc = [2, 3, 4, 5] + result = idx.delete(loc) + expected = idx[:2] + tm.assert_index_equal(result, expected, exact=True) + + result = idx.delete(loc[::-1]) + tm.assert_index_equal(result, expected, exact=True) + + def test_delete_preserves_rangeindex_list_middle(self): + idx = RangeIndex(0, 6, 1) + + loc = [1, 2, 3, 4] + result = idx.delete(loc) + expected = RangeIndex(0, 6, 5) + tm.assert_index_equal(result, expected, exact=True) + + result = idx.delete(loc[::-1]) + tm.assert_index_equal(result, expected, exact=True) + + def test_delete_all_preserves_rangeindex(self): + idx = RangeIndex(0, 6, 1) + + loc = [0, 1, 2, 3, 4, 5] + result = idx.delete(loc) + expected = idx[:0] + tm.assert_index_equal(result, expected, exact=True) + + result = idx.delete(loc[::-1]) + tm.assert_index_equal(result, expected, exact=True) + + def test_delete_not_preserving_rangeindex(self): + idx = RangeIndex(0, 6, 1) + + loc = [0, 3, 5] + result = idx.delete(loc) + expected = Index([1, 2, 4]) + tm.assert_index_equal(result, expected, exact=True) + + result = idx.delete(loc[::-1]) + tm.assert_index_equal(result, expected, exact=True) + + def test_view(self): + i = RangeIndex(0, name="Foo") + i_view = i.view() + assert i_view.name == "Foo" + + i_view = i.view("i8") + tm.assert_numpy_array_equal(i.values, i_view) + + def test_dtype(self, simple_index): + index = simple_index + assert index.dtype == np.int64 + + def test_cache(self): + # GH 26565, GH26617, GH35432, GH53387 + # This test checks whether _cache has been set. + # Calling RangeIndex._cache["_data"] creates an int64 array of the same length + # as the RangeIndex and stores it in _cache. + idx = RangeIndex(0, 100, 10) + + assert idx._cache == {} + + repr(idx) + assert idx._cache == {} + + str(idx) + assert idx._cache == {} + + idx.get_loc(20) + assert idx._cache == {} + + 90 in idx # True + assert idx._cache == {} + + 91 in idx # False + assert idx._cache == {} + + idx.all() + assert idx._cache == {} + + idx.any() + assert idx._cache == {} + + for _ in idx: + pass + assert idx._cache == {} + + df = pd.DataFrame({"a": range(10)}, index=idx) + + # df.__repr__ should not populate index cache + str(df) + assert idx._cache == {} + + df.loc[50] + assert idx._cache == {} + + with pytest.raises(KeyError, match="51"): + df.loc[51] + assert idx._cache == {} + + df.loc[10:50] + assert idx._cache == {} + + df.iloc[5:10] + assert idx._cache == {} + + # after calling take, _cache may contain other keys, but not "_data" + idx.take([3, 0, 1]) + assert "_data" not in idx._cache + + df.loc[[50]] + assert "_data" not in idx._cache + + df.iloc[[5, 6, 7, 8, 9]] + assert "_data" not in idx._cache + + # idx._cache should contain a _data entry after call to idx._data + idx._data + assert isinstance(idx._data, np.ndarray) + assert idx._data is idx._data # check cached value is reused + assert "_data" in idx._cache + expected = np.arange(0, 100, 10, dtype="int64") + tm.assert_numpy_array_equal(idx._cache["_data"], expected) + + def test_is_monotonic(self): + index = RangeIndex(0, 20, 2) + assert index.is_monotonic_increasing is True + assert index.is_monotonic_increasing is True + assert index.is_monotonic_decreasing is False + assert index._is_strictly_monotonic_increasing is True + assert index._is_strictly_monotonic_decreasing is False + + index = RangeIndex(4, 0, -1) + assert index.is_monotonic_increasing is False + assert index._is_strictly_monotonic_increasing is False + assert index.is_monotonic_decreasing is True + assert index._is_strictly_monotonic_decreasing is True + + index = RangeIndex(1, 2) + assert index.is_monotonic_increasing is True + assert index.is_monotonic_increasing is True + assert index.is_monotonic_decreasing is True + assert index._is_strictly_monotonic_increasing is True + assert index._is_strictly_monotonic_decreasing is True + + index = RangeIndex(2, 1) + assert index.is_monotonic_increasing is True + assert index.is_monotonic_increasing is True + assert index.is_monotonic_decreasing is True + assert index._is_strictly_monotonic_increasing is True + assert index._is_strictly_monotonic_decreasing is True + + index = RangeIndex(1, 1) + assert index.is_monotonic_increasing is True + assert index.is_monotonic_increasing is True + assert index.is_monotonic_decreasing is True + assert index._is_strictly_monotonic_increasing is True + assert index._is_strictly_monotonic_decreasing is True + + @pytest.mark.parametrize( + "left,right", + [ + (RangeIndex(0, 9, 2), RangeIndex(0, 10, 2)), + (RangeIndex(0), RangeIndex(1, -1, 3)), + (RangeIndex(1, 2, 3), RangeIndex(1, 3, 4)), + (RangeIndex(0, -9, -2), RangeIndex(0, -10, -2)), + ], + ) + def test_equals_range(self, left, right): + assert left.equals(right) + assert right.equals(left) + + def test_logical_compat(self, simple_index): + idx = simple_index + assert idx.all() == idx.values.all() + assert idx.any() == idx.values.any() + + def test_identical(self, simple_index): + index = simple_index + i = Index(index.copy()) + assert i.identical(index) + + # we don't allow object dtype for RangeIndex + if isinstance(index, RangeIndex): + return + + same_values_different_type = Index(i, dtype=object) + assert not i.identical(same_values_different_type) + + i = index.copy(dtype=object) + i = i.rename("foo") + same_values = Index(i, dtype=object) + assert same_values.identical(index.copy(dtype=object)) + + assert not i.identical(index) + assert Index(same_values, name="foo", dtype=object).identical(i) + + assert not index.copy(dtype=object).identical(index.copy(dtype="int64")) + + def test_nbytes(self): + # memory savings vs int index + idx = RangeIndex(0, 1000) + assert idx.nbytes < Index(idx._values).nbytes / 10 + + # constant memory usage + i2 = RangeIndex(0, 10) + assert idx.nbytes == i2.nbytes + + @pytest.mark.parametrize( + "start,stop,step", + [ + # can't + ("foo", "bar", "baz"), + # shouldn't + ("0", "1", "2"), + ], + ) + def test_cant_or_shouldnt_cast(self, start, stop, step): + msg = f"Wrong type {type(start)} for value {start}" + with pytest.raises(TypeError, match=msg): + RangeIndex(start, stop, step) + + def test_view_index(self, simple_index): + index = simple_index + msg = ( + "Cannot change data-type for array of references.|" + "Cannot change data-type for object array.|" + ) + with pytest.raises(TypeError, match=msg): + index.view(Index) + + def test_prevent_casting(self, simple_index): + index = simple_index + result = index.astype("O") + assert result.dtype == np.object_ + + def test_repr_roundtrip(self, simple_index): + index = simple_index + tm.assert_index_equal(eval(repr(index)), index) + + def test_slice_keep_name(self): + idx = RangeIndex(1, 2, name="asdf") + assert idx.name == idx[1:].name + + @pytest.mark.parametrize( + "index", + [ + RangeIndex(start=0, stop=20, step=2, name="foo"), + RangeIndex(start=18, stop=-1, step=-2, name="bar"), + ], + ids=["index_inc", "index_dec"], + ) + def test_has_duplicates(self, index): + assert index.is_unique + assert not index.has_duplicates + + def test_extended_gcd(self, simple_index): + index = simple_index + result = index._extended_gcd(6, 10) + assert result[0] == result[1] * 6 + result[2] * 10 + assert 2 == result[0] + + result = index._extended_gcd(10, 6) + assert 2 == result[1] * 10 + result[2] * 6 + assert 2 == result[0] + + def test_min_fitting_element(self): + result = min_fitting_element(0, 2, 1) + assert 2 == result + + result = min_fitting_element(1, 1, 1) + assert 1 == result + + result = min_fitting_element(18, -2, 1) + assert 2 == result + + result = min_fitting_element(5, -1, 1) + assert 1 == result + + big_num = 500000000000000000000000 + + result = min_fitting_element(5, 1, big_num) + assert big_num == result + + def test_slice_specialised(self, simple_index): + index = simple_index + index.name = "foo" + + # scalar indexing + res = index[1] + expected = 2 + assert res == expected + + res = index[-1] + expected = 18 + assert res == expected + + # slicing + # slice value completion + index_slice = index[:] + expected = index + tm.assert_index_equal(index_slice, expected) + + # positive slice values + index_slice = index[7:10:2] + expected = Index([14, 18], name="foo") + tm.assert_index_equal(index_slice, expected, exact="equiv") + + # negative slice values + index_slice = index[-1:-5:-2] + expected = Index([18, 14], name="foo") + tm.assert_index_equal(index_slice, expected, exact="equiv") + + # stop overshoot + index_slice = index[2:100:4] + expected = Index([4, 12], name="foo") + tm.assert_index_equal(index_slice, expected, exact="equiv") + + # reverse + index_slice = index[::-1] + expected = Index(index.values[::-1], name="foo") + tm.assert_index_equal(index_slice, expected, exact="equiv") + + index_slice = index[-8::-1] + expected = Index([4, 2, 0], name="foo") + tm.assert_index_equal(index_slice, expected, exact="equiv") + + index_slice = index[-40::-1] + expected = Index(np.array([], dtype=np.int64), name="foo") + tm.assert_index_equal(index_slice, expected, exact="equiv") + + index_slice = index[40::-1] + expected = Index(index.values[40::-1], name="foo") + tm.assert_index_equal(index_slice, expected, exact="equiv") + + index_slice = index[10::-1] + expected = Index(index.values[::-1], name="foo") + tm.assert_index_equal(index_slice, expected, exact="equiv") + + @pytest.mark.parametrize("step", set(range(-5, 6)) - {0}) + def test_len_specialised(self, step): + # make sure that our len is the same as np.arange calc + start, stop = (0, 5) if step > 0 else (5, 0) + + arr = np.arange(start, stop, step) + index = RangeIndex(start, stop, step) + assert len(index) == len(arr) + + index = RangeIndex(stop, start, step) + assert len(index) == 0 + + @pytest.mark.parametrize( + "indices, expected", + [ + ([RangeIndex(1, 12, 5)], RangeIndex(1, 12, 5)), + ([RangeIndex(0, 6, 4)], RangeIndex(0, 6, 4)), + ([RangeIndex(1, 3), RangeIndex(3, 7)], RangeIndex(1, 7)), + ([RangeIndex(1, 5, 2), RangeIndex(5, 6)], RangeIndex(1, 6, 2)), + ([RangeIndex(1, 3, 2), RangeIndex(4, 7, 3)], RangeIndex(1, 7, 3)), + ([RangeIndex(-4, 3, 2), RangeIndex(4, 7, 2)], RangeIndex(-4, 7, 2)), + ([RangeIndex(-4, -8), RangeIndex(-8, -12)], RangeIndex(0, 0)), + ([RangeIndex(-4, -8), RangeIndex(3, -4)], RangeIndex(0, 0)), + ([RangeIndex(-4, -8), RangeIndex(3, 5)], RangeIndex(3, 5)), + ([RangeIndex(-4, -2), RangeIndex(3, 5)], Index([-4, -3, 3, 4])), + ([RangeIndex(-2), RangeIndex(3, 5)], RangeIndex(3, 5)), + ([RangeIndex(2), RangeIndex(2)], Index([0, 1, 0, 1])), + ([RangeIndex(2), RangeIndex(2, 5), RangeIndex(5, 8, 4)], RangeIndex(0, 6)), + ( + [RangeIndex(2), RangeIndex(3, 5), RangeIndex(5, 8, 4)], + Index([0, 1, 3, 4, 5]), + ), + ( + [RangeIndex(-2, 2), RangeIndex(2, 5), RangeIndex(5, 8, 4)], + RangeIndex(-2, 6), + ), + ([RangeIndex(3), Index([-1, 3, 15])], Index([0, 1, 2, -1, 3, 15])), + ([RangeIndex(3), Index([-1, 3.1, 15.0])], Index([0, 1, 2, -1, 3.1, 15.0])), + ([RangeIndex(3), Index(["a", None, 14])], Index([0, 1, 2, "a", None, 14])), + ([RangeIndex(3, 1), Index(["a", None, 14])], Index(["a", None, 14])), + ], + ) + def test_append(self, indices, expected): + # GH16212 + result = indices[0].append(indices[1:]) + tm.assert_index_equal(result, expected, exact=True) + + if len(indices) == 2: + # Append single item rather than list + result2 = indices[0].append(indices[1]) + tm.assert_index_equal(result2, expected, exact=True) + + def test_engineless_lookup(self): + # GH 16685 + # Standard lookup on RangeIndex should not require the engine to be + # created + idx = RangeIndex(2, 10, 3) + + assert idx.get_loc(5) == 1 + tm.assert_numpy_array_equal( + idx.get_indexer([2, 8]), ensure_platform_int(np.array([0, 2])) + ) + with pytest.raises(KeyError, match="3"): + idx.get_loc(3) + + assert "_engine" not in idx._cache + + # Different types of scalars can be excluded immediately, no need to + # use the _engine + with pytest.raises(KeyError, match="'a'"): + idx.get_loc("a") + + assert "_engine" not in idx._cache + + @pytest.mark.parametrize( + "ri", + [ + RangeIndex(0, -1, -1), + RangeIndex(0, 1, 1), + RangeIndex(1, 3, 2), + RangeIndex(0, -1, -2), + RangeIndex(-3, -5, -2), + ], + ) + def test_append_len_one(self, ri): + # GH39401 + result = ri.append([]) + tm.assert_index_equal(result, ri, exact=True) + + @pytest.mark.parametrize("base", [RangeIndex(0, 2), Index([0, 1])]) + def test_isin_range(self, base): + # GH#41151 + values = RangeIndex(0, 1) + result = base.isin(values) + expected = np.array([True, False]) + tm.assert_numpy_array_equal(result, expected) + + def test_sort_values_key(self): + # GH#43666, GH#52764 + sort_order = {8: 2, 6: 0, 4: 8, 2: 10, 0: 12} + values = RangeIndex(0, 10, 2) + result = values.sort_values(key=lambda x: x.map(sort_order)) + expected = Index([6, 8, 4, 2, 0], dtype="int64") + tm.assert_index_equal(result, expected, check_exact=True) + + # check this matches the Series.sort_values behavior + ser = values.to_series() + result2 = ser.sort_values(key=lambda x: x.map(sort_order)) + tm.assert_series_equal(result2, expected.to_series(), check_exact=True) + + def test_range_index_rsub_by_const(self): + # GH#53255 + result = 3 - RangeIndex(0, 4, 1) + expected = RangeIndex(3, -1, -1) + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize( + "rng, decimals", + [ + [range(5), 0], + [range(5), 2], + [range(10, 30, 10), -1], + [range(30, 10, -10), -1], + ], +) +def test_range_round_returns_rangeindex(rng, decimals): + ri = RangeIndex(rng) + expected = ri.copy() + result = ri.round(decimals=decimals) + tm.assert_index_equal(result, expected, exact=True) + + +@pytest.mark.parametrize( + "rng, decimals", + [ + [range(10, 30, 1), -1], + [range(30, 10, -1), -1], + [range(11, 14), -10], + ], +) +def test_range_round_returns_index(rng, decimals): + ri = RangeIndex(rng) + expected = Index(list(rng)).round(decimals=decimals) + result = ri.round(decimals=decimals) + tm.assert_index_equal(result, expected, exact=True) + + +def test_reindex_1_value_returns_rangeindex(): + ri = RangeIndex(0, 10, 2, name="foo") + result, result_indexer = ri.reindex([2]) + expected = RangeIndex(2, 4, 2, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + expected_indexer = np.array([1], dtype=np.intp) + tm.assert_numpy_array_equal(result_indexer, expected_indexer) + + +def test_reindex_empty_returns_rangeindex(): + ri = RangeIndex(0, 10, 2, name="foo") + result, result_indexer = ri.reindex([]) + expected = RangeIndex(0, 0, 2, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + expected_indexer = np.array([], dtype=np.intp) + tm.assert_numpy_array_equal(result_indexer, expected_indexer) + + +def test_insert_empty_0_loc(): + ri = RangeIndex(0, step=10, name="foo") + result = ri.insert(0, 5) + expected = RangeIndex(5, 15, 10, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + +def test_append_non_rangeindex_return_rangeindex(): + ri = RangeIndex(1) + result = ri.append(Index([1])) + expected = RangeIndex(2) + tm.assert_index_equal(result, expected, exact=True) + + +def test_append_non_rangeindex_return_index(): + ri = RangeIndex(1) + result = ri.append(Index([1, 3, 4])) + expected = Index([0, 1, 3, 4]) + tm.assert_index_equal(result, expected, exact=True) + + +def test_reindex_returns_rangeindex(): + ri = RangeIndex(2, name="foo") + result, result_indexer = ri.reindex([1, 2, 3]) + expected = RangeIndex(1, 4, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + expected_indexer = np.array([1, -1, -1], dtype=np.intp) + tm.assert_numpy_array_equal(result_indexer, expected_indexer) + + +def test_reindex_returns_index(): + ri = RangeIndex(4, name="foo") + result, result_indexer = ri.reindex([0, 1, 3]) + expected = Index([0, 1, 3], name="foo") + tm.assert_index_equal(result, expected, exact=True) + + expected_indexer = np.array([0, 1, 3], dtype=np.intp) + tm.assert_numpy_array_equal(result_indexer, expected_indexer) + + +def test_take_return_rangeindex(): + ri = RangeIndex(5, name="foo") + result = ri.take([]) + expected = RangeIndex(0, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + result = ri.take([3, 4]) + expected = RangeIndex(3, 5, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + +def test__getitem__boolean_numpyextensionarray(): + ri = RangeIndex(1) + result = ri[pd.arrays.NumpyExtensionArray(np.array([True]))] + tm.assert_index_equal(ri, result) + + +@pytest.mark.parametrize( + "container", + [np.array, pd.Series, lambda x: pd.arrays.NumpyExtensionArray(np.array(x))], + ids=["numpy-array", "series", "numpy-extension-array"], +) +def test__getitem__boolean_arraylike(container): + ri = RangeIndex(5) + result = ri[container([True, True, False, False, True])] + expected = Index([0, 1, 4], dtype="int64") + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize( + "rng, exp_rng", + [ + [range(5), range(3, 4)], + [range(0, -10, -2), range(-6, -8, -2)], + [range(0, 10, 2), range(6, 8, 2)], + ], +) +def test_take_1_value_returns_rangeindex(rng, exp_rng): + ri = RangeIndex(rng, name="foo") + result = ri.take([3]) + expected = RangeIndex(exp_rng, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + +def test_append_one_nonempty_preserve_step(): + expected = RangeIndex(0, -1, -1) + result = RangeIndex(0).append([expected]) + tm.assert_index_equal(result, expected, exact=True) + + +def test_getitem_boolmask_all_true(): + ri = RangeIndex(3, name="foo") + expected = ri.copy() + result = ri[[True] * 3] + tm.assert_index_equal(result, expected, exact=True) + + +def test_getitem_boolmask_all_false(): + ri = RangeIndex(3, name="foo") + result = ri[[False] * 3] + expected = RangeIndex(0, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + +def test_getitem_boolmask_returns_rangeindex(): + ri = RangeIndex(3, name="foo") + result = ri[[False, True, True]] + expected = RangeIndex(1, 3, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + result = ri[[True, False, True]] + expected = RangeIndex(0, 3, 2, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + +def test_getitem_boolmask_returns_index(): + ri = RangeIndex(4, name="foo") + result = ri[[True, True, False, True]] + expected = Index([0, 1, 3], name="foo") + tm.assert_index_equal(result, expected) + + +def test_getitem_boolmask_wrong_length(): + ri = RangeIndex(4, name="foo") + with pytest.raises(IndexError, match="Boolean index has wrong length"): + ri[[True]] + + +def test_pos_returns_rangeindex(): + ri = RangeIndex(2, name="foo") + expected = ri.copy() + result = +ri + tm.assert_index_equal(result, expected, exact=True) + + +def test_neg_returns_rangeindex(): + ri = RangeIndex(2, name="foo") + result = -ri + expected = RangeIndex(0, -2, -1, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + ri = RangeIndex(-2, 2, name="foo") + result = -ri + expected = RangeIndex(2, -2, -1, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + +@pytest.mark.parametrize( + "rng, exp_rng", + [ + [range(0), range(0)], + [range(10), range(10)], + [range(-2, 1, 1), range(2, -1, -1)], + [range(0, -10, -1), range(0, 10, 1)], + ], +) +def test_abs_returns_rangeindex(rng, exp_rng): + ri = RangeIndex(rng, name="foo") + expected = RangeIndex(exp_rng, name="foo") + result = abs(ri) + tm.assert_index_equal(result, expected, exact=True) + + +def test_abs_returns_index(): + ri = RangeIndex(-2, 2, name="foo") + result = abs(ri) + expected = Index([2, 1, 0, 1], name="foo") + tm.assert_index_equal(result, expected, exact=True) + + +@pytest.mark.parametrize( + "rng", + [ + range(0), + range(5), + range(0, -5, -1), + range(-2, 2, 1), + range(2, -2, -2), + range(0, 5, 2), + ], +) +def test_invert_returns_rangeindex(rng): + ri = RangeIndex(rng, name="foo") + result = ~ri + assert isinstance(result, RangeIndex) + expected = ~Index(list(rng), name="foo") + tm.assert_index_equal(result, expected, exact=False) + + +@pytest.mark.parametrize( + "rng", + [ + range(0, 5, 1), + range(0, 5, 2), + range(10, 15, 1), + range(10, 5, -1), + range(10, 5, -2), + range(5, 0, -1), + ], +) +@pytest.mark.parametrize("meth", ["argmax", "argmin"]) +def test_arg_min_max(rng, meth): + ri = RangeIndex(rng) + idx = Index(list(rng)) + assert getattr(ri, meth)() == getattr(idx, meth)() + + +@pytest.mark.parametrize("meth", ["argmin", "argmax"]) +def test_empty_argmin_argmax_raises(meth): + with pytest.raises(ValueError, match=f"attempt to get {meth} of an empty sequence"): + getattr(RangeIndex(0), meth)() + + +def test_getitem_integers_return_rangeindex(): + result = RangeIndex(0, 10, 2, name="foo")[[0, -1]] + expected = RangeIndex(start=0, stop=16, step=8, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + result = RangeIndex(0, 10, 2, name="foo")[[3]] + expected = RangeIndex(start=6, stop=8, step=2, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + +def test_getitem_empty_return_rangeindex(): + result = RangeIndex(0, 10, 2, name="foo")[[]] + expected = RangeIndex(start=0, stop=0, step=1, name="foo") + tm.assert_index_equal(result, expected, exact=True) + + +def test_getitem_integers_return_index(): + result = RangeIndex(0, 10, 2, name="foo")[[0, 1, -1]] + expected = Index([0, 2, 8], dtype="int64", name="foo") + tm.assert_index_equal(result, expected) + + +@pytest.mark.parametrize("normalize", [True, False]) +@pytest.mark.parametrize( + "rng", + [ + range(3), + range(0), + range(0, 3, 2), + range(3, -3, -2), + ], +) +def test_value_counts(sort, dropna, ascending, normalize, rng): + ri = RangeIndex(rng, name="A") + result = ri.value_counts( + normalize=normalize, sort=sort, ascending=ascending, dropna=dropna + ) + expected = Index(list(rng), name="A").value_counts( + normalize=normalize, sort=sort, ascending=ascending, dropna=dropna + ) + tm.assert_series_equal(result, expected, check_index_type=False) + + +@pytest.mark.parametrize("side", ["left", "right"]) +@pytest.mark.parametrize("value", [0, -5, 5, -3, np.array([-5, -3, 0, 5])]) +def test_searchsorted(side, value): + ri = RangeIndex(-3, 3, 2) + result = ri.searchsorted(value=value, side=side) + expected = Index(list(ri)).searchsorted(value=value, side=side) + if isinstance(value, int): + assert result == expected + else: + tm.assert_numpy_array_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_setops.py b/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_setops.py new file mode 100644 index 0000000000000000000000000000000000000000..55ae84fdbc23b0a9a032bf976321f2e12281d6a1 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/ranges/test_setops.py @@ -0,0 +1,494 @@ +from datetime import ( + datetime, + timedelta, +) + +from hypothesis import ( + assume, + given, + strategies as st, +) +import numpy as np +import pytest + +from pandas import ( + Index, + RangeIndex, +) +import pandas._testing as tm + + +class TestRangeIndexSetOps: + @pytest.mark.parametrize("dtype", [None, "int64", "uint64"]) + def test_intersection_mismatched_dtype(self, dtype): + # check that we cast to float, not object + index = RangeIndex(start=0, stop=20, step=2, name="foo") + index = Index(index, dtype=dtype) + + flt = index.astype(np.float64) + + # bc index.equals(flt), we go through fastpath and get RangeIndex back + result = index.intersection(flt) + tm.assert_index_equal(result, index, exact=True) + + result = flt.intersection(index) + tm.assert_index_equal(result, flt, exact=True) + + # neither empty, not-equals + result = index.intersection(flt[1:]) + tm.assert_index_equal(result, flt[1:], exact=True) + + result = flt[1:].intersection(index) + tm.assert_index_equal(result, flt[1:], exact=True) + + # empty other + result = index.intersection(flt[:0]) + tm.assert_index_equal(result, flt[:0], exact=True) + + result = flt[:0].intersection(index) + tm.assert_index_equal(result, flt[:0], exact=True) + + def test_intersection_empty(self, sort, names): + # name retention on empty intersections + index = RangeIndex(start=0, stop=20, step=2, name=names[0]) + + # empty other + result = index.intersection(index[:0].rename(names[1]), sort=sort) + tm.assert_index_equal(result, index[:0].rename(names[2]), exact=True) + + # empty self + result = index[:0].intersection(index.rename(names[1]), sort=sort) + tm.assert_index_equal(result, index[:0].rename(names[2]), exact=True) + + def test_intersection(self, sort): + # intersect with Index with dtype int64 + index = RangeIndex(start=0, stop=20, step=2) + other = Index(np.arange(1, 6)) + result = index.intersection(other, sort=sort) + expected = Index(np.sort(np.intersect1d(index.values, other.values))) + tm.assert_index_equal(result, expected) + + result = other.intersection(index, sort=sort) + expected = Index( + np.sort(np.asarray(np.intersect1d(index.values, other.values))) + ) + tm.assert_index_equal(result, expected) + + # intersect with increasing RangeIndex + other = RangeIndex(1, 6) + result = index.intersection(other, sort=sort) + expected = Index(np.sort(np.intersect1d(index.values, other.values))) + tm.assert_index_equal(result, expected, exact="equiv") + + # intersect with decreasing RangeIndex + other = RangeIndex(5, 0, -1) + result = index.intersection(other, sort=sort) + expected = Index(np.sort(np.intersect1d(index.values, other.values))) + tm.assert_index_equal(result, expected, exact="equiv") + + # reversed (GH 17296) + result = other.intersection(index, sort=sort) + tm.assert_index_equal(result, expected, exact="equiv") + + # GH 17296: intersect two decreasing RangeIndexes + first = RangeIndex(10, -2, -2) + other = RangeIndex(5, -4, -1) + expected = RangeIndex(start=4, stop=-2, step=-2) + result = first.intersection(other, sort=sort) + tm.assert_index_equal(result, expected) + + # reversed + result = other.intersection(first, sort=sort) + tm.assert_index_equal(result, expected) + + index = RangeIndex(5, name="foo") + + # intersect of non-overlapping indices + other = RangeIndex(5, 10, 1, name="foo") + result = index.intersection(other, sort=sort) + expected = RangeIndex(0, 0, 1, name="foo") + tm.assert_index_equal(result, expected) + + other = RangeIndex(-1, -5, -1) + result = index.intersection(other, sort=sort) + expected = RangeIndex(0, 0, 1) + tm.assert_index_equal(result, expected) + + # intersection of empty indices + other = RangeIndex(0, 0, 1) + result = index.intersection(other, sort=sort) + expected = RangeIndex(0, 0, 1) + tm.assert_index_equal(result, expected) + + result = other.intersection(index, sort=sort) + tm.assert_index_equal(result, expected) + + def test_intersection_non_overlapping_gcd(self, sort, names): + # intersection of non-overlapping values based on start value and gcd + index = RangeIndex(1, 10, 2, name=names[0]) + other = RangeIndex(0, 10, 4, name=names[1]) + result = index.intersection(other, sort=sort) + expected = RangeIndex(0, 0, 1, name=names[2]) + tm.assert_index_equal(result, expected) + + def test_union_noncomparable(self, sort): + # corner case, Index with non-int64 dtype + index = RangeIndex(start=0, stop=20, step=2) + other = Index([datetime.now() + timedelta(i) for i in range(4)], dtype=object) + result = index.union(other, sort=sort) + expected = Index(np.concatenate((index, other))) + tm.assert_index_equal(result, expected) + + result = other.union(index, sort=sort) + expected = Index(np.concatenate((other, index))) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "idx1, idx2, expected_sorted, expected_notsorted", + [ + ( + RangeIndex(0, 10, 1), + RangeIndex(0, 10, 1), + RangeIndex(0, 10, 1), + RangeIndex(0, 10, 1), + ), + ( + RangeIndex(0, 10, 1), + RangeIndex(5, 20, 1), + RangeIndex(0, 20, 1), + RangeIndex(0, 20, 1), + ), + ( + RangeIndex(0, 10, 1), + RangeIndex(10, 20, 1), + RangeIndex(0, 20, 1), + RangeIndex(0, 20, 1), + ), + ( + RangeIndex(0, -10, -1), + RangeIndex(0, -10, -1), + RangeIndex(0, -10, -1), + RangeIndex(0, -10, -1), + ), + ( + RangeIndex(0, -10, -1), + RangeIndex(-10, -20, -1), + RangeIndex(-19, 1, 1), + RangeIndex(0, -20, -1), + ), + ( + RangeIndex(0, 10, 2), + RangeIndex(1, 10, 2), + RangeIndex(0, 10, 1), + Index(list(range(0, 10, 2)) + list(range(1, 10, 2))), + ), + ( + RangeIndex(0, 11, 2), + RangeIndex(1, 12, 2), + RangeIndex(0, 12, 1), + Index(list(range(0, 11, 2)) + list(range(1, 12, 2))), + ), + ( + RangeIndex(0, 21, 4), + RangeIndex(-2, 24, 4), + RangeIndex(-2, 24, 2), + Index(list(range(0, 21, 4)) + list(range(-2, 24, 4))), + ), + ( + RangeIndex(0, -20, -2), + RangeIndex(-1, -21, -2), + RangeIndex(-19, 1, 1), + Index(list(range(0, -20, -2)) + list(range(-1, -21, -2))), + ), + ( + RangeIndex(0, 100, 5), + RangeIndex(0, 100, 20), + RangeIndex(0, 100, 5), + RangeIndex(0, 100, 5), + ), + ( + RangeIndex(0, -100, -5), + RangeIndex(5, -100, -20), + RangeIndex(-95, 10, 5), + Index([*list(range(0, -100, -5)), 5]), + ), + ( + RangeIndex(0, -11, -1), + RangeIndex(1, -12, -4), + RangeIndex(-11, 2, 1), + Index([*list(range(0, -11, -1)), 1, -11]), + ), + (RangeIndex(0), RangeIndex(0), RangeIndex(0), RangeIndex(0)), + ( + RangeIndex(0, -10, -2), + RangeIndex(0), + RangeIndex(0, -10, -2), + RangeIndex(0, -10, -2), + ), + ( + RangeIndex(0, 100, 2), + RangeIndex(100, 150, 200), + RangeIndex(0, 102, 2), + RangeIndex(0, 102, 2), + ), + ( + RangeIndex(0, -100, -2), + RangeIndex(-100, 50, 102), + RangeIndex(-100, 4, 2), + Index([*list(range(0, -100, -2)), -100, 2]), + ), + ( + RangeIndex(0, -100, -1), + RangeIndex(0, -50, -3), + RangeIndex(-99, 1, 1), + RangeIndex(0, -100, -1), + ), + ( + RangeIndex(0, 1, 1), + RangeIndex(5, 6, 10), + RangeIndex(0, 6, 5), + RangeIndex(0, 10, 5), + ), + ( + RangeIndex(0, 10, 5), + RangeIndex(-5, -6, -20), + RangeIndex(-5, 10, 5), + Index([0, 5, -5]), + ), + ( + RangeIndex(0, 3, 1), + RangeIndex(4, 5, 1), + Index([0, 1, 2, 4]), + Index([0, 1, 2, 4]), + ), + ( + RangeIndex(0, 10, 1), + Index([], dtype=np.int64), + RangeIndex(0, 10, 1), + RangeIndex(0, 10, 1), + ), + ( + RangeIndex(0), + Index([1, 5, 6]), + Index([1, 5, 6]), + Index([1, 5, 6]), + ), + # GH 43885 + ( + RangeIndex(0, 10), + RangeIndex(0, 5), + RangeIndex(0, 10), + RangeIndex(0, 10), + ), + ], + ids=lambda x: repr(x) if isinstance(x, RangeIndex) else x, + ) + def test_union_sorted(self, idx1, idx2, expected_sorted, expected_notsorted): + res1 = idx1.union(idx2, sort=None) + tm.assert_index_equal(res1, expected_sorted, exact=True) + + res1 = idx1.union(idx2, sort=False) + tm.assert_index_equal(res1, expected_notsorted, exact=True) + + res2 = idx2.union(idx1, sort=None) + res3 = Index(idx1._values, name=idx1.name).union(idx2, sort=None) + tm.assert_index_equal(res2, expected_sorted, exact=True) + tm.assert_index_equal(res3, expected_sorted, exact="equiv") + + def test_union_same_step_misaligned(self): + # GH#44019 + left = RangeIndex(range(0, 20, 4)) + right = RangeIndex(range(1, 21, 4)) + + result = left.union(right) + expected = Index([0, 1, 4, 5, 8, 9, 12, 13, 16, 17]) + tm.assert_index_equal(result, expected, exact=True) + + def test_difference(self): + # GH#12034 Cases where we operate against another RangeIndex and may + # get back another RangeIndex + obj = RangeIndex.from_range(range(1, 10), name="foo") + + result = obj.difference(obj) + expected = RangeIndex.from_range(range(0), name="foo") + tm.assert_index_equal(result, expected, exact=True) + + result = obj.difference(expected.rename("bar")) + tm.assert_index_equal(result, obj.rename(None), exact=True) + + result = obj.difference(obj[:3]) + tm.assert_index_equal(result, obj[3:], exact=True) + + result = obj.difference(obj[-3:]) + tm.assert_index_equal(result, obj[:-3], exact=True) + + # Flipping the step of 'other' doesn't affect the result, but + # flipping the stepof 'self' does when sort=None + result = obj[::-1].difference(obj[-3:]) + tm.assert_index_equal(result, obj[:-3], exact=True) + + result = obj[::-1].difference(obj[-3:], sort=False) + tm.assert_index_equal(result, obj[:-3][::-1], exact=True) + + result = obj[::-1].difference(obj[-3:][::-1]) + tm.assert_index_equal(result, obj[:-3], exact=True) + + result = obj[::-1].difference(obj[-3:][::-1], sort=False) + tm.assert_index_equal(result, obj[:-3][::-1], exact=True) + + result = obj.difference(obj[2:6]) + expected = Index([1, 2, 7, 8, 9], name="foo") + tm.assert_index_equal(result, expected, exact=True) + + def test_difference_sort(self): + # GH#44085 ensure we respect the sort keyword + + idx = Index(range(4))[::-1] + other = Index(range(3, 4)) + + result = idx.difference(other) + expected = Index(range(3)) + tm.assert_index_equal(result, expected, exact=True) + + result = idx.difference(other, sort=False) + expected = expected[::-1] + tm.assert_index_equal(result, expected, exact=True) + + # case where the intersection is empty + other = range(10, 12) + result = idx.difference(other, sort=None) + expected = idx[::-1] + tm.assert_index_equal(result, expected, exact=True) + + def test_difference_mismatched_step(self): + obj = RangeIndex.from_range(range(1, 10), name="foo") + + result = obj.difference(obj[::2]) + expected = obj[1::2] + tm.assert_index_equal(result, expected, exact=True) + + result = obj[::-1].difference(obj[::2], sort=False) + tm.assert_index_equal(result, expected[::-1], exact=True) + + result = obj.difference(obj[1::2]) + expected = obj[::2] + tm.assert_index_equal(result, expected, exact=True) + + result = obj[::-1].difference(obj[1::2], sort=False) + tm.assert_index_equal(result, expected[::-1], exact=True) + + def test_difference_interior_overlap_endpoints_preserved(self): + left = RangeIndex(range(4)) + right = RangeIndex(range(1, 3)) + + result = left.difference(right) + expected = RangeIndex(0, 4, 3) + assert expected.tolist() == [0, 3] + tm.assert_index_equal(result, expected, exact=True) + + def test_difference_endpoints_overlap_interior_preserved(self): + left = RangeIndex(-8, 20, 7) + right = RangeIndex(13, -9, -3) + + result = left.difference(right) + expected = RangeIndex(-1, 13, 7) + assert expected.tolist() == [-1, 6] + tm.assert_index_equal(result, expected, exact=True) + + def test_difference_interior_non_preserving(self): + # case with intersection of length 1 but RangeIndex is not preserved + idx = Index(range(10)) + + other = idx[3:4] + result = idx.difference(other) + expected = Index([0, 1, 2, 4, 5, 6, 7, 8, 9]) + tm.assert_index_equal(result, expected, exact=True) + + # case with other.step / self.step > 2 + other = idx[::3] + result = idx.difference(other) + expected = Index([1, 2, 4, 5, 7, 8]) + tm.assert_index_equal(result, expected, exact=True) + + # cases with only reaching one end of left + obj = Index(range(20)) + other = obj[:10:2] + result = obj.difference(other) + expected = Index([1, 3, 5, 7, 9, *list(range(10, 20))]) + tm.assert_index_equal(result, expected, exact=True) + + other = obj[1:11:2] + result = obj.difference(other) + expected = Index([0, 2, 4, 6, 8, 10, *list(range(11, 20))]) + tm.assert_index_equal(result, expected, exact=True) + + def test_symmetric_difference(self): + # GH#12034 Cases where we operate against another RangeIndex and may + # get back another RangeIndex + left = RangeIndex.from_range(range(1, 10), name="foo") + + result = left.symmetric_difference(left) + expected = RangeIndex.from_range(range(0), name="foo") + tm.assert_index_equal(result, expected) + + result = left.symmetric_difference(expected.rename("bar")) + tm.assert_index_equal(result, left.rename(None)) + + result = left[:-2].symmetric_difference(left[2:]) + expected = Index([1, 2, 8, 9], name="foo") + tm.assert_index_equal(result, expected, exact=True) + + right = RangeIndex.from_range(range(10, 15)) + + result = left.symmetric_difference(right) + expected = RangeIndex.from_range(range(1, 15)) + tm.assert_index_equal(result, expected) + + result = left.symmetric_difference(right[1:]) + expected = Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14]) + tm.assert_index_equal(result, expected, exact=True) + + +def assert_range_or_not_is_rangelike(index): + """ + Check that we either have a RangeIndex or that this index *cannot* + be represented as a RangeIndex. + """ + if not isinstance(index, RangeIndex) and len(index) > 0: + diff = index[:-1] - index[1:] + assert not (diff == diff[0]).all() + + +@pytest.mark.slow +@given( + st.integers(-20, 20), + st.integers(-20, 20), + st.integers(-20, 20), + st.integers(-20, 20), + st.integers(-20, 20), + st.integers(-20, 20), +) +def test_range_difference(start1, stop1, step1, start2, stop2, step2): + # test that + # a) we match Index[int64].difference and + # b) we return RangeIndex whenever it is possible to do so. + assume(step1 != 0) + assume(step2 != 0) + + left = RangeIndex(start1, stop1, step1) + right = RangeIndex(start2, stop2, step2) + + result = left.difference(right, sort=None) + assert_range_or_not_is_rangelike(result) + + left_int64 = Index(left.to_numpy()) + right_int64 = Index(right.to_numpy()) + + alt = left_int64.difference(right_int64, sort=None) + tm.assert_index_equal(result, alt, exact="equiv") + + result = left.difference(right, sort=False) + assert_range_or_not_is_rangelike(result) + + alt = left_int64.difference(right_int64, sort=False) + tm.assert_index_equal(result, alt, exact="equiv") diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/string/__init__.py b/lib/python3.11/site-packages/pandas/tests/indexes/string/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/string/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/pandas/tests/indexes/string/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..e03498a4873ea92f69009976e94565ef7eb82965 Binary files /dev/null and b/lib/python3.11/site-packages/pandas/tests/indexes/string/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/string/__pycache__/test_astype.cpython-311.pyc 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0000000000000000000000000000000000000000..0349d85f2316707d6ecba2c2289fde49930cbbac --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/string/test_astype.py @@ -0,0 +1,21 @@ +from pandas import ( + Index, + Series, +) +import pandas._testing as tm + + +def test_astype_str_from_bytes(): + # https://github.com/pandas-dev/pandas/issues/38607 + # GH#49658 pre-2.0 Index called .values.astype(str) here, which effectively + # did a .decode() on the bytes object. In 2.0 we go through + # ensure_string_array which does f"{val}" + idx = Index(["あ", b"a"], dtype="object") + result = idx.astype(str) + expected = Index(["あ", "a"], dtype="str") + tm.assert_index_equal(result, expected) + + # while we're here, check that Series.astype behaves the same + result = Series(idx).astype(str) + expected = Series(expected, dtype="str") + tm.assert_series_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/string/test_indexing.py b/lib/python3.11/site-packages/pandas/tests/indexes/string/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..648ee47ddc34c1d4ae90bd986a283880743ac415 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/string/test_indexing.py @@ -0,0 +1,199 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import Index +import pandas._testing as tm + + +def _isnan(val): + try: + return val is not pd.NA and np.isnan(val) + except TypeError: + return False + + +def _equivalent_na(dtype, null): + if dtype.na_value is pd.NA and null is pd.NA: + return True + elif _isnan(dtype.na_value) and _isnan(null): + return True + else: + return False + + +class TestGetLoc: + def test_get_loc(self, any_string_dtype): + index = Index(["a", "b", "c"], dtype=any_string_dtype) + assert index.get_loc("b") == 1 + + def test_get_loc_raises(self, any_string_dtype): + index = Index(["a", "b", "c"], dtype=any_string_dtype) + with pytest.raises(KeyError, match="d"): + index.get_loc("d") + + def test_get_loc_invalid_value(self, any_string_dtype): + index = Index(["a", "b", "c"], dtype=any_string_dtype) + with pytest.raises(KeyError, match="1"): + index.get_loc(1) + + def test_get_loc_non_unique(self, any_string_dtype): + index = Index(["a", "b", "a"], dtype=any_string_dtype) + result = index.get_loc("a") + expected = np.array([True, False, True]) + tm.assert_numpy_array_equal(result, expected) + + def test_get_loc_non_missing(self, any_string_dtype, nulls_fixture): + index = Index(["a", "b", "c"], dtype=any_string_dtype) + with pytest.raises(KeyError): + index.get_loc(nulls_fixture) + + def test_get_loc_missing(self, any_string_dtype, nulls_fixture): + index = Index(["a", "b", nulls_fixture], dtype=any_string_dtype) + assert index.get_loc(nulls_fixture) == 2 + + +class TestGetIndexer: + @pytest.mark.parametrize( + "method,expected", + [ + ("pad", [-1, 0, 1, 1]), + ("backfill", [0, 0, 1, -1]), + ], + ) + def test_get_indexer_strings(self, any_string_dtype, method, expected): + expected = np.array(expected, dtype=np.intp) + index = Index(["b", "c"], dtype=any_string_dtype) + actual = index.get_indexer(["a", "b", "c", "d"], method=method) + + tm.assert_numpy_array_equal(actual, expected) + + def test_get_indexer_strings_raises(self, any_string_dtype): + index = Index(["b", "c"], dtype=any_string_dtype) + + msg = "|".join( + [ + "operation 'sub' not supported for dtype 'str", + r"unsupported operand type\(s\) for -: 'str' and 'str'", + ] + ) + with pytest.raises(TypeError, match=msg): + index.get_indexer(["a", "b", "c", "d"], method="nearest") + + with pytest.raises(TypeError, match=msg): + index.get_indexer(["a", "b", "c", "d"], method="pad", tolerance=2) + + with pytest.raises(TypeError, match=msg): + index.get_indexer( + ["a", "b", "c", "d"], method="pad", tolerance=[2, 2, 2, 2] + ) + + @pytest.mark.parametrize("null", [None, np.nan, float("nan"), pd.NA]) + def test_get_indexer_missing(self, any_string_dtype, null, using_infer_string): + # NaT and Decimal("NaN") from null_fixture are not supported for string dtype + index = Index(["a", "b", null], dtype=any_string_dtype) + result = index.get_indexer(["a", null, "c"]) + if using_infer_string: + expected = np.array([0, 2, -1], dtype=np.intp) + elif any_string_dtype == "string" and not _equivalent_na( + any_string_dtype, null + ): + expected = np.array([0, -1, -1], dtype=np.intp) + else: + expected = np.array([0, 2, -1], dtype=np.intp) + + tm.assert_numpy_array_equal(result, expected) + + +class TestGetIndexerNonUnique: + @pytest.mark.parametrize("null", [None, np.nan, float("nan"), pd.NA]) + def test_get_indexer_non_unique_nas( + self, any_string_dtype, null, using_infer_string + ): + index = Index(["a", "b", null], dtype=any_string_dtype) + indexer, missing = index.get_indexer_non_unique(["a", null]) + + if using_infer_string: + expected_indexer = np.array([0, 2], dtype=np.intp) + expected_missing = np.array([], dtype=np.intp) + elif any_string_dtype == "string" and not _equivalent_na( + any_string_dtype, null + ): + expected_indexer = np.array([0, -1], dtype=np.intp) + expected_missing = np.array([1], dtype=np.intp) + else: + expected_indexer = np.array([0, 2], dtype=np.intp) + expected_missing = np.array([], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected_indexer) + tm.assert_numpy_array_equal(missing, expected_missing) + + # actually non-unique + index = Index(["a", null, "b", null], dtype=any_string_dtype) + indexer, missing = index.get_indexer_non_unique(["a", null]) + + if using_infer_string: + expected_indexer = np.array([0, 1, 3], dtype=np.intp) + elif any_string_dtype == "string" and not _equivalent_na( + any_string_dtype, null + ): + pass + else: + expected_indexer = np.array([0, 1, 3], dtype=np.intp) + tm.assert_numpy_array_equal(indexer, expected_indexer) + tm.assert_numpy_array_equal(missing, expected_missing) + + +class TestSliceLocs: + @pytest.mark.parametrize( + "in_slice,expected", + [ + # error: Slice index must be an integer or None + (pd.IndexSlice[::-1], "yxdcb"), + (pd.IndexSlice["b":"y":-1], ""), # type: ignore[misc] + (pd.IndexSlice["b"::-1], "b"), # type: ignore[misc] + (pd.IndexSlice[:"b":-1], "yxdcb"), # type: ignore[misc] + (pd.IndexSlice[:"y":-1], "y"), # type: ignore[misc] + (pd.IndexSlice["y"::-1], "yxdcb"), # type: ignore[misc] + (pd.IndexSlice["y"::-4], "yb"), # type: ignore[misc] + # absent labels + (pd.IndexSlice[:"a":-1], "yxdcb"), # type: ignore[misc] + (pd.IndexSlice[:"a":-2], "ydb"), # type: ignore[misc] + (pd.IndexSlice["z"::-1], "yxdcb"), # type: ignore[misc] + (pd.IndexSlice["z"::-3], "yc"), # type: ignore[misc] + (pd.IndexSlice["m"::-1], "dcb"), # type: ignore[misc] + (pd.IndexSlice[:"m":-1], "yx"), # type: ignore[misc] + (pd.IndexSlice["a":"a":-1], ""), # type: ignore[misc] + (pd.IndexSlice["z":"z":-1], ""), # type: ignore[misc] + (pd.IndexSlice["m":"m":-1], ""), # type: ignore[misc] + ], + ) + def test_slice_locs_negative_step(self, in_slice, expected, any_string_dtype): + index = Index(list("bcdxy"), dtype=any_string_dtype) + + s_start, s_stop = index.slice_locs(in_slice.start, in_slice.stop, in_slice.step) + result = index[s_start : s_stop : in_slice.step] + expected = Index(list(expected), dtype=any_string_dtype) + tm.assert_index_equal(result, expected) + + def test_slice_locs_negative_step_oob(self, any_string_dtype): + index = Index(list("bcdxy"), dtype=any_string_dtype) + + result = index[-10:5:1] 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days", periods=4, freq="D", name="idx") + expected_list = [ + Timedelta("1 days"), + Timedelta("2 days"), + Timedelta("3 days"), + Timedelta("4 days"), + ] + result = idx.astype(object) + expected = Index(expected_list, dtype=object, name="idx") + tm.assert_index_equal(result, expected) + assert idx.tolist() == expected_list + + def test_astype_object_with_nat(self): + idx = TimedeltaIndex( + [timedelta(days=1), timedelta(days=2), NaT, timedelta(days=4)], name="idx" + ) + expected_list = [ + Timedelta("1 days"), + Timedelta("2 days"), + NaT, + Timedelta("4 days"), + ] + result = idx.astype(object) + expected = Index(expected_list, dtype=object, name="idx") + tm.assert_index_equal(result, expected) + assert idx.tolist() == expected_list + + def test_astype(self, using_infer_string): + # GH 13149, GH 13209 + idx = TimedeltaIndex([1e14, "NaT", NaT, np.nan], name="idx") + + result = idx.astype(object) + expected = Index( + [Timedelta("1 days 03:46:40")] + [NaT] * 3, dtype=object, name="idx" + ) + tm.assert_index_equal(result, expected) + + result = idx.astype(np.int64) + expected = Index( + [100000000000000] + [-9223372036854775808] * 3, dtype=np.int64, name="idx" + ) + tm.assert_index_equal(result, expected) + + result = idx.astype(str) + if using_infer_string: + expected = Index( + [str(x) if x is not NaT else None for x in idx], name="idx", dtype="str" + ) + else: + expected = Index([str(x) for x in idx], name="idx", dtype=object) + tm.assert_index_equal(result, expected) + + rng = timedelta_range("1 days", periods=10) + result = rng.astype("i8") + tm.assert_index_equal(result, Index(rng.asi8)) + tm.assert_numpy_array_equal(rng.asi8, result.values) + + def test_astype_uint(self): + arr = timedelta_range("1h", periods=2) + + with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"): + arr.astype("uint64") + with pytest.raises(TypeError, match=r"Do obj.astype\('int64'\)"): + arr.astype("uint32") + + def test_astype_timedelta64(self): + # GH 13149, GH 13209 + idx = TimedeltaIndex([1e14, "NaT", NaT, np.nan]) + + msg = ( + r"Cannot convert from timedelta64\[ns\] to timedelta64. " + "Supported resolutions are 's', 'ms', 'us', 'ns'" + ) + with pytest.raises(ValueError, match=msg): + idx.astype("timedelta64") + + result = idx.astype("timedelta64[ns]") + tm.assert_index_equal(result, idx) + assert result is not idx + + result = idx.astype("timedelta64[ns]", copy=False) + tm.assert_index_equal(result, idx) + assert result is idx + + def test_astype_to_td64d_raises(self, index_or_series): + # We don't support "D" reso + scalar = Timedelta(days=31) + td = index_or_series( + [scalar, scalar, scalar + timedelta(minutes=5, seconds=3), NaT], + dtype="m8[ns]", + ) + msg = ( + r"Cannot convert from timedelta64\[ns\] to timedelta64\[D\]. " + "Supported resolutions are 's', 'ms', 'us', 'ns'" + ) + with pytest.raises(ValueError, match=msg): + td.astype("timedelta64[D]") + + def test_astype_ms_to_s(self, index_or_series): + scalar = Timedelta(days=31) + td = index_or_series( + [scalar, scalar, scalar + timedelta(minutes=5, seconds=3), NaT], + dtype="m8[ns]", + ) + + exp_values = np.asarray(td).astype("m8[s]") + exp_tda = TimedeltaArray._simple_new(exp_values, dtype=exp_values.dtype) + expected = index_or_series(exp_tda) + assert expected.dtype == "m8[s]" + result = td.astype("timedelta64[s]") + tm.assert_equal(result, expected) + + def test_astype_freq_conversion(self): + # pre-2.0 td64 astype converted to float64. now for supported units + # (s, ms, us, ns) this converts to the requested dtype. + # This matches TDA and Series + tdi = timedelta_range("1 Day", periods=30) + + res = tdi.astype("m8[s]") + exp_values = np.asarray(tdi).astype("m8[s]") + exp_tda = TimedeltaArray._simple_new( + exp_values, dtype=exp_values.dtype, freq=tdi.freq + ) + expected = Index(exp_tda) + assert expected.dtype == "m8[s]" + tm.assert_index_equal(res, expected) + + # check this matches Series and TimedeltaArray + res = tdi._data.astype("m8[s]") + tm.assert_equal(res, expected._values) + + res = tdi.to_series().astype("m8[s]") + tm.assert_equal(res._values, expected._values._with_freq(None)) + + @pytest.mark.parametrize("dtype", [float, "datetime64", "datetime64[ns]"]) + def test_astype_raises(self, dtype): + # GH 13149, GH 13209 + idx = TimedeltaIndex([1e14, "NaT", NaT, np.nan]) + msg = "Cannot cast TimedeltaIndex to dtype" + with pytest.raises(TypeError, match=msg): + idx.astype(dtype) + + def test_astype_category(self): + obj = timedelta_range("1h", periods=2, freq="h") + + result = obj.astype("category") + expected = pd.CategoricalIndex([Timedelta("1h"), Timedelta("2h")]) + tm.assert_index_equal(result, expected) + + result = obj._data.astype("category") + expected = expected.values + tm.assert_categorical_equal(result, expected) + + def test_astype_array_fallback(self): + obj = timedelta_range("1h", periods=2) + result = obj.astype(bool) + expected = Index(np.array([True, True])) + tm.assert_index_equal(result, expected) + + result = obj._data.astype(bool) + expected = np.array([True, True]) + tm.assert_numpy_array_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py new file mode 100644 index 0000000000000000000000000000000000000000..24ab3888412d08b54543ed22910c67ce9bdf328f --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py @@ -0,0 +1,40 @@ +import numpy as np + +from pandas import ( + TimedeltaIndex, + factorize, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndexFactorize: + def test_factorize(self): + idx1 = TimedeltaIndex(["1 day", "1 day", "2 day", "2 day", "3 day", "3 day"]) + + exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp) + exp_idx = TimedeltaIndex(["1 day", "2 day", "3 day"]) + + arr, idx = idx1.factorize() + tm.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, exp_idx) + assert idx.freq == exp_idx.freq + + arr, idx = idx1.factorize(sort=True) + tm.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, exp_idx) + assert idx.freq == exp_idx.freq + + def test_factorize_preserves_freq(self): + # GH#38120 freq should be preserved + idx3 = timedelta_range("1 day", periods=4, freq="s") + exp_arr = np.array([0, 1, 2, 3], dtype=np.intp) + arr, idx = idx3.factorize() + tm.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, idx3) + assert idx.freq == idx3.freq + + arr, idx = factorize(idx3) + tm.assert_numpy_array_equal(arr, exp_arr) + tm.assert_index_equal(idx, idx3) + assert idx.freq == idx3.freq diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_fillna.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_fillna.py new file mode 100644 index 0000000000000000000000000000000000000000..40aa95d0a46058d2dc3fc5208ca39328d96b23fb --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_fillna.py @@ -0,0 +1,22 @@ +from pandas import ( + Index, + NaT, + Timedelta, + TimedeltaIndex, +) +import pandas._testing as tm + + +class TestFillNA: + def test_fillna_timedelta(self): + # GH#11343 + idx = TimedeltaIndex(["1 day", NaT, "3 day"]) + + exp = TimedeltaIndex(["1 day", "2 day", "3 day"]) + tm.assert_index_equal(idx.fillna(Timedelta("2 day")), exp) + + exp = TimedeltaIndex(["1 day", "3 hour", "3 day"]) + idx.fillna(Timedelta("3 hour")) + + exp = Index([Timedelta("1 day"), "x", Timedelta("3 day")], dtype=object) + tm.assert_index_equal(idx.fillna("x"), exp) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py new file mode 100644 index 0000000000000000000000000000000000000000..c70a0956e825147941cd31dd20ff816f00276816 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_insert.py @@ -0,0 +1,145 @@ +from datetime import timedelta + +import numpy as np +import pytest + +from pandas._libs import lib + +import pandas as pd +from pandas import ( + Index, + Timedelta, + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndexInsert: + def test_insert(self): + idx = TimedeltaIndex(["4day", "1day", "2day"], name="idx") + + result = idx.insert(2, timedelta(days=5)) + exp = TimedeltaIndex(["4day", "1day", "5day", "2day"], name="idx") + tm.assert_index_equal(result, exp) + + # insertion of non-datetime should coerce to object index + result = idx.insert(1, "inserted") + expected = Index( + [Timedelta("4day"), "inserted", Timedelta("1day"), Timedelta("2day")], + name="idx", + ) + assert not isinstance(result, TimedeltaIndex) + tm.assert_index_equal(result, expected) + assert result.name == expected.name + + idx = timedelta_range("1day 00:00:01", periods=3, freq="s", name="idx") + + # preserve freq + expected_0 = TimedeltaIndex( + ["1day", "1day 00:00:01", "1day 00:00:02", "1day 00:00:03"], + name="idx", + freq="s", + ) + expected_3 = TimedeltaIndex( + ["1day 00:00:01", "1day 00:00:02", "1day 00:00:03", "1day 00:00:04"], + name="idx", + freq="s", + ) + + # reset freq to None + expected_1_nofreq = TimedeltaIndex( + ["1day 00:00:01", "1day 00:00:01", "1day 00:00:02", "1day 00:00:03"], + name="idx", + freq=None, + ) + expected_3_nofreq = TimedeltaIndex( + ["1day 00:00:01", "1day 00:00:02", "1day 00:00:03", "1day 00:00:05"], + name="idx", + freq=None, + ) + + cases = [ + (0, Timedelta("1day"), expected_0), + (-3, Timedelta("1day"), expected_0), + (3, Timedelta("1day 00:00:04"), expected_3), + (1, Timedelta("1day 00:00:01"), expected_1_nofreq), + (3, Timedelta("1day 00:00:05"), expected_3_nofreq), + ] + + for n, d, expected in cases: + result = idx.insert(n, d) + tm.assert_index_equal(result, expected) + assert result.name == expected.name + assert result.freq == expected.freq + + @pytest.mark.parametrize( + "null", [None, np.nan, np.timedelta64("NaT", "ns"), pd.NaT, pd.NA] + ) + def test_insert_nat(self, null): + # GH 18295 (test missing) + idx = timedelta_range("1day", "3day") + result = idx.insert(1, null) + expected = TimedeltaIndex(["1day", pd.NaT, "2day", "3day"]) + tm.assert_index_equal(result, expected) + + def test_insert_invalid_na(self): + idx = TimedeltaIndex(["4day", "1day", "2day"], name="idx") + + item = np.datetime64("NaT", "ns") + result = idx.insert(0, item) + + expected = Index([item, *list(idx)], dtype=object, name="idx") + tm.assert_index_equal(result, expected) + + # Also works if we pass a different dt64nat object + item2 = np.datetime64("NaT", "ns") + result = idx.insert(0, item2) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "item", [0, np.int64(0), np.float64(0), np.array(0), np.datetime64(456, "us")] + ) + def test_insert_mismatched_types_raises(self, item): + # GH#33703 dont cast these to td64 + tdi = TimedeltaIndex(["4day", "1day", "2day"], name="idx") + + result = tdi.insert(1, item) + + expected = Index( + [tdi[0], lib.item_from_zerodim(item), *list(tdi[1:])], + dtype=object, + name="idx", + ) + tm.assert_index_equal(result, expected) + + def test_insert_castable_str(self): + idx = timedelta_range("1day", "3day") + + result = idx.insert(0, "1 Day") + + expected = TimedeltaIndex([idx[0], *list(idx)]) + tm.assert_index_equal(result, expected) + + def test_insert_non_castable_str(self): + idx = timedelta_range("1day", "3day") + + result = idx.insert(0, "foo") + + expected = Index(["foo", *list(idx)], dtype=object) + tm.assert_index_equal(result, expected) + + def test_insert_empty(self): + # Corner case inserting with length zero doesn't raise IndexError + # GH#33573 for freq preservation + idx = timedelta_range("1 Day", periods=3) + td = idx[0] + + result = idx[:0].insert(0, td) + assert result.freq == "D" + + with pytest.raises(IndexError, match="loc must be an integer between"): + result = idx[:0].insert(1, td) + + with pytest.raises(IndexError, match="loc must be an integer between"): + result = idx[:0].insert(-1, td) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_repeat.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_repeat.py new file mode 100644 index 0000000000000000000000000000000000000000..2a9b58d1bf322938e9344d0cbacfaa79674fcf0e --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_repeat.py @@ -0,0 +1,34 @@ +import numpy as np + +from pandas import ( + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + + +class TestRepeat: + def test_repeat(self): + index = timedelta_range("1 days", periods=2, freq="D") + exp = TimedeltaIndex(["1 days", "1 days", "2 days", "2 days"]) + for res in [index.repeat(2), np.repeat(index, 2)]: + tm.assert_index_equal(res, exp) + assert res.freq is None + + index = TimedeltaIndex(["1 days", "NaT", "3 days"]) + exp = TimedeltaIndex( + [ + "1 days", + "1 days", + "1 days", + "NaT", + "NaT", + "NaT", + "3 days", + "3 days", + "3 days", + ] + ) + for res in [index.repeat(3), np.repeat(index, 3)]: + tm.assert_index_equal(res, exp) + assert res.freq is None diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_shift.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_shift.py new file mode 100644 index 0000000000000000000000000000000000000000..166eeb671867f79be62c31423a3ec8bddd70209a --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/methods/test_shift.py @@ -0,0 +1,78 @@ +import pytest + +from pandas.errors import NullFrequencyError + +import pandas as pd +from pandas import TimedeltaIndex +import pandas._testing as tm + + +class TestTimedeltaIndexShift: + # ------------------------------------------------------------- + # TimedeltaIndex.shift is used by __add__/__sub__ + + def test_tdi_shift_empty(self): + # GH#9903 + idx = TimedeltaIndex([], name="xxx") + tm.assert_index_equal(idx.shift(0, freq="h"), idx) + tm.assert_index_equal(idx.shift(3, freq="h"), idx) + + def test_tdi_shift_hours(self): + # GH#9903 + idx = TimedeltaIndex(["5 hours", "6 hours", "9 hours"], name="xxx") + tm.assert_index_equal(idx.shift(0, freq="h"), idx) + exp = TimedeltaIndex(["8 hours", "9 hours", "12 hours"], name="xxx") + tm.assert_index_equal(idx.shift(3, freq="h"), exp) + exp = TimedeltaIndex(["2 hours", "3 hours", "6 hours"], name="xxx") + tm.assert_index_equal(idx.shift(-3, freq="h"), exp) + + def test_tdi_shift_minutes(self): + # GH#9903 + idx = TimedeltaIndex(["5 hours", "6 hours", "9 hours"], name="xxx") + tm.assert_index_equal(idx.shift(0, freq="min"), idx) + exp = TimedeltaIndex(["05:03:00", "06:03:00", "9:03:00"], name="xxx") + tm.assert_index_equal(idx.shift(3, freq="min"), exp) + exp = TimedeltaIndex(["04:57:00", "05:57:00", "8:57:00"], name="xxx") + tm.assert_index_equal(idx.shift(-3, freq="min"), exp) + + def test_tdi_shift_int(self): + # GH#8083 + tdi = pd.to_timedelta(range(5), unit="D") + trange = tdi._with_freq("infer") + pd.offsets.Hour(1) + result = trange.shift(1) + expected = TimedeltaIndex( + [ + "1 days 01:00:00", + "2 days 01:00:00", + "3 days 01:00:00", + "4 days 01:00:00", + "5 days 01:00:00", + ], + freq="D", + dtype="m8[s]", + ) + tm.assert_index_equal(result, expected) + + def test_tdi_shift_nonstandard_freq(self): + # GH#8083 + tdi = pd.to_timedelta(range(5), unit="D") + trange = tdi._with_freq("infer") + pd.offsets.Hour(1) + result = trange.shift(3, freq="2D 1s") + expected = TimedeltaIndex( + [ + "6 days 01:00:03", + "7 days 01:00:03", + "8 days 01:00:03", + "9 days 01:00:03", + "10 days 01:00:03", + ], + freq="D", + dtype="m8[s]", + ) + tm.assert_index_equal(result, expected) + + def test_shift_no_freq(self): + # GH#19147 + tdi = TimedeltaIndex(["1 days 01:00:00", "2 days 01:00:00"], freq=None) + with pytest.raises(NullFrequencyError, match="Cannot shift with no freq"): + tdi.shift(2) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_arithmetic.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_arithmetic.py new file mode 100644 index 0000000000000000000000000000000000000000..a431e10dc18ab15da0fd07f798d54b6dead26073 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_arithmetic.py @@ -0,0 +1,51 @@ +# Arithmetic tests for TimedeltaIndex are generally about the result's `freq` attribute. +# Other cases can be shared in tests.arithmetic.test_timedelta64 +import numpy as np + +from pandas import ( + NaT, + Timedelta, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndexArithmetic: + def test_arithmetic_zero_freq(self): + # GH#51575 don't get a .freq with freq.n = 0 + tdi = timedelta_range(0, periods=100, freq="ns") + result = tdi / 2 + assert result.freq is None + expected = tdi[:50].repeat(2) + tm.assert_index_equal(result, expected) + + result2 = tdi // 2 + assert result2.freq is None + expected2 = expected + tm.assert_index_equal(result2, expected2) + + result3 = tdi * 0 + assert result3.freq is None + expected3 = tdi[:1].repeat(100) + tm.assert_index_equal(result3, expected3) + + def test_tdi_division(self, index_or_series): + # doc example + + scalar = Timedelta(days=31) + td = index_or_series( + [scalar, scalar, scalar + Timedelta(minutes=5, seconds=3), NaT], + dtype="m8[ns]", + ) + + result = td / np.timedelta64(1, "D") + expected = index_or_series( + [31, 31, (31 * 86400 + 5 * 60 + 3) / 86400.0, np.nan] + ) + tm.assert_equal(result, expected) + + result = td / np.timedelta64(1, "s") + expected = index_or_series( + [31 * 86400, 31 * 86400, 31 * 86400 + 5 * 60 + 3, np.nan] + ) + tm.assert_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_constructors.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_constructors.py new file mode 100644 index 0000000000000000000000000000000000000000..a0be8f4b3c74c26fd67e7da4dc8fa5850e6596a3 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_constructors.py @@ -0,0 +1,271 @@ +from datetime import timedelta + +import numpy as np +import pytest + +from pandas.errors import Pandas4Warning + +import pandas as pd +from pandas import ( + Timedelta, + TimedeltaIndex, + timedelta_range, + to_timedelta, +) +import pandas._testing as tm +from pandas.core.arrays.timedeltas import TimedeltaArray + + +class TestTimedeltaIndex: + def test_array_of_dt64_nat_raises(self): + # GH#39462 + nat = np.datetime64("NaT", "ns") + arr = np.array([nat], dtype=object) + + msg = "Invalid type for timedelta scalar" + with pytest.raises(TypeError, match=msg): + TimedeltaIndex(arr) + + with pytest.raises(TypeError, match=msg): + TimedeltaArray._from_sequence(arr, dtype="m8[ns]") + + with pytest.raises(TypeError, match=msg): + to_timedelta(arr) + + def test_int64_nocopy(self): + # GH#23539 check that a copy isn't made when we pass int64 data + # and copy=False + arr = np.arange(10, dtype=np.int64) + tdi = TimedeltaIndex(arr, copy=False) + assert tdi._data._ndarray.base is arr + + def test_infer_from_tdi(self): + # GH#23539 + # fast-path for inferring a frequency if the passed data already + # has one + tdi = timedelta_range("1 second", periods=10**7, freq="1s") + + result = TimedeltaIndex(tdi, freq="infer") + assert result.freq == tdi.freq + + # check that inferred_freq was not called by checking that the + # value has not been cached + assert "inferred_freq" not in getattr(result, "_cache", {}) + + def test_infer_from_tdi_mismatch(self): + # GH#23539 + # fast-path for invalidating a frequency if the passed data already + # has one and it does not match the `freq` input + tdi = timedelta_range("1 second", periods=100, freq="1s") + + msg = ( + "Inferred frequency .* from passed values does " + "not conform to passed frequency" + ) + with pytest.raises(ValueError, match=msg): + TimedeltaIndex(tdi, freq="D") + + with pytest.raises(ValueError, match=msg): + TimedeltaIndex(tdi._data, freq="D") + + def test_dt64_data_invalid(self): + # GH#23539 + # passing tz-aware DatetimeIndex raises, naive or ndarray[datetime64] + # raise as of GH#29794 + dti = pd.date_range("2016-01-01", periods=3) + + msg = "cannot be converted to timedelta64" + with pytest.raises(TypeError, match=msg): + TimedeltaIndex(dti.tz_localize("Europe/Brussels")) + + with pytest.raises(TypeError, match=msg): + TimedeltaIndex(dti) + + with pytest.raises(TypeError, match=msg): + TimedeltaIndex(np.asarray(dti)) + + def test_float64_ns_rounded(self): + # GH#23539 without specifying a unit, floats are regarded as nanos, + # and fractional portions are truncated + tdi = TimedeltaIndex([2.3, 9.7]) + expected = TimedeltaIndex([2, 9]) + tm.assert_index_equal(tdi, expected) + + # integral floats are non-lossy + tdi = TimedeltaIndex([2.0, 9.0]) + expected = TimedeltaIndex([2, 9]) + tm.assert_index_equal(tdi, expected) + + # NaNs get converted to NaT + tdi = TimedeltaIndex([2.0, np.nan]) + expected = TimedeltaIndex([Timedelta(nanoseconds=2), pd.NaT]) + tm.assert_index_equal(tdi, expected) + + def test_float64_unit_conversion(self): + # GH#23539 + tdi = to_timedelta([1.5, 2.25], unit="D") + expected = TimedeltaIndex( + [Timedelta(days=1.5), Timedelta(days=2.25)], dtype="m8[ns]" + ) + tm.assert_index_equal(tdi, expected) + + def test_construction_base_constructor(self): + arr = [Timedelta("1 days"), pd.NaT, Timedelta("3 days")] + tm.assert_index_equal(pd.Index(arr), TimedeltaIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), TimedeltaIndex(np.array(arr))) + + arr = [np.nan, pd.NaT, Timedelta("1 days")] + tm.assert_index_equal(pd.Index(arr), TimedeltaIndex(arr)) + tm.assert_index_equal(pd.Index(np.array(arr)), TimedeltaIndex(np.array(arr))) + + def test_constructor(self): + expected = TimedeltaIndex( + [ + "1 days", + "1 days 00:00:05", + "2 days", + "2 days 00:00:02", + "0 days 00:00:03", + ] + ) + result = TimedeltaIndex( + [ + "1 days", + "1 days, 00:00:05", + np.timedelta64(2, "D"), + timedelta(days=2, seconds=2), + pd.offsets.Second(3), + ] + ) + tm.assert_index_equal(result, expected) + + def test_constructor_iso(self): + # GH #21877 + expected = timedelta_range("1s", periods=9, freq="s") + durations = [f"P0DT0H0M{i}S" for i in range(1, 10)] + result = to_timedelta(durations) + tm.assert_index_equal(result, expected) + + def test_timedelta_range_fractional_period(self): + msg = "periods must be an integer" + with pytest.raises(TypeError, match=msg): + timedelta_range("1 days", periods=10.5) + + def test_constructor_coverage(self): + msg = "periods must be an integer, got foo" + with pytest.raises(TypeError, match=msg): + timedelta_range(start="1 days", periods="foo", freq="D") + + msg = ( + r"TimedeltaIndex\(\.\.\.\) must be called with a collection of some kind, " + "'1 days' was passed" + ) + with pytest.raises(TypeError, match=msg): + TimedeltaIndex("1 days") + + # generator expression + gen = (timedelta(i) for i in range(10)) + result = TimedeltaIndex(gen) + expected = TimedeltaIndex([timedelta(i) for i in range(10)]) + tm.assert_index_equal(result, expected) + + # NumPy string array + strings = np.array(["1 days", "2 days", "3 days"]) + result = TimedeltaIndex(strings) + expected = to_timedelta([1, 2, 3], unit="D").as_unit("us") + tm.assert_index_equal(result, expected) + + from_ints = TimedeltaIndex(expected.as_unit("ns").asi8) + tm.assert_index_equal(from_ints, expected.as_unit("ns")) + + # non-conforming freq + msg = ( + "Inferred frequency None from passed values does not conform to " + "passed frequency D" + ) + with pytest.raises(ValueError, match=msg): + TimedeltaIndex(["1 days", "2 days", "4 days"], freq="D") + + msg = ( + "Of the four parameters: start, end, periods, and freq, exactly " + "three must be specified" + ) + with pytest.raises(ValueError, match=msg): + timedelta_range(periods=10, freq="D") + + def test_constructor_name(self): + idx = timedelta_range(start="1 days", periods=1, freq="D", name="TEST") + assert idx.name == "TEST" + + # GH10025 + idx2 = TimedeltaIndex(idx, name="something else") + assert idx2.name == "something else" + + def test_constructor_no_precision_raises(self): + # GH-24753, GH-24739 + + msg = "with no precision is not allowed" + with pytest.raises(ValueError, match=msg): + TimedeltaIndex(["2000"], dtype="timedelta64") + + msg = "The 'timedelta64' dtype has no unit. Please pass in" + with pytest.raises(ValueError, match=msg): + pd.Index(["2000"], dtype="timedelta64") + + def test_constructor_wrong_precision_raises(self): + msg = "Supported timedelta64 resolutions are 's', 'ms', 'us', 'ns'" + with pytest.raises(ValueError, match=msg): + TimedeltaIndex(["2000"], dtype="timedelta64[D]") + + # "timedelta64[us]" was unsupported pre-2.0, but now this works. + tdi = TimedeltaIndex(["2000"], dtype="timedelta64[us]") + assert tdi.dtype == "m8[us]" + + def test_explicit_none_freq(self): + # Explicitly passing freq=None is respected + tdi = timedelta_range(1, periods=5) + assert tdi.freq is not None + + result = TimedeltaIndex(tdi, freq=None) + assert result.freq is None + + result = TimedeltaIndex(tdi._data, freq=None) + assert result.freq is None + + def test_from_categorical(self): + tdi = timedelta_range(1, periods=5) + + cat = pd.Categorical(tdi) + + result = TimedeltaIndex(cat) + tm.assert_index_equal(result, tdi) + + ci = pd.CategoricalIndex(tdi) + result = TimedeltaIndex(ci) + tm.assert_index_equal(result, tdi) + + @pytest.mark.parametrize( + "unit,unit_depr", + [ + ("W", "w"), + ("D", "d"), + ("min", "MIN"), + ("s", "S"), + ("h", "H"), + ("ms", "MS"), + ("us", "US"), + ], + ) + def test_unit_deprecated(self, unit, unit_depr): + # GH#52536, GH#59051 + msg = f"'{unit_depr}' is deprecated and will be removed in a future version." + + expected = TimedeltaIndex([f"1{unit}", f"2{unit}"]) + with tm.assert_produces_warning(Pandas4Warning, match=msg): + result = TimedeltaIndex([f"1{unit_depr}", f"2{unit_depr}"]) + tm.assert_index_equal(result, expected) + + with tm.assert_produces_warning(Pandas4Warning, match=msg): + tdi = to_timedelta([1, 2], unit=unit_depr) + exp_unit = unit if unit in ["s", "ms", "us"] else "s" + tm.assert_index_equal(tdi, expected.as_unit(exp_unit)) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_delete.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_delete.py new file mode 100644 index 0000000000000000000000000000000000000000..f49af7cd0befda87b591ce3f1d12c00f5b012635 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_delete.py @@ -0,0 +1,71 @@ +from pandas import ( + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndexDelete: + def test_delete(self): + idx = timedelta_range(start="1 Days", periods=5, freq="D", name="idx") + + # preserve freq + expected_0 = timedelta_range(start="2 Days", periods=4, freq="D", name="idx") + expected_4 = timedelta_range(start="1 Days", periods=4, freq="D", name="idx") + + # reset freq to None + expected_1 = TimedeltaIndex( + ["1 day", "3 day", "4 day", "5 day"], freq=None, name="idx" + ) + + cases = { + 0: expected_0, + -5: expected_0, + -1: expected_4, + 4: expected_4, + 1: expected_1, + } + for n, expected in cases.items(): + result = idx.delete(n) + tm.assert_index_equal(result, expected) + assert result.name == expected.name + assert result.freq == expected.freq + + with tm.external_error_raised((IndexError, ValueError)): + # either depending on numpy version + idx.delete(5) + + def test_delete_slice(self): + idx = timedelta_range(start="1 days", periods=10, freq="D", name="idx") + + # preserve freq + expected_0_2 = timedelta_range(start="4 days", periods=7, freq="D", name="idx") + expected_7_9 = timedelta_range(start="1 days", periods=7, freq="D", name="idx") + + # reset freq to None + expected_3_5 = TimedeltaIndex( + ["1 D", "2 D", "3 D", "7 D", "8 D", "9 D", "10D"], freq=None, name="idx" + ) + + cases = { + (0, 1, 2): expected_0_2, + (7, 8, 9): expected_7_9, + (3, 4, 5): expected_3_5, + } + for n, expected in cases.items(): + result = idx.delete(n) + tm.assert_index_equal(result, expected) + assert result.name == expected.name + assert result.freq == expected.freq + + result = idx.delete(slice(n[0], n[-1] + 1)) + tm.assert_index_equal(result, expected) + assert result.name == expected.name + assert result.freq == expected.freq + + def test_delete_doesnt_infer_freq(self): + # GH#30655 behavior matches DatetimeIndex + + tdi = TimedeltaIndex(["1 Day", "2 Days", None, "3 Days", "4 Days"]) + result = tdi.delete(2) + assert result.freq is None diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_formats.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..0ec4c999aa838c0b9550b14fcd877a64efd6404d --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_formats.py @@ -0,0 +1,106 @@ +import pytest + +import pandas as pd +from pandas import ( + Series, + TimedeltaIndex, +) + + +class TestTimedeltaIndexRendering: + def test_repr_round_days_non_nano(self): + # GH#55405 + # we should get "1 days", not "1 days 00:00:00" with non-nano + tdi = TimedeltaIndex(["1 days"], freq="D").as_unit("s") + result = repr(tdi) + expected = "TimedeltaIndex(['1 days'], dtype='timedelta64[s]', freq='D')" + assert result == expected + + result2 = repr(Series(tdi)) + expected2 = "0 1 days\ndtype: timedelta64[s]" + assert result2 == expected2 + + @pytest.mark.parametrize("method", ["__repr__", "__str__"]) + def test_representation(self, method): + idx1 = TimedeltaIndex([], freq="D", dtype="m8[ns]") + idx2 = TimedeltaIndex(["1 days"], freq="D") + idx3 = TimedeltaIndex(["1 days", "2 days"], freq="D") + idx4 = TimedeltaIndex(["1 days", "2 days", "3 days"], freq="D") + idx5 = TimedeltaIndex(["1 days 00:00:01", "2 days", "3 days"]) + + exp1 = "TimedeltaIndex([], dtype='timedelta64[ns]', freq='D')" + + exp2 = "TimedeltaIndex(['1 days'], dtype='timedelta64[us]', freq='D')" + + exp3 = "TimedeltaIndex(['1 days', '2 days'], dtype='timedelta64[us]', freq='D')" + + exp4 = ( + "TimedeltaIndex(['1 days', '2 days', '3 days'], " + "dtype='timedelta64[us]', freq='D')" + ) + + exp5 = ( + "TimedeltaIndex(['1 days 00:00:01', '2 days 00:00:00', " + "'3 days 00:00:00'], dtype='timedelta64[us]', freq=None)" + ) + + with pd.option_context("display.width", 300): + for idx, expected in zip( + [idx1, idx2, idx3, idx4, idx5], [exp1, exp2, exp3, exp4, exp5] + ): + result = getattr(idx, method)() + assert result == expected + + # TODO: this is a Series.__repr__ test + def test_representation_to_series(self): + idx1 = TimedeltaIndex([], freq="D", dtype="m8[ns]") + idx2 = TimedeltaIndex(["1 days"], freq="D") + idx3 = TimedeltaIndex(["1 days", "2 days"], freq="D") + idx4 = TimedeltaIndex(["1 days", "2 days", "3 days"], freq="D") + idx5 = TimedeltaIndex(["1 days 00:00:01", "2 days", "3 days"]) + + exp1 = """Series([], dtype: timedelta64[ns])""" + + exp2 = "0 1 days\ndtype: timedelta64[us]" + + exp3 = "0 1 days\n1 2 days\ndtype: timedelta64[us]" + + exp4 = "0 1 days\n1 2 days\n2 3 days\ndtype: timedelta64[us]" + + exp5 = ( + "0 1 days 00:00:01\n" + "1 2 days 00:00:00\n" + "2 3 days 00:00:00\n" + "dtype: timedelta64[us]" + ) + + with pd.option_context("display.width", 300): + for idx, expected in zip( + [idx1, idx2, idx3, idx4, idx5], [exp1, exp2, exp3, exp4, exp5] + ): + result = repr(Series(idx)) + assert result == expected + + def test_summary(self): + # GH#9116 + idx1 = TimedeltaIndex([], freq="D", dtype="m8[ns]") + idx2 = TimedeltaIndex(["1 days"], freq="D") + idx3 = TimedeltaIndex(["1 days", "2 days"], freq="D") + idx4 = TimedeltaIndex(["1 days", "2 days", "3 days"], freq="D") + idx5 = TimedeltaIndex(["1 days 00:00:01", "2 days", "3 days"]) + + exp1 = "TimedeltaIndex: 0 entries\nFreq: D" + + exp2 = "TimedeltaIndex: 1 entries, 1 days to 1 days\nFreq: D" + + exp3 = "TimedeltaIndex: 2 entries, 1 days to 2 days\nFreq: D" + + exp4 = "TimedeltaIndex: 3 entries, 1 days to 3 days\nFreq: D" + + exp5 = "TimedeltaIndex: 3 entries, 1 days 00:00:01 to 3 days 00:00:00" + + for idx, expected in zip( + [idx1, idx2, idx3, idx4, idx5], [exp1, exp2, exp3, exp4, exp5] + ): + result = idx._summary() + assert result == expected diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_freq_attr.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_freq_attr.py new file mode 100644 index 0000000000000000000000000000000000000000..1912c49d3000fcbef45dd081213778bfb387e38e --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_freq_attr.py @@ -0,0 +1,72 @@ +import pytest + +from pandas import TimedeltaIndex + +from pandas.tseries.offsets import ( + DateOffset, + Day, + Hour, + MonthEnd, +) + + +class TestFreq: + @pytest.mark.parametrize("values", [["0 days", "2 days", "4 days"], []]) + @pytest.mark.parametrize("freq", ["2D", Day(2), "48h", Hour(48)]) + def test_freq_setter(self, values, freq): + # GH#20678 + idx = TimedeltaIndex(values) + + # can set to an offset, converting from string if necessary + idx._data.freq = freq + assert idx.freq == freq + assert isinstance(idx.freq, DateOffset) + + # can reset to None + idx._data.freq = None + assert idx.freq is None + + def test_with_freq_empty_requires_tick(self): + idx = TimedeltaIndex([]) + + off = MonthEnd(1) + msg = "TimedeltaArray/Index freq must be a Tick" + with pytest.raises(TypeError, match=msg): + idx._with_freq(off) + with pytest.raises(TypeError, match=msg): + idx._data._with_freq(off) + + def test_freq_setter_errors(self): + # GH#20678 + idx = TimedeltaIndex(["0 days", "2 days", "4 days"]) + + # setting with an incompatible freq + msg = ( + "Inferred frequency 2D from passed values does not conform to " + "passed frequency 5D" + ) + with pytest.raises(ValueError, match=msg): + idx._data.freq = "5D" + + # setting with a non-fixed frequency + msg = r"<2 \* BusinessDays> is a non-fixed frequency" + with pytest.raises(ValueError, match=msg): + idx._data.freq = "2B" + + # setting with non-freq string + with pytest.raises(ValueError, match="Invalid frequency"): + idx._data.freq = "foo" + + def test_freq_view_safe(self): + # Setting the freq for one TimedeltaIndex shouldn't alter the freq + # for another that views the same data + + tdi = TimedeltaIndex(["0 days", "2 days", "4 days"], freq="2D") + tda = tdi._data + + tdi2 = TimedeltaIndex(tda)._with_freq(None) + assert tdi2.freq is None + + # Original was not altered + assert tdi.freq == "2D" + assert tda.freq == "2D" diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_indexing.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..83ea239c03a1f9c0e402a4be6ecd0cdb102476f8 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_indexing.py @@ -0,0 +1,352 @@ +from datetime import datetime +import re + +import numpy as np +import pytest + +from pandas.errors import Pandas4Warning + +from pandas import ( + Index, + NaT, + Timedelta, + TimedeltaIndex, + Timestamp, + notna, + offsets, + timedelta_range, + to_timedelta, +) +import pandas._testing as tm + + +class TestGetItem: + def test_getitem_slice_keeps_name(self): + # GH#4226, GH#59051 + msg = "'d' is deprecated and will be removed in a future version." + with tm.assert_produces_warning(Pandas4Warning, match=msg): + tdi = timedelta_range("1d", "5d", freq="h", name="timebucket") + assert tdi[1:].name == tdi.name + + def test_getitem(self): + idx1 = timedelta_range("1 day", "31 day", freq="D", name="idx") + + for idx in [idx1]: + result = idx[0] + assert result == Timedelta("1 day") + + result = idx[0:5] + expected = timedelta_range("1 day", "5 day", freq="D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx[0:10:2] + expected = timedelta_range("1 day", "9 day", freq="2D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx[-20:-5:3] + expected = timedelta_range("12 day", "24 day", freq="3D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx[4::-1] + expected = TimedeltaIndex( + ["5 day", "4 day", "3 day", "2 day", "1 day"], freq="-1D", name="idx" + ) + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + @pytest.mark.parametrize( + "key", + [ + Timestamp("1970-01-01"), + Timestamp("1970-01-02"), + datetime(1970, 1, 1), + Timestamp("1970-01-03").to_datetime64(), + # non-matching NA values + np.datetime64("NaT", "ns"), + ], + ) + def test_timestamp_invalid_key(self, key): + # GH#20464 + tdi = timedelta_range(0, periods=10) + with pytest.raises(KeyError, match=re.escape(repr(key))): + tdi.get_loc(key) + + +class TestGetLoc: + def test_get_loc_key_unit_mismatch(self): + idx = to_timedelta(["0 days", "1 days", "2 days"]) + key = idx[1].as_unit("ms") + loc = idx.get_loc(key) + assert loc == 1 + + def test_get_loc_key_unit_mismatch_not_castable(self): + tdi = to_timedelta(["0 days", "1 days", "2 days"]).astype("m8[s]") + assert tdi.dtype == "m8[s]" + key = tdi[0].as_unit("ns") + Timedelta(1) + + with pytest.raises(KeyError, match=r"Timedelta\('0 days 00:00:00.000000001'\)"): + tdi.get_loc(key) + + assert key not in tdi + + def test_get_loc(self): + idx = to_timedelta(["0 days", "1 days", "2 days"]) + + # GH 16909 + assert idx.get_loc(idx[1].to_timedelta64()) == 1 + + # GH 16896 + assert idx.get_loc("0 days") == 0 + + def test_get_loc_nat(self): + tidx = TimedeltaIndex(["1 days 01:00:00", "NaT", "2 days 01:00:00"]) + + assert tidx.get_loc(NaT) == 1 + assert tidx.get_loc(None) == 1 + assert tidx.get_loc(float("nan")) == 1 + assert tidx.get_loc(np.nan) == 1 + + +class TestGetIndexer: + def test_get_indexer(self): + idx = to_timedelta(["0 days", "1 days", "2 days"]) + tm.assert_numpy_array_equal( + idx.get_indexer(idx), np.array([0, 1, 2], dtype=np.intp) + ) + + target = to_timedelta(["-1 hour", "12 hours", "1 day 1 hour"]) + tm.assert_numpy_array_equal( + idx.get_indexer(target, "pad"), np.array([-1, 0, 1], dtype=np.intp) + ) + tm.assert_numpy_array_equal( + idx.get_indexer(target, "backfill"), np.array([0, 1, 2], dtype=np.intp) + ) + tm.assert_numpy_array_equal( + idx.get_indexer(target, "nearest"), np.array([0, 1, 1], dtype=np.intp) + ) + + res = idx.get_indexer(target, "nearest", tolerance=Timedelta("1 hour")) + tm.assert_numpy_array_equal(res, np.array([0, -1, 1], dtype=np.intp)) + + +class TestWhere: + def test_where_doesnt_retain_freq(self): + tdi = timedelta_range("1 day", periods=3, freq="D", name="idx") + cond = [True, True, False] + expected = TimedeltaIndex([tdi[0], tdi[1], tdi[0]], freq=None, name="idx") + + result = tdi.where(cond, tdi[::-1]) + tm.assert_index_equal(result, expected) + + def test_where_invalid_dtypes(self, fixed_now_ts): + tdi = timedelta_range("1 day", periods=3, freq="D", name="idx") + + tail = tdi[2:].tolist() + i2 = Index([NaT, NaT, *tail]) + mask = notna(i2) + + expected = Index([NaT._value, NaT._value, *tail], dtype=object, name="idx") + assert isinstance(expected[0], int) + result = tdi.where(mask, i2.asi8) + tm.assert_index_equal(result, expected) + + ts = i2 + fixed_now_ts + expected = Index([ts[0], ts[1], *tail], dtype=object, name="idx") + result = tdi.where(mask, ts) + tm.assert_index_equal(result, expected) + + per = (i2 + fixed_now_ts).to_period("D") + expected = Index([per[0], per[1], *tail], dtype=object, name="idx") + result = tdi.where(mask, per) + tm.assert_index_equal(result, expected) + + ts = fixed_now_ts + expected = Index([ts, ts, *tail], dtype=object, name="idx") + result = tdi.where(mask, ts) + tm.assert_index_equal(result, expected) + + def test_where_mismatched_nat(self): + tdi = timedelta_range("1 day", periods=3, freq="D", name="idx") + cond = np.array([True, False, False]) + + dtnat = np.datetime64("NaT", "ns") + expected = Index([tdi[0], dtnat, dtnat], dtype=object, name="idx") + assert expected[2] is dtnat + result = tdi.where(cond, dtnat) + tm.assert_index_equal(result, expected) + + +class TestTake: + def test_take(self): + # GH 10295 + idx1 = timedelta_range("1 day", "31 day", freq="D", name="idx") + + for idx in [idx1]: + result = idx.take([0]) + assert result == Timedelta("1 day") + + result = idx.take([-1]) + assert result == Timedelta("31 day") + + result = idx.take([0, 1, 2]) + expected = timedelta_range("1 day", "3 day", freq="D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx.take([0, 2, 4]) + expected = timedelta_range("1 day", "5 day", freq="2D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx.take([7, 4, 1]) + expected = timedelta_range("8 day", "2 day", freq="-3D", name="idx") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + result = idx.take([3, 2, 5]) + expected = TimedeltaIndex(["4 day", "3 day", "6 day"], name="idx") + tm.assert_index_equal(result, expected) + assert result.freq is None + + result = idx.take([-3, 2, 5]) + expected = TimedeltaIndex(["29 day", "3 day", "6 day"], name="idx") + tm.assert_index_equal(result, expected) + assert result.freq is None + + def test_take_invalid_kwargs(self): + idx = timedelta_range("1 day", "31 day", freq="D", name="idx") + indices = [1, 6, 5, 9, 10, 13, 15, 3] + + msg = r"take\(\) got an unexpected keyword argument 'foo'" + with pytest.raises(TypeError, match=msg): + idx.take(indices, foo=2) + + msg = "the 'out' parameter is not supported" + with pytest.raises(ValueError, match=msg): + idx.take(indices, out=indices) + + msg = "the 'mode' parameter is not supported" + with pytest.raises(ValueError, match=msg): + idx.take(indices, mode="clip") + + def test_take_equiv_getitem(self): + tds = ["1day 02:00:00", "1 day 04:00:00", "1 day 10:00:00"] + idx = timedelta_range(start="1D", end="2D", freq="h", name="idx") + expected = TimedeltaIndex(tds, freq=None, name="idx") + + taken1 = idx.take([2, 4, 10]) + taken2 = idx[[2, 4, 10]] + + for taken in [taken1, taken2]: + tm.assert_index_equal(taken, expected) + assert isinstance(taken, TimedeltaIndex) + assert taken.freq is None + assert taken.name == expected.name + + def test_take_fill_value(self): + # GH 12631 + idx = TimedeltaIndex(["1 days", "2 days", "3 days"], name="xxx") + result = idx.take(np.array([1, 0, -1])) + expected = TimedeltaIndex(["2 days", "1 days", "3 days"], name="xxx") + tm.assert_index_equal(result, expected) + + # fill_value + result = idx.take(np.array([1, 0, -1]), fill_value=True) + expected = TimedeltaIndex(["2 days", "1 days", "NaT"], name="xxx") + tm.assert_index_equal(result, expected) + + # allow_fill=False + result = idx.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True) + expected = TimedeltaIndex(["2 days", "1 days", "3 days"], name="xxx") + tm.assert_index_equal(result, expected) + + msg = ( + "When allow_fill=True and fill_value is not None, all indices must be >= -1" + ) + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -2]), fill_value=True) + with pytest.raises(ValueError, match=msg): + idx.take(np.array([1, 0, -5]), fill_value=True) + + msg = "index -5 is out of bounds for (axis 0 with )?size 3" + with pytest.raises(IndexError, match=msg): + idx.take(np.array([1, -5])) + + +class TestMaybeCastSliceBound: + @pytest.fixture(params=["increasing", "decreasing", None]) + def monotonic(self, request): + return request.param + + @pytest.fixture + def tdi(self, monotonic): + tdi = timedelta_range("1 Day", periods=10) + if monotonic == "decreasing": + tdi = tdi[::-1] + elif monotonic is None: + taker = np.arange(10, dtype=np.intp) + np.random.default_rng(2).shuffle(taker) + tdi = tdi.take(taker) + return tdi + + def test_maybe_cast_slice_bound_invalid_str(self, tdi): + # test the low-level _maybe_cast_slice_bound and that we get the + # expected exception+message all the way up the stack + msg = ( + "cannot do slice indexing on TimedeltaIndex with these " + r"indexers \[foo\] of type str" + ) + with pytest.raises(TypeError, match=msg): + tdi._maybe_cast_slice_bound("foo", side="left") + with pytest.raises(TypeError, match=msg): + tdi.get_slice_bound("foo", side="left") + with pytest.raises(TypeError, match=msg): + tdi.slice_locs("foo", None, None) + + def test_slice_invalid_str_with_timedeltaindex( + self, tdi, frame_or_series, indexer_sl + ): + obj = frame_or_series(range(10), index=tdi) + + msg = ( + "cannot do slice indexing on TimedeltaIndex with these " + r"indexers \[foo\] of type str" + ) + with pytest.raises(TypeError, match=msg): + indexer_sl(obj)["foo":] + with pytest.raises(TypeError, match=msg): + indexer_sl(obj)["foo":-1] + with pytest.raises(TypeError, match=msg): + indexer_sl(obj)[:"foo"] + with pytest.raises(TypeError, match=msg): + indexer_sl(obj)[tdi[0] : "foo"] + + +class TestContains: + def test_contains_nonunique(self): + # GH#9512 + for vals in ( + [0, 1, 0], + [0, 0, -1], + [0, -1, -1], + ["00:01:00", "00:01:00", "00:02:00"], + ["00:01:00", "00:01:00", "00:00:01"], + ): + idx = TimedeltaIndex(vals) + assert idx[0] in idx + + def test_contains(self): + # Checking for any NaT-like objects + # GH#13603, GH#59051 + msg = "'d' is deprecated and will be removed in a future version." + with tm.assert_produces_warning(Pandas4Warning, match=msg): + td = to_timedelta(range(5), unit="d") + offsets.Hour(1) + for v in [NaT, None, float("nan"), np.nan]: + assert v not in td + + td = to_timedelta([NaT]) + for v in [NaT, None, float("nan"), np.nan]: + assert v in td diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_join.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_join.py new file mode 100644 index 0000000000000000000000000000000000000000..cbd7a5de71b10a6004cd7a3f798fecd8e7631750 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_join.py @@ -0,0 +1,47 @@ +import numpy as np + +from pandas import ( + DataFrame, + Index, + Timedelta, + timedelta_range, +) +import pandas._testing as tm + + +class TestJoin: + def test_append_join_nondatetimeindex(self): + rng = timedelta_range("1 days", periods=10) + idx = Index(["a", "b", "c", "d"]) + + result = rng.append(idx) + assert isinstance(result[0], Timedelta) + + # it works + rng.join(idx, how="outer") + + def test_join_self(self, join_type): + index = timedelta_range("1 day", periods=10) + joined = index.join(index, how=join_type) + tm.assert_index_equal(index, joined) + + def test_does_not_convert_mixed_integer(self): + df = DataFrame(np.ones((5, 5)), columns=timedelta_range("1 day", periods=5)) + + cols = df.columns.join(df.index, how="outer") + joined = cols.join(df.columns) + assert cols.dtype == np.dtype("O") + assert cols.dtype == joined.dtype + tm.assert_index_equal(cols, joined) + + def test_join_preserves_freq(self): + # GH#32157 + tdi = timedelta_range("1 day", periods=10) + result = tdi[:5].join(tdi[5:], how="outer") + assert result.freq == tdi.freq + tm.assert_index_equal(result, tdi) + + result = tdi[:5].join(tdi[6:], how="outer") + assert result.freq is None + expected = tdi.delete(5) + tm.assert_index_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_ops.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..f6013baf86edcd566a17cd3127467a7443ac475a --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_ops.py @@ -0,0 +1,14 @@ +from pandas import ( + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndexOps: + def test_infer_freq(self, freq_sample): + # GH#11018 + idx = timedelta_range("1", freq=freq_sample, periods=10) + result = TimedeltaIndex(idx.asi8, freq="infer") + tm.assert_index_equal(idx, result) + assert result.freq == freq_sample diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_pickle.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_pickle.py new file mode 100644 index 0000000000000000000000000000000000000000..d58e15960ebf1ea75c2e6ddb1e7ecafca2bb5e86 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_pickle.py @@ -0,0 +1,11 @@ +from pandas import timedelta_range +import pandas._testing as tm + + +class TestPickle: + def test_pickle_after_set_freq(self, temp_file): + tdi = timedelta_range("1 day", periods=4, freq="s") + tdi = tdi._with_freq(None) + + res = tm.round_trip_pickle(tdi, temp_file) + tm.assert_index_equal(res, tdi) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_scalar_compat.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_scalar_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..80c02f9b1bd38430b17dc1b1cb2c71597879a4a9 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_scalar_compat.py @@ -0,0 +1,147 @@ +""" +Tests for TimedeltaIndex methods behaving like their Timedelta counterparts +""" + +import numpy as np +import pytest + +from pandas._libs.tslibs.offsets import INVALID_FREQ_ERR_MSG +from pandas.errors import Pandas4Warning + +from pandas import ( + Index, + Series, + Timedelta, + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + + +class TestVectorizedTimedelta: + def test_tdi_total_seconds(self): + # GH#10939 + # test index + rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s") + expt = [ + 1 * 86400 + 10 * 3600 + 11 * 60 + 12 + 100123456.0 / 1e9, + 1 * 86400 + 10 * 3600 + 11 * 60 + 13 + 100123456.0 / 1e9, + ] + tm.assert_almost_equal(rng.total_seconds(), Index(expt)) + + # test Series + ser = Series(rng) + s_expt = Series(expt, index=[0, 1]) + tm.assert_series_equal(ser.dt.total_seconds(), s_expt) + + # with nat + ser[1] = np.nan + s_expt = Series( + [1 * 86400 + 10 * 3600 + 11 * 60 + 12 + 100123456.0 / 1e9, np.nan], + index=[0, 1], + ) + tm.assert_series_equal(ser.dt.total_seconds(), s_expt) + + def test_tdi_total_seconds_all_nat(self): + # with both nat + ser = Series([np.nan, np.nan], dtype="timedelta64[ns]") + result = ser.dt.total_seconds() + expected = Series([np.nan, np.nan]) + tm.assert_series_equal(result, expected) + + def test_tdi_round(self): + td = timedelta_range(start="16801 days", periods=5, freq="30Min") + elt = td[1] + + expected_rng = TimedeltaIndex( + [ + Timedelta("16801 days 00:00:00"), + Timedelta("16801 days 00:00:00"), + Timedelta("16801 days 01:00:00"), + Timedelta("16801 days 02:00:00"), + Timedelta("16801 days 02:00:00"), + ] + ) + expected_elt = expected_rng[1] + + tm.assert_index_equal(td.round(freq="h"), expected_rng) + assert elt.round(freq="h") == expected_elt + + msg = INVALID_FREQ_ERR_MSG + with pytest.raises(ValueError, match=msg): + td.round(freq="foo") + with pytest.raises(ValueError, match=msg): + elt.round(freq="foo") + + msg = " is a non-fixed frequency" + with pytest.raises(ValueError, match=msg): + td.round(freq="ME") + with pytest.raises(ValueError, match=msg): + elt.round(freq="ME") + + @pytest.mark.parametrize( + "freq,msg", + [ + ("YE", " is a non-fixed frequency"), + ("ME", " is a non-fixed frequency"), + ("foobar", "Invalid frequency: foobar"), + ], + ) + def test_tdi_round_invalid(self, freq, msg): + t1 = timedelta_range("1 days", periods=3, freq="1 min 2 s 3 us") + + with pytest.raises(ValueError, match=msg): + t1.round(freq) + with pytest.raises(ValueError, match=msg): + # Same test for TimedeltaArray + t1._data.round(freq) + + # TODO: de-duplicate with test_tdi_round + def test_round(self): + t1 = timedelta_range("1 days", periods=3, freq="1 min 2 s 3 us") + t2 = -1 * t1 + t1a = timedelta_range("1 days", periods=3, freq="1 min 2 s") + t1c = TimedeltaIndex(np.array([1, 1, 1], "m8[D]")).as_unit("us") + + # note that negative times round DOWN! so don't give whole numbers + msg = "'d' is deprecated and will be removed in a future version." + + with tm.assert_produces_warning(Pandas4Warning, match=msg): + for freq, s1, s2 in [ + ("ns", t1, t2), + ("us", t1, t2), + ( + "ms", + t1a, + TimedeltaIndex( + ["-1 days +00:00:00", "-2 days +23:58:58", "-2 days +23:57:56"] + ), + ), + ( + "s", + t1a, + TimedeltaIndex( + ["-1 days +00:00:00", "-2 days +23:58:58", "-2 days +23:57:56"] + ), + ), + ("12min", t1c, TimedeltaIndex(["-1 days", "-1 days", "-1 days"])), + ("h", t1c, TimedeltaIndex(["-1 days", "-1 days", "-1 days"])), + ("d", t1c, -1 * t1c), + ]: + r1 = t1.round(freq) + r2 = t2.round(freq) + + tm.assert_index_equal(r1, s1) + tm.assert_index_equal(r2, s2) + + def test_components(self): + rng = timedelta_range("1 days, 10:11:12", periods=2, freq="s") + rng.components + + # with nat + s = Series(rng) + s[1] = np.nan + + result = s.dt.components + assert not result.iloc[0].isna().all() + assert result.iloc[1].isna().all() diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_searchsorted.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_searchsorted.py new file mode 100644 index 0000000000000000000000000000000000000000..710571ef383970097985f44e09ecba77fcf63f74 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_searchsorted.py @@ -0,0 +1,28 @@ +import numpy as np +import pytest + +from pandas import ( + TimedeltaIndex, + Timestamp, +) +import pandas._testing as tm + + +class TestSearchSorted: + def test_searchsorted_different_argument_classes(self, listlike_box): + idx = TimedeltaIndex(["1 day", "2 days", "3 days"]) + result = idx.searchsorted(listlike_box(idx)) + expected = np.arange(len(idx), dtype=result.dtype) + tm.assert_numpy_array_equal(result, expected) + + result = idx._data.searchsorted(listlike_box(idx)) + tm.assert_numpy_array_equal(result, expected) + + @pytest.mark.parametrize( + "arg", [[1, 2], ["a", "b"], [Timestamp("2020-01-01", tz="Europe/London")] * 2] + ) + def test_searchsorted_invalid_argument_dtype(self, arg): + idx = TimedeltaIndex(["1 day", "2 days", "3 days"]) + msg = "value should be a 'Timedelta', 'NaT', or array of those. Got" + with pytest.raises(TypeError, match=msg): + idx.searchsorted(arg) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_setops.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_setops.py new file mode 100644 index 0000000000000000000000000000000000000000..424961a4b652bd00874f408f36a9f28b0f880d9a --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_setops.py @@ -0,0 +1,260 @@ +import numpy as np +import pytest + +from pandas.errors import Pandas4Warning + +import pandas as pd +from pandas import ( + Index, + TimedeltaIndex, + timedelta_range, +) +import pandas._testing as tm + +from pandas.tseries.offsets import Hour + + +class TestTimedeltaIndex: + def test_union(self): + i1 = timedelta_range("1day", periods=5) + i2 = timedelta_range("3day", periods=5) + result = i1.union(i2) + expected = timedelta_range("1day", periods=7) + tm.assert_index_equal(result, expected) + + i1 = Index(np.arange(0, 20, 2, dtype=np.int64)) + i2 = timedelta_range(start="1 day", periods=10, freq="D") + i1.union(i2) # Works + i2.union(i1) # Fails with "AttributeError: can't set attribute" + + def test_union_sort_false(self): + tdi = timedelta_range("1day", periods=5) + + left = tdi[3:] + right = tdi[:3] + + # Check that we are testing the desired code path + assert left._can_fast_union(right) + + result = left.union(right) + tm.assert_index_equal(result, tdi) + + result = left.union(right, sort=False) + expected = TimedeltaIndex(["4 Days", "5 Days", "1 Days", "2 Day", "3 Days"]) + tm.assert_index_equal(result, expected) + + def test_union_coverage(self): + # GH#59051 + msg = "'d' is deprecated and will be removed in a future version." + with tm.assert_produces_warning(Pandas4Warning, match=msg): + idx = TimedeltaIndex(["3d", "1d", "2d"]) + ordered = TimedeltaIndex(idx.sort_values(), freq="infer") + result = ordered.union(idx) + tm.assert_index_equal(result, ordered) + + result = ordered[:0].union(ordered) + tm.assert_index_equal(result, ordered) + assert result.freq == ordered.freq + + def test_union_bug_1730(self): + rng_a = timedelta_range("1 day", periods=4, freq="3h") + rng_b = timedelta_range("1 day", periods=4, freq="4h") + + result = rng_a.union(rng_b) + exp = TimedeltaIndex(sorted(set(rng_a) | set(rng_b))) + tm.assert_index_equal(result, exp) + + def test_union_bug_1745(self): + left = TimedeltaIndex(["1 day 15:19:49.695000"]) + right = TimedeltaIndex( + ["2 day 13:04:21.322000", "1 day 15:27:24.873000", "1 day 15:31:05.350000"] + ) + + result = left.union(right) + exp = TimedeltaIndex(sorted(set(left) | set(right))) + tm.assert_index_equal(result, exp) + + def test_union_bug_4564(self): + left = timedelta_range("1 day", "30D") + right = left + pd.offsets.Minute(15) + + result = left.union(right) + exp = TimedeltaIndex(sorted(set(left) | set(right))) + tm.assert_index_equal(result, exp) + + def test_union_freq_infer(self): + # When taking the union of two TimedeltaIndexes, we infer + # a freq even if the arguments don't have freq. This matches + # DatetimeIndex behavior. + tdi = timedelta_range("1 Day", periods=5) + left = tdi[[0, 1, 3, 4]] + right = tdi[[2, 3, 1]] + + assert left.freq is None + assert right.freq is None + + result = left.union(right) + tm.assert_index_equal(result, tdi) + assert result.freq == "D" + + def test_intersection_bug_1708(self): + index_1 = timedelta_range("1 day", periods=4, freq="h") + index_2 = index_1 + pd.offsets.Hour(5) + + result = index_1.intersection(index_2) + assert len(result) == 0 + + index_1 = timedelta_range("1 day", periods=4, freq="h") + index_2 = index_1 + pd.offsets.Hour(1) + + result = index_1.intersection(index_2) + expected = timedelta_range("1 day 01:00:00", periods=3, freq="h") + tm.assert_index_equal(result, expected) + assert result.freq == expected.freq + + def test_intersection_equal(self, sort): + # GH 24471 Test intersection outcome given the sort keyword + # GH#63169 intersection returns a copy to prevent shared mutable state + first = timedelta_range("1 day", periods=4, freq="h") + second = timedelta_range("1 day", periods=4, freq="h") + intersect = first.intersection(second, sort=sort) + if sort is None: + tm.assert_index_equal(intersect, second.sort_values()) + tm.assert_index_equal(intersect, second) + + # Corner cases + inter = first.intersection(first, sort=sort) + assert inter is not first + tm.assert_index_equal(inter, first) + + @pytest.mark.parametrize("period_1, period_2", [(0, 4), (4, 0)]) + def test_intersection_zero_length(self, period_1, period_2, sort): + # GH 24471 test for non overlap the intersection should be zero length + index_1 = timedelta_range("1 day", periods=period_1, freq="h") + index_2 = timedelta_range("1 day", periods=period_2, freq="h") + expected = timedelta_range("1 day", periods=0, freq="h") + result = index_1.intersection(index_2, sort=sort) + tm.assert_index_equal(result, expected) + + def test_zero_length_input_index(self, sort): + # GH 24966 test for 0-len intersections are copied + index_1 = timedelta_range("1 day", periods=0, freq="h") + index_2 = timedelta_range("1 day", periods=3, freq="h") + result = index_1.intersection(index_2, sort=sort) + assert index_1 is not result + assert index_2 is not result + tm.assert_copy(result, index_1) + + @pytest.mark.parametrize( + "rng, expected", + # if target has the same name, it is preserved + [ + ( + timedelta_range("1 day", periods=5, freq="h", name="idx"), + timedelta_range("1 day", periods=4, freq="h", name="idx"), + ), + # if target name is different, it will be reset + ( + timedelta_range("1 day", periods=5, freq="h", name="other"), + timedelta_range("1 day", periods=4, freq="h", name=None), + ), + # if no overlap exists return empty index + ( + timedelta_range("1 day", periods=10, freq="h", name="idx")[5:], + TimedeltaIndex([], freq="h", name="idx", dtype="m8[us]"), + ), + ], + ) + def test_intersection(self, rng, expected, sort): + # GH 4690 (with tz) + base = timedelta_range("1 day", periods=4, freq="h", name="idx") + result = base.intersection(rng, sort=sort) + if sort is None: + expected = expected.sort_values() + tm.assert_index_equal(result, expected) + assert result.name == expected.name + assert result.freq == expected.freq + + @pytest.mark.parametrize( + "rng, expected", + # part intersection works + [ + ( + TimedeltaIndex(["5 hour", "2 hour", "4 hour", "9 hour"], name="idx"), + TimedeltaIndex(["2 hour", "4 hour"], name="idx"), + ), + # reordered part intersection + ( + TimedeltaIndex(["2 hour", "5 hour", "5 hour", "1 hour"], name="other"), + TimedeltaIndex(["1 hour", "2 hour"], name=None), + ), + # reversed index + ( + TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx")[ + ::-1 + ], + TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx"), + ), + ], + ) + def test_intersection_non_monotonic(self, rng, expected, sort): + # 24471 non-monotonic + base = TimedeltaIndex(["1 hour", "2 hour", "4 hour", "3 hour"], name="idx") + result = base.intersection(rng, sort=sort) + if sort is None: + expected = expected.sort_values() + tm.assert_index_equal(result, expected) + assert result.name == expected.name + + # if reversed order, frequency is still the same + if all(base == rng[::-1]) and sort is None: + assert isinstance(result.freq, Hour) + else: + assert result.freq is None + + +class TestTimedeltaIndexDifference: + def test_difference_freq(self, sort): + # GH14323: Difference of TimedeltaIndex should not preserve frequency + + index = timedelta_range("0 days", "5 days", freq="D") + + other = timedelta_range("1 days", "4 days", freq="D") + expected = TimedeltaIndex(["0 days", "5 days"], freq=None) + idx_diff = index.difference(other, sort) + tm.assert_index_equal(idx_diff, expected) + tm.assert_attr_equal("freq", idx_diff, expected) + + # preserve frequency when the difference is a contiguous + # subset of the original range + other = timedelta_range("2 days", "5 days", freq="D") + idx_diff = index.difference(other, sort) + expected = TimedeltaIndex(["0 days", "1 days"], freq="D") + tm.assert_index_equal(idx_diff, expected) + tm.assert_attr_equal("freq", idx_diff, expected) + + def test_difference_sort(self, sort): + index = TimedeltaIndex( + ["5 days", "3 days", "2 days", "4 days", "1 days", "0 days"] + ) + + other = timedelta_range("1 days", "4 days", freq="D") + idx_diff = index.difference(other, sort) + + expected = TimedeltaIndex(["5 days", "0 days"], freq=None) + + if sort is None: + expected = expected.sort_values() + + tm.assert_index_equal(idx_diff, expected) + tm.assert_attr_equal("freq", idx_diff, expected) + + other = timedelta_range("2 days", "5 days", freq="D") + idx_diff = index.difference(other, sort) + expected = TimedeltaIndex(["1 days", "0 days"], freq=None) + + if sort is None: + expected = expected.sort_values() + + tm.assert_index_equal(idx_diff, expected) + tm.assert_attr_equal("freq", idx_diff, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py new file mode 100644 index 0000000000000000000000000000000000000000..2066be8976e7fa33c68c70a8b79a3dc65587de32 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_timedelta.py @@ -0,0 +1,61 @@ +import numpy as np +import pytest + +from pandas import ( + Index, + Series, + Timedelta, + timedelta_range, +) +import pandas._testing as tm + + +class TestTimedeltaIndex: + def test_misc_coverage(self): + rng = timedelta_range("1 day", periods=5) + result = rng.groupby(rng.days) + assert isinstance(next(iter(result.values()))[0], Timedelta) + + def test_map(self): + # test_map_dictlike generally tests + + rng = timedelta_range("1 day", periods=10) + + f = lambda x: x.days + result = rng.map(f) + exp = Index([f(x) for x in rng], dtype=np.int64) + tm.assert_index_equal(result, exp) + + def test_fields(self): + rng = timedelta_range("1 days, 10:11:12.100123456", periods=2, freq="s") + tm.assert_index_equal(rng.days, Index([1, 1], dtype=np.int64)) + tm.assert_index_equal( + rng.seconds, + Index([10 * 3600 + 11 * 60 + 12, 10 * 3600 + 11 * 60 + 13], dtype=np.int32), + ) + tm.assert_index_equal( + rng.microseconds, + Index([100 * 1000 + 123, 100 * 1000 + 123], dtype=np.int32), + ) + tm.assert_index_equal(rng.nanoseconds, Index([456, 456], dtype=np.int32)) + + msg = "'TimedeltaIndex' object has no attribute '{}'" + with pytest.raises(AttributeError, match=msg.format("hours")): + rng.hours + with pytest.raises(AttributeError, match=msg.format("minutes")): + rng.minutes + with pytest.raises(AttributeError, match=msg.format("milliseconds")): + rng.milliseconds + + # with nat + s = Series(rng) + s[1] = np.nan + + tm.assert_series_equal(s.dt.days, Series([1, np.nan], index=range(2))) + tm.assert_series_equal( + s.dt.seconds, Series([10 * 3600 + 11 * 60 + 12, np.nan], index=range(2)) + ) + + # preserve name (GH15589) + rng.name = "name" + assert rng.days.name == "name" diff --git a/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_timedelta_range.py b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_timedelta_range.py new file mode 100644 index 0000000000000000000000000000000000000000..1cca77e7e44c9ca5e5ab9d8f4baafb047967dd7f --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexes/timedeltas/test_timedelta_range.py @@ -0,0 +1,187 @@ +import numpy as np +import pytest + +from pandas.errors import Pandas4Warning + +from pandas import ( + Timedelta, + timedelta_range, + to_timedelta, +) +import pandas._testing as tm + +from pandas.tseries.offsets import ( + Day, + Second, +) + + +class TestTimedeltas: + def test_timedelta_range_unit(self): + # GH#49824 + tdi = timedelta_range("0 Days", periods=10, freq="100000D", unit="s") + exp_arr = (np.arange(10, dtype="i8") * 100_000).view("m8[D]").astype("m8[s]") + tm.assert_numpy_array_equal(tdi.to_numpy(), exp_arr) + + def test_timedelta_range(self): + expected = to_timedelta(np.arange(5), unit="D").as_unit("us") + result = timedelta_range("0 days", periods=5, freq="D") + tm.assert_index_equal(result, expected) + + expected = to_timedelta(np.arange(11), unit="D").as_unit("us") + result = timedelta_range("0 days", "10 days", freq="D") + tm.assert_index_equal(result, expected) + + expected = ( + to_timedelta(np.arange(5), unit="D").as_unit("us") + Second(2) + Day() + ) + result = timedelta_range("1 days, 00:00:02", "5 days, 00:00:02", freq="D") + tm.assert_index_equal(result, expected) + + expected = to_timedelta([1, 3, 5, 7, 9], unit="D").as_unit("us") + Second(2) + result = timedelta_range("1 days, 00:00:02", periods=5, freq="2D") + tm.assert_index_equal(result, expected) + + expected = to_timedelta(np.arange(50), unit="min").as_unit("us") * 30 + result = timedelta_range("0 days", freq="30min", periods=50) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("depr_unit, unit", [("H", "hour"), ("S", "second")]) + def test_timedelta_units_H_S_deprecated(self, depr_unit, unit): + # GH#52536 + depr_msg = ( + f"'{depr_unit}' is deprecated and will be removed in a future version." + ) + expected = to_timedelta(np.arange(5), unit=unit) + with tm.assert_produces_warning(Pandas4Warning, match=depr_msg): + result = to_timedelta(np.arange(5), unit=depr_unit) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("unit", ["T", "t", "L", "l", "U", "u", "N", "n"]) + def test_timedelta_unit_T_L_U_N_raises(self, unit): + msg = f"invalid unit abbreviation: {unit}" + + with pytest.raises(ValueError, match=msg): + to_timedelta(np.arange(5), unit=unit) + + @pytest.mark.parametrize( + "periods, freq", [(3, "2D"), (5, "D"), (6, "19h12min"), (7, "16h"), (9, "12h")] + ) + def test_linspace_behavior(self, periods, freq): + # GH 20976 + result = timedelta_range(start="0 days", end="4 days", periods=periods) + expected = timedelta_range(start="0 days", end="4 days", freq=freq) + tm.assert_index_equal(result, expected) + + def test_timedelta_range_H_raises(self): + # GH#52536 + msg = "Invalid frequency: H" + + with pytest.raises(ValueError, match=msg): + timedelta_range(start="0 days", end="4 days", freq="19H12min") + + def test_timedelta_range_T_raises(self): + msg = "Invalid frequency: T" + + with pytest.raises(ValueError, match=msg): + timedelta_range(start="0 days", end="4 days", freq="19h12T") + + def test_errors(self): + # not enough params + msg = ( + "Of the four parameters: start, end, periods, and freq, " + "exactly three must be specified" + ) + with pytest.raises(ValueError, match=msg): + timedelta_range(start="0 days") + + with pytest.raises(ValueError, match=msg): + timedelta_range(end="5 days") + + with pytest.raises(ValueError, match=msg): + timedelta_range(periods=2) + + with pytest.raises(ValueError, match=msg): + timedelta_range() + + # too many params + with pytest.raises(ValueError, match=msg): + timedelta_range(start="0 days", end="5 days", periods=10, freq="h") + + @pytest.mark.parametrize( + "start, end, freq, expected_periods", + [ + ("1D", "10D", "2D", (10 - 1) // 2 + 1), + ("2D", "30D", "3D", (30 - 2) // 3 + 1), + ("2s", "50s", "5s", (50 - 2) // 5 + 1), + # tests that worked before GH 33498: + ("4D", "16D", "3D", (16 - 4) // 3 + 1), + ("8D", "16D", "40s", (16 * 3600 * 24 - 8 * 3600 * 24) // 40 + 1), + ], + ) + def test_timedelta_range_freq_divide_end(self, start, end, freq, expected_periods): + # GH 33498 only the cases where `(end % freq) == 0` used to fail + res = timedelta_range(start=start, end=end, freq=freq) + assert Timedelta(start) == res[0] + assert Timedelta(end) >= res[-1] + assert len(res) == expected_periods + + def test_timedelta_range_infer_freq(self): + # https://github.com/pandas-dev/pandas/issues/35897 + result = timedelta_range("0s", "1s", periods=31) + assert result.freq is None + + @pytest.mark.parametrize( + "freq_depr, start, end", + [ + ( + "3.5l", + "05:03:01", + "05:03:10", + ), + ( + "2.5T", + "5 hours", + "5 hours 8 minutes", + ), + ( + "3.5S", + "05:03:01", + "05:03:10", + ), + ], + ) + def test_timedelta_range_removed_freq(self, freq_depr, start, end): + # GH#59143 + msg = f"Invalid frequency: {freq_depr}" + with pytest.raises(ValueError, match=msg): + timedelta_range(start=start, end=end, freq=freq_depr) + + +class TestTimedeltaRangeUnitInference: + def test_timedelta_range_unit_inference_matching_unit(self, unit): + start = Timedelta(0).as_unit(unit) + end = Timedelta(days=1).as_unit(unit) + + tdi = timedelta_range(start, end, freq="D") + assert tdi.unit == unit + + def test_timedelta_range_unit_inference_mismatched_unit(self, unit): + start = Timedelta(0).as_unit(unit) + end = Timedelta(days=1).as_unit("s") + + tdi = timedelta_range(start, end, freq="D") + assert tdi.unit == unit + + tdi = timedelta_range(start, end.as_unit("ns"), freq="D") + assert tdi.unit == "ns" + + def 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b/lib/python3.11/site-packages/pandas/tests/indexing/interval/test_interval.py @@ -0,0 +1,234 @@ +import numpy as np +import pytest + +from pandas._libs import index as libindex +from pandas.compat import WASM + +import pandas as pd +from pandas import ( + DataFrame, + IntervalIndex, + Series, +) +import pandas._testing as tm + + +class TestIntervalIndex: + @pytest.fixture + def series_with_interval_index(self): + return Series(np.arange(5), IntervalIndex.from_breaks(np.arange(6))) + + def test_getitem_with_scalar(self, series_with_interval_index, indexer_sl): + ser = series_with_interval_index.copy() + + expected = ser.iloc[:3] + tm.assert_series_equal(expected, indexer_sl(ser)[:3]) + tm.assert_series_equal(expected, indexer_sl(ser)[:2.5]) + tm.assert_series_equal(expected, indexer_sl(ser)[0.1:2.5]) + if indexer_sl is tm.loc: + tm.assert_series_equal(expected, ser.loc[-1:3]) + + expected = ser.iloc[1:4] + tm.assert_series_equal(expected, indexer_sl(ser)[[1.5, 2.5, 3.5]]) + tm.assert_series_equal(expected, indexer_sl(ser)[[2, 3, 4]]) + tm.assert_series_equal(expected, indexer_sl(ser)[[1.5, 3, 4]]) + + expected = ser.iloc[2:5] + tm.assert_series_equal(expected, indexer_sl(ser)[ser >= 2]) + + @pytest.mark.parametrize("direction", ["increasing", "decreasing"]) + def test_getitem_nonoverlapping_monotonic(self, direction, closed, indexer_sl): + tpls = [(0, 1), (2, 3), (4, 5)] + if direction == "decreasing": + tpls = tpls[::-1] + + idx = IntervalIndex.from_tuples(tpls, closed=closed) + ser = Series(list("abc"), idx) + + for key, expected in zip(idx.left, ser): + if idx.closed_left: + assert indexer_sl(ser)[key] == expected + else: + with pytest.raises(KeyError, match=str(key)): + indexer_sl(ser)[key] + + for key, expected in zip(idx.right, ser): + if idx.closed_right: + assert indexer_sl(ser)[key] == expected + else: + with pytest.raises(KeyError, match=str(key)): + indexer_sl(ser)[key] + + for key, expected in zip(idx.mid, ser): + assert indexer_sl(ser)[key] == expected + + def test_getitem_non_matching(self, series_with_interval_index, indexer_sl): + ser = series_with_interval_index.copy() + + # this is a departure from our current + # indexing scheme, but simpler + with pytest.raises(KeyError, match=r"\[-1\] not in index"): + indexer_sl(ser)[[-1, 3, 4, 5]] + + with pytest.raises(KeyError, match=r"\[-1\] not in index"): + indexer_sl(ser)[[-1, 3]] + + def test_loc_getitem_large_series(self, monkeypatch): + size_cutoff = 20 + with monkeypatch.context(): + monkeypatch.setattr(libindex, "_SIZE_CUTOFF", size_cutoff) + ser = Series( + np.arange(size_cutoff), + index=IntervalIndex.from_breaks(np.arange(size_cutoff + 1)), + ) + + result1 = ser.loc[:8] + result2 = ser.loc[0:8] + result3 = ser.loc[0:8:1] + tm.assert_series_equal(result1, result2) + tm.assert_series_equal(result1, result3) + + def test_loc_getitem_frame(self): + # CategoricalIndex with IntervalIndex categories + df = DataFrame({"A": range(10)}) + ser = pd.cut(df.A, 5) + df["B"] = ser + df = df.set_index("B") + + result = df.loc[4] + expected = df.iloc[4:6] + tm.assert_frame_equal(result, expected) + + with pytest.raises(KeyError, match="10"): + df.loc[10] + + # single list-like + result = df.loc[[4]] + expected = df.iloc[4:6] + tm.assert_frame_equal(result, expected) + + # non-unique + result = df.loc[[4, 5]] + expected = df.take([4, 5, 4, 5]) + tm.assert_frame_equal(result, expected) + + msg = ( + r"None of \[Index\(\[10\], dtype='object', name='B'\)\] " + r"are in the \[index\]" + ) + with pytest.raises(KeyError, match=msg): + df.loc[[10]] + + # partial missing + with pytest.raises(KeyError, match=r"\[10\] not in index"): + df.loc[[10, 4]] + + def test_getitem_interval_with_nans(self, frame_or_series, indexer_sl): + # GH#41831 + + index = IntervalIndex([np.nan, np.nan]) + key = index[:-1] + + obj = frame_or_series(range(2), index=index) + if frame_or_series is DataFrame and indexer_sl is tm.setitem: + obj = obj.T + + result = indexer_sl(obj)[key] + expected = obj + + tm.assert_equal(result, expected) + + def test_setitem_interval_with_slice(self): + # GH#54722 + ii = IntervalIndex.from_breaks(range(4, 15)) + ser = Series(range(10), index=ii) + + orig = ser.copy() + + # This should be a no-op (used to raise) + ser.loc[1:3] = 20 + tm.assert_series_equal(ser, orig) + + ser.loc[6:8] = 19 + orig.iloc[1:4] = 19 + tm.assert_series_equal(ser, orig) + + ser2 = Series(range(5), index=ii[::2]) + orig2 = ser2.copy() + + # this used to raise + ser2.loc[6:8] = 22 # <- raises on main, sets on branch + orig2.iloc[1] = 22 + tm.assert_series_equal(ser2, orig2) + + ser2.loc[5:7] = 21 + orig2.iloc[:2] = 21 + tm.assert_series_equal(ser2, orig2) + + +class TestIntervalIndexInsideMultiIndex: + def test_mi_intervalindex_slicing_with_scalar(self): + # GH#27456 + ii = IntervalIndex.from_arrays( + [0, 1, 10, 11, 0, 1, 10, 11], [1, 2, 11, 12, 1, 2, 11, 12], name="MP" + ) + idx = pd.MultiIndex.from_arrays( + [ + pd.Index(["FC", "FC", "FC", "FC", "OWNER", "OWNER", "OWNER", "OWNER"]), + pd.Index( + ["RID1", "RID1", "RID2", "RID2", "RID1", "RID1", "RID2", "RID2"] + ), + ii, + ] + ) + + idx.names = ["Item", "RID", "MP"] + df = DataFrame({"value": [1, 2, 3, 4, 5, 6, 7, 8]}) + df.index = idx + + query_df = DataFrame( + { + "Item": ["FC", "OWNER", "FC", "OWNER", "OWNER"], + "RID": ["RID1", "RID1", "RID1", "RID2", "RID2"], + "MP": [0.2, 1.5, 1.6, 11.1, 10.9], + } + ) + + query_df = query_df.sort_index() + + idx = pd.MultiIndex.from_arrays([query_df.Item, query_df.RID, query_df.MP]) + query_df.index = idx + result = df.value.loc[query_df.index] + + # the IntervalIndex level is indexed with floats, which map to + # the intervals containing them. Matching the behavior we would get + # with _only_ an IntervalIndex, we get an IntervalIndex level back. + sliced_level = ii.take([0, 1, 1, 3, 2]) + expected_index = pd.MultiIndex.from_arrays( + [idx.get_level_values(0), idx.get_level_values(1), sliced_level] + ) + expected = Series([1, 6, 2, 8, 7], index=expected_index, name="value") + tm.assert_series_equal(result, expected) + + @pytest.mark.xfail(WASM, reason="GH 23440") + @pytest.mark.parametrize("base", [101, 1010]) + def test_reindex_behavior_with_interval_index(self, base): + # GH 51826 + + ser = Series( + range(base), + index=IntervalIndex.from_arrays(range(base), range(1, base + 1)), + ) + expected_result = Series([np.nan, 0], index=[np.nan, 1.0], dtype=float) + result = ser.reindex(index=[np.nan, 1.0]) + tm.assert_series_equal(result, expected_result) + + def test_multiindex_with_interval_index(self): + # for GH#25298 + intIndex = IntervalIndex.from_arrays([1, 5, 8, 13, 16], [4, 9, 12, 17, 20]) + multiIndex = pd.MultiIndex.from_arrays([["a", "a", "b", "b", "c"], intIndex]) + data = [(1, 2), (3, 4), (5, 6), (7, 8), (9, 10)] + df = DataFrame(data, index=multiIndex) + result = df.loc[("b", 16)] + expected = Series([7, 8], name=("b", pd.Interval(13, 17, closed="right"))) + tm.assert_series_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/interval/test_interval_new.py b/lib/python3.11/site-packages/pandas/tests/indexing/interval/test_interval_new.py new file mode 100644 index 0000000000000000000000000000000000000000..051dc7b98f2aa3c8294c715bc5dbb2637d99552a --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/interval/test_interval_new.py @@ -0,0 +1,235 @@ +import re + +import numpy as np +import pytest + +from pandas.compat import WASM + +from pandas import ( + Index, + Interval, + IntervalIndex, + Series, +) +import pandas._testing as tm + + +class TestIntervalIndex: + @pytest.fixture + def series_with_interval_index(self): + """ + Fixture providing a Series with an IntervalIndex. + """ + return Series(np.arange(5), IntervalIndex.from_breaks(np.arange(6))) + + def test_loc_with_interval(self, series_with_interval_index, indexer_sl): + # loc with single label / list of labels: + # - Intervals: only exact matches + # - scalars: those that contain it + + ser = series_with_interval_index.copy() + + expected = 0 + result = indexer_sl(ser)[Interval(0, 1)] + assert result == expected + + expected = ser.iloc[3:5] + result = indexer_sl(ser)[[Interval(3, 4), Interval(4, 5)]] + tm.assert_series_equal(expected, result) + + # missing or not exact + with pytest.raises(KeyError, match=re.escape("Interval(3, 5, closed='left')")): + indexer_sl(ser)[Interval(3, 5, closed="left")] + + with pytest.raises(KeyError, match=re.escape("Interval(3, 5, closed='right')")): + indexer_sl(ser)[Interval(3, 5)] + + with pytest.raises( + KeyError, match=re.escape("Interval(-2, 0, closed='right')") + ): + indexer_sl(ser)[Interval(-2, 0)] + + with pytest.raises(KeyError, match=re.escape("Interval(5, 6, closed='right')")): + indexer_sl(ser)[Interval(5, 6)] + + def test_loc_with_scalar(self, series_with_interval_index, indexer_sl): + # loc with single label / list of labels: + # - Intervals: only exact matches + # - scalars: those that contain it + + ser = series_with_interval_index.copy() + + assert indexer_sl(ser)[1] == 0 + assert indexer_sl(ser)[1.5] == 1 + assert indexer_sl(ser)[2] == 1 + + expected = ser.iloc[1:4] + tm.assert_series_equal(expected, indexer_sl(ser)[[1.5, 2.5, 3.5]]) + tm.assert_series_equal(expected, indexer_sl(ser)[[2, 3, 4]]) + tm.assert_series_equal(expected, indexer_sl(ser)[[1.5, 3, 4]]) + + expected = ser.iloc[[1, 1, 2, 1]] + tm.assert_series_equal(expected, indexer_sl(ser)[[1.5, 2, 2.5, 1.5]]) + + expected = ser.iloc[2:5] + tm.assert_series_equal(expected, indexer_sl(ser)[ser >= 2]) + + def test_loc_with_slices(self, series_with_interval_index, indexer_sl): + # loc with slices: + # - Interval objects: only works with exact matches + # - scalars: only works for non-overlapping, monotonic intervals, + # and start/stop select location based on the interval that + # contains them: + # (slice_loc(start, stop) == (idx.get_loc(start), idx.get_loc(stop)) + + ser = series_with_interval_index.copy() + + # slice of interval + + expected = ser.iloc[:3] + result = indexer_sl(ser)[Interval(0, 1) : Interval(2, 3)] + tm.assert_series_equal(expected, result) + + expected = ser.iloc[3:] + result = indexer_sl(ser)[Interval(3, 4) :] + tm.assert_series_equal(expected, result) + + msg = "Interval objects are not currently supported" + with pytest.raises(NotImplementedError, match=msg): + indexer_sl(ser)[Interval(3, 6) :] + + with pytest.raises(NotImplementedError, match=msg): + indexer_sl(ser)[Interval(3, 4, closed="left") :] + + def test_slice_step_ne1(self, series_with_interval_index): + # GH#31658 slice of scalar with step != 1 + ser = series_with_interval_index.copy() + expected = ser.iloc[0:4:2] + + result = ser[0:4:2] + tm.assert_series_equal(result, expected) + + result2 = ser[0:4][::2] + tm.assert_series_equal(result2, expected) + + def test_slice_float_start_stop(self, series_with_interval_index): + # GH#31658 slicing with integers is positional, with floats is not + # supported + ser = series_with_interval_index.copy() + + msg = "label-based slicing with step!=1 is not supported for IntervalIndex" + with pytest.raises(ValueError, match=msg): + ser[1.5:9.5:2] + + def test_slice_interval_step(self, series_with_interval_index): + # GH#31658 allows for integer step!=1, not Interval step + ser = series_with_interval_index.copy() + msg = "label-based slicing with step!=1 is not supported for IntervalIndex" + with pytest.raises(ValueError, match=msg): + ser[0 : 4 : Interval(0, 1)] + + def test_loc_with_overlap(self, indexer_sl): + idx = IntervalIndex.from_tuples([(1, 5), (3, 7)]) + ser = Series(range(len(idx)), index=idx) + + # scalar + expected = ser + result = indexer_sl(ser)[4] + tm.assert_series_equal(expected, result) + + result = indexer_sl(ser)[[4]] + tm.assert_series_equal(expected, result) + + # interval + expected = 0 + result = indexer_sl(ser)[Interval(1, 5)] + assert expected == result + + expected = ser + result = indexer_sl(ser)[[Interval(1, 5), Interval(3, 7)]] + tm.assert_series_equal(expected, result) + + with pytest.raises(KeyError, match=re.escape("Interval(3, 5, closed='right')")): + indexer_sl(ser)[Interval(3, 5)] + + msg = ( + r"None of \[IntervalIndex\(\[\(3, 5\]\], " + r"dtype='interval\[int64, right\]'\)\] are in the \[index\]" + ) + with pytest.raises(KeyError, match=msg): + indexer_sl(ser)[[Interval(3, 5)]] + + # slices with interval (only exact matches) + expected = ser + result = indexer_sl(ser)[Interval(1, 5) : Interval(3, 7)] + tm.assert_series_equal(expected, result) + + msg = ( + "'can only get slices from an IntervalIndex if bounds are " + "non-overlapping and all monotonic increasing or decreasing'" + ) + with pytest.raises(KeyError, match=msg): + indexer_sl(ser)[Interval(1, 6) : Interval(3, 8)] + + if indexer_sl is tm.loc: + # slices with scalar raise for overlapping intervals + # TODO KeyError is the appropriate error? + with pytest.raises(KeyError, match=msg): + ser.loc[1:4] + + def test_non_unique(self, indexer_sl): + idx = IntervalIndex.from_tuples([(1, 3), (3, 7)]) + ser = Series(range(len(idx)), index=idx) + + result = indexer_sl(ser)[Interval(1, 3)] + assert result == 0 + + result = indexer_sl(ser)[[Interval(1, 3)]] + expected = ser.iloc[0:1] + tm.assert_series_equal(expected, result) + + def test_non_unique_moar(self, indexer_sl): + idx = IntervalIndex.from_tuples([(1, 3), (1, 3), (3, 7)]) + ser = Series(range(len(idx)), index=idx) + + expected = ser.iloc[[0, 1]] + result = indexer_sl(ser)[Interval(1, 3)] + tm.assert_series_equal(expected, result) + + expected = ser + result = indexer_sl(ser)[Interval(1, 3) :] + tm.assert_series_equal(expected, result) + + expected = ser.iloc[[0, 1]] + result = indexer_sl(ser)[[Interval(1, 3)]] + tm.assert_series_equal(expected, result) + + def test_loc_getitem_missing_key_error_message( + self, frame_or_series, series_with_interval_index + ): + # GH#27365 + ser = series_with_interval_index.copy() + obj = frame_or_series(ser) + with pytest.raises(KeyError, match=r"\[6\]"): + obj.loc[[4, 5, 6]] + + +@pytest.mark.xfail(WASM, reason="GH 23440") +@pytest.mark.parametrize( + "intervals", + [ + ([Interval(-np.inf, 0.0), Interval(0.0, 1.0)]), + ([Interval(-np.inf, -2.0), Interval(-2.0, -1.0)]), + ([Interval(-1.0, 0.0), Interval(0.0, np.inf)]), + ([Interval(1.0, 2.0), Interval(2.0, np.inf)]), + ], +) +def test_repeating_interval_index_with_infs(intervals): + # GH 46658 + + interval_index = Index(intervals * 51) + + expected = np.arange(1, 102, 2, dtype=np.intp) + result = interval_index.get_indexer_for([intervals[1]]) + + tm.assert_equal(result, expected) diff --git 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0000000000000000000000000000000000000000..943752bf4aaa8d7ecb8b9f18c03bd086eea99bcc Binary files /dev/null and b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/__pycache__/test_sorted.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_chaining_and_caching.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_chaining_and_caching.py new file mode 100644 index 0000000000000000000000000000000000000000..c7ed21a2cc001ff523b328bacee953c05d278099 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_chaining_and_caching.py @@ -0,0 +1,87 @@ +import numpy as np + +from pandas._libs import index as libindex + +from pandas import ( + DataFrame, + MultiIndex, + RangeIndex, + Series, +) +import pandas._testing as tm + + +def test_detect_chained_assignment(): + # Inplace ops, originally from: + # https://stackoverflow.com/questions/20508968/series-fillna-in-a-multiindex-dataframe-does-not-fill-is-this-a-bug + a = [12, 23] + b = [123, None] + c = [1234, 2345] + d = [12345, 23456] + tuples = [("eyes", "left"), ("eyes", "right"), ("ears", "left"), ("ears", "right")] + events = { + ("eyes", "left"): a, + ("eyes", "right"): b, + ("ears", "left"): c, + ("ears", "right"): d, + } + multiind = MultiIndex.from_tuples(tuples, names=["part", "side"]) + zed = DataFrame(events, index=["a", "b"], columns=multiind) + + with tm.raises_chained_assignment_error(): + zed["eyes"]["right"].fillna(value=555, inplace=True) + + +def test_cache_updating(): + # 5216 + # make sure that we don't try to set a dead cache + a = np.random.default_rng(2).random((10, 3)) + df = DataFrame(a, columns=["x", "y", "z"]) + df_original = df.copy() + tuples = [(i, j) for i in range(5) for j in range(2)] + index = MultiIndex.from_tuples(tuples) + df.index = index + + # setting via chained assignment + # but actually works, since everything is a view + + with tm.raises_chained_assignment_error(): + df.loc[0]["z"].iloc[0] = 1.0 + + assert df.loc[(0, 0), "z"] == df_original.loc[0, "z"] + + # correct setting + df.loc[(0, 0), "z"] = 2 + result = df.loc[(0, 0), "z"] + assert result == 2 + + +def test_indexer_caching(monkeypatch): + # GH5727 + # make sure that indexers are in the _internal_names_set + size_cutoff = 20 + with monkeypatch.context(): + monkeypatch.setattr(libindex, "_SIZE_CUTOFF", size_cutoff) + index = MultiIndex.from_arrays([np.arange(size_cutoff), np.arange(size_cutoff)]) + s = Series(np.zeros(size_cutoff), index=index) + + # setitem + s[s == 0] = 1 + expected = Series(np.ones(size_cutoff), index=index) + tm.assert_series_equal(s, expected) + + +def test_set_names_only_clears_level_cache(): + mi = MultiIndex.from_arrays([range(4), range(4)], names=["a", "b"]) + mi.dtypes + mi.is_monotonic_increasing + mi._engine + mi.levels + old_cache_keys = sorted(mi._cache.keys()) + assert old_cache_keys == ["_engine", "dtypes", "is_monotonic_increasing", "levels"] + mi.names = ["A", "B"] + new_cache_keys = sorted(mi._cache.keys()) + assert new_cache_keys == ["_engine", "dtypes", "is_monotonic_increasing"] + new_levels = mi.levels + tm.assert_index_equal(new_levels[0], RangeIndex(4, name="A")) + tm.assert_index_equal(new_levels[1], RangeIndex(4, name="B")) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_datetime.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_datetime.py new file mode 100644 index 0000000000000000000000000000000000000000..d325971e7baf69fb3119afc018c6f90da93e0d3b --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_datetime.py @@ -0,0 +1,50 @@ +from datetime import datetime + +import numpy as np + +from pandas import ( + DataFrame, + Index, + MultiIndex, + Period, + Series, + period_range, + to_datetime, +) +import pandas._testing as tm + + +def test_multiindex_period_datetime(): + # GH4861, using datetime in period of multiindex raises exception + + idx1 = Index(["a", "a", "a", "b", "b"]) + idx2 = period_range("2012-01", periods=len(idx1), freq="M") + s = Series(np.random.default_rng(2).standard_normal(len(idx1)), [idx1, idx2]) + + # try Period as index + expected = s.iloc[0] + result = s.loc["a", Period("2012-01")] + assert result == expected + + # try datetime as index + result = s.loc["a", datetime(2012, 1, 1)] + assert result == expected + + +def test_multiindex_datetime_columns(): + # GH35015, using datetime as column indices raises exception + + mi = MultiIndex.from_tuples( + [(to_datetime("02/29/2020"), to_datetime("03/01/2020"))], names=["a", "b"] + ) + + df = DataFrame([], columns=mi) + + expected_df = DataFrame( + [], + columns=MultiIndex.from_arrays( + [[to_datetime("02/29/2020")], [to_datetime("03/01/2020")]], names=["a", "b"] + ), + ) + + tm.assert_frame_equal(df, expected_df) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_getitem.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_getitem.py new file mode 100644 index 0000000000000000000000000000000000000000..41e90fca281e1ac950771c523c65198f390851b6 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_getitem.py @@ -0,0 +1,414 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, +) +import pandas._testing as tm +from pandas.core.indexing import IndexingError + +# ---------------------------------------------------------------------------- +# test indexing of Series with multi-level Index +# ---------------------------------------------------------------------------- + + +@pytest.mark.parametrize( + "access_method", + [lambda s, x: s[:, x], lambda s, x: s.loc[:, x], lambda s, x: s.xs(x, level=1)], +) +@pytest.mark.parametrize( + "level1_value, expected", + [(0, Series([1], index=[0])), (1, Series([2, 3], index=[1, 2]))], +) +def test_series_getitem_multiindex(access_method, level1_value, expected): + # GH 6018 + # series regression getitem with a multi-index + + mi = MultiIndex.from_tuples([(0, 0), (1, 1), (2, 1)], names=["A", "B"]) + ser = Series([1, 2, 3], index=mi) + expected.index.name = "A" + + result = access_method(ser, level1_value) + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("level0_value", ["D", "A"]) +def test_series_getitem_duplicates_multiindex(level0_value): + # GH 5725 the 'A' happens to be a valid Timestamp so the doesn't raise + # the appropriate error, only in PY3 of course! + + index = MultiIndex( + levels=[[level0_value, "B", "C"], [0, 26, 27, 37, 57, 67, 75, 82]], + codes=[[0, 0, 0, 1, 2, 2, 2, 2, 2, 2], [1, 3, 4, 6, 0, 2, 2, 3, 5, 7]], + names=["tag", "day"], + ) + arr = np.random.default_rng(2).standard_normal((len(index), 1)) + df = DataFrame(arr, index=index, columns=["val"]) + + # confirm indexing on missing value raises KeyError + if level0_value != "A": + with pytest.raises(KeyError, match=r"^'A'$"): + df.val["A"] + + with pytest.raises(KeyError, match=r"^'X'$"): + df.val["X"] + + result = df.val[level0_value] + expected = Series( + arr.ravel()[0:3], name="val", index=Index([26, 37, 57], name="day") + ) + tm.assert_series_equal(result, expected) + + +def test_series_getitem(multiindex_year_month_day_dataframe_random_data, indexer_sl): + s = multiindex_year_month_day_dataframe_random_data["A"] + expected = s.reindex(s.index[42:65]) + expected.index = expected.index.droplevel(0).droplevel(0) + + result = indexer_sl(s)[2000, 3] + tm.assert_series_equal(result, expected) + + +def test_series_getitem_returns_scalar( + multiindex_year_month_day_dataframe_random_data, indexer_sl +): + s = multiindex_year_month_day_dataframe_random_data["A"] + expected = s.iloc[49] + + result = indexer_sl(s)[2000, 3, 10] + assert result == expected + + +@pytest.mark.parametrize( + "indexer,expected_error,expected_error_msg", + [ + (lambda s: s.__getitem__((2000, 3, 4)), KeyError, r"^\(2000, 3, 4\)$"), + (lambda s: s[(2000, 3, 4)], KeyError, r"^\(2000, 3, 4\)$"), + (lambda s: s.loc[(2000, 3, 4)], KeyError, r"^\(2000, 3, 4\)$"), + (lambda s: s.loc[(2000, 3, 4, 5)], IndexingError, "Too many indexers"), + ( + lambda s: s.__getitem__(len(s)), + KeyError, + "100", + ), # match should include len(s) + (lambda s: s[len(s)], KeyError, "100"), # match should include len(s) + ( + lambda s: s.iloc[len(s)], + IndexError, + "single positional indexer is out-of-bounds", + ), + ], +) +def test_series_getitem_indexing_errors( + multiindex_year_month_day_dataframe_random_data, + indexer, + expected_error, + expected_error_msg, +): + s = multiindex_year_month_day_dataframe_random_data["A"] + with pytest.raises(expected_error, match=expected_error_msg): + indexer(s) + + +def test_series_getitem_corner_generator( + multiindex_year_month_day_dataframe_random_data, +): + s = multiindex_year_month_day_dataframe_random_data["A"] + result = s[(x > 0 for x in s)] + expected = s[s > 0] + tm.assert_series_equal(result, expected) + + +# ---------------------------------------------------------------------------- +# test indexing of DataFrame with multi-level Index +# ---------------------------------------------------------------------------- + + +def test_getitem_simple(multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data.T + expected = df.values[:, 0] + result = df["foo", "one"].values + tm.assert_almost_equal(result, expected) + + +@pytest.mark.parametrize( + "indexer,expected_error_msg", + [ + (lambda df: df[("foo", "four")], r"^\('foo', 'four'\)$"), + (lambda df: df["foobar"], r"^'foobar'$"), + ], +) +def test_frame_getitem_simple_key_error( + multiindex_dataframe_random_data, indexer, expected_error_msg +): + df = multiindex_dataframe_random_data.T + with pytest.raises(KeyError, match=expected_error_msg): + indexer(df) + + +def test_tuple_string_column_names(): + # GH#50372 + mi = MultiIndex.from_tuples([("a", "aa"), ("a", "ab"), ("b", "ba"), ("b", "bb")]) + df = DataFrame([range(4), range(1, 5), range(2, 6)], columns=mi) + df["single_index"] = 0 + + df_flat = df.copy() + df_flat.columns = df_flat.columns.to_flat_index() + df_flat["new_single_index"] = 0 + + result = df_flat[[("a", "aa"), "new_single_index"]] + expected = DataFrame( + [[0, 0], [1, 0], [2, 0]], columns=Index([("a", "aa"), "new_single_index"]) + ) + tm.assert_frame_equal(result, expected) + + +def test_frame_getitem_multicolumn_empty_level(): + df = DataFrame({"a": ["1", "2", "3"], "b": ["2", "3", "4"]}) + df.columns = [ + ["level1 item1", "level1 item2"], + ["", "level2 item2"], + ["level3 item1", "level3 item2"], + ] + + result = df["level1 item1"] + expected = DataFrame( + [["1"], ["2"], ["3"]], index=df.index, columns=["level3 item1"] + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "indexer,expected_slice", + [ + (lambda df: df["foo"], slice(3)), + (lambda df: df["bar"], slice(3, 5)), + (lambda df: df.loc[:, "bar"], slice(3, 5)), + ], +) +def test_frame_getitem_toplevel( + multiindex_dataframe_random_data, indexer, expected_slice +): + df = multiindex_dataframe_random_data.T + expected = df.reindex(columns=df.columns[expected_slice]) + expected.columns = expected.columns.droplevel(0) + result = indexer(df) + tm.assert_frame_equal(result, expected) + + +def test_frame_mixed_depth_get(): + arrays = [ + ["a", "top", "top", "routine1", "routine1", "routine2"], + ["", "OD", "OD", "result1", "result2", "result1"], + ["", "wx", "wy", "", "", ""], + ] + + tuples = sorted(zip(*arrays)) + index = MultiIndex.from_tuples(tuples) + df = DataFrame(np.random.default_rng(2).standard_normal((4, 6)), columns=index) + + result = df["a"] + expected = df["a", "", ""].rename("a") + tm.assert_series_equal(result, expected) + + result = df["routine1", "result1"] + expected = df["routine1", "result1", ""] + expected = expected.rename(("routine1", "result1")) + tm.assert_series_equal(result, expected) + + +def test_frame_getitem_nan_multiindex(nulls_fixture): + # GH#29751 + # loc on a multiindex containing nan values + n = nulls_fixture # for code readability + cols = ["a", "b", "c"] + df = DataFrame( + [[11, n, 13], [21, n, 23], [31, n, 33], [41, n, 43]], + columns=cols, + ).set_index(["a", "b"]) + df["c"] = df["c"].astype("int64") + + idx = (21, n) + result = df.loc[:idx] + expected = DataFrame([[11, n, 13], [21, n, 23]], columns=cols).set_index(["a", "b"]) + expected["c"] = expected["c"].astype("int64") + tm.assert_frame_equal(result, expected) + + result = df.loc[idx:] + expected = DataFrame( + [[21, n, 23], [31, n, 33], [41, n, 43]], columns=cols + ).set_index(["a", "b"]) + expected["c"] = expected["c"].astype("int64") + tm.assert_frame_equal(result, expected) + + idx1, idx2 = (21, n), (31, n) + result = df.loc[idx1:idx2] + expected = DataFrame([[21, n, 23], [31, n, 33]], columns=cols).set_index(["a", "b"]) + expected["c"] = expected["c"].astype("int64") + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "indexer,expected", + [ + ( + (["b"], ["bar", np.nan]), + ( + DataFrame( + [[2, 3], [5, 6]], + columns=MultiIndex.from_tuples([("b", "bar"), ("b", np.nan)]), + dtype="int64", + ) + ), + ), + ( + (["a", "b"]), + ( + DataFrame( + [[1, 2, 3], [4, 5, 6]], + columns=MultiIndex.from_tuples( + [("a", "foo"), ("b", "bar"), ("b", np.nan)] + ), + dtype="int64", + ) + ), + ), + ( + (["b"]), + ( + DataFrame( + [[2, 3], [5, 6]], + columns=MultiIndex.from_tuples([("b", "bar"), ("b", np.nan)]), + dtype="int64", + ) + ), + ), + ( + (["b"], ["bar"]), + ( + DataFrame( + [[2], [5]], + columns=MultiIndex.from_tuples([("b", "bar")]), + dtype="int64", + ) + ), + ), + ( + (["b"], [np.nan]), + ( + DataFrame( + [[3], [6]], + columns=MultiIndex( + codes=[[1], [-1]], levels=[["a", "b"], ["bar", "foo"]] + ), + dtype="int64", + ) + ), + ), + (("b", np.nan), Series([3, 6], dtype="int64", name=("b", np.nan))), + ], +) +def test_frame_getitem_nan_cols_multiindex( + indexer, + expected, + nulls_fixture, +): + # Slicing MultiIndex including levels with nan values, for more information + # see GH#25154 + df = DataFrame( + [[1, 2, 3], [4, 5, 6]], + columns=MultiIndex.from_tuples( + [("a", "foo"), ("b", "bar"), ("b", nulls_fixture)] + ), + dtype="int64", + ) + + result = df.loc[:, indexer] + tm.assert_equal(result, expected) + + +# ---------------------------------------------------------------------------- +# test indexing of DataFrame with multi-level Index with duplicates +# ---------------------------------------------------------------------------- + + +@pytest.fixture +def dataframe_with_duplicate_index(): + """Fixture for DataFrame used in tests for gh-4145 and gh-4146""" + data = [["a", "d", "e", "c", "f", "b"], [1, 4, 5, 3, 6, 2], [1, 4, 5, 3, 6, 2]] + index = ["h1", "h3", "h5"] + columns = MultiIndex( + levels=[["A", "B"], ["A1", "A2", "B1", "B2"]], + codes=[[0, 0, 0, 1, 1, 1], [0, 3, 3, 0, 1, 2]], + names=["main", "sub"], + ) + return DataFrame(data, index=index, columns=columns) + + +@pytest.mark.parametrize( + "indexer", [lambda df: df[("A", "A1")], lambda df: df.loc[:, ("A", "A1")]] +) +def test_frame_mi_access(dataframe_with_duplicate_index, indexer): + # GH 4145 + df = dataframe_with_duplicate_index + index = Index(["h1", "h3", "h5"]) + columns = MultiIndex.from_tuples([("A", "A1")], names=["main", "sub"]) + expected = DataFrame([["a", 1, 1]], index=columns, columns=index).T + + result = indexer(df) + tm.assert_frame_equal(result, expected) + + +def test_frame_mi_access_returns_series(dataframe_with_duplicate_index): + # GH 4146, not returning a block manager when selecting a unique index + # from a duplicate index + # as of 4879, this returns a Series (which is similar to what happens + # with a non-unique) + df = dataframe_with_duplicate_index + expected = Series(["a", 1, 1], index=["h1", "h3", "h5"], name="A1") + result = df["A"]["A1"] + tm.assert_series_equal(result, expected) + + +def test_frame_mi_access_returns_frame(dataframe_with_duplicate_index): + # selecting a non_unique from the 2nd level + df = dataframe_with_duplicate_index + expected = DataFrame( + [["d", 4, 4], ["e", 5, 5]], + index=Index(["B2", "B2"], name="sub"), + columns=["h1", "h3", "h5"], + ).T + result = df["A"]["B2"] + tm.assert_frame_equal(result, expected) + + +def test_frame_mi_empty_slice(): + # GH 15454 + df = DataFrame(0, index=range(2), columns=MultiIndex.from_product([[1], [2]])) + result = df[[]] + expected = DataFrame( + index=[0, 1], columns=MultiIndex(levels=[[1], [2]], codes=[[], []]) + ) + tm.assert_frame_equal(result, expected) + + +def test_loc_empty_multiindex(): + # GH#36936 + arrays = [["a", "a", "b", "a"], ["a", "a", "b", "b"]] + index = MultiIndex.from_arrays(arrays, names=("idx1", "idx2")) + df = DataFrame([1, 2, 3, 4], index=index, columns=["value"]) + + # loc on empty multiindex == loc with False mask + empty_multiindex = df.loc[df.loc[:, "value"] == 0, :].index + result = df.loc[empty_multiindex, :] + expected = df.loc[[False] * len(df.index), :] + tm.assert_frame_equal(result, expected) + + # replacing value with loc on empty multiindex + df.loc[df.loc[df.loc[:, "value"] == 0].index, "value"] = 5 + result = df + expected = DataFrame([1, 2, 3, 4], index=index, columns=["value"]) + tm.assert_frame_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_iloc.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_iloc.py new file mode 100644 index 0000000000000000000000000000000000000000..8939ecc78000be08812afb702358e7eee1ae9499 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_iloc.py @@ -0,0 +1,171 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + MultiIndex, + Series, +) +import pandas._testing as tm + + +@pytest.fixture +def simple_multiindex_dataframe(): + """ + Factory function to create simple 3 x 3 dataframe with + both columns and row MultiIndex using supplied data or + random data by default. + """ + + data = np.random.default_rng(2).standard_normal((3, 3)) + return DataFrame( + data, columns=[[2, 2, 4], [6, 8, 10]], index=[[4, 4, 8], [8, 10, 12]] + ) + + +@pytest.mark.parametrize( + "indexer, expected", + [ + ( + lambda df: df.iloc[0], + lambda arr: Series(arr[0], index=[[2, 2, 4], [6, 8, 10]], name=(4, 8)), + ), + ( + lambda df: df.iloc[2], + lambda arr: Series(arr[2], index=[[2, 2, 4], [6, 8, 10]], name=(8, 12)), + ), + ( + lambda df: df.iloc[:, 2], + lambda arr: Series(arr[:, 2], index=[[4, 4, 8], [8, 10, 12]], name=(4, 10)), + ), + ], +) +def test_iloc_returns_series(indexer, expected, simple_multiindex_dataframe): + df = simple_multiindex_dataframe + arr = df.values + result = indexer(df) + expected = expected(arr) + tm.assert_series_equal(result, expected) + + +def test_iloc_returns_dataframe(simple_multiindex_dataframe): + df = simple_multiindex_dataframe + result = df.iloc[[0, 1]] + expected = df.xs(4, drop_level=False) + tm.assert_frame_equal(result, expected) + + +def test_iloc_returns_scalar(simple_multiindex_dataframe): + df = simple_multiindex_dataframe + arr = df.values + result = df.iloc[2, 2] + expected = arr[2, 2] + assert result == expected + + +def test_iloc_getitem_multiple_items(): + # GH 5528 + tup = zip(*[["a", "a", "b", "b"], ["x", "y", "x", "y"]]) + index = MultiIndex.from_tuples(tup) + df = DataFrame(np.random.default_rng(2).standard_normal((4, 4)), index=index) + result = df.iloc[[2, 3]] + expected = df.xs("b", drop_level=False) + tm.assert_frame_equal(result, expected) + + +def test_iloc_getitem_labels(): + # this is basically regular indexing + arr = np.random.default_rng(2).standard_normal((4, 3)) + df = DataFrame( + arr, + columns=[["i", "i", "j"], ["A", "A", "B"]], + index=[["i", "i", "j", "k"], ["X", "X", "Y", "Y"]], + ) + result = df.iloc[2, 2] + expected = arr[2, 2] + assert result == expected + + +def test_frame_getitem_slice(multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + result = df.iloc[:4] + expected = df[:4] + tm.assert_frame_equal(result, expected) + + +def test_frame_setitem_slice(multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + df.iloc[:4] = 0 + + assert (df.values[:4] == 0).all() + assert (df.values[4:] != 0).all() + + +def test_indexing_ambiguity_bug_1678(): + # GH 1678 + columns = MultiIndex.from_tuples( + [("Ohio", "Green"), ("Ohio", "Red"), ("Colorado", "Green")] + ) + index = MultiIndex.from_tuples([("a", 1), ("a", 2), ("b", 1), ("b", 2)]) + + df = DataFrame(np.arange(12).reshape((4, 3)), index=index, columns=columns) + + result = df.iloc[:, 1] + expected = df.loc[:, ("Ohio", "Red")] + tm.assert_series_equal(result, expected) + + +def test_iloc_integer_locations(): + # GH 13797 + data = [ + ["str00", "str01"], + ["str10", "str11"], + ["str20", "srt21"], + ["str30", "str31"], + ["str40", "str41"], + ] + + index = MultiIndex.from_tuples( + [("CC", "A"), ("CC", "B"), ("CC", "B"), ("BB", "a"), ("BB", "b")] + ) + + expected = DataFrame(data) + df = DataFrame(data, index=index) + + result = DataFrame([[df.iloc[r, c] for c in range(2)] for r in range(5)]) + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "data, indexes, values, expected_k", + [ + # test without indexer value in first level of MultiIndex + ([[2, 22, 5], [2, 33, 6]], [0, -1, 1], [2, 3, 1], [7, 10]), + # test like code sample 1 in the issue + ([[1, 22, 555], [1, 33, 666]], [0, -1, 1], [200, 300, 100], [755, 1066]), + # test like code sample 2 in the issue + ([[1, 3, 7], [2, 4, 8]], [0, -1, 1], [10, 10, 1000], [17, 1018]), + # test like code sample 3 in the issue + ([[1, 11, 4], [2, 22, 5], [3, 33, 6]], [0, -1, 1], [4, 7, 10], [8, 15, 13]), + ], +) +def test_iloc_setitem_int_multiindex_series(data, indexes, values, expected_k): + # GH17148 + df = DataFrame(data=data, columns=["i", "j", "k"]) + df = df.set_index(["i", "j"]) + + series = df.k.copy() + for i, v in zip(indexes, values): + series.iloc[i] += v + + df["k"] = expected_k + expected = df.k + tm.assert_series_equal(series, expected) + + +def test_getitem_iloc(multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + result = df.iloc[2] + expected = df.xs(df.index[2]) + tm.assert_series_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_indexing_slow.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_indexing_slow.py new file mode 100644 index 0000000000000000000000000000000000000000..8c7f7f3bb05acca8acef5cda78438ded431800e0 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_indexing_slow.py @@ -0,0 +1,119 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Series, +) +import pandas._testing as tm + + +@pytest.fixture +def m(): + return 5 + + +@pytest.fixture +def n(): + return 100 + + +@pytest.fixture +def cols(): + return ["jim", "joe", "jolie", "joline", "jolia"] + + +@pytest.fixture +def vals(n): + vals = [ + np.random.default_rng(2).integers(0, 10, n), + np.random.default_rng(2).choice(list("abcdefghij"), n), + np.random.default_rng(2).choice( + pd.date_range("20141009", periods=10).tolist(), n + ), + np.random.default_rng(2).choice(list("ZYXWVUTSRQ"), n), + np.random.default_rng(2).standard_normal(n), + ] + vals = list(map(tuple, zip(*vals))) + return vals + + +@pytest.fixture +def keys(n, m, vals): + # bunch of keys for testing + keys = [ + np.random.default_rng(2).integers(0, 11, m), + np.random.default_rng(2).choice(list("abcdefghijk"), m), + np.random.default_rng(2).choice( + pd.date_range("20141009", periods=11).tolist(), m + ), + np.random.default_rng(2).choice(list("ZYXWVUTSRQP"), m), + ] + keys = list(map(tuple, zip(*keys))) + keys += [t[:-1] for t in vals[:: n // m]] + return keys + + +# covers both unique index and non-unique index +@pytest.fixture +def df(vals, cols): + return DataFrame(vals, columns=cols) + + +@pytest.fixture +def a(df): + return pd.concat([df, df]) + + +@pytest.fixture +def b(df, cols): + return df.drop_duplicates(subset=cols[:-1]) + + +@pytest.mark.slow +@pytest.mark.filterwarnings("ignore::pandas.errors.PerformanceWarning") +@pytest.mark.parametrize("lexsort_depth", list(range(5))) +@pytest.mark.parametrize("frame_fixture", ["a", "b"]) +def test_multiindex_get_loc(request, lexsort_depth, keys, frame_fixture, cols): + # GH7724, GH2646 + + frame = request.getfixturevalue(frame_fixture) + if lexsort_depth == 0: + df = frame.copy(deep=False) + else: + df = frame.sort_values(by=cols[:lexsort_depth]) + + mi = df.set_index(cols[:-1]) + assert not mi.index._lexsort_depth < lexsort_depth + for key in keys: + mask = np.ones(len(df), dtype=bool) + + # test for all partials of this key + for i, k in enumerate(key): + mask &= df.iloc[:, i] == k + + if not mask.any(): + assert key[: i + 1] not in mi.index + continue + + assert key[: i + 1] in mi.index + right = df[mask].copy(deep=False) + + if i + 1 != len(key): # partial key + return_value = right.drop(cols[: i + 1], axis=1, inplace=True) + assert return_value is None + return_value = right.set_index(cols[i + 1 : -1], inplace=True) + assert return_value is None + tm.assert_frame_equal(mi.loc[key[: i + 1]], right) + + else: # full key + return_value = right.set_index(cols[:-1], inplace=True) + assert return_value is None + if len(right) == 1: # single hit + right = Series( + right["jolia"].values, name=right.index[0], index=["jolia"] + ) + tm.assert_series_equal(mi.loc[key[: i + 1]], right) + else: # multi hit + tm.assert_frame_equal(mi.loc[key[: i + 1]], right) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_loc.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_loc.py new file mode 100644 index 0000000000000000000000000000000000000000..51c8d5b3569f561db53fae65da66ad34eb674ea3 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_loc.py @@ -0,0 +1,1012 @@ +import numpy as np +import pytest + +from pandas.errors import IndexingError + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, +) +import pandas._testing as tm + + +@pytest.fixture +def frame_random_data_integer_multi_index(): + levels = [[0, 1], [0, 1, 2]] + codes = [[0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]] + index = MultiIndex(levels=levels, codes=codes) + return DataFrame(np.random.default_rng(2).standard_normal((6, 2)), index=index) + + +class TestMultiIndexLoc: + @pytest.mark.parametrize("has_ref", [True, False]) + def test_loc_setitem_frame_with_multiindex( + self, multiindex_dataframe_random_data, has_ref + ): + frame = multiindex_dataframe_random_data + if has_ref: + view = frame[:] + frame.loc[("bar", "two"), "B"] = 5 + assert frame.loc[("bar", "two"), "B"] == 5 + + # with integer labels + df = frame.copy() + df.columns = list(range(3)) + if has_ref: + view = df[:] # noqa: F841 + df.loc[("bar", "two"), 1] = 7 + assert df.loc[("bar", "two"), 1] == 7 + + def test_loc_getitem_general(self, performance_warning, any_real_numpy_dtype): + # GH#2817 + dtype = any_real_numpy_dtype + data = { + "amount": {0: 700, 1: 600, 2: 222, 3: 333, 4: 444}, + "col": {0: 3.5, 1: 3.5, 2: 4.0, 3: 4.0, 4: 4.0}, + "num": {0: 12, 1: 11, 2: 12, 3: 12, 4: 12}, + } + df = DataFrame(data) + df = df.astype({"col": dtype, "num": dtype}) + df = df.set_index(keys=["col", "num"]) + key = 4.0, 12 + + with tm.assert_produces_warning(performance_warning): + tm.assert_frame_equal(df.loc[key], df.iloc[2:]) + + # this is ok + return_value = df.sort_index(inplace=True) + assert return_value is None + res = df.loc[key] + + # col has float dtype, result should be float64 Index + col_arr = np.array([4.0] * 3, dtype=dtype) + year_arr = np.array([12] * 3, dtype=dtype) + index = MultiIndex.from_arrays([col_arr, year_arr], names=["col", "num"]) + expected = DataFrame({"amount": [222, 333, 444]}, index=index) + tm.assert_frame_equal(res, expected) + + def test_loc_getitem_multiindex_missing_label_raises(self): + # GH#21593 + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + columns=[[2, 2, 4], [6, 8, 10]], + index=[[4, 4, 8], [8, 10, 12]], + ) + + with pytest.raises(KeyError, match=r"^2$"): + df.loc[2] + + def test_loc_getitem_list_of_tuples_with_multiindex( + self, multiindex_year_month_day_dataframe_random_data + ): + ser = multiindex_year_month_day_dataframe_random_data["A"] + expected = ser.reindex(ser.index[49:51]) + result = ser.loc[[(2000, 3, 10), (2000, 3, 13)]] + tm.assert_series_equal(result, expected) + + def test_loc_getitem_series(self): + # GH14730 + # passing a series as a key with a MultiIndex + index = MultiIndex.from_product([[1, 2, 3], ["A", "B", "C"]]) + x = Series(index=index, data=range(9), dtype=np.float64) + y = Series([1, 3]) + expected = Series( + data=[0, 1, 2, 6, 7, 8], + index=MultiIndex.from_product([[1, 3], ["A", "B", "C"]]), + dtype=np.float64, + ) + result = x.loc[y] + tm.assert_series_equal(result, expected) + + result = x.loc[[1, 3]] + tm.assert_series_equal(result, expected) + + # GH15424 + y1 = Series([1, 3], index=[1, 2]) + result = x.loc[y1] + tm.assert_series_equal(result, expected) + + empty = Series(data=[], dtype=np.float64) + expected = Series( + [], + index=MultiIndex(levels=index.levels, codes=[[], []]), + dtype=np.float64, + ) + result = x.loc[empty] + tm.assert_series_equal(result, expected) + + def test_loc_getitem_array(self): + # GH15434 + # passing an array as a key with a MultiIndex + index = MultiIndex.from_product([[1, 2, 3], ["A", "B", "C"]]) + x = Series(index=index, data=range(9), dtype=np.float64) + y = np.array([1, 3]) + expected = Series( + data=[0, 1, 2, 6, 7, 8], + index=MultiIndex.from_product([[1, 3], ["A", "B", "C"]]), + dtype=np.float64, + ) + result = x.loc[y] + tm.assert_series_equal(result, expected) + + # empty array: + empty = np.array([]) + expected = Series( + [], + index=MultiIndex(levels=index.levels, codes=[[], []]), + dtype="float64", + ) + result = x.loc[empty] + tm.assert_series_equal(result, expected) + + # 0-dim array (scalar): + scalar = np.int64(1) + expected = Series(data=[0, 1, 2], index=["A", "B", "C"], dtype=np.float64) + result = x.loc[scalar] + tm.assert_series_equal(result, expected) + + def test_loc_multiindex_labels(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + columns=[["i", "i", "j"], ["A", "A", "B"]], + index=[["i", "i", "j"], ["X", "X", "Y"]], + ) + + # the first 2 rows + expected = df.iloc[[0, 1]].droplevel(0) + result = df.loc["i"] + tm.assert_frame_equal(result, expected) + + # 2nd (last) column + expected = df.iloc[:, [2]].droplevel(0, axis=1) + result = df.loc[:, "j"] + tm.assert_frame_equal(result, expected) + + # bottom right corner + expected = df.iloc[[2], [2]].droplevel(0).droplevel(0, axis=1) + result = df.loc["j"].loc[:, "j"] + tm.assert_frame_equal(result, expected) + + # with a tuple + expected = df.iloc[[0, 1]] + result = df.loc[("i", "X")] + tm.assert_frame_equal(result, expected) + + def test_loc_multiindex_ints(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + columns=[[2, 2, 4], [6, 8, 10]], + index=[[4, 4, 8], [8, 10, 12]], + ) + expected = df.iloc[[0, 1]].droplevel(0) + result = df.loc[4] + tm.assert_frame_equal(result, expected) + + def test_loc_multiindex_missing_label_raises(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + columns=[[2, 2, 4], [6, 8, 10]], + index=[[4, 4, 8], [8, 10, 12]], + ) + + with pytest.raises(KeyError, match=r"^2$"): + df.loc[2] + + @pytest.mark.parametrize("key, pos", [([2, 4], [0, 1]), ([2], []), ([2, 3], [])]) + def test_loc_multiindex_list_missing_label(self, key, pos): + # GH 27148 - lists with missing labels _do_ raise + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + columns=[[2, 2, 4], [6, 8, 10]], + index=[[4, 4, 8], [8, 10, 12]], + ) + + with pytest.raises(KeyError, match="not in index"): + df.loc[key] + + def test_loc_multiindex_too_many_dims_raises(self): + # GH 14885 + s = Series( + range(8), + index=MultiIndex.from_product([["a", "b"], ["c", "d"], ["e", "f"]]), + ) + + with pytest.raises(KeyError, match=r"^\('a', 'b'\)$"): + s.loc["a", "b"] + with pytest.raises(KeyError, match=r"^\('a', 'd', 'g'\)$"): + s.loc["a", "d", "g"] + with pytest.raises(IndexingError, match="Too many indexers"): + s.loc["a", "d", "g", "j"] + + def test_loc_multiindex_indexer_none(self): + # GH6788 + # multi-index indexer is None (meaning take all) + attributes = ["Attribute" + str(i) for i in range(1)] + attribute_values = ["Value" + str(i) for i in range(5)] + + index = MultiIndex.from_product([attributes, attribute_values]) + df = 0.1 * np.random.default_rng(2).standard_normal((10, 1 * 5)) + 0.5 + df = DataFrame(df, columns=index) + result = df[attributes] + tm.assert_frame_equal(result, df) + + # GH 7349 + # loc with a multi-index seems to be doing fallback + df = DataFrame( + np.arange(12).reshape(-1, 1), + index=MultiIndex.from_product([[1, 2, 3, 4], [1, 2, 3]]), + ) + + expected = df.loc[([1, 2],), :] + result = df.loc[[1, 2]] + tm.assert_frame_equal(result, expected) + + def test_loc_multiindex_incomplete(self): + # GH 7399 + # incomplete indexers + s = Series( + np.arange(15, dtype="int64"), + MultiIndex.from_product([range(5), ["a", "b", "c"]]), + ) + expected = s.loc[:, "a":"c"] + + result = s.loc[0:4, "a":"c"] + tm.assert_series_equal(result, expected) + + result = s.loc[:4, "a":"c"] + tm.assert_series_equal(result, expected) + + result = s.loc[0:, "a":"c"] + tm.assert_series_equal(result, expected) + + # GH 7400 + # multiindexer getitem with list of indexers skips wrong element + s = Series( + np.arange(15, dtype="int64"), + MultiIndex.from_product([range(5), ["a", "b", "c"]]), + ) + expected = s.iloc[[6, 7, 8, 12, 13, 14]] + result = s.loc[2:4:2, "a":"c"] + tm.assert_series_equal(result, expected) + + def test_get_loc_single_level(self): + single_level = MultiIndex( + levels=[["foo", "bar", "baz", "qux"]], codes=[[0, 1, 2, 3]], names=["first"] + ) + s = Series( + np.random.default_rng(2).standard_normal(len(single_level)), + index=single_level, + ) + for k in single_level.values: + s[k] + + def test_loc_getitem_int_slice(self): + # GH 3053 + # loc should treat integer slices like label slices + + index = MultiIndex.from_product([[6, 7, 8], ["a", "b"]]) + df = DataFrame(np.random.default_rng(2).standard_normal((6, 6)), index, index) + result = df.loc[6:8, :] + expected = df + tm.assert_frame_equal(result, expected) + + index = MultiIndex.from_product([[10, 20, 30], ["a", "b"]]) + df = DataFrame(np.random.default_rng(2).standard_normal((6, 6)), index, index) + result = df.loc[20:30, :] + expected = df.iloc[2:] + tm.assert_frame_equal(result, expected) + + # doc examples + result = df.loc[10, :] + expected = df.iloc[0:2] + expected.index = ["a", "b"] + tm.assert_frame_equal(result, expected) + + result = df.loc[:, 10] + expected = df[10] + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "indexer_type_1", (list, tuple, set, slice, np.ndarray, Series, Index) + ) + @pytest.mark.parametrize( + "indexer_type_2", (list, tuple, set, slice, np.ndarray, Series, Index) + ) + def test_loc_getitem_nested_indexer(self, indexer_type_1, indexer_type_2): + # GH #19686 + # .loc should work with nested indexers which can be + # any list-like objects (see `is_list_like` (`pandas.api.types`)) or slices + + def convert_nested_indexer(indexer_type, keys): + if indexer_type == np.ndarray: + return np.array(keys) + if indexer_type == slice: + return slice(*keys) + return indexer_type(keys) + + a = [10, 20, 30] + b = [1, 2, 3] + index = MultiIndex.from_product([a, b]) + df = DataFrame( + np.arange(len(index), dtype="int64"), index=index, columns=["Data"] + ) + + keys = ([10, 20], [2, 3]) + types = (indexer_type_1, indexer_type_2) + + # check indexers with all the combinations of nested objects + # of all the valid types + indexer = tuple( + convert_nested_indexer(indexer_type, k) + for indexer_type, k in zip(types, keys) + ) + if indexer_type_1 is set or indexer_type_2 is set: + with pytest.raises(TypeError, match="as an indexer is not supported"): + df.loc[indexer, "Data"] + + return + else: + result = df.loc[indexer, "Data"] + expected = Series( + [1, 2, 4, 5], name="Data", index=MultiIndex.from_product(keys) + ) + + tm.assert_series_equal(result, expected) + + def test_multiindex_loc_one_dimensional_tuple(self, frame_or_series): + # GH#37711 + mi = MultiIndex.from_tuples([("a", "A"), ("b", "A")]) + obj = frame_or_series([1, 2], index=mi) + obj.loc[("a",)] = 0 + expected = frame_or_series([0, 2], index=mi) + tm.assert_equal(obj, expected) + + @pytest.mark.parametrize("indexer", [("a",), ("a")]) + def test_multiindex_one_dimensional_tuple_columns(self, indexer): + # GH#37711 + mi = MultiIndex.from_tuples([("a", "A"), ("b", "A")]) + obj = DataFrame([1, 2], index=mi) + obj.loc[indexer, :] = 0 + expected = DataFrame([0, 2], index=mi) + tm.assert_frame_equal(obj, expected) + + @pytest.mark.parametrize( + "indexer, exp_value", [(slice(None), 1.0), ((1, 2), np.nan)] + ) + def test_multiindex_setitem_columns_enlarging(self, indexer, exp_value): + # GH#39147 + mi = MultiIndex.from_tuples([(1, 2), (3, 4)]) + df = DataFrame([[1, 2], [3, 4]], index=mi, columns=["a", "b"]) + df.loc[indexer, ["c", "d"]] = 1.0 + expected = DataFrame( + [[1, 2, 1.0, 1.0], [3, 4, exp_value, exp_value]], + index=mi, + columns=["a", "b", "c", "d"], + ) + tm.assert_frame_equal(df, expected) + + def test_multiindex_setitem_axis_set(self): + # GH#58116 + dates = pd.date_range("2001-01-01", freq="D", periods=2) + ids = ["i1", "i2", "i3"] + index = MultiIndex.from_product([dates, ids], names=["date", "identifier"]) + df = DataFrame(0.0, index=index, columns=["A", "B"]) + df.loc(axis=0)["2001-01-01", ["i1", "i3"]] = None + + expected = DataFrame( + [ + [None, None], + [0.0, 0.0], + [None, None], + [0.0, 0.0], + [0.0, 0.0], + [0.0, 0.0], + ], + index=index, + columns=["A", "B"], + ) + + tm.assert_frame_equal(df, expected) + + def test_sorted_multiindex_after_union(self): + # GH#44752 + midx = MultiIndex.from_product( + [pd.date_range("20110101", periods=2), Index(["a", "b"])] + ) + ser1 = Series(1, index=midx) + ser2 = Series(1, index=midx[:2]) + df = pd.concat([ser1, ser2], axis=1) + expected = df.copy() + result = df.loc["2011-01-01":"2011-01-02"] + tm.assert_frame_equal(result, expected) + + df = DataFrame({0: ser1, 1: ser2}) + result = df.loc["2011-01-01":"2011-01-02"] + tm.assert_frame_equal(result, expected) + + df = pd.concat([ser1, ser2.reindex(ser1.index)], axis=1) + result = df.loc["2011-01-01":"2011-01-02"] + tm.assert_frame_equal(result, expected) + + def test_loc_no_second_level_index(self): + # GH#43599 + df = DataFrame( + index=MultiIndex.from_product([list("ab"), list("cd"), list("e")]), + columns=["Val"], + ) + res = df.loc[np.s_[:, "c", :]] + expected = DataFrame( + index=MultiIndex.from_product([list("ab"), list("e")]), columns=["Val"] + ) + tm.assert_frame_equal(res, expected) + + def test_loc_multi_index_key_error(self): + # GH 51892 + df = DataFrame( + { + (1, 2): ["a", "b", "c"], + (1, 3): ["d", "e", "f"], + (2, 2): ["g", "h", "i"], + (2, 4): ["j", "k", "l"], + } + ) + with pytest.raises(KeyError, match=r"(1, 4)"): + df.loc[0, (1, 4)] + + +@pytest.mark.parametrize( + "indexer, pos", + [ + ([], []), # empty ok + (["A"], slice(3)), + (["A", "D"], []), # "D" isn't present -> raise + (["D", "E"], []), # no values found -> raise + (["D"], []), # same, with single item list: GH 27148 + (pd.IndexSlice[:, ["foo"]], slice(2, None, 3)), + (pd.IndexSlice[:, ["foo", "bah"]], slice(2, None, 3)), + ], +) +def test_loc_getitem_duplicates_multiindex_missing_indexers(indexer, pos): + # GH 7866 + # multi-index slicing with missing indexers + idx = MultiIndex.from_product( + [["A", "B", "C"], ["foo", "bar", "baz"]], names=["one", "two"] + ) + ser = Series(np.arange(9, dtype="int64"), index=idx).sort_index() + expected = ser.iloc[pos] + + if expected.size == 0 and indexer != []: + with pytest.raises(KeyError, match=str(indexer)): + ser.loc[indexer] + elif indexer == (slice(None), ["foo", "bah"]): + # "bah" is not in idx.levels[1], raising KeyError enforced in 2.0 + with pytest.raises(KeyError, match="'bah'"): + ser.loc[indexer] + else: + result = ser.loc[indexer] + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize("columns_indexer", [([], slice(None)), (["foo"], [])]) +def test_loc_getitem_duplicates_multiindex_empty_indexer(columns_indexer): + # GH 8737 + # empty indexer + multi_index = MultiIndex.from_product((["foo", "bar", "baz"], ["alpha", "beta"])) + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 6)), + index=range(5), + columns=multi_index, + ) + df = df.sort_index(level=0, axis=1) + + expected = DataFrame(index=range(5), columns=multi_index.reindex([])[0]) + result = df.loc[:, columns_indexer] + tm.assert_frame_equal(result, expected) + + +def test_loc_getitem_duplicates_multiindex_non_scalar_type_object(): + # regression from < 0.14.0 + # GH 7914 + df = DataFrame( + [[np.mean, np.median], ["mean", "median"]], + columns=MultiIndex.from_tuples([("functs", "mean"), ("functs", "median")]), + index=["function", "name"], + ) + result = df.loc["function", ("functs", "mean")] + expected = np.mean + assert result == expected + + +def test_loc_getitem_tuple_plus_slice(): + # GH 671 + df = DataFrame( + { + "a": np.arange(10), + "b": np.arange(10), + "c": np.random.default_rng(2).standard_normal(10), + "d": np.random.default_rng(2).standard_normal(10), + } + ).set_index(["a", "b"]) + expected = df.loc[0, 0] + result = df.loc[(0, 0), :] + tm.assert_series_equal(result, expected) + + +def test_loc_getitem_int(frame_random_data_integer_multi_index): + df = frame_random_data_integer_multi_index + result = df.loc[1] + expected = df[-3:] + expected.index = expected.index.droplevel(0) + tm.assert_frame_equal(result, expected) + + +def test_loc_getitem_int_raises_exception(frame_random_data_integer_multi_index): + df = frame_random_data_integer_multi_index + with pytest.raises(KeyError, match=r"^3$"): + df.loc[3] + + +def test_loc_getitem_lowerdim_corner(multiindex_dataframe_random_data): + df = multiindex_dataframe_random_data + + # test setup - check key not in dataframe + with pytest.raises(KeyError, match=r"^\('bar', 'three'\)$"): + df.loc[("bar", "three"), "B"] + + # in theory should be inserting in a sorted space???? + df.loc[("bar", "three"), "B"] = 0 + expected = 0 + result = df.sort_index().loc[("bar", "three"), "B"] + assert result == expected + + +def test_loc_setitem_single_column_slice(): + # case from https://github.com/pandas-dev/pandas/issues/27841 + df = DataFrame( + "string", + index=list("abcd"), + columns=MultiIndex.from_product([["Main"], ("another", "one")]), + ) + df["labels"] = "a" + df.loc[:, "labels"] = df.index + tm.assert_numpy_array_equal(np.asarray(df["labels"]), np.asarray(df.index)) + + # test with non-object block + df = DataFrame( + np.nan, + index=range(4), + columns=MultiIndex.from_tuples([("A", "1"), ("A", "2"), ("B", "1")]), + ) + expected = df.copy() + df.loc[:, "B"] = np.arange(4) + expected.iloc[:, 2] = np.arange(4) + tm.assert_frame_equal(df, expected) + + +def test_loc_nan_multiindex(using_infer_string): + # GH 5286 + tups = [ + ("Good Things", "C", np.nan), + ("Good Things", "R", np.nan), + ("Bad Things", "C", np.nan), + ("Bad Things", "T", np.nan), + ("Okay Things", "N", "B"), + ("Okay Things", "N", "D"), + ("Okay Things", "B", np.nan), + ("Okay Things", "D", np.nan), + ] + df = DataFrame( + np.ones((8, 4)), + columns=Index(["d1", "d2", "d3", "d4"]), + index=MultiIndex.from_tuples(tups, names=["u1", "u2", "u3"]), + ) + result = df.loc["Good Things"].loc["C"] + expected = DataFrame( + np.ones((1, 4)), + index=Index( + [np.nan], + dtype="object" if not using_infer_string else "str", + name="u3", + ), + columns=Index(["d1", "d2", "d3", "d4"]), + ) + tm.assert_frame_equal(result, expected) + + +def test_loc_period_string_indexing(): + # GH 9892 + a = pd.period_range("2013Q1", "2013Q4", freq="Q") + i = (1111, 2222, 3333) + idx = MultiIndex.from_product((a, i), names=("Period", "CVR")) + df = DataFrame( + index=idx, + columns=( + "OMS", + "OMK", + "RES", + "DRIFT_IND", + "OEVRIG_IND", + "FIN_IND", + "VARE_UD", + "LOEN_UD", + "FIN_UD", + ), + ) + result = df.loc[("2013Q1", 1111), "OMS"] + + alt = df.loc[(a[0], 1111), "OMS"] + assert np.isnan(alt) + + # Because the resolution of the string matches, it is an exact lookup, + # not a slice + assert np.isnan(result) + + alt = df.loc[("2013Q1", 1111), "OMS"] + assert np.isnan(alt) + + +def test_loc_datetime_mask_slicing(): + # GH 16699 + dt_idx = pd.to_datetime(["2017-05-04", "2017-05-05"]) + m_idx = MultiIndex.from_product([dt_idx, dt_idx], names=["Idx1", "Idx2"]) + df = DataFrame( + data=[[1, 2], [3, 4], [5, 6], [7, 6]], index=m_idx, columns=["C1", "C2"] + ) + result = df.loc[(dt_idx[0], (df.index.get_level_values(1) > "2017-05-04")), "C1"] + expected = Series( + [3], + name="C1", + index=MultiIndex.from_tuples( + [(pd.Timestamp("2017-05-04"), pd.Timestamp("2017-05-05"))], + names=["Idx1", "Idx2"], + ), + ) + tm.assert_series_equal(result, expected) + + +def test_loc_datetime_series_tuple_slicing(): + # https://github.com/pandas-dev/pandas/issues/35858 + date = pd.Timestamp("2000") + ser = Series( + 1, + index=MultiIndex.from_tuples([("a", date)], names=["a", "b"]), + name="c", + ) + result = ser.loc[:, [date]] + tm.assert_series_equal(result, ser) + + +def test_loc_with_mi_indexer(): + # https://github.com/pandas-dev/pandas/issues/35351 + df = DataFrame( + data=[["a", 1], ["a", 0], ["b", 1], ["c", 2]], + index=MultiIndex.from_tuples( + [(0, 1), (1, 0), (1, 1), (1, 1)], names=["index", "date"] + ), + columns=["author", "price"], + ) + idx = MultiIndex.from_tuples([(0, 1), (1, 1)], names=["index", "date"]) + result = df.loc[idx, :] + expected = DataFrame( + [["a", 1], ["b", 1], ["c", 2]], + index=MultiIndex.from_tuples([(0, 1), (1, 1), (1, 1)], names=["index", "date"]), + columns=["author", "price"], + ) + tm.assert_frame_equal(result, expected) + + +def test_loc_mi_with_level1_named_0(): + # GH#37194 + dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific") + + ser = Series(range(3), index=dti) + df = ser.to_frame() + df[1] = dti + + df2 = df.set_index(0, append=True) + assert df2.index.names == (None, 0) + df2.index.get_loc(dti[0]) # smoke test + + result = df2.loc[dti[0]] + expected = df2.iloc[[0]].droplevel(None) + tm.assert_frame_equal(result, expected) + + ser2 = df2[1] + assert ser2.index.names == (None, 0) + + result = ser2.loc[dti[0]] + expected = ser2.iloc[[0]].droplevel(None) + tm.assert_series_equal(result, expected) + + +def test_getitem_str_slice(): + # GH#15928 + df = DataFrame( + [ + ["20160525 13:30:00.023", "MSFT", "51.95", "51.95"], + ["20160525 13:30:00.048", "GOOG", "720.50", "720.93"], + ["20160525 13:30:00.076", "AAPL", "98.55", "98.56"], + ["20160525 13:30:00.131", "AAPL", "98.61", "98.62"], + ["20160525 13:30:00.135", "MSFT", "51.92", "51.95"], + ["20160525 13:30:00.135", "AAPL", "98.61", "98.62"], + ], + columns="time,ticker,bid,ask".split(","), + ) + df2 = df.set_index(["ticker", "time"]).sort_index() + + res = df2.loc[("AAPL", slice("2016-05-25 13:30:00")), :].droplevel(0) + expected = df2.loc["AAPL"].loc[slice("2016-05-25 13:30:00"), :] + tm.assert_frame_equal(res, expected) + + +def test_3levels_leading_period_index(): + # GH#24091 + pi = pd.PeriodIndex( + ["20181101 1100", "20181101 1200", "20181102 1300", "20181102 1400"], + name="datetime", + freq="D", + ) + lev2 = ["A", "A", "Z", "W"] + lev3 = ["B", "C", "Q", "F"] + mi = MultiIndex.from_arrays([pi, lev2, lev3]) + + ser = Series(range(4), index=mi, dtype=np.float64) + result = ser.loc[(pi[0], "A", "B")] + assert result == 0.0 + + +class TestKeyErrorsWithMultiIndex: + def test_missing_keys_raises_keyerror(self): + # GH#27420 KeyError, not TypeError + df = DataFrame(np.arange(12).reshape(4, 3), columns=["A", "B", "C"]) + df2 = df.set_index(["A", "B"]) + + with pytest.raises(KeyError, match="6"): + df2.loc[(1, 6)] + + def test_missing_key_raises_keyerror2(self): + # GH#21168 KeyError, not "IndexingError: Too many indexers" + ser = Series(-1, index=MultiIndex.from_product([[0, 1]] * 2)) + + with pytest.raises(KeyError, match=r"\(0, 3\)"): + ser.loc[0, 3] + + def test_missing_key_combination(self): + # GH: 19556 + mi = MultiIndex.from_arrays( + [ + np.array(["a", "a", "b", "b"]), + np.array(["1", "2", "2", "3"]), + np.array(["c", "d", "c", "d"]), + ], + names=["one", "two", "three"], + ) + df = DataFrame(np.random.default_rng(2).random((4, 3)), index=mi) + msg = r"\('b', '1', slice\(None, None, None\)\)" + with pytest.raises(KeyError, match=msg): + df.loc[("b", "1", slice(None)), :] + with pytest.raises(KeyError, match=msg): + df.index.get_locs(("b", "1", slice(None))) + with pytest.raises(KeyError, match=r"\('b', '1'\)"): + df.loc[("b", "1"), :] + + +def test_getitem_loc_commutability(multiindex_year_month_day_dataframe_random_data): + df = multiindex_year_month_day_dataframe_random_data + ser = df["A"] + result = ser[2000, 5] + expected = df.loc[2000, 5]["A"] + tm.assert_series_equal(result, expected) + + +def test_loc_with_nan(): + # GH: 27104 + df = DataFrame( + {"col": [1, 2, 5], "ind1": ["a", "d", np.nan], "ind2": [1, 4, 5]} + ).set_index(["ind1", "ind2"]) + result = df.loc[["a"]] + expected = DataFrame( + {"col": [1]}, index=MultiIndex.from_tuples([("a", 1)], names=["ind1", "ind2"]) + ) + tm.assert_frame_equal(result, expected) + + result = df.loc["a"] + expected = DataFrame({"col": [1]}, index=Index([1], name="ind2")) + tm.assert_frame_equal(result, expected) + + +def test_getitem_non_found_tuple(): + # GH: 25236 + df = DataFrame([[1, 2, 3, 4]], columns=["a", "b", "c", "d"]).set_index( + ["a", "b", "c"] + ) + with pytest.raises(KeyError, match=r"\(2\.0, 2\.0, 3\.0\)"): + df.loc[(2.0, 2.0, 3.0)] + + +def test_get_loc_datetime_index(): + # GH#24263 + index = pd.date_range("2001-01-01", periods=100) + mi = MultiIndex.from_arrays([index]) + # Check if get_loc matches for Index and MultiIndex + assert mi.get_loc("2001-01") == slice(0, 31, None) + assert index.get_loc("2001-01") == slice(0, 31, None) + + loc = mi[::2].get_loc("2001-01") + expected = index[::2].get_loc("2001-01") + assert loc == expected + + loc = mi.repeat(2).get_loc("2001-01") + expected = index.repeat(2).get_loc("2001-01") + assert loc == expected + + loc = mi.append(mi).get_loc("2001-01") + expected = index.append(index).get_loc("2001-01") + # TODO: standardize return type for MultiIndex.get_loc + tm.assert_numpy_array_equal(loc.nonzero()[0], expected) + + +def test_loc_setitem_indexer_differently_ordered(): + # GH#34603 + mi = MultiIndex.from_product([["a", "b"], [0, 1]]) + df = DataFrame([[1, 2], [3, 4], [5, 6], [7, 8]], index=mi) + + indexer = ("a", [1, 0]) + df.loc[indexer, :] = np.array([[9, 10], [11, 12]]) + expected = DataFrame([[11, 12], [9, 10], [5, 6], [7, 8]], index=mi) + tm.assert_frame_equal(df, expected) + + +def test_loc_getitem_index_differently_ordered_slice_none(): + # GH#31330 + df = DataFrame( + [[1, 2], [3, 4], [5, 6], [7, 8]], + index=[["a", "a", "b", "b"], [1, 2, 1, 2]], + columns=["a", "b"], + ) + result = df.loc[(slice(None), [2, 1]), :] + expected = DataFrame( + [[3, 4], [7, 8], [1, 2], [5, 6]], + index=[["a", "b", "a", "b"], [2, 2, 1, 1]], + columns=["a", "b"], + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize("indexer", [[1, 2, 7, 6, 2, 3, 8, 7], [1, 2, 7, 6, 3, 8]]) +def test_loc_getitem_index_differently_ordered_slice_none_duplicates(indexer): + # GH#40978 + df = DataFrame( + [1] * 8, + index=MultiIndex.from_tuples( + [(1, 1), (1, 2), (1, 7), (1, 6), (2, 2), (2, 3), (2, 8), (2, 7)] + ), + columns=["a"], + ) + result = df.loc[(slice(None), indexer), :] + expected = DataFrame( + [1] * 8, + index=[[1, 1, 2, 1, 2, 1, 2, 2], [1, 2, 2, 7, 7, 6, 3, 8]], + columns=["a"], + ) + tm.assert_frame_equal(result, expected) + + result = df.loc[df.index.isin(indexer, level=1), :] + tm.assert_frame_equal(result, df) + + +def test_loc_getitem_drops_levels_for_one_row_dataframe(): + # GH#10521 "x" and "z" are both scalar indexing, so those levels are dropped + mi = MultiIndex.from_arrays([["x"], ["y"], ["z"]], names=["a", "b", "c"]) + df = DataFrame({"d": [0]}, index=mi) + expected = df.droplevel([0, 2]) + result = df.loc["x", :, "z"] + tm.assert_frame_equal(result, expected) + + ser = Series([0], index=mi) + result = ser.loc["x", :, "z"] + expected = Series([0], index=Index(["y"], name="b")) + tm.assert_series_equal(result, expected) + + +def test_mi_columns_loc_list_label_order(): + # GH 10710 + cols = MultiIndex.from_product([["A", "B", "C"], [1, 2]]) + df = DataFrame(np.zeros((5, 6)), columns=cols) + result = df.loc[:, ["B", "A"]] + expected = DataFrame( + np.zeros((5, 4)), + columns=MultiIndex.from_tuples([("B", 1), ("B", 2), ("A", 1), ("A", 2)]), + ) + tm.assert_frame_equal(result, expected) + + +def test_mi_partial_indexing_list_raises(): + # GH 13501 + frame = DataFrame( + np.arange(12).reshape((4, 3)), + index=[["a", "a", "b", "b"], [1, 2, 1, 2]], + columns=[["Ohio", "Ohio", "Colorado"], ["Green", "Red", "Green"]], + ) + frame.index.names = ["key1", "key2"] + frame.columns.names = ["state", "color"] + with pytest.raises(KeyError, match="\\[2\\] not in index"): + frame.loc[["b", 2], "Colorado"] + + +def test_mi_indexing_list_nonexistent_raises(): + # GH 15452 + s = Series(range(4), index=MultiIndex.from_product([[1, 2], ["a", "b"]])) + with pytest.raises(KeyError, match="\\['not' 'found'\\] not in index"): + s.loc[["not", "found"]] + + +def test_mi_add_cell_missing_row_non_unique(): + # GH 16018 + result = DataFrame( + [[1, 2, 5, 6], [3, 4, 7, 8]], + index=["a", "a"], + columns=MultiIndex.from_product([[1, 2], ["A", "B"]]), + ) + result.loc["c"] = -1 + result.loc["c", (1, "A")] = 3 + result.loc["d", (1, "A")] = 3 + expected = DataFrame( + [ + [1.0, 2.0, 5.0, 6.0], + [3.0, 4.0, 7.0, 8.0], + [3.0, -1.0, -1, -1], + [3.0, np.nan, np.nan, np.nan], + ], + index=["a", "a", "c", "d"], + columns=MultiIndex.from_product([[1, 2], ["A", "B"]]), + ) + tm.assert_frame_equal(result, expected) + + +def test_loc_get_scalar_casting_to_float(): + # GH#41369 + df = DataFrame( + {"a": 1.0, "b": 2}, index=MultiIndex.from_arrays([[3], [4]], names=["c", "d"]) + ) + result = df.loc[(3, 4), "b"] + assert result == 2 + assert isinstance(result, np.int64) + result = df.loc[[(3, 4)], "b"].iloc[0] + assert result == 2 + assert isinstance(result, np.int64) + + +def test_loc_empty_single_selector_with_names(): + # GH 19517 + idx = MultiIndex.from_product([["a", "b"], ["A", "B"]], names=[1, 0]) + s2 = Series(index=idx, dtype=np.float64) + result = s2.loc["a"] + expected = Series([np.nan, np.nan], index=Index(["A", "B"], name=0)) + tm.assert_series_equal(result, expected) + + +def test_loc_keyerror_rightmost_key_missing(): + # GH 20951 + + df = DataFrame( + { + "A": [100, 100, 200, 200, 300, 300], + "B": [10, 10, 20, 21, 31, 33], + "C": range(6), + } + ) + df = df.set_index(["A", "B"]) + with pytest.raises(KeyError, match="^1$"): + df.loc[(100, 1)] + + +def test_multindex_series_loc_with_tuple_label(): + # GH#43908 + mi = MultiIndex.from_tuples([(1, 2), (3, (4, 5))]) + ser = Series([1, 2], index=mi) + result = ser.loc[(3, (4, 5))] + assert result == 2 diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_multiindex.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_multiindex.py new file mode 100644 index 0000000000000000000000000000000000000000..35ef7625b6e76f6baa9fc7a0059463a33e5cc54e --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_multiindex.py @@ -0,0 +1,294 @@ +import numpy as np +import pytest + +import pandas._libs.index as libindex + +import pandas as pd +from pandas import ( + CategoricalDtype, + DataFrame, + Index, + MultiIndex, + Series, +) +import pandas._testing as tm +from pandas.core.arrays.boolean import BooleanDtype + + +class TestMultiIndexBasic: + def test_multiindex_perf_warn(self, performance_warning): + df = DataFrame( + { + "jim": [0, 0, 1, 1], + "joe": ["x", "x", "z", "y"], + "jolie": np.random.default_rng(2).random(4), + } + ).set_index(["jim", "joe"]) + + with tm.assert_produces_warning(performance_warning): + df.loc[(1, "z")] + + df = df.iloc[[2, 1, 3, 0]] + with tm.assert_produces_warning(performance_warning): + df.loc[(0,)] + + @pytest.mark.parametrize("offset", [-5, 5]) + def test_indexing_over_hashtable_size_cutoff(self, monkeypatch, offset): + size_cutoff = 20 + n = size_cutoff + offset + + with monkeypatch.context(): + monkeypatch.setattr(libindex, "_SIZE_CUTOFF", size_cutoff) + s = Series(np.arange(n), MultiIndex.from_arrays((["a"] * n, np.arange(n)))) + + # hai it works! + assert s[("a", 5)] == 5 + assert s[("a", 6)] == 6 + assert s[("a", 7)] == 7 + + def test_multi_nan_indexing(self): + # GH 3588 + df = DataFrame( + { + "a": ["R1", "R2", np.nan, "R4"], + "b": ["C1", "C2", "C3", "C4"], + "c": [10, 15, np.nan, 20], + } + ) + result = df.set_index(["a", "b"], drop=False) + expected = DataFrame( + { + "a": ["R1", "R2", np.nan, "R4"], + "b": ["C1", "C2", "C3", "C4"], + "c": [10, 15, np.nan, 20], + }, + index=[ + Index(["R1", "R2", np.nan, "R4"], name="a"), + Index(["C1", "C2", "C3", "C4"], name="b"), + ], + ) + tm.assert_frame_equal(result, expected) + + def test_exclusive_nat_column_indexing(self): + # GH 38025 + # test multi indexing when one column exclusively contains NaT values + df = DataFrame( + { + "a": [pd.NaT, pd.NaT, pd.NaT, pd.NaT], + "b": ["C1", "C2", "C3", "C4"], + "c": [10, 15, np.nan, 20], + } + ) + df = df.set_index(["a", "b"]) + expected = DataFrame( + { + "c": [10, 15, np.nan, 20], + }, + index=[ + Index([pd.NaT, pd.NaT, pd.NaT, pd.NaT], name="a"), + Index(["C1", "C2", "C3", "C4"], name="b"), + ], + ) + tm.assert_frame_equal(df, expected) + + def test_nested_tuples_duplicates(self): + # GH#30892 + + dti = pd.to_datetime(["20190101", "20190101", "20190102"]) + idx = Index(["a", "a", "c"]) + mi = MultiIndex.from_arrays([dti, idx], names=["index1", "index2"]) + + df = DataFrame({"c1": [1, 2, 3], "c2": [np.nan, np.nan, np.nan]}, index=mi) + + expected = DataFrame({"c1": df["c1"], "c2": [1.0, 1.0, np.nan]}, index=mi) + + df2 = df.copy(deep=True) + df2.loc[(dti[0], "a"), "c2"] = 1.0 + tm.assert_frame_equal(df2, expected) + + df3 = df.copy(deep=True) + df3.loc[[(dti[0], "a")], "c2"] = 1.0 + tm.assert_frame_equal(df3, expected) + + def test_multiindex_with_datatime_level_preserves_freq(self): + # https://github.com/pandas-dev/pandas/issues/35563 + idx = Index(range(2), name="A") + dti = pd.date_range("2020-01-01", periods=7, freq="D", name="B") + mi = MultiIndex.from_product([idx, dti]) + df = DataFrame(np.random.default_rng(2).standard_normal((14, 2)), index=mi) + result = df.loc[0].index + tm.assert_index_equal(result, dti) + assert result.freq == dti.freq + + def test_multiindex_complex(self): + # GH#42145 + complex_data = [1 + 2j, 4 - 3j, 10 - 1j] + non_complex_data = [3, 4, 5] + result = DataFrame( + { + "x": complex_data, + "y": non_complex_data, + "z": non_complex_data, + } + ) + result.set_index(["x", "y"], inplace=True) + expected = DataFrame( + {"z": non_complex_data}, + index=MultiIndex.from_arrays( + [complex_data, non_complex_data], + names=("x", "y"), + ), + ) + tm.assert_frame_equal(result, expected) + + def test_rename_multiindex_with_duplicates(self): + # GH 38015 + mi = MultiIndex.from_tuples([("A", "cat"), ("B", "cat"), ("B", "cat")]) + df = DataFrame(index=mi) + df = df.rename(index={"A": "Apple"}, level=0) + + mi2 = MultiIndex.from_tuples([("Apple", "cat"), ("B", "cat"), ("B", "cat")]) + expected = DataFrame(index=mi2) + tm.assert_frame_equal(df, expected) + + def test_series_align_multiindex_with_nan_overlap_only(self): + # GH 38439 + mi1 = MultiIndex.from_arrays([[81.0, np.nan], [np.nan, np.nan]]) + mi2 = MultiIndex.from_arrays([[np.nan, 82.0], [np.nan, np.nan]]) + ser1 = Series([1, 2], index=mi1) + ser2 = Series([1, 2], index=mi2) + result1, result2 = ser1.align(ser2) + + mi = MultiIndex.from_arrays([[81.0, 82.0, np.nan], [np.nan, np.nan, np.nan]]) + expected1 = Series([1.0, np.nan, 2.0], index=mi) + expected2 = Series([np.nan, 2.0, 1.0], index=mi) + + tm.assert_series_equal(result1, expected1) + tm.assert_series_equal(result2, expected2) + + def test_series_align_multiindex_with_nan(self): + # GH 38439 + mi1 = MultiIndex.from_arrays([[81.0, np.nan], [np.nan, np.nan]]) + mi2 = MultiIndex.from_arrays([[np.nan, 81.0], [np.nan, np.nan]]) + ser1 = Series([1, 2], index=mi1) + ser2 = Series([1, 2], index=mi2) + result1, result2 = ser1.align(ser2) + + mi = MultiIndex.from_arrays([[81.0, np.nan], [np.nan, np.nan]]) + expected1 = Series([1, 2], index=mi) + expected2 = Series([2, 1], index=mi) + + tm.assert_series_equal(result1, expected1) + tm.assert_series_equal(result2, expected2) + + def test_nunique_smoke(self): + # GH 34019 + n = DataFrame([[1, 2], [1, 2]]).set_index([0, 1]).index.nunique() + assert n == 1 + + def test_multiindex_repeated_keys(self): + # GH19414 + tm.assert_series_equal( + Series([1, 2], MultiIndex.from_arrays([["a", "b"]])).loc[ + ["a", "a", "b", "b"] + ], + Series([1, 1, 2, 2], MultiIndex.from_arrays([["a", "a", "b", "b"]])), + ) + + def test_multiindex_with_na_missing_key(self): + # GH46173 + df = DataFrame.from_dict( + { + ("foo",): [1, 2, 3], + ("bar",): [5, 6, 7], + (None,): [8, 9, 0], + } + ) + with pytest.raises(KeyError, match="missing_key"): + df[[("missing_key",)]] + + def test_multiindex_dtype_preservation(self): + # GH51261 + columns = MultiIndex.from_tuples([("A", "B")], names=["lvl1", "lvl2"]) + df = DataFrame(["value"], columns=columns).astype("category") + df_no_multiindex = df["A"] + assert isinstance(df_no_multiindex["B"].dtype, CategoricalDtype) + + # geopandas 1763 analogue + df = DataFrame( + [[1, 0], [0, 1]], + columns=[ + ["foo", "foo"], + ["location", "location"], + ["x", "y"], + ], + ).assign(bools=Series([True, False], dtype="boolean")) + assert isinstance(df["bools"].dtype, BooleanDtype) + + def test_multiindex_from_tuples_with_nan(self): + # GH#23578 + result = MultiIndex.from_tuples([("a", "b", "c"), np.nan, ("d", "", "")]) + expected = MultiIndex.from_tuples( + [("a", "b", "c"), (np.nan, np.nan, np.nan), ("d", "", "")] + ) + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("operation", ["div", "mul", "add", "sub"]) + def test_groupyby_rename_categories_operation_with_multiindex(self, operation): + # GH#51500 + data = DataFrame( + [["C", "B", "B"], ["B", "A", "A"], ["B", "A", "B"]], columns=["0", "1", "2"] + ) + data["0"] = data["0"].astype("category") + data["0"] = data["0"].cat.rename_categories({"C": "B", "B": "C"}) + + a = data.groupby(by=["0", "1"])["2"].value_counts() + b = data.groupby(by=["0", "1"]).size() + + result = getattr(a, operation)(b) + expected = getattr(a, operation)(b.sort_index(ascending=False)) + + tm.assert_series_equal(result, expected) + + def test_multiindex_assign_aligns_as_implicit_tuple(self): + # GH 61841 + cols = MultiIndex.from_tuples([("A", "B")]) + df1 = DataFrame([[i] for i in range(3)], columns=cols) + df2 = df1.copy() + df3 = df1.copy() + s1 = df1["A"].rolling(2).mean() + s2 = s1.copy() + s3 = s1.copy() + + df2["C"] = s2 + df3[("C", "")] = s3 + tm.assert_frame_equal(df2, df3) + + df1["C"] = s1 + tm.assert_frame_equal(df1, df2) + tm.assert_frame_equal(df1, df3) + + df1["C"] = s1 + tm.assert_frame_equal(df1, df2) + tm.assert_frame_equal(df1, df3) + + def test_multiindex_assign_alignment_with_non_string_dtype(self): + # GH 62518 + columns = MultiIndex.from_arrays( + [["a", "a", "z", "z"], pd.Categorical([1, 2, 1, 2])] + ) + + meta = DataFrame(columns=columns, dtype=object) + meta["z"] = meta["z"].astype("int64") + + result = DataFrame( + data={ + ("a", 1): Series([], dtype=object), + ("a", 2): Series([], dtype=object), + ("z", 1): Series([], dtype="int64"), + ("z", 2): Series([], dtype="int64"), + }, + columns=columns, + ) + + tm.assert_frame_equal(meta, result) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_partial.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_partial.py new file mode 100644 index 0000000000000000000000000000000000000000..cdb3e6bb1f9518b9688e5fc77efded30f606bb08 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_partial.py @@ -0,0 +1,255 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + DatetimeIndex, + MultiIndex, + date_range, +) +import pandas._testing as tm + + +class TestMultiIndexPartial: + def test_getitem_partial_int(self): + # GH 12416 + # with single item + l1 = [10, 20] + l2 = ["a", "b"] + df = DataFrame(index=range(2), columns=MultiIndex.from_product([l1, l2])) + expected = DataFrame(index=range(2), columns=l2) + result = df[20] + tm.assert_frame_equal(result, expected) + + # with list + expected = DataFrame( + index=range(2), columns=MultiIndex.from_product([l1[1:], l2]) + ) + result = df[[20]] + tm.assert_frame_equal(result, expected) + + # missing item: + with pytest.raises(KeyError, match="1"): + df[1] + with pytest.raises(KeyError, match=r"'\[1\] not in index'"): + df[[1]] + + def test_series_slice_partial(self): + pass + + def test_xs_partial( + self, + multiindex_dataframe_random_data, + multiindex_year_month_day_dataframe_random_data, + ): + frame = multiindex_dataframe_random_data + ymd = multiindex_year_month_day_dataframe_random_data + result = frame.xs("foo") + result2 = frame.loc["foo"] + expected = frame.T["foo"].T + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result, result2) + + result = ymd.xs((2000, 4)) + expected = ymd.loc[2000, 4] + tm.assert_frame_equal(result, expected) + + # ex from #1796 + index = MultiIndex( + levels=[["foo", "bar"], ["one", "two"], [-1, 1]], + codes=[ + [0, 0, 0, 0, 1, 1, 1, 1], + [0, 0, 1, 1, 0, 0, 1, 1], + [0, 1, 0, 1, 0, 1, 0, 1], + ], + ) + df = DataFrame( + np.random.default_rng(2).standard_normal((8, 4)), + index=index, + columns=list("abcd"), + ) + + result = df.xs(("foo", "one")) + expected = df.loc["foo", "one"] + tm.assert_frame_equal(result, expected) + + def test_getitem_partial(self, multiindex_year_month_day_dataframe_random_data): + ymd = multiindex_year_month_day_dataframe_random_data + ymd = ymd.T + result = ymd[2000, 2] + + expected = ymd.reindex(columns=ymd.columns[ymd.columns.codes[1] == 1]) + expected.columns = expected.columns.droplevel(0).droplevel(0) + tm.assert_frame_equal(result, expected) + + def test_fancy_slice_partial( + self, + multiindex_dataframe_random_data, + multiindex_year_month_day_dataframe_random_data, + ): + frame = multiindex_dataframe_random_data + result = frame.loc["bar":"baz"] + expected = frame[3:7] + tm.assert_frame_equal(result, expected) + + ymd = multiindex_year_month_day_dataframe_random_data + result = ymd.loc[(2000, 2) : (2000, 4)] + lev = ymd.index.codes[1] + expected = ymd[(lev >= 1) & (lev <= 3)] + tm.assert_frame_equal(result, expected) + + def test_getitem_partial_column_select(self): + idx = MultiIndex( + codes=[[0, 0, 0], [0, 1, 1], [1, 0, 1]], + levels=[["a", "b"], ["x", "y"], ["p", "q"]], + ) + df = DataFrame(np.random.default_rng(2).random((3, 2)), index=idx) + + result = df.loc[("a", "y"), :] + expected = df.loc[("a", "y")] + tm.assert_frame_equal(result, expected) + + result = df.loc[("a", "y"), [1, 0]] + expected = df.loc[("a", "y")][[1, 0]] + tm.assert_frame_equal(result, expected) + + with pytest.raises(KeyError, match=r"\('a', 'foo'\)"): + df.loc[("a", "foo"), :] + + def test_partial_set( + self, + multiindex_year_month_day_dataframe_random_data, + ): + # GH #397 + ymd = multiindex_year_month_day_dataframe_random_data + df = ymd.copy() + exp = ymd.copy() + df.loc[2000, 4] = 0 + exp.iloc[65:85] = 0 + tm.assert_frame_equal(df, exp) + + with tm.raises_chained_assignment_error(): + df["A"].loc[2000, 4] = 1 + df.loc[(2000, 4), "A"] = 1 + exp.iloc[65:85, 0] = 1 + tm.assert_frame_equal(df, exp) + + df.loc[2000] = 5 + exp.iloc[:100] = 5 + tm.assert_frame_equal(df, exp) + + # this works...for now + with tm.raises_chained_assignment_error(): + df["A"].iloc[14] = 5 + assert df["A"].iloc[14] == exp["A"].iloc[14] + + @pytest.mark.parametrize("dtype", [int, float]) + def test_getitem_intkey_leading_level( + self, multiindex_year_month_day_dataframe_random_data, dtype + ): + # GH#33355 dont fall-back to positional when leading level is int + ymd = multiindex_year_month_day_dataframe_random_data + levels = ymd.index.levels + ymd.index = ymd.index.set_levels([levels[0].astype(dtype), *levels[1:]]) + ser = ymd["A"] + mi = ser.index + assert isinstance(mi, MultiIndex) + if dtype is int: + assert mi.levels[0].dtype == np.dtype(int) + else: + assert mi.levels[0].dtype == np.float64 + + assert 14 not in mi.levels[0] + assert not mi.levels[0]._should_fallback_to_positional + assert not mi._should_fallback_to_positional + + with pytest.raises(KeyError, match="14"): + ser[14] + + # --------------------------------------------------------------------- + + def test_setitem_multiple_partial(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + expected = frame.copy() + result = frame.copy() + result.loc[["foo", "bar"]] = 0 + expected.loc["foo"] = 0 + expected.loc["bar"] = 0 + tm.assert_frame_equal(result, expected) + + expected = frame.copy() + result = frame.copy() + result.loc["foo":"bar"] = 0 + expected.loc["foo"] = 0 + expected.loc["bar"] = 0 + tm.assert_frame_equal(result, expected) + + expected = frame["A"].copy() + result = frame["A"].copy() + result.loc[["foo", "bar"]] = 0 + expected.loc["foo"] = 0 + expected.loc["bar"] = 0 + tm.assert_series_equal(result, expected) + + expected = frame["A"].copy() + result = frame["A"].copy() + result.loc["foo":"bar"] = 0 + expected.loc["foo"] = 0 + expected.loc["bar"] = 0 + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "indexer, exp_idx, exp_values", + [ + ( + slice("2019-2", None), + DatetimeIndex(["2019-02-01"], dtype="M8[ns]"), + [2, 3], + ), + ( + slice(None, "2019-2"), + date_range("2019", periods=2, freq="MS", unit="ns"), + [0, 1, 2, 3], + ), + ], + ) + def test_partial_getitem_loc_datetime(self, indexer, exp_idx, exp_values): + # GH: 25165 + date_idx = date_range("2019", periods=2, freq="MS", unit="ns") + df = DataFrame( + list(range(4)), + index=MultiIndex.from_product([date_idx, [0, 1]], names=["x", "y"]), + ) + expected = DataFrame( + exp_values, + index=MultiIndex.from_product([exp_idx, [0, 1]], names=["x", "y"]), + ) + result = df[indexer] + tm.assert_frame_equal(result, expected) + result = df.loc[indexer] + tm.assert_frame_equal(result, expected) + + result = df.loc(axis=0)[indexer] + tm.assert_frame_equal(result, expected) + + result = df.loc[indexer, :] + tm.assert_frame_equal(result, expected) + + df2 = df.swaplevel(0, 1).sort_index() + expected = expected.swaplevel(0, 1).sort_index() + + result = df2.loc[:, indexer, :] + tm.assert_frame_equal(result, expected) + + +def test_loc_getitem_partial_both_axis(): + # gh-12660 + iterables = [["a", "b"], [2, 1]] + columns = MultiIndex.from_product(iterables, names=["col1", "col2"]) + rows = MultiIndex.from_product(iterables, names=["row1", "row2"]) + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), index=rows, columns=columns + ) + expected = df.iloc[:2, 2:].droplevel("row1").droplevel("col1", axis=1) + result = df.loc["a", "b"] + tm.assert_frame_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_setitem.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_setitem.py new file mode 100644 index 0000000000000000000000000000000000000000..615a0eb6e1af19cf3135edff68e06def06a95987 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_setitem.py @@ -0,0 +1,553 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + MultiIndex, + Series, + date_range, + isna, + notna, +) +import pandas._testing as tm + + +def assert_equal(a, b): + assert a == b + + +class TestMultiIndexSetItem: + def check(self, target, indexers, value, compare_fn=assert_equal, expected=None): + target.loc[indexers] = value + result = target.loc[indexers] + if expected is None: + expected = value + compare_fn(result, expected) + + def test_setitem_multiindex(self): + # GH#7190 + cols = ["A", "w", "l", "a", "x", "X", "d", "profit"] + index = MultiIndex.from_product( + [np.arange(0, 100), np.arange(0, 80)], names=["time", "firm"] + ) + t, n = 0, 2 + + df = DataFrame( + np.nan, + columns=cols, + index=index, + ) + self.check(target=df, indexers=((t, n), "X"), value=0) + + df = DataFrame(-999, columns=cols, index=index) + self.check(target=df, indexers=((t, n), "X"), value=1) + + df = DataFrame(columns=cols, index=index) + self.check(target=df, indexers=((t, n), "X"), value=2) + + # gh-7218: assigning with 0-dim arrays + df = DataFrame(-999, columns=cols, index=index) + self.check( + target=df, + indexers=((t, n), "X"), + value=np.array(3), + expected=3, + ) + + def test_setitem_multiindex2(self): + # GH#5206 + df = DataFrame( + np.arange(25).reshape(5, 5), columns="A,B,C,D,E".split(","), dtype=float + ) + df["F"] = 99 + row_selection = df["A"] % 2 == 0 + col_selection = ["B", "C"] + df.loc[row_selection, col_selection] = df["F"] + output = DataFrame(99.0, index=[0, 2, 4], columns=["B", "C"]) + tm.assert_frame_equal(df.loc[row_selection, col_selection], output) + self.check( + target=df, + indexers=(row_selection, col_selection), + value=df["F"], + compare_fn=tm.assert_frame_equal, + expected=output, + ) + + def test_setitem_multiindex3(self): + # GH#11372 + idx = MultiIndex.from_product( + [["A", "B", "C"], date_range("2015-01-01", "2015-04-01", freq="MS")] + ) + cols = MultiIndex.from_product( + [["foo", "bar"], date_range("2016-01-01", "2016-02-01", freq="MS")] + ) + + df = DataFrame( + np.random.default_rng(2).random((12, 4)), index=idx, columns=cols + ) + + subidx = MultiIndex.from_arrays( + [["A", "A"], date_range("2015-01-01", "2015-02-01", freq="MS")] + ) + subcols = MultiIndex.from_arrays( + [["foo", "foo"], date_range("2016-01-01", "2016-02-01", freq="MS")] + ) + + vals = DataFrame( + np.random.default_rng(2).random((2, 2)), index=subidx, columns=subcols + ) + self.check( + target=df, + indexers=(subidx, subcols), + value=vals, + compare_fn=tm.assert_frame_equal, + ) + # set all columns + vals = DataFrame( + np.random.default_rng(2).random((2, 4)), index=subidx, columns=cols + ) + self.check( + target=df, + indexers=(subidx, slice(None, None, None)), + value=vals, + compare_fn=tm.assert_frame_equal, + ) + # identity + copy = df.copy() + self.check( + target=df, + indexers=(df.index, df.columns), + value=df, + compare_fn=tm.assert_frame_equal, + expected=copy, + ) + + def test_multiindex_setitem(self): + # GH 3738 + # setting with a multi-index right hand side + arrays = [ + np.array(["bar", "bar", "baz", "qux", "qux", "bar"]), + np.array(["one", "two", "one", "one", "two", "one"]), + np.arange(0, 6, 1), + ] + + df_orig = DataFrame( + np.random.default_rng(2).standard_normal((6, 3)), + index=arrays, + columns=["A", "B", "C"], + ).sort_index() + + expected = df_orig.loc[["bar"]] * 2 + df = df_orig.copy() + df.loc[["bar"]] *= 2 + tm.assert_frame_equal(df.loc[["bar"]], expected) + + # raise because these have differing levels + msg = "cannot align on a multi-index with out specifying the join levels" + with pytest.raises(TypeError, match=msg): + df.loc["bar"] *= 2 + + def test_multiindex_setitem2(self): + # from SO + # https://stackoverflow.com/questions/24572040/pandas-access-the-level-of-multiindex-for-inplace-operation + df_orig = DataFrame.from_dict( + { + "price": { + ("DE", "Coal", "Stock"): 2, + ("DE", "Gas", "Stock"): 4, + ("DE", "Elec", "Demand"): 1, + ("FR", "Gas", "Stock"): 5, + ("FR", "Solar", "SupIm"): 0, + ("FR", "Wind", "SupIm"): 0, + } + } + ) + df_orig.index = MultiIndex.from_tuples( + df_orig.index, names=["Sit", "Com", "Type"] + ) + + expected = df_orig.copy() + expected.iloc[[0, 1, 3]] *= 2 + + idx = pd.IndexSlice + df = df_orig.copy() + df.loc[idx[:, :, "Stock"], :] *= 2 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[idx[:, :, "Stock"], "price"] *= 2 + tm.assert_frame_equal(df, expected) + + def test_multiindex_assignment(self): + # GH3777 part 2 + + # mixed dtype + df = DataFrame( + np.random.default_rng(2).integers(5, 10, size=9).reshape(3, 3), + columns=list("abc"), + index=[[4, 4, 8], [8, 10, 12]], + ) + df["d"] = np.nan + arr = np.array([0.0, 1.0]) + + df.loc[4, "d"] = arr + tm.assert_series_equal(df.loc[4, "d"], Series(arr, index=[8, 10], name="d")) + + def test_multiindex_assignment_single_dtype(self): + # GH3777 part 2b + # single dtype + arr = np.array([0.0, 1.0]) + + df = DataFrame( + np.random.default_rng(2).integers(5, 10, size=9).reshape(3, 3), + columns=list("abc"), + index=[[4, 4, 8], [8, 10, 12]], + dtype=np.int64, + ) + + # arr can be losslessly cast to int, so this setitem is inplace + df.loc[4, "c"] = arr + exp = Series(arr, index=[8, 10], name="c", dtype="int64") + result = df.loc[4, "c"] + tm.assert_series_equal(result, exp) + + # arr + 0.5 cannot be cast losslessly to int, so we upcast + with pytest.raises(TypeError, match="Invalid value"): + df.loc[4, "c"] = arr + 0.5 + # Upcast so that we can add .5 + df = df.astype({"c": "float64"}) + df.loc[4, "c"] = arr + 0.5 + + # scalar ok + df.loc[4, "c"] = 10 + exp = Series(10, index=[8, 10], name="c", dtype="float64") + tm.assert_series_equal(df.loc[4, "c"], exp) + + # invalid assignments + msg = "Must have equal len keys and value when setting with an iterable" + with pytest.raises(ValueError, match=msg): + df.loc[4, "c"] = [0, 1, 2, 3] + + with pytest.raises(ValueError, match=msg): + df.loc[4, "c"] = [0] + + # But with a length-1 listlike column indexer this behaves like + # `df.loc[4, "c"] = 0 + df.loc[4, ["c"]] = [0] + assert (df.loc[4, "c"] == 0).all() + + def test_groupby_example(self): + # groupby example + NUM_ROWS = 100 + NUM_COLS = 10 + col_names = ["A" + num for num in map(str, np.arange(NUM_COLS).tolist())] + index_cols = col_names[:5] + + df = DataFrame( + np.random.default_rng(2).integers(5, size=(NUM_ROWS, NUM_COLS)), + dtype=np.int64, + columns=col_names, + ) + df = df.set_index(index_cols).sort_index() + grp = df.groupby(level=index_cols[:4]) + df["new_col"] = np.nan + + # we are actually operating on a copy here + # but in this case, that's ok + for name, df2 in grp: + new_vals = np.arange(df2.shape[0]) + df.loc[name, "new_col"] = new_vals + + def test_series_setitem(self, multiindex_year_month_day_dataframe_random_data): + ymd = multiindex_year_month_day_dataframe_random_data + s = ymd["A"] + + s[2000, 3] = np.nan + assert isna(s.values[42:65]).all() + assert notna(s.values[:42]).all() + assert notna(s.values[65:]).all() + + s[2000, 3, 10] = np.nan + assert isna(s.iloc[49]) + + with pytest.raises(KeyError, match="49"): + # GH#33355 dont fall-back to positional when leading level is int + s[49] + + def test_frame_getitem_setitem_boolean(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + df = frame.T.copy() + values = df.values.copy() + + result = df[df > 0] + expected = df.where(df > 0) + tm.assert_frame_equal(result, expected) + + df[df > 0] = 5 + values[values > 0] = 5 + tm.assert_almost_equal(df.values, values) + + df[df == 5] = 0 + values[values == 5] = 0 + tm.assert_almost_equal(df.values, values) + + # a df that needs alignment first + df[df[:-1] < 0] = 2 + np.putmask(values[:-1], values[:-1] < 0, 2) + tm.assert_almost_equal(df.values, values) + + with pytest.raises(TypeError, match="boolean values only"): + df[df * 0] = 2 + + def test_frame_getitem_setitem_multislice(self): + levels = [["t1", "t2"], ["a", "b", "c"]] + codes = [[0, 0, 0, 1, 1], [0, 1, 2, 0, 1]] + midx = MultiIndex(codes=codes, levels=levels, names=[None, "id"]) + df = DataFrame({"value": [1, 2, 3, 7, 8]}, index=midx) + + result = df.loc[:, "value"] + tm.assert_series_equal(df["value"], result) + + result = df.loc[df.index[1:3], "value"] + tm.assert_series_equal(df["value"][1:3], result) + + result = df.loc[:, :] + tm.assert_frame_equal(df, result) + + result = df + df.loc[:, "value"] = 10 + result["value"] = 10 + tm.assert_frame_equal(df, result) + + df.loc[:, :] = 10 + tm.assert_frame_equal(df, result) + + def test_frame_setitem_multi_column(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + columns=[["a", "a", "b", "b"], [0, 1, 0, 1]], + ) + + cp = df.copy() + cp["a"] = cp["b"] + tm.assert_frame_equal(cp["a"], cp["b"]) + + # set with ndarray + cp = df.copy() + cp["a"] = cp["b"].values + tm.assert_frame_equal(cp["a"], cp["b"]) + + def test_frame_setitem_multi_column2(self): + # --------------------------------------- + # GH#1803 + columns = MultiIndex.from_tuples([("A", "1"), ("A", "2"), ("B", "1")]) + df = DataFrame(index=[1, 3, 5], columns=columns) + + # Works, but adds a column instead of updating the two existing ones + df["A"] = 0.0 # Doesn't work + assert (df["A"].values == 0).all() + + # it broadcasts + df["B", "1"] = [1, 2, 3] + df["A"] = df["B", "1"] + + sliced_a1 = df["A", "1"] + sliced_a2 = df["A", "2"] + sliced_b1 = df["B", "1"] + tm.assert_series_equal(sliced_a1, sliced_b1, check_names=False) + tm.assert_series_equal(sliced_a2, sliced_b1, check_names=False) + assert sliced_a1.name == ("A", "1") + assert sliced_a2.name == ("A", "2") + assert sliced_b1.name == ("B", "1") + + def test_loc_getitem_tuple_plus_columns( + self, multiindex_year_month_day_dataframe_random_data + ): + # GH #1013 + ymd = multiindex_year_month_day_dataframe_random_data + df = ymd[:5] + + result = df.loc[(2000, 1, 6), ["A", "B", "C"]] + expected = df.loc[2000, 1, 6][["A", "B", "C"]] + tm.assert_series_equal(result, expected) + + @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning") + def test_loc_getitem_setitem_slice_integers(self, frame_or_series): + index = MultiIndex( + levels=[[0, 1, 2], [0, 2]], codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]] + ) + + obj = DataFrame( + np.random.default_rng(2).standard_normal((len(index), 4)), + index=index, + columns=["a", "b", "c", "d"], + ) + obj = tm.get_obj(obj, frame_or_series) + + res = obj.loc[1:2] + exp = obj.reindex(obj.index[2:]) + tm.assert_equal(res, exp) + + obj.loc[1:2] = 7 + assert (obj.loc[1:2] == 7).values.all() + + def test_setitem_change_dtype(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + dft = frame.T + s = dft["foo", "two"] + dft["foo", "two"] = s > s.median() + tm.assert_series_equal(dft["foo", "two"], s > s.median()) + # assert isinstance(dft._data.blocks[1].items, MultiIndex) + + reindexed = dft.reindex(columns=[("foo", "two")]) + tm.assert_series_equal(reindexed["foo", "two"], s > s.median()) + + def test_set_column_scalar_with_loc(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + subset = frame.index[[1, 4, 5]] + + frame.loc[subset] = 99 + assert (frame.loc[subset].values == 99).all() + + frame_original = frame.copy() + col = frame["B"] + col[subset] = 97 + # chained setitem doesn't work with CoW + tm.assert_frame_equal(frame, frame_original) + + def test_nonunique_assignment_1750(self): + df = DataFrame( + [[1, 1, "x", "X"], [1, 1, "y", "Y"], [1, 2, "z", "Z"]], columns=list("ABCD") + ) + + df = df.set_index(["A", "B"]) + mi = MultiIndex.from_tuples([(1, 1)]) + + df.loc[mi, "C"] = "_" + + assert (df.xs((1, 1))["C"] == "_").all() + + def test_astype_assignment_with_dups(self): + # GH 4686 + # assignment with dups that has a dtype change + cols = MultiIndex.from_tuples([("A", "1"), ("B", "1"), ("A", "2")]) + df = DataFrame(np.arange(3).reshape((1, 3)), columns=cols, dtype=object) + index = df.index.copy() + + df["A"] = df["A"].astype(np.float64) + tm.assert_index_equal(df.index, index) + + def test_setitem_nonmonotonic(self): + # https://github.com/pandas-dev/pandas/issues/31449 + index = MultiIndex.from_tuples( + [("a", "c"), ("b", "x"), ("a", "d")], names=["l1", "l2"] + ) + df = DataFrame(data=[0, 1, 2], index=index, columns=["e"]) + df.loc["a", "e"] = np.arange(99, 101, dtype="int64") + expected = DataFrame({"e": [99, 1, 100]}, index=index) + tm.assert_frame_equal(df, expected) + + +class TestSetitemWithExpansionMultiIndex: + def test_setitem_new_column_mixed_depth(self): + arrays = [ + ["a", "top", "top", "routine1", "routine1", "routine2"], + ["", "OD", "OD", "result1", "result2", "result1"], + ["", "wx", "wy", "", "", ""], + ] + + tuples = sorted(zip(*arrays)) + index = MultiIndex.from_tuples(tuples) + df = DataFrame(np.random.default_rng(2).standard_normal((4, 6)), columns=index) + + result = df.copy() + expected = df.copy() + result["b"] = [1, 2, 3, 4] + expected["b", "", ""] = [1, 2, 3, 4] + tm.assert_frame_equal(result, expected) + + def test_setitem_new_column_all_na(self): + # GH#1534 + mix = MultiIndex.from_tuples([("1a", "2a"), ("1a", "2b"), ("1a", "2c")]) + df = DataFrame([[1, 2], [3, 4], [5, 6]], index=mix) + s = Series({(1, 1): 1, (1, 2): 2}) + df["new"] = s + assert df["new"].isna().all() + + def test_setitem_enlargement_keep_index_names(self): + # GH#53053 + mi = MultiIndex.from_tuples([(1, 2, 3)], names=["i1", "i2", "i3"]) + df = DataFrame(data=[[10, 20, 30]], index=mi, columns=["A", "B", "C"]) + df.loc[(0, 0, 0)] = df.loc[(1, 2, 3)] + mi_expected = MultiIndex.from_tuples( + [(1, 2, 3), (0, 0, 0)], names=["i1", "i2", "i3"] + ) + expected = DataFrame( + data=[[10, 20, 30], [10, 20, 30]], + index=mi_expected, + columns=["A", "B", "C"], + ) + tm.assert_frame_equal(df, expected) + + def test_setitem_enlargement_multiindex_with_none(self): + # GH#59153 + # enlarging a DataFrame with a MultiIndex containing None values + index = MultiIndex.from_tuples( + [("A", "a1"), ("A", "a2"), ("B", "b1"), ("B", None)] + ) + df = DataFrame([(0.0, 6.0), (1.0, 5.0), (2.0, 4.0), (3.0, 7.0)], index=index) + df.loc[("A", None), :] = [12.0, 13.0] + expected_index = MultiIndex.from_tuples( + [("A", "a1"), ("A", "a2"), ("B", "b1"), ("B", None), ("A", None)] + ) + expected = DataFrame( + [[0.0, 6.0], [1.0, 5.0], [2.0, 4.0], [3.0, 7.0], [12.0, 13.0]], + index=expected_index, + columns=[0, 1], + ) + tm.assert_frame_equal(df, expected, check_index_type=False) + + +def test_frame_setitem_view_direct(multiindex_dataframe_random_data): + # this works because we are modifying the underlying array + # really a no-no + df = multiindex_dataframe_random_data.T + with pytest.raises(ValueError, match="read-only"): + df["foo"].values[:] = 0 + assert (df["foo"].values != 0).all() + + +def test_frame_setitem_copy_raises(multiindex_dataframe_random_data): + # will raise/warn as its chained assignment + df = multiindex_dataframe_random_data.T + with tm.raises_chained_assignment_error(): + df["foo"]["one"] = 2 + + +def test_frame_setitem_copy_no_write(multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data.T + expected = frame + df = frame.copy() + with tm.raises_chained_assignment_error(): + df["foo"]["one"] = 2 + + tm.assert_frame_equal(df, expected) + + +def test_frame_setitem_partial_multiindex(): + # GH 54875 + df = DataFrame( + { + "a": [1, 2, 3], + "b": [3, 4, 5], + "c": 6, + "d": 7, + } + ).set_index(["a", "b", "c"]) + ser = Series(8, index=df.index.droplevel("c")) + result = df.copy() + result["d"] = ser + expected = df.copy() + expected["d"] = 8 + tm.assert_frame_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_slice.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_slice.py new file mode 100644 index 0000000000000000000000000000000000000000..7f298e9bdd375d226b295455a6db1443f2ffaf77 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_slice.py @@ -0,0 +1,798 @@ +from datetime import ( + datetime, + timedelta, +) + +import numpy as np +import pytest + +from pandas.errors import UnsortedIndexError + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, + Timestamp, +) +import pandas._testing as tm +from pandas.tests.indexing.common import _mklbl + + +class TestMultiIndexSlicers: + def test_per_axis_per_level_getitem(self): + # GH6134 + # example test case + ix = MultiIndex.from_product( + [_mklbl("A", 5), _mklbl("B", 7), _mklbl("C", 4), _mklbl("D", 2)] + ) + df = DataFrame(np.arange(len(ix.to_numpy())), index=ix) + + result = df.loc[(slice("A1", "A3"), slice(None), ["C1", "C3"]), :] + expected = df.loc[ + [ + ( + a, + b, + c, + d, + ) + for a, b, c, d in df.index.values + if a in ("A1", "A2", "A3") and c in ("C1", "C3") + ] + ] + tm.assert_frame_equal(result, expected) + + expected = df.loc[ + [ + ( + a, + b, + c, + d, + ) + for a, b, c, d in df.index.values + if a in ("A1", "A2", "A3") and c in ("C1", "C2", "C3") + ] + ] + result = df.loc[(slice("A1", "A3"), slice(None), slice("C1", "C3")), :] + tm.assert_frame_equal(result, expected) + + # test multi-index slicing with per axis and per index controls + index = MultiIndex.from_tuples( + [("A", 1), ("A", 2), ("A", 3), ("B", 1)], names=["one", "two"] + ) + columns = MultiIndex.from_tuples( + [("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")], + names=["lvl0", "lvl1"], + ) + + df = DataFrame( + np.arange(16, dtype="int64").reshape(4, 4), index=index, columns=columns + ) + df = df.sort_index(axis=0).sort_index(axis=1) + + # identity + result = df.loc[(slice(None), slice(None)), :] + tm.assert_frame_equal(result, df) + result = df.loc[(slice(None), slice(None)), (slice(None), slice(None))] + tm.assert_frame_equal(result, df) + result = df.loc[:, (slice(None), slice(None))] + tm.assert_frame_equal(result, df) + + # index + result = df.loc[(slice(None), [1]), :] + expected = df.iloc[[0, 3]] + tm.assert_frame_equal(result, expected) + + result = df.loc[(slice(None), 1), :] + expected = df.iloc[[0, 3]] + tm.assert_frame_equal(result, expected) + + # columns + result = df.loc[:, (slice(None), ["foo"])] + expected = df.iloc[:, [1, 3]] + tm.assert_frame_equal(result, expected) + + # both + result = df.loc[(slice(None), 1), (slice(None), ["foo"])] + expected = df.iloc[[0, 3], [1, 3]] + tm.assert_frame_equal(result, expected) + + result = df.loc["A", "a"] + expected = DataFrame( + {"bar": [1, 5, 9], "foo": [0, 4, 8]}, + index=Index([1, 2, 3], name="two"), + columns=Index(["bar", "foo"], name="lvl1"), + ) + tm.assert_frame_equal(result, expected) + + result = df.loc[(slice(None), [1, 2]), :] + expected = df.iloc[[0, 1, 3]] + tm.assert_frame_equal(result, expected) + + # multi-level series + s = Series(np.arange(len(ix.to_numpy())), index=ix) + result = s.loc["A1":"A3", :, ["C1", "C3"]] + expected = s.loc[ + [ + ( + a, + b, + c, + d, + ) + for a, b, c, d in s.index.values + if a in ("A1", "A2", "A3") and c in ("C1", "C3") + ] + ] + tm.assert_series_equal(result, expected) + + # boolean indexers + result = df.loc[(slice(None), df.loc[:, ("a", "bar")] > 5), :] + expected = df.iloc[[2, 3]] + tm.assert_frame_equal(result, expected) + + msg = ( + "cannot index with a boolean indexer " + "that is not the same length as the index" + ) + with pytest.raises(ValueError, match=msg): + df.loc[(slice(None), np.array([True, False])), :] + + with pytest.raises(KeyError, match=r"\[1\] not in index"): + # slice(None) is on the index, [1] is on the columns, but 1 is + # not in the columns, so we raise + # This used to treat [1] as positional GH#16396 + df.loc[slice(None), [1]] + + # not lexsorted + assert df.index._lexsort_depth == 2 + df = df.sort_index(level=1, axis=0) + assert df.index._lexsort_depth == 0 + + msg = ( + "MultiIndex slicing requires the index to be " + r"lexsorted: slicing on levels \[1\], lexsort depth 0" + ) + with pytest.raises(UnsortedIndexError, match=msg): + df.loc[(slice(None), slice("bar")), :] + + # GH 16734: not sorted, but no real slicing + result = df.loc[(slice(None), df.loc[:, ("a", "bar")] > 5), :] + tm.assert_frame_equal(result, df.iloc[[1, 3], :]) + + def test_multiindex_slicers_non_unique(self): + # GH 7106 + # non-unique mi index support + df = ( + DataFrame( + { + "A": ["foo", "foo", "foo", "foo"], + "B": ["a", "a", "a", "a"], + "C": [1, 2, 1, 3], + "D": [1, 2, 3, 4], + } + ) + .set_index(["A", "B", "C"]) + .sort_index() + ) + assert not df.index.is_unique + expected = ( + DataFrame({"A": ["foo", "foo"], "B": ["a", "a"], "C": [1, 1], "D": [1, 3]}) + .set_index(["A", "B", "C"]) + .sort_index() + ) + result = df.loc[(slice(None), slice(None), 1), :] + tm.assert_frame_equal(result, expected) + + # this is equivalent of an xs expression + result = df.xs(1, level=2, drop_level=False) + tm.assert_frame_equal(result, expected) + + df = ( + DataFrame( + { + "A": ["foo", "foo", "foo", "foo"], + "B": ["a", "a", "a", "a"], + "C": [1, 2, 1, 2], + "D": [1, 2, 3, 4], + } + ) + .set_index(["A", "B", "C"]) + .sort_index() + ) + assert not df.index.is_unique + expected = ( + DataFrame({"A": ["foo", "foo"], "B": ["a", "a"], "C": [1, 1], "D": [1, 3]}) + .set_index(["A", "B", "C"]) + .sort_index() + ) + result = df.loc[(slice(None), slice(None), 1), :] + assert not result.index.is_unique + tm.assert_frame_equal(result, expected) + + # GH12896 + # numpy-implementation dependent bug + ints = [ + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 12, + 12, + 13, + 14, + 14, + 16, + 17, + 18, + 19, + 200000, + 200000, + ] + n = len(ints) + idx = MultiIndex.from_arrays([["a"] * n, ints]) + result = Series([1] * n, index=idx) + result = result.sort_index() + result = result.loc[(slice(None), slice(100000))] + expected = Series([1] * (n - 2), index=idx[:-2]).sort_index() + tm.assert_series_equal(result, expected) + + def test_multiindex_slicers_datetimelike(self): + # GH 7429 + # buggy/inconsistent behavior when slicing with datetime-like + dates = [datetime(2012, 1, 1, 12, 12, 12) + timedelta(days=i) for i in range(6)] + freq = [1, 2] + index = MultiIndex.from_product([dates, freq], names=["date", "frequency"]) + + df = DataFrame( + np.arange(6 * 2 * 4, dtype="int64").reshape(-1, 4), + index=index, + columns=list("ABCD"), + ) + + # multi-axis slicing + idx = pd.IndexSlice + expected = df.iloc[[0, 2, 4], [0, 1]] + result = df.loc[ + ( + slice( + Timestamp("2012-01-01 12:12:12"), Timestamp("2012-01-03 12:12:12") + ), + slice(1, 1), + ), + slice("A", "B"), + ] + tm.assert_frame_equal(result, expected) + + result = df.loc[ + ( + idx[ + Timestamp("2012-01-01 12:12:12") : Timestamp("2012-01-03 12:12:12") + ], + idx[1:1], + ), + slice("A", "B"), + ] + tm.assert_frame_equal(result, expected) + + result = df.loc[ + ( + slice( + Timestamp("2012-01-01 12:12:12"), Timestamp("2012-01-03 12:12:12") + ), + 1, + ), + slice("A", "B"), + ] + tm.assert_frame_equal(result, expected) + + # with strings + result = df.loc[ + (slice("2012-01-01 12:12:12", "2012-01-03 12:12:12"), slice(1, 1)), + slice("A", "B"), + ] + tm.assert_frame_equal(result, expected) + + result = df.loc[ + (idx["2012-01-01 12:12:12":"2012-01-03 12:12:12"], 1), idx["A", "B"] + ] + tm.assert_frame_equal(result, expected) + + def test_multiindex_slicers_edges(self): + # GH 8132 + # various edge cases + df = DataFrame( + { + "A": ["A0"] * 5 + ["A1"] * 5 + ["A2"] * 5, + "B": ["B0", "B0", "B1", "B1", "B2"] * 3, + "DATE": [ + "2013-06-11", + "2013-07-02", + "2013-07-09", + "2013-07-30", + "2013-08-06", + "2013-06-11", + "2013-07-02", + "2013-07-09", + "2013-07-30", + "2013-08-06", + "2013-09-03", + "2013-10-01", + "2013-07-09", + "2013-08-06", + "2013-09-03", + ], + "VALUES": [22, 35, 14, 9, 4, 40, 18, 4, 2, 5, 1, 2, 3, 4, 2], + } + ) + + df["DATE"] = pd.to_datetime(df["DATE"]) + df1 = df.set_index(["A", "B", "DATE"]) + df1 = df1.sort_index() + + # A1 - Get all values under "A0" and "A1" + result = df1.loc[(slice("A1")), :] + expected = df1.iloc[0:10] + tm.assert_frame_equal(result, expected) + + # A2 - Get all values from the start to "A2" + result = df1.loc[(slice("A2")), :] + expected = df1 + tm.assert_frame_equal(result, expected) + + # A3 - Get all values under "B1" or "B2" + result = df1.loc[(slice(None), slice("B1", "B2")), :] + expected = df1.iloc[[2, 3, 4, 7, 8, 9, 12, 13, 14]] + tm.assert_frame_equal(result, expected) + + # A4 - Get all values between 2013-07-02 and 2013-07-09 + result = df1.loc[(slice(None), slice(None), slice("20130702", "20130709")), :] + expected = df1.iloc[[1, 2, 6, 7, 12]] + tm.assert_frame_equal(result, expected) + + # B1 - Get all values in B0 that are also under A0, A1 and A2 + result = df1.loc[(slice("A2"), slice("B0")), :] + expected = df1.iloc[[0, 1, 5, 6, 10, 11]] + tm.assert_frame_equal(result, expected) + + # B2 - Get all values in B0, B1 and B2 (similar to what #2 is doing for + # the As) + result = df1.loc[(slice(None), slice("B2")), :] + expected = df1 + tm.assert_frame_equal(result, expected) + + # B3 - Get all values from B1 to B2 and up to 2013-08-06 + result = df1.loc[(slice(None), slice("B1", "B2"), slice("2013-08-06")), :] + expected = df1.iloc[[2, 3, 4, 7, 8, 9, 12, 13]] + tm.assert_frame_equal(result, expected) + + # B4 - Same as A4 but the start of the date slice is not a key. + # shows indexing on a partial selection slice + result = df1.loc[(slice(None), slice(None), slice("20130701", "20130709")), :] + expected = df1.iloc[[1, 2, 6, 7, 12]] + tm.assert_frame_equal(result, expected) + + def test_per_axis_per_level_doc_examples(self): + # test index maker + idx = pd.IndexSlice + + # from indexing.rst / advanced + index = MultiIndex.from_product( + [_mklbl("A", 4), _mklbl("B", 2), _mklbl("C", 4), _mklbl("D", 2)] + ) + columns = MultiIndex.from_tuples( + [("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")], + names=["lvl0", "lvl1"], + ) + df = DataFrame( + np.arange(len(index) * len(columns), dtype="int64").reshape( + (len(index), len(columns)) + ), + index=index, + columns=columns, + ) + result = df.loc[(slice("A1", "A3"), slice(None), ["C1", "C3"]), :] + expected = df.loc[ + [ + ( + a, + b, + c, + d, + ) + for a, b, c, d in df.index.values + if a in ("A1", "A2", "A3") and c in ("C1", "C3") + ] + ] + tm.assert_frame_equal(result, expected) + result = df.loc[idx["A1":"A3", :, ["C1", "C3"]], :] + tm.assert_frame_equal(result, expected) + + result = df.loc[(slice(None), slice(None), ["C1", "C3"]), :] + expected = df.loc[ + [ + ( + a, + b, + c, + d, + ) + for a, b, c, d in df.index.values + if c in ("C1", "C3") + ] + ] + tm.assert_frame_equal(result, expected) + result = df.loc[idx[:, :, ["C1", "C3"]], :] + tm.assert_frame_equal(result, expected) + + # not sorted + msg = ( + "MultiIndex slicing requires the index to be lexsorted: " + r"slicing on levels \[1\], lexsort depth 1" + ) + with pytest.raises(UnsortedIndexError, match=msg): + df.loc["A1", ("a", slice("foo"))] + + # GH 16734: not sorted, but no real slicing + tm.assert_frame_equal( + df.loc["A1", (slice(None), "foo")], df.loc["A1"].iloc[:, [0, 2]] + ) + + df = df.sort_index(axis=1) + + # slicing + df.loc["A1", (slice(None), "foo")] + df.loc[(slice(None), slice(None), ["C1", "C3"]), (slice(None), "foo")] + + # setitem + df.loc(axis=0)[:, :, ["C1", "C3"]] = -10 + + def test_loc_axis_arguments(self): + index = MultiIndex.from_product( + [_mklbl("A", 4), _mklbl("B", 2), _mklbl("C", 4), _mklbl("D", 2)] + ) + columns = MultiIndex.from_tuples( + [("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")], + names=["lvl0", "lvl1"], + ) + df = ( + DataFrame( + np.arange(len(index) * len(columns), dtype="int64").reshape( + (len(index), len(columns)) + ), + index=index, + columns=columns, + ) + .sort_index() + .sort_index(axis=1) + ) + + # axis 0 + result = df.loc(axis=0)["A1":"A3", :, ["C1", "C3"]] + expected = df.loc[ + [ + ( + a, + b, + c, + d, + ) + for a, b, c, d in df.index.values + if a in ("A1", "A2", "A3") and c in ("C1", "C3") + ] + ] + tm.assert_frame_equal(result, expected) + + result = df.loc(axis="index")[:, :, ["C1", "C3"]] + expected = df.loc[ + [ + ( + a, + b, + c, + d, + ) + for a, b, c, d in df.index.values + if c in ("C1", "C3") + ] + ] + tm.assert_frame_equal(result, expected) + + # axis 1 + result = df.loc(axis=1)[:, "foo"] + expected = df.loc[:, (slice(None), "foo")] + tm.assert_frame_equal(result, expected) + + result = df.loc(axis="columns")[:, "foo"] + expected = df.loc[:, (slice(None), "foo")] + tm.assert_frame_equal(result, expected) + + # invalid axis + for i in [-1, 2, "foo"]: + msg = f"No axis named {i} for object type DataFrame" + with pytest.raises(ValueError, match=msg): + df.loc(axis=i)[:, :, ["C1", "C3"]] + + def test_loc_axis_single_level_multi_col_indexing_multiindex_col_df(self): + # GH29519 + df = DataFrame( + np.arange(27).reshape(3, 9), + columns=MultiIndex.from_product([["a1", "a2", "a3"], ["b1", "b2", "b3"]]), + ) + result = df.loc(axis=1)["a1":"a2"] + expected = df.iloc[:, :-3] + + tm.assert_frame_equal(result, expected) + + def test_loc_axis_single_level_single_col_indexing_multiindex_col_df(self): + # GH29519 + df = DataFrame( + np.arange(27).reshape(3, 9), + columns=MultiIndex.from_product([["a1", "a2", "a3"], ["b1", "b2", "b3"]]), + ) + result = df.loc(axis=1)["a1"] + expected = df.iloc[:, :3] + expected.columns = ["b1", "b2", "b3"] + + tm.assert_frame_equal(result, expected) + + def test_loc_ax_single_level_indexer_simple_df(self): + # GH29519 + # test single level indexing on single index column data frame + df = DataFrame(np.arange(9).reshape(3, 3), columns=["a", "b", "c"]) + result = df.loc(axis=1)["a"] + expected = Series(np.array([0, 3, 6]), name="a") + tm.assert_series_equal(result, expected) + + def test_per_axis_per_level_setitem(self): + # test index maker + idx = pd.IndexSlice + + # test multi-index slicing with per axis and per index controls + index = MultiIndex.from_tuples( + [("A", 1), ("A", 2), ("A", 3), ("B", 1)], names=["one", "two"] + ) + columns = MultiIndex.from_tuples( + [("a", "foo"), ("a", "bar"), ("b", "foo"), ("b", "bah")], + names=["lvl0", "lvl1"], + ) + + df_orig = DataFrame( + np.arange(16, dtype="int64").reshape(4, 4), index=index, columns=columns + ) + df_orig = df_orig.sort_index(axis=0).sort_index(axis=1) + + # identity + df = df_orig.copy() + df.loc[(slice(None), slice(None)), :] = 100 + expected = df_orig.copy() + expected.iloc[:, :] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc(axis=0)[:, :] = 100 + expected = df_orig.copy() + expected.iloc[:, :] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[(slice(None), slice(None)), (slice(None), slice(None))] = 100 + expected = df_orig.copy() + expected.iloc[:, :] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[:, (slice(None), slice(None))] = 100 + expected = df_orig.copy() + expected.iloc[:, :] = 100 + tm.assert_frame_equal(df, expected) + + # index + df = df_orig.copy() + df.loc[(slice(None), [1]), :] = 100 + expected = df_orig.copy() + expected.iloc[[0, 3]] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[(slice(None), 1), :] = 100 + expected = df_orig.copy() + expected.iloc[[0, 3]] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc(axis=0)[:, 1] = 100 + expected = df_orig.copy() + expected.iloc[[0, 3]] = 100 + tm.assert_frame_equal(df, expected) + + # columns + df = df_orig.copy() + df.loc[:, (slice(None), ["foo"])] = 100 + expected = df_orig.copy() + expected.iloc[:, [1, 3]] = 100 + tm.assert_frame_equal(df, expected) + + # both + df = df_orig.copy() + df.loc[(slice(None), 1), (slice(None), ["foo"])] = 100 + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[idx[:, 1], idx[:, ["foo"]]] = 100 + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] = 100 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc["A", "a"] = 100 + expected = df_orig.copy() + expected.iloc[0:3, 0:2] = 100 + tm.assert_frame_equal(df, expected) + + # setting with a list-like + df = df_orig.copy() + df.loc[(slice(None), 1), (slice(None), ["foo"])] = np.array( + [[100, 100], [100, 100]], dtype="int64" + ) + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] = 100 + tm.assert_frame_equal(df, expected) + + # not enough values + df = df_orig.copy() + + msg = "setting an array element with a sequence." + with pytest.raises(ValueError, match=msg): + df.loc[(slice(None), 1), (slice(None), ["foo"])] = np.array( + [[100], [100, 100]], dtype="int64" + ) + + msg = "Must have equal len keys and value when setting with an iterable" + with pytest.raises(ValueError, match=msg): + df.loc[(slice(None), 1), (slice(None), ["foo"])] = np.array( + [100, 100, 100, 100], dtype="int64" + ) + + # with an alignable rhs + df = df_orig.copy() + df.loc[(slice(None), 1), (slice(None), ["foo"])] = ( + df.loc[(slice(None), 1), (slice(None), ["foo"])] * 5 + ) + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] = expected.iloc[[0, 3], [1, 3]] * 5 + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + df.loc[(slice(None), 1), (slice(None), ["foo"])] *= df.loc[ + (slice(None), 1), (slice(None), ["foo"]) + ] + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] *= expected.iloc[[0, 3], [1, 3]] + tm.assert_frame_equal(df, expected) + + rhs = df_orig.loc[(slice(None), 1), (slice(None), ["foo"])].copy() + rhs.loc[:, ("c", "bah")] = 10 + df = df_orig.copy() + df.loc[(slice(None), 1), (slice(None), ["foo"])] *= rhs + expected = df_orig.copy() + expected.iloc[[0, 3], [1, 3]] *= expected.iloc[[0, 3], [1, 3]] + tm.assert_frame_equal(df, expected) + + def test_multiindex_label_slicing_with_negative_step(self): + ser = Series( + np.arange(20), MultiIndex.from_product([list("abcde"), np.arange(4)]) + ) + SLC = pd.IndexSlice + + tm.assert_indexing_slices_equivalent(ser, SLC[::-1], SLC[::-1]) + + tm.assert_indexing_slices_equivalent(ser, SLC["d"::-1], SLC[15::-1]) + tm.assert_indexing_slices_equivalent(ser, SLC[("d",) :: -1], SLC[15::-1]) + + tm.assert_indexing_slices_equivalent(ser, SLC[:"d":-1], SLC[:11:-1]) + tm.assert_indexing_slices_equivalent(ser, SLC[: ("d",) : -1], SLC[:11:-1]) + + tm.assert_indexing_slices_equivalent(ser, SLC["d":"b":-1], SLC[15:3:-1]) + tm.assert_indexing_slices_equivalent(ser, SLC[("d",) : "b" : -1], SLC[15:3:-1]) + tm.assert_indexing_slices_equivalent(ser, SLC["d" : ("b",) : -1], SLC[15:3:-1]) + tm.assert_indexing_slices_equivalent( + ser, SLC[("d",) : ("b",) : -1], SLC[15:3:-1] + ) + tm.assert_indexing_slices_equivalent(ser, SLC["b":"d":-1], SLC[:0]) + + tm.assert_indexing_slices_equivalent(ser, SLC[("c", 2) :: -1], SLC[10::-1]) + tm.assert_indexing_slices_equivalent(ser, SLC[: ("c", 2) : -1], SLC[:9:-1]) + tm.assert_indexing_slices_equivalent( + ser, SLC[("e", 0) : ("c", 2) : -1], SLC[16:9:-1] + ) + + def test_multiindex_slice_first_level(self): + # GH 12697 + freq = ["a", "b", "c", "d"] + idx = MultiIndex.from_product([freq, range(500)]) + df = DataFrame(list(range(2000)), index=idx, columns=["Test"]) + df_slice = df.loc[pd.IndexSlice[:, 30:70], :] + result = df_slice.loc["a"] + expected = DataFrame(list(range(30, 71)), columns=["Test"], index=range(30, 71)) + tm.assert_frame_equal(result, expected) + result = df_slice.loc["d"] + expected = DataFrame( + list(range(1530, 1571)), columns=["Test"], index=range(30, 71) + ) + tm.assert_frame_equal(result, expected) + + def test_int_series_slicing(self, multiindex_year_month_day_dataframe_random_data): + ymd = multiindex_year_month_day_dataframe_random_data + s = ymd["A"] + result = s[5:] + expected = s.reindex(s.index[5:]) + tm.assert_series_equal(result, expected) + + s = ymd["A"].copy() + exp = ymd["A"].copy() + s[5:] = 0 + exp.iloc[5:] = 0 + tm.assert_numpy_array_equal(s.values, exp.values) + + result = ymd[5:] + expected = ymd.reindex(s.index[5:]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "dtype, loc, iloc", + [ + # dtype = int, step = -1 + ("int", slice(None, None, -1), slice(None, None, -1)), + ("int", slice(3, None, -1), slice(3, None, -1)), + ("int", slice(None, 1, -1), slice(None, 0, -1)), + ("int", slice(3, 1, -1), slice(3, 0, -1)), + # dtype = int, step = -2 + ("int", slice(None, None, -2), slice(None, None, -2)), + ("int", slice(3, None, -2), slice(3, None, -2)), + ("int", slice(None, 1, -2), slice(None, 0, -2)), + ("int", slice(3, 1, -2), slice(3, 0, -2)), + # dtype = str, step = -1 + ("str", slice(None, None, -1), slice(None, None, -1)), + ("str", slice("d", None, -1), slice(3, None, -1)), + ("str", slice(None, "b", -1), slice(None, 0, -1)), + ("str", slice("d", "b", -1), slice(3, 0, -1)), + # dtype = str, step = -2 + ("str", slice(None, None, -2), slice(None, None, -2)), + ("str", slice("d", None, -2), slice(3, None, -2)), + ("str", slice(None, "b", -2), slice(None, 0, -2)), + ("str", slice("d", "b", -2), slice(3, 0, -2)), + ], + ) + def test_loc_slice_negative_stepsize(self, dtype, loc, iloc): + # GH#38071 + labels = { + "str": list("abcde"), + "int": range(5), + }[dtype] + + mi = MultiIndex.from_arrays([labels] * 2) + df = DataFrame(1.0, index=mi, columns=["A"]) + + SLC = pd.IndexSlice + + expected = df.iloc[iloc, :] + result_get_loc = df.loc[SLC[loc], :] + result_get_locs_level_0 = df.loc[SLC[loc, :], :] + result_get_locs_level_1 = df.loc[SLC[:, loc], :] + + tm.assert_frame_equal(result_get_loc, expected) + tm.assert_frame_equal(result_get_locs_level_0, expected) + tm.assert_frame_equal(result_get_locs_level_1, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_sorted.py b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_sorted.py new file mode 100644 index 0000000000000000000000000000000000000000..53a2e1ce05d2a1c48f6df009b1d6ad7187d0ae7d --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/multiindex/test_sorted.py @@ -0,0 +1,153 @@ +import numpy as np +import pytest + +from pandas import ( + NA, + DataFrame, + MultiIndex, + Series, + array, +) +import pandas._testing as tm + + +class TestMultiIndexSorted: + def test_getitem_multilevel_index_tuple_not_sorted(self): + index_columns = list("abc") + df = DataFrame( + [[0, 1, 0, "x"], [0, 0, 1, "y"]], columns=[*index_columns, "data"] + ) + df = df.set_index(index_columns) + query_index = df.index[:1] + rs = df.loc[query_index, "data"] + + xp_idx = MultiIndex.from_tuples([(0, 1, 0)], names=["a", "b", "c"]) + xp = Series(["x"], index=xp_idx, name="data") + tm.assert_series_equal(rs, xp) + + def test_getitem_slice_not_sorted(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + df = frame.sort_index(level=1).T + + # buglet with int typechecking + result = df.iloc[:, : np.int32(3)] + expected = df.reindex(columns=df.columns[:3]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("key", [None, lambda x: x]) + def test_frame_getitem_not_sorted2(self, key): + # 13431 + df = DataFrame( + { + "col1": ["b", "d", "b", "a"], + "col2": [3, 1, 1, 2], + "data": ["one", "two", "three", "four"], + } + ) + + df2 = df.set_index(["col1", "col2"]) + df2_original = df2.copy() + + df2.index = df2.index.set_levels(["b", "d", "a"], level="col1") + df2.index = df2.index.set_codes([0, 1, 0, 2], level="col1") + assert not df2.index.is_monotonic_increasing + + assert df2_original.index.equals(df2.index) + expected = df2.sort_index(key=key) + assert expected.index.is_monotonic_increasing + + result = df2.sort_index(level=0, key=key) + assert result.index.is_monotonic_increasing + tm.assert_frame_equal(result, expected) + + def test_sort_values_key(self): + arrays = [ + ["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"], + ["one", "two", "one", "two", "one", "two", "one", "two"], + ] + tuples = zip(*arrays) + index = MultiIndex.from_tuples(tuples) + index = index.sort_values( # sort by third letter + key=lambda x: x.map(lambda entry: entry[2]) + ) + result = DataFrame(range(8), index=index) + + arrays = [ + ["foo", "foo", "bar", "bar", "qux", "qux", "baz", "baz"], + ["one", "two", "one", "two", "one", "two", "one", "two"], + ] + tuples = zip(*arrays) + index = MultiIndex.from_tuples(tuples) + expected = DataFrame(range(8), index=index) + + tm.assert_frame_equal(result, expected) + + def test_argsort_with_na(self): + # GH48495 + arrays = [ + array([2, NA, 1], dtype="Int64"), + array([1, 2, 3], dtype="Int64"), + ] + index = MultiIndex.from_arrays(arrays) + result = index.argsort() + expected = np.array([2, 0, 1], dtype=np.intp) + tm.assert_numpy_array_equal(result, expected) + + def test_sort_values_with_na(self): + # GH48495 + arrays = [ + array([2, NA, 1], dtype="Int64"), + array([1, 2, 3], dtype="Int64"), + ] + index = MultiIndex.from_arrays(arrays) + index = index.sort_values() + result = DataFrame(range(3), index=index) + + arrays = [ + array([1, 2, NA], dtype="Int64"), + array([3, 1, 2], dtype="Int64"), + ] + index = MultiIndex.from_arrays(arrays) + expected = DataFrame(range(3), index=index) + + tm.assert_frame_equal(result, expected) + + def test_frame_getitem_not_sorted(self, multiindex_dataframe_random_data): + frame = multiindex_dataframe_random_data + df = frame.T + df["foo", "four"] = "foo" + + arrays = [np.array(x) for x in zip(*df.columns.values)] + + result = df["foo"] + result2 = df.loc[:, "foo"] + expected = df.reindex(columns=df.columns[arrays[0] == "foo"]) + expected.columns = expected.columns.droplevel(0) + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result2, expected) + + df = df.T + result = df.xs("foo") + result2 = df.loc["foo"] + expected = df.reindex(df.index[arrays[0] == "foo"]) + expected.index = expected.index.droplevel(0) + tm.assert_frame_equal(result, expected) + tm.assert_frame_equal(result2, expected) + + def test_series_getitem_not_sorted(self): + arrays = [ + ["bar", "bar", "baz", "baz", "qux", "qux", "foo", "foo"], + ["one", "two", "one", "two", "one", "two", "one", "two"], + ] + tuples = zip(*arrays) + index = MultiIndex.from_tuples(tuples) + s = Series(np.random.default_rng(2).standard_normal(8), index=index) + + arrays = [np.array(x) for x in zip(*index.values)] + + result = s["qux"] + result2 = s.loc["qux"] + expected = s[arrays[0] == "qux"] + expected.index = expected.index.droplevel(0) + tm.assert_series_equal(result, expected) + tm.assert_series_equal(result2, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_at.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_at.py new file mode 100644 index 0000000000000000000000000000000000000000..d24d3433326693e61ad18141339b769d4eaf0f61 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_at.py @@ -0,0 +1,229 @@ +from datetime import ( + datetime, + timezone, +) + +import numpy as np +import pytest + +from pandas.errors import InvalidIndexError + +from pandas import ( + CategoricalDtype, + CategoricalIndex, + DataFrame, + DatetimeIndex, + MultiIndex, + Series, + Timestamp, +) +import pandas._testing as tm + + +def test_at_timezone(): + # https://github.com/pandas-dev/pandas/issues/33544 + result = DataFrame({"foo": [datetime(2000, 1, 1)]}) + with pytest.raises(TypeError, match="Invalid value"): + result.at[0, "foo"] = datetime(2000, 1, 2, tzinfo=timezone.utc) + + +def test_selection_methods_of_assigned_col(): + # GH 29282 + df = DataFrame(data={"a": [1, 2, 3], "b": [4, 5, 6]}) + df2 = DataFrame(data={"c": [7, 8, 9]}, index=[2, 1, 0]) + df["c"] = df2["c"] + df.at[1, "c"] = 11 + result = df + expected = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [9, 11, 7]}) + tm.assert_frame_equal(result, expected) + result = df.at[1, "c"] + assert result == 11 + + result = df["c"] + expected = Series([9, 11, 7], name="c") + tm.assert_series_equal(result, expected) + + result = df[["c"]] + expected = DataFrame({"c": [9, 11, 7]}) + tm.assert_frame_equal(result, expected) + + +class TestAtSetItem: + def test_at_setitem_mixed_index_assignment(self): + # GH#19860 + ser = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2]) + ser.at["a"] = 11 + assert ser.iat[0] == 11 + ser.at[1] = 22 + assert ser.iat[3] == 22 + + def test_at_setitem_categorical_missing(self): + df = DataFrame( + index=range(3), columns=range(3), dtype=CategoricalDtype(["foo", "bar"]) + ) + df.at[1, 1] = "foo" + + expected = DataFrame( + [ + [np.nan, np.nan, np.nan], + [np.nan, "foo", np.nan], + [np.nan, np.nan, np.nan], + ], + dtype=CategoricalDtype(["foo", "bar"]), + ) + + tm.assert_frame_equal(df, expected) + + def test_at_setitem_multiindex(self): + df = DataFrame( + np.zeros((3, 2), dtype="int64"), + columns=MultiIndex.from_tuples([("a", 0), ("a", 1)]), + ) + df.at[0, "a"] = 10 + expected = DataFrame( + [[10, 10], [0, 0], [0, 0]], + columns=MultiIndex.from_tuples([("a", 0), ("a", 1)]), + ) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("row", (Timestamp("2019-01-01"), "2019-01-01")) + def test_at_datetime_index(self, row): + # Set float64 dtype to avoid upcast when setting .5 + df = DataFrame( + data=[[1] * 2], index=DatetimeIndex(data=["2019-01-01", "2019-01-02"]) + ).astype({0: "float64"}) + expected = DataFrame( + data=[[0.5, 1], [1.0, 1]], + index=DatetimeIndex(data=["2019-01-01", "2019-01-02"]), + ) + + df.at[row, 0] = 0.5 + tm.assert_frame_equal(df, expected) + + +class TestAtSetItemWithExpansion: + def test_at_setitem_expansion_series_dt64tz_value(self, tz_naive_fixture): + # GH#25506 + ts = ( + Timestamp("2017-08-05 00:00:00+0100", tz=tz_naive_fixture) + if tz_naive_fixture is not None + else Timestamp("2017-08-05 00:00:00+0100") + ) + result = Series(ts) + result.at[1] = ts + expected = Series([ts, ts]) + tm.assert_series_equal(result, expected) + + +class TestAtWithDuplicates: + def test_at_with_duplicate_axes_requires_scalar_lookup(self): + # GH#33041 check that falling back to loc doesn't allow non-scalar + # args to slip in + + arr = np.random.default_rng(2).standard_normal(6).reshape(3, 2) + df = DataFrame(arr, columns=["A", "A"]) + + msg = "Invalid call for scalar access" + with pytest.raises(ValueError, match=msg): + df.at[[1, 2]] + with pytest.raises(ValueError, match=msg): + df.at[1, ["A"]] + with pytest.raises(ValueError, match=msg): + df.at[:, "A"] + + with pytest.raises(ValueError, match=msg): + df.at[[1, 2]] = 1 + with pytest.raises(ValueError, match=msg): + df.at[1, ["A"]] = 1 + with pytest.raises(ValueError, match=msg): + df.at[:, "A"] = 1 + + +class TestAtErrors: + # TODO: De-duplicate/parametrize + # test_at_series_raises_key_error2, test_at_frame_raises_key_error2 + + def test_at_series_raises_key_error(self, indexer_al): + # GH#31724 .at should match .loc + + ser = Series([1, 2, 3], index=[3, 2, 1]) + result = indexer_al(ser)[1] + assert result == 3 + + with pytest.raises(KeyError, match="a"): + indexer_al(ser)["a"] + + def test_at_frame_raises_key_error(self, indexer_al): + # GH#31724 .at should match .loc + + df = DataFrame({0: [1, 2, 3]}, index=[3, 2, 1]) + + result = indexer_al(df)[1, 0] + assert result == 3 + + with pytest.raises(KeyError, match="a"): + indexer_al(df)["a", 0] + + with pytest.raises(KeyError, match="a"): + indexer_al(df)[1, "a"] + + def test_at_series_raises_key_error2(self, indexer_al): + # at should not fallback + # GH#7814 + # GH#31724 .at should match .loc + ser = Series([1, 2, 3], index=list("abc")) + result = indexer_al(ser)["a"] + assert result == 1 + + with pytest.raises(KeyError, match="^0$"): + indexer_al(ser)[0] + + def test_at_frame_raises_key_error2(self, indexer_al): + # GH#31724 .at should match .loc + df = DataFrame({"A": [1, 2, 3]}, index=list("abc")) + result = indexer_al(df)["a", "A"] + assert result == 1 + + with pytest.raises(KeyError, match="^0$"): + indexer_al(df)["a", 0] + + def test_at_frame_multiple_columns(self): + # GH#48296 - at shouldn't modify multiple columns + df = DataFrame({"a": [1, 2], "b": [3, 4]}) + new_row = [6, 7] + with pytest.raises( + InvalidIndexError, + match=f"You can only assign a scalar value not a \\{type(new_row)}", + ): + df.at[5] = new_row + + def test_at_getitem_mixed_index_no_fallback(self): + # GH#19860 + ser = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2]) + with pytest.raises(KeyError, match="^0$"): + ser.at[0] + with pytest.raises(KeyError, match="^4$"): + ser.at[4] + + def test_at_categorical_integers(self): + # CategoricalIndex with integer categories that don't happen to match + # the Categorical's codes + ci = CategoricalIndex([3, 4]) + + arr = np.arange(4).reshape(2, 2) + frame = DataFrame(arr, index=ci) + + for df in [frame, frame.T]: + for key in [0, 1]: + with pytest.raises(KeyError, match=str(key)): + df.at[key, key] + + def test_at_applied_for_rows(self): + # GH#48729 .at should raise InvalidIndexError when assigning rows + df = DataFrame(index=["a"], columns=["col1", "col2"]) + new_row = [123, 15] + with pytest.raises( + InvalidIndexError, + match=f"You can only assign a scalar value not a \\{type(new_row)}", + ): + df.at["a"] = new_row diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_categorical.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_categorical.py new file mode 100644 index 0000000000000000000000000000000000000000..a31f463d0b17e9ca6f47279456b1f9f843b6b44e --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_categorical.py @@ -0,0 +1,595 @@ +import re + +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + Categorical, + CategoricalDtype, + CategoricalIndex, + DataFrame, + Index, + Interval, + Series, + Timedelta, + Timestamp, + option_context, +) +import pandas._testing as tm + + +@pytest.fixture +def df(): + return DataFrame( + { + "A": np.arange(6, dtype="int64"), + }, + index=CategoricalIndex( + list("aabbca"), dtype=CategoricalDtype(list("cab")), name="B" + ), + ) + + +@pytest.fixture +def df2(): + return DataFrame( + { + "A": np.arange(6, dtype="int64"), + }, + index=CategoricalIndex( + list("aabbca"), dtype=CategoricalDtype(list("cabe")), name="B" + ), + ) + + +class TestCategoricalIndex: + def test_loc_scalar(self, df): + dtype = CategoricalDtype(list("cab")) + result = df.loc["a"] + bidx = Series(list("aaa"), name="B").astype(dtype) + assert bidx.dtype == dtype + + expected = DataFrame({"A": [0, 1, 5]}, index=Index(bidx)) + tm.assert_frame_equal(result, expected) + + df = df.copy() + df.loc["a"] = 20 + bidx2 = Series(list("aabbca"), name="B").astype(dtype) + assert bidx2.dtype == dtype + expected = DataFrame( + { + "A": [20, 20, 2, 3, 4, 20], + }, + index=Index(bidx2), + ) + tm.assert_frame_equal(df, expected) + + # value not in the categories + with pytest.raises(KeyError, match=r"^'d'$"): + df.loc["d"] + + df2 = df.copy() + expected = df2.copy() + expected.index = expected.index.astype(object) + expected.loc["d"] = 10 + df2.loc["d"] = 10 + tm.assert_frame_equal(df2, expected) + + def test_loc_setitem_with_expansion_non_category(self, df): + # Setting-with-expansion with a new key "d" that is not among caegories + df.loc["a"] = 20 + + # Setting a new row on an existing column + df3 = df.copy() + df3.loc["d", "A"] = 10 + bidx3 = Index(list("aabbcad"), name="B") + expected3 = DataFrame( + { + "A": [20, 20, 2, 3, 4, 20, 10.0], + }, + index=Index(bidx3), + ) + tm.assert_frame_equal(df3, expected3) + + # Setting a new row _and_ new column + df4 = df.copy() + df4.loc["d", "C"] = 10 + expected3 = DataFrame( + { + "A": [20, 20, 2, 3, 4, 20, np.nan], + "C": [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 10], + }, + index=Index(bidx3), + ) + tm.assert_frame_equal(df4, expected3) + + def test_loc_getitem_scalar_non_category(self, df): + with pytest.raises(KeyError, match="^1$"): + df.loc[1] + + def test_slicing(self): + cat = Series(Categorical([1, 2, 3, 4])) + reverse = cat[::-1] + exp = np.array([4, 3, 2, 1], dtype=np.int64) + tm.assert_numpy_array_equal(reverse.__array__(), exp) + + df = DataFrame({"value": (np.arange(100) + 1).astype("int64")}) + df["D"] = pd.cut(df.value, bins=[0, 25, 50, 75, 100]) + + expected = Series([11, Interval(0, 25)], index=["value", "D"], name=10) + result = df.iloc[10] + tm.assert_series_equal(result, expected) + + expected = DataFrame( + {"value": np.arange(11, 21).astype("int64")}, + index=np.arange(10, 20).astype("int64"), + ) + expected["D"] = pd.cut(expected.value, bins=[0, 25, 50, 75, 100]) + result = df.iloc[10:20] + tm.assert_frame_equal(result, expected) + + expected = Series([9, Interval(0, 25)], index=["value", "D"], name=8) + result = df.loc[8] + tm.assert_series_equal(result, expected) + + def test_slicing_and_getting_ops(self): + # systematically test the slicing operations: + # for all slicing ops: + # - returning a dataframe + # - returning a column + # - returning a row + # - returning a single value + + cats = Categorical( + ["a", "c", "b", "c", "c", "c", "c"], categories=["a", "b", "c"] + ) + idx = Index(["h", "i", "j", "k", "l", "m", "n"]) + values = [1, 2, 3, 4, 5, 6, 7] + df = DataFrame({"cats": cats, "values": values}, index=idx) + + # the expected values + cats2 = Categorical(["b", "c"], categories=["a", "b", "c"]) + idx2 = Index(["j", "k"]) + values2 = [3, 4] + + # 2:4,: | "j":"k",: + exp_df = DataFrame({"cats": cats2, "values": values2}, index=idx2) + + # :,"cats" | :,0 + exp_col = Series(cats, index=idx, name="cats") + + # "j",: | 2,: + exp_row = Series(["b", 3], index=["cats", "values"], dtype="object", name="j") + + # "j","cats | 2,0 + exp_val = "b" + + # iloc + # frame + res_df = df.iloc[2:4, :] + tm.assert_frame_equal(res_df, exp_df) + assert isinstance(res_df["cats"].dtype, CategoricalDtype) + + # row + res_row = df.iloc[2, :] + tm.assert_series_equal(res_row, exp_row) + assert isinstance(res_row["cats"], str) + + # col + res_col = df.iloc[:, 0] + tm.assert_series_equal(res_col, exp_col) + assert isinstance(res_col.dtype, CategoricalDtype) + + # single value + res_val = df.iloc[2, 0] + assert res_val == exp_val + + # loc + # frame + res_df = df.loc["j":"k", :] + tm.assert_frame_equal(res_df, exp_df) + assert isinstance(res_df["cats"].dtype, CategoricalDtype) + + # row + res_row = df.loc["j", :] + tm.assert_series_equal(res_row, exp_row) + assert isinstance(res_row["cats"], str) + + # col + res_col = df.loc[:, "cats"] + tm.assert_series_equal(res_col, exp_col) + assert isinstance(res_col.dtype, CategoricalDtype) + + # single value + res_val = df.loc["j", "cats"] + assert res_val == exp_val + + # single value + res_val = df.loc["j", df.columns[0]] + assert res_val == exp_val + + # iat + res_val = df.iat[2, 0] + assert res_val == exp_val + + # at + res_val = df.at["j", "cats"] + assert res_val == exp_val + + # fancy indexing + exp_fancy = df.iloc[[2]] + + res_fancy = df[df["cats"] == "b"] + tm.assert_frame_equal(res_fancy, exp_fancy) + res_fancy = df[df["values"] == 3] + tm.assert_frame_equal(res_fancy, exp_fancy) + + # get_value + res_val = df.at["j", "cats"] + assert res_val == exp_val + + # i : int, slice, or sequence of integers + res_row = df.iloc[2] + tm.assert_series_equal(res_row, exp_row) + assert isinstance(res_row["cats"], str) + + res_df = df.iloc[slice(2, 4)] + tm.assert_frame_equal(res_df, exp_df) + assert isinstance(res_df["cats"].dtype, CategoricalDtype) + + res_df = df.iloc[[2, 3]] + tm.assert_frame_equal(res_df, exp_df) + assert isinstance(res_df["cats"].dtype, CategoricalDtype) + + res_col = df.iloc[:, 0] + tm.assert_series_equal(res_col, exp_col) + assert isinstance(res_col.dtype, CategoricalDtype) + + res_df = df.iloc[:, slice(0, 2)] + tm.assert_frame_equal(res_df, df) + assert isinstance(res_df["cats"].dtype, CategoricalDtype) + + res_df = df.iloc[:, [0, 1]] + tm.assert_frame_equal(res_df, df) + assert isinstance(res_df["cats"].dtype, CategoricalDtype) + + def test_slicing_doc_examples(self): + # GH 7918 + cats = Categorical( + ["a", "b", "b", "b", "c", "c", "c"], categories=["a", "b", "c"] + ) + idx = Index(["h", "i", "j", "k", "l", "m", "n"]) + values = [1, 2, 2, 2, 3, 4, 5] + df = DataFrame({"cats": cats, "values": values}, index=idx) + + result = df.iloc[2:4, :] + expected = DataFrame( + { + "cats": Categorical(["b", "b"], categories=["a", "b", "c"]), + "values": [2, 2], + }, + index=["j", "k"], + ) + tm.assert_frame_equal(result, expected) + + result = df.iloc[2:4, :].dtypes + expected = Series(["category", "int64"], ["cats", "values"], dtype=object) + tm.assert_series_equal(result, expected) + + result = df.loc["h":"j", "cats"] + expected = Series( + Categorical(["a", "b", "b"], categories=["a", "b", "c"]), + index=["h", "i", "j"], + name="cats", + ) + tm.assert_series_equal(result, expected) + + result = df.loc["h":"j", df.columns[0:1]] + expected = DataFrame( + {"cats": Categorical(["a", "b", "b"], categories=["a", "b", "c"])}, + index=["h", "i", "j"], + ) + tm.assert_frame_equal(result, expected) + + def test_loc_getitem_listlike_labels(self, df): + # list of labels + result = df.loc[["c", "a"]] + expected = df.iloc[[4, 0, 1, 5]] + tm.assert_frame_equal(result, expected, check_index_type=True) + + def test_loc_getitem_listlike_unused_category(self, df2): + # GH#37901 a label that is in index.categories but not in index + # listlike containing an element in the categories but not in the values + with pytest.raises(KeyError, match=re.escape("['e'] not in index")): + df2.loc[["a", "b", "e"]] + + def test_loc_getitem_label_unused_category(self, df2): + # element in the categories but not in the values + with pytest.raises(KeyError, match=r"^'e'$"): + df2.loc["e"] + + def test_loc_getitem_non_category(self, df2): + # not all labels in the categories + with pytest.raises(KeyError, match=re.escape("['d'] not in index")): + df2.loc[["a", "d"]] + + def test_loc_setitem_expansion_label_unused_category(self, df2): + # assigning with a label that is in the categories but not in the index + df = df2.copy() + df.loc["e"] = 20 + result = df.loc[["a", "b", "e"]] + exp_index = CategoricalIndex(list("aaabbe"), categories=list("cabe"), name="B") + expected = DataFrame({"A": [0, 1, 5, 2, 3, 20]}, index=exp_index) + tm.assert_frame_equal(result, expected) + + def test_loc_listlike_dtypes(self): + # GH 11586 + + # unique categories and codes + index = CategoricalIndex(["a", "b", "c"]) + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=index) + + # unique slice + res = df.loc[["a", "b"]] + exp_index = CategoricalIndex(["a", "b"], categories=index.categories) + exp = DataFrame({"A": [1, 2], "B": [4, 5]}, index=exp_index) + tm.assert_frame_equal(res, exp, check_index_type=True) + + # duplicated slice + res = df.loc[["a", "a", "b"]] + + exp_index = CategoricalIndex(["a", "a", "b"], categories=index.categories) + exp = DataFrame({"A": [1, 1, 2], "B": [4, 4, 5]}, index=exp_index) + tm.assert_frame_equal(res, exp, check_index_type=True) + + with pytest.raises(KeyError, match=re.escape("['x'] not in index")): + df.loc[["a", "x"]] + + def test_loc_listlike_dtypes_duplicated_categories_and_codes(self): + # duplicated categories and codes + index = CategoricalIndex(["a", "b", "a"]) + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}, index=index) + + # unique slice + res = df.loc[["a", "b"]] + exp = DataFrame( + {"A": [1, 3, 2], "B": [4, 6, 5]}, index=CategoricalIndex(["a", "a", "b"]) + ) + tm.assert_frame_equal(res, exp, check_index_type=True) + + # duplicated slice + res = df.loc[["a", "a", "b"]] + exp = DataFrame( + {"A": [1, 3, 1, 3, 2], "B": [4, 6, 4, 6, 5]}, + index=CategoricalIndex(["a", "a", "a", "a", "b"]), + ) + tm.assert_frame_equal(res, exp, check_index_type=True) + + with pytest.raises(KeyError, match=re.escape("['x'] not in index")): + df.loc[["a", "x"]] + + def test_loc_listlike_dtypes_unused_category(self): + # contains unused category + index = CategoricalIndex(["a", "b", "a", "c"], categories=list("abcde")) + df = DataFrame({"A": [1, 2, 3, 4], "B": [5, 6, 7, 8]}, index=index) + + res = df.loc[["a", "b"]] + exp = DataFrame( + {"A": [1, 3, 2], "B": [5, 7, 6]}, + index=CategoricalIndex(["a", "a", "b"], categories=list("abcde")), + ) + tm.assert_frame_equal(res, exp, check_index_type=True) + + # duplicated slice + res = df.loc[["a", "a", "b"]] + exp = DataFrame( + {"A": [1, 3, 1, 3, 2], "B": [5, 7, 5, 7, 6]}, + index=CategoricalIndex(["a", "a", "a", "a", "b"], categories=list("abcde")), + ) + tm.assert_frame_equal(res, exp, check_index_type=True) + + with pytest.raises(KeyError, match=re.escape("['x'] not in index")): + df.loc[["a", "x"]] + + def test_loc_getitem_listlike_unused_category_raises_keyerror(self): + # key that is an *unused* category raises + index = CategoricalIndex(["a", "b", "a", "c"], categories=list("abcde")) + df = DataFrame({"A": [1, 2, 3, 4], "B": [5, 6, 7, 8]}, index=index) + + with pytest.raises(KeyError, match="e"): + # For comparison, check the scalar behavior + df.loc["e"] + + with pytest.raises(KeyError, match=re.escape("['e'] not in index")): + df.loc[["a", "e"]] + + def test_ix_categorical_index(self): + # GH 12531 + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + index=list("ABC"), + columns=list("XYZ"), + ) + cdf = df.copy() + cdf.index = CategoricalIndex(df.index) + cdf.columns = CategoricalIndex(df.columns) + + expect = Series(df.loc["A", :], index=cdf.columns, name="A") + tm.assert_series_equal(cdf.loc["A", :], expect) + + expect = Series(df.loc[:, "X"], index=cdf.index, name="X") + tm.assert_series_equal(cdf.loc[:, "X"], expect) + + exp_index = CategoricalIndex(list("AB"), categories=["A", "B", "C"]) + expect = DataFrame(df.loc[["A", "B"], :], columns=cdf.columns, index=exp_index) + tm.assert_frame_equal(cdf.loc[["A", "B"], :], expect) + + exp_columns = CategoricalIndex(list("XY"), categories=["X", "Y", "Z"]) + expect = DataFrame(df.loc[:, ["X", "Y"]], index=cdf.index, columns=exp_columns) + tm.assert_frame_equal(cdf.loc[:, ["X", "Y"]], expect) + + @pytest.mark.parametrize( + "infer_string", [False, pytest.param(True, marks=td.skip_if_no("pyarrow"))] + ) + def test_ix_categorical_index_non_unique(self, infer_string): + # non-unique + with option_context("future.infer_string", infer_string): + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + index=list("ABA"), + columns=list("XYX"), + ) + cdf = df.copy() + cdf.index = CategoricalIndex(df.index) + cdf.columns = CategoricalIndex(df.columns) + + exp_index = CategoricalIndex(list("AA"), categories=["A", "B"]) + expect = DataFrame(df.loc["A", :], columns=cdf.columns, index=exp_index) + tm.assert_frame_equal(cdf.loc["A", :], expect) + + exp_columns = CategoricalIndex(list("XX"), categories=["X", "Y"]) + expect = DataFrame(df.loc[:, "X"], index=cdf.index, columns=exp_columns) + tm.assert_frame_equal(cdf.loc[:, "X"], expect) + + expect = DataFrame( + df.loc[["A", "B"], :], + columns=cdf.columns, + index=CategoricalIndex(list("AAB")), + ) + tm.assert_frame_equal(cdf.loc[["A", "B"], :], expect) + + expect = DataFrame( + df.loc[:, ["X", "Y"]], + index=cdf.index, + columns=CategoricalIndex(list("XXY")), + ) + tm.assert_frame_equal(cdf.loc[:, ["X", "Y"]], expect) + + def test_loc_slice(self, df): + # GH9748 + msg = ( + "cannot do slice indexing on CategoricalIndex with these " + r"indexers \[1\] of type int" + ) + with pytest.raises(TypeError, match=msg): + df.loc[1:5] + + result = df.loc["b":"c"] + expected = df.iloc[[2, 3, 4]] + tm.assert_frame_equal(result, expected) + + def test_loc_and_at_with_categorical_index(self): + # GH 20629 + df = DataFrame( + [[1, 2], [3, 4], [5, 6]], index=CategoricalIndex(["A", "B", "C"]) + ) + + s = df[0] + assert s.loc["A"] == 1 + assert s.at["A"] == 1 + + assert df.loc["B", 1] == 4 + assert df.at["B", 1] == 4 + + @pytest.mark.parametrize( + "idx_values", + [ + # python types + [1, 2, 3], + [-1, -2, -3], + [1.5, 2.5, 3.5], + [-1.5, -2.5, -3.5], + # numpy int/uint + *(np.array([1, 2, 3], dtype=dtype) for dtype in tm.ALL_INT_NUMPY_DTYPES), + # numpy floats + *(np.array([1.5, 2.5, 3.5], dtype=dtyp) for dtyp in tm.FLOAT_NUMPY_DTYPES), + # numpy object + np.array([1, "b", 3.5], dtype=object), + # pandas scalars + [Interval(1, 4), Interval(4, 6), Interval(6, 9)], + [Timestamp(2019, 1, 1), Timestamp(2019, 2, 1), Timestamp(2019, 3, 1)], + [Timedelta(1, "D"), Timedelta(2, "D"), Timedelta(3, "D")], + # pandas Integer arrays + *(pd.array([1, 2, 3], dtype=dtype) for dtype in tm.ALL_INT_EA_DTYPES), + # other pandas arrays + pd.IntervalIndex.from_breaks([1, 4, 6, 9]).array, + pd.date_range("2019-01-01", periods=3).array, + pd.timedelta_range(start="1D", periods=3).array, + ], + ) + def test_loc_getitem_with_non_string_categories(self, idx_values, ordered): + # GH-17569 + cat_idx = CategoricalIndex(idx_values, ordered=ordered) + df = DataFrame({"A": ["foo", "bar", "baz"]}, index=cat_idx) + sl = slice(idx_values[0], idx_values[1]) + + # scalar selection + result = df.loc[idx_values[0]] + expected = Series(["foo"], index=["A"], name=idx_values[0]) + tm.assert_series_equal(result, expected) + + # list selection + result = df.loc[idx_values[:2]] + expected = DataFrame(["foo", "bar"], index=cat_idx[:2], columns=["A"]) + tm.assert_frame_equal(result, expected) + + # slice selection + result = df.loc[sl] + expected = DataFrame(["foo", "bar"], index=cat_idx[:2], columns=["A"]) + tm.assert_frame_equal(result, expected) + + # scalar assignment + result = df.copy() + result.loc[idx_values[0]] = "qux" + expected = DataFrame({"A": ["qux", "bar", "baz"]}, index=cat_idx) + tm.assert_frame_equal(result, expected) + + # list assignment + result = df.copy() + result.loc[idx_values[:2], "A"] = ["qux", "qux2"] + expected = DataFrame({"A": ["qux", "qux2", "baz"]}, index=cat_idx) + tm.assert_frame_equal(result, expected) + + # slice assignment + result = df.copy() + result.loc[sl, "A"] = ["qux", "qux2"] + expected = DataFrame({"A": ["qux", "qux2", "baz"]}, index=cat_idx) + tm.assert_frame_equal(result, expected) + + def test_getitem_categorical_with_nan(self): + # GH#41933 + ci = CategoricalIndex(["A", "B", np.nan]) + + ser = Series(range(3), index=ci) + + assert ser[np.nan] == 2 + assert ser.loc[np.nan] == 2 + + df = DataFrame(ser) + assert df.loc[np.nan, 0] == 2 + assert df.loc[np.nan][0] == 2 + + def test_getitem_row_categorical_with_nan(self): + # GH#58954 + df = DataFrame({"a": [1, 2], "b": CategoricalIndex([1, None])}) + + res = df.iloc[1] + expected = Series([2, np.nan], index=df.columns, name=1) + tm.assert_series_equal(res, expected) + + res = df.loc[1] + tm.assert_series_equal(res, expected) + + def test_getitem_row_categorical_with_nan_bool(self): + # GH#58954 + df = DataFrame({"a": [True, False], "b": CategoricalIndex([False, None])}) + + res = df.iloc[1] + expected = Series([False, np.nan], index=df.columns, dtype=object, name=1) + tm.assert_series_equal(res, expected) + + res = df.loc[1] + tm.assert_series_equal(res, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_chaining_and_caching.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_chaining_and_caching.py new file mode 100644 index 0000000000000000000000000000000000000000..f1f8bf8ae9bf8dd4fba6b10abebca88ea8792b82 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_chaining_and_caching.py @@ -0,0 +1,361 @@ +from string import ascii_letters + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + Series, + Timestamp, + date_range, + option_context, +) +import pandas._testing as tm + + +class TestCaching: + @pytest.mark.parametrize("do_ref", [True, False]) + def test_setitem_cache_updating(self, do_ref): + # GH 5424 + cont = ["one", "two", "three", "four", "five", "six", "seven"] + + df = DataFrame({"a": cont, "b": cont[3:] + cont[:3], "c": np.arange(7)}) + + # ref the cache + if do_ref: + df.loc[0, "c"] + + # set it + df.loc[7, "c"] = 1 + + assert df.loc[0, "c"] == 0.0 + assert df.loc[7, "c"] == 1.0 + + def test_setitem_cache_updating_slices(self): + # GH 7084 + # not updating cache on series setting with slices + expected = DataFrame( + {"A": [600, 600, 600]}, index=date_range("5/7/2014", "5/9/2014") + ) + out = DataFrame({"A": [0, 0, 0]}, index=date_range("5/7/2014", "5/9/2014")) + df = DataFrame({"C": ["A", "A", "A"], "D": [100, 200, 300]}) + + # loop through df to update out + six = Timestamp("5/7/2014") + eix = Timestamp("5/9/2014") + for ix, row in df.iterrows(): + out.loc[six:eix, row["C"]] = out.loc[six:eix, row["C"]] + row["D"] + + tm.assert_frame_equal(out, expected) + tm.assert_series_equal(out["A"], expected["A"]) + + # try via a chain indexing + # this actually works + out = DataFrame({"A": [0, 0, 0]}, index=date_range("5/7/2014", "5/9/2014")) + out_original = out.copy() + for ix, row in df.iterrows(): + v = out[row["C"]][six:eix] + row["D"] + with tm.raises_chained_assignment_error(): + out[row["C"]][six:eix] = v + + tm.assert_frame_equal(out, out_original) + tm.assert_series_equal(out["A"], out_original["A"]) + + out = DataFrame({"A": [0, 0, 0]}, index=date_range("5/7/2014", "5/9/2014")) + for ix, row in df.iterrows(): + out.loc[six:eix, row["C"]] += row["D"] + + tm.assert_frame_equal(out, expected) + tm.assert_series_equal(out["A"], expected["A"]) + + +class TestChaining: + def test_setitem_chained_setfault(self): + # GH6026 + data = ["right", "left", "left", "left", "right", "left", "timeout"] + + df = DataFrame({"response": np.array(data)}) + mask = df.response == "timeout" + with tm.raises_chained_assignment_error(): + df.response[mask] = "none" + tm.assert_frame_equal(df, DataFrame({"response": data})) + + recarray = np.rec.fromarrays([data], names=["response"]) + df = DataFrame(recarray) + mask = df.response == "timeout" + with tm.raises_chained_assignment_error(): + df.response[mask] = "none" + tm.assert_frame_equal(df, DataFrame({"response": data})) + + df = DataFrame({"response": data, "response1": data}) + df_original = df.copy() + mask = df.response == "timeout" + with tm.raises_chained_assignment_error(): + df.response[mask] = "none" + tm.assert_frame_equal(df, df_original) + + # GH 6056 + expected = DataFrame({"A": [np.nan, "bar", "bah", "foo", "bar"]}) + df = DataFrame({"A": np.array(["foo", "bar", "bah", "foo", "bar"])}) + with tm.raises_chained_assignment_error(): + df["A"].iloc[0] = np.nan + expected = DataFrame({"A": ["foo", "bar", "bah", "foo", "bar"]}) + result = df.head() + tm.assert_frame_equal(result, expected) + + df = DataFrame({"A": np.array(["foo", "bar", "bah", "foo", "bar"])}) + with tm.raises_chained_assignment_error(): + df.A.iloc[0] = np.nan + result = df.head() + tm.assert_frame_equal(result, expected) + + @pytest.mark.arm_slow + def test_detect_chained_assignment(self): + with option_context("chained_assignment", "raise"): + # work with the chain + df = DataFrame( + np.arange(4).reshape(2, 2), columns=list("AB"), dtype="int64" + ) + df_original = df.copy() + + with tm.raises_chained_assignment_error(): + df["A"][0] = -5 + with tm.raises_chained_assignment_error(): + df["A"][1] = -6 + tm.assert_frame_equal(df, df_original) + + @pytest.mark.arm_slow + def test_detect_chained_assignment_raises(self): + # test with the chaining + df = DataFrame( + { + "A": Series(range(2), dtype="int64"), + "B": np.array(np.arange(2, 4), dtype=np.float64), + } + ) + df_original = df.copy() + with tm.raises_chained_assignment_error(): + df["A"][0] = -5 + with tm.raises_chained_assignment_error(): + df["A"][1] = -6 + tm.assert_frame_equal(df, df_original) + + @pytest.mark.arm_slow + def test_detect_chained_assignment_fails(self): + # Using a copy (the chain), fails + df = DataFrame( + { + "A": Series(range(2), dtype="int64"), + "B": np.array(np.arange(2, 4), dtype=np.float64), + } + ) + + with tm.raises_chained_assignment_error(): + df.loc[0]["A"] = -5 + + @pytest.mark.arm_slow + def test_detect_chained_assignment_doc_example(self): + # Doc example + df = DataFrame( + { + "a": ["one", "one", "two", "three", "two", "one", "six"], + "c": Series(range(7), dtype="int64"), + } + ) + + indexer = df.a.str.startswith("o") + with tm.raises_chained_assignment_error(): + df[indexer]["c"] = 42 + + @pytest.mark.arm_slow + def test_detect_chained_assignment_object_dtype(self): + df = DataFrame( + {"A": Series(["aaa", "bbb", "ccc"], dtype=object), "B": [1, 2, 3]} + ) + df_original = df.copy() + + with tm.raises_chained_assignment_error(): + df["A"][0] = 111 + tm.assert_frame_equal(df, df_original) + + @pytest.mark.arm_slow + def test_detect_chained_assignment_is_copy_pickle(self, temp_file): + # gh-5475: Make sure that is_copy is picked up reconstruction + df = DataFrame({"A": [1, 2]}) + + path = str(temp_file) + df.to_pickle(path) + df2 = pd.read_pickle(path) + df2["B"] = df2["A"] + df2["B"] = df2["A"] + + @pytest.mark.arm_slow + def test_detect_chained_assignment_str(self): + idxs = np.random.default_rng(2).integers(len(ascii_letters), size=(100, 2)) + idxs.sort(axis=1) + strings = [ascii_letters[x[0] : x[1]] for x in idxs] + + df = DataFrame(strings, columns=["letters"]) + indexer = df.letters.apply(lambda x: len(x) > 10) + df.loc[indexer, "letters"] = df.loc[indexer, "letters"].apply(str.lower) + + @pytest.mark.arm_slow + def test_detect_chained_assignment_sorting(self): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))) + ser = df.iloc[:, 0].sort_values() + + tm.assert_series_equal(ser, df.iloc[:, 0].sort_values()) + tm.assert_series_equal(ser, df[0].sort_values()) + + @pytest.mark.arm_slow + def test_detect_chained_assignment_false_positives(self): + # see gh-6025: false positives + df = DataFrame({"column1": ["a", "a", "a"], "column2": [4, 8, 9]}) + str(df) + + df["column1"] = df["column1"] + "b" + str(df) + + df = df[df["column2"] != 8] + str(df) + + df["column1"] = df["column1"] + "c" + str(df) + + @pytest.mark.arm_slow + def test_detect_chained_assignment_undefined_column(self): + # from SO: + # https://stackoverflow.com/questions/24054495/potential-bug-setting-value-for-undefined-column-using-iloc + df = DataFrame(np.arange(0, 9), columns=["count"]) + df["group"] = "b" + df_original = df.copy() + with tm.raises_chained_assignment_error(): + df.iloc[0:5]["group"] = "a" + tm.assert_frame_equal(df, df_original) + + @pytest.mark.arm_slow + def test_detect_chained_assignment_changing_dtype(self): + # Mixed type setting but same dtype & changing dtype + df = DataFrame( + { + "A": date_range("20130101", periods=5), + "B": np.random.default_rng(2).standard_normal(5), + "C": np.arange(5, dtype="int64"), + "D": ["a", "b", "c", "d", "e"], + } + ) + df_original = df.copy() + + with tm.raises_chained_assignment_error(): + df.loc[2]["D"] = "foo" + with tm.raises_chained_assignment_error(): + df.loc[2]["C"] = "foo" + tm.assert_frame_equal(df, df_original) + # TODO: Use tm.raises_chained_assignment_error() when PDEP-6 is enforced + with pytest.raises(TypeError, match="Invalid value"): + with tm.raises_chained_assignment_error(): + df["C"][2] = "foo" + + def test_setting_with_copy_bug(self): + # operating on a copy + df = DataFrame( + {"a": list(range(4)), "b": list("ab.."), "c": ["a", "b", np.nan, "d"]} + ) + df_original = df.copy() + mask = pd.isna(df.c) + with tm.raises_chained_assignment_error(): + df[["c"]][mask] = df[["b"]][mask] + tm.assert_frame_equal(df, df_original) + + def test_setting_with_copy_bug_no_warning(self): + # invalid warning as we are returning a new object + # GH 8730 + df1 = DataFrame({"x": Series(["a", "b", "c"]), "y": Series(["d", "e", "f"])}) + df2 = df1[["x"]] + + # this should not raise + df2["y"] = ["g", "h", "i"] + + def test_detect_chained_assignment_warnings_errors(self): + df = DataFrame({"A": ["aaa", "bbb", "ccc"], "B": [1, 2, 3]}) + with tm.raises_chained_assignment_error(): + df.loc[0]["A"] = 111 + + @pytest.mark.parametrize("rhs", [3, DataFrame({0: [1, 2, 3, 4]})]) + def test_detect_chained_assignment_warning_stacklevel(self, rhs): + # GH#42570 + df = DataFrame(np.arange(25).reshape(5, 5)) + df_original = df.copy() + chained = df.loc[:3] + chained[2] = rhs + tm.assert_frame_equal(df, df_original) + + def test_chained_getitem_with_lists(self): + # GH6394 + # Regression in chained getitem indexing with embedded list-like from + # 0.12 + + df = DataFrame({"A": 5 * [np.zeros(3)], "B": 5 * [np.ones(3)]}) + expected = df["A"].iloc[2] + result = df.loc[2, "A"] + tm.assert_numpy_array_equal(result, expected) + result2 = df.iloc[2]["A"] + tm.assert_numpy_array_equal(result2, expected) + result3 = df["A"].loc[2] + tm.assert_numpy_array_equal(result3, expected) + result4 = df["A"].iloc[2] + tm.assert_numpy_array_equal(result4, expected) + + def test_cache_updating(self): + # GH 4939, make sure to update the cache on setitem + + df = DataFrame( + np.zeros((10, 4)), + columns=Index(list("ABCD"), dtype=object), + ) + df["A"] # cache series + df.loc["Hello Friend"] = df.iloc[0] + assert "Hello Friend" in df["A"].index + assert "Hello Friend" in df["B"].index + + def test_cache_updating2(self): + # 10264 + df = DataFrame( + np.zeros((5, 5), dtype="int64"), + columns=["a", "b", "c", "d", "e"], + index=range(5), + ) + df["f"] = 0 + df_orig = df.copy() + with pytest.raises(ValueError, match="read-only"): + df.f.values[3] = 1 + tm.assert_frame_equal(df, df_orig) + + def test_iloc_setitem_chained_assignment(self): + # GH#3970 + with option_context("chained_assignment", None): + df = DataFrame({"aa": range(5), "bb": [2.2] * 5}) + df["cc"] = 0.0 + + ck = [True] * len(df) + + with tm.raises_chained_assignment_error(): + df["bb"].iloc[0] = 0.13 + + # GH#3970 this lookup used to break the chained setting to 0.15 + df.iloc[ck] + + with tm.raises_chained_assignment_error(): + df["bb"].iloc[0] = 0.15 + + assert df["bb"].iloc[0] == 2.2 + + def test_getitem_loc_assignment_slice_state(self): + # GH 13569 + df = DataFrame({"a": [10, 20, 30]}) + with tm.raises_chained_assignment_error(): + df["a"].loc[4] = 40 + tm.assert_frame_equal(df, DataFrame({"a": [10, 20, 30]})) + tm.assert_series_equal(df["a"], Series([10, 20, 30], name="a")) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_check_indexer.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_check_indexer.py new file mode 100644 index 0000000000000000000000000000000000000000..975a31b873792c6afe59a23e5fef43b56ce7e46e --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_check_indexer.py @@ -0,0 +1,105 @@ +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm +from pandas.api.indexers import check_array_indexer + + +@pytest.mark.parametrize( + "indexer, expected", + [ + # integer + ([1, 2], np.array([1, 2], dtype=np.intp)), + (np.array([1, 2], dtype="int64"), np.array([1, 2], dtype=np.intp)), + (pd.array([1, 2], dtype="Int32"), np.array([1, 2], dtype=np.intp)), + (pd.Index([1, 2]), np.array([1, 2], dtype=np.intp)), + # boolean + ([True, False, True], np.array([True, False, True], dtype=np.bool_)), + (np.array([True, False, True]), np.array([True, False, True], dtype=np.bool_)), + ( + pd.array([True, False, True], dtype="boolean"), + np.array([True, False, True], dtype=np.bool_), + ), + # other + ([], np.array([], dtype=np.intp)), + ], +) +def test_valid_input(indexer, expected): + arr = np.array([1, 2, 3]) + result = check_array_indexer(arr, indexer) + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize( + "indexer", [[True, False, None], pd.array([True, False, None], dtype="boolean")] +) +def test_boolean_na_returns_indexer(indexer): + # https://github.com/pandas-dev/pandas/issues/31503 + arr = np.array([1, 2, 3]) + + result = check_array_indexer(arr, indexer) + expected = np.array([True, False, False], dtype=bool) + + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize( + "indexer", + [ + [True, False], + pd.array([True, False], dtype="boolean"), + np.array([True, False], dtype=np.bool_), + ], +) +def test_bool_raise_length(indexer): + arr = np.array([1, 2, 3]) + + msg = "Boolean index has wrong length" + with pytest.raises(IndexError, match=msg): + check_array_indexer(arr, indexer) + + +@pytest.mark.parametrize( + "indexer", [[0, 1, None], pd.array([0, 1, pd.NA], dtype="Int64")] +) +def test_int_raise_missing_values(indexer): + arr = np.array([1, 2, 3]) + + msg = "Cannot index with an integer indexer containing NA values" + with pytest.raises(ValueError, match=msg): + check_array_indexer(arr, indexer) + + +@pytest.mark.parametrize( + "indexer", + [ + [0.0, 1.0], + np.array([1.0, 2.0], dtype="float64"), + np.array([True, False], dtype=object), + pd.Index([True, False], dtype=object), + ], +) +def test_raise_invalid_array_dtypes(indexer): + arr = np.array([1, 2, 3]) + + msg = "arrays used as indices must be of integer or boolean type" + with pytest.raises(IndexError, match=msg): + check_array_indexer(arr, indexer) + + +def test_raise_nullable_string_dtype(nullable_string_dtype): + indexer = pd.array(["a", "b"], dtype=nullable_string_dtype) + arr = np.array([1, 2, 3]) + + msg = "arrays used as indices must be of integer or boolean type" + with pytest.raises(IndexError, match=msg): + check_array_indexer(arr, indexer) + + +@pytest.mark.parametrize("indexer", [None, Ellipsis, slice(0, 3), (None,)]) +def test_pass_through_non_array_likes(indexer): + arr = np.array([1, 2, 3]) + + result = check_array_indexer(arr, indexer) + assert result == indexer diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_coercion.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_coercion.py new file mode 100644 index 0000000000000000000000000000000000000000..662028bb246ba0126b4af0a947933f664ea1225c --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_coercion.py @@ -0,0 +1,895 @@ +from __future__ import annotations + +from datetime import ( + datetime, + timedelta, +) + +import numpy as np +import pytest + +from pandas.compat import ( + IS64, + is_platform_windows, +) +from pandas.compat.numpy import np_version_gt2 + +import pandas as pd +import pandas._testing as tm + +############################################################### +# Index / Series common tests which may trigger dtype coercions +############################################################### + + +class CoercionBase: + klasses = ["index", "series"] + dtypes = [ + "object", + "int64", + "float64", + "complex128", + "bool", + "datetime64", + "datetime64tz", + "timedelta64", + "period", + ] + + @property + def method(self): + raise NotImplementedError(self) + + +class TestSetitemCoercion(CoercionBase): + method = "setitem" + + # disable comprehensiveness tests, as most of these have been moved to + # tests.series.indexing.test_setitem in SetitemCastingEquivalents subclasses. + klasses: list[str] = [] + + def test_setitem_series_no_coercion_from_values_list(self): + # GH35865 - int casted to str when internally calling np.array(ser.values) + ser = pd.Series(["a", 1]) + ser[:] = list(ser.values) + + expected = pd.Series(["a", 1]) + + tm.assert_series_equal(ser, expected) + + def _assert_setitem_index_conversion( + self, original_series, loc_key, expected_index, expected_dtype + ): + """test index's coercion triggered by assign key""" + temp = original_series.copy() + # GH#33469 pre-2.0 with int loc_key and temp.index.dtype == np.float64 + # `temp[loc_key] = 5` treated loc_key as positional + temp[loc_key] = 5 + exp = pd.Series([1, 2, 3, 4, 5], index=expected_index) + tm.assert_series_equal(temp, exp) + # check dtype explicitly for sure + assert temp.index.dtype == expected_dtype + + temp = original_series.copy() + temp.loc[loc_key] = 5 + exp = pd.Series([1, 2, 3, 4, 5], index=expected_index) + tm.assert_series_equal(temp, exp) + # check dtype explicitly for sure + assert temp.index.dtype == expected_dtype + + @pytest.mark.parametrize( + "val,exp_dtype", [("x", object), (5, IndexError), (1.1, object)] + ) + def test_setitem_index_object(self, val, exp_dtype): + obj = pd.Series([1, 2, 3, 4], index=pd.Index(list("abcd"), dtype=object)) + assert obj.index.dtype == object + + exp_index = pd.Index([*list("abcd"), val], dtype=object) + self._assert_setitem_index_conversion(obj, val, exp_index, exp_dtype) + + @pytest.mark.parametrize( + "val,exp_dtype", [(5, np.int64), (1.1, np.float64), ("x", object)] + ) + def test_setitem_index_int64(self, val, exp_dtype): + obj = pd.Series([1, 2, 3, 4]) + assert obj.index.dtype == np.int64 + + exp_index = pd.Index([0, 1, 2, 3, val]) + self._assert_setitem_index_conversion(obj, val, exp_index, exp_dtype) + + @pytest.mark.parametrize( + "val,exp_dtype", [(5, np.float64), (5.1, np.float64), ("x", object)] + ) + def test_setitem_index_float64(self, val, exp_dtype, request): + obj = pd.Series([1, 2, 3, 4], index=[1.1, 2.1, 3.1, 4.1]) + assert obj.index.dtype == np.float64 + + exp_index = pd.Index([1.1, 2.1, 3.1, 4.1, val]) + self._assert_setitem_index_conversion(obj, val, exp_index, exp_dtype) + + @pytest.mark.xfail(reason="Test not implemented") + def test_setitem_series_period(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_setitem_index_complex128(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_setitem_index_bool(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_setitem_index_datetime64(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_setitem_index_datetime64tz(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_setitem_index_timedelta64(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_setitem_index_period(self): + raise NotImplementedError + + +class TestInsertIndexCoercion(CoercionBase): + klasses = ["index"] + method = "insert" + + def _assert_insert_conversion(self, original, value, expected, expected_dtype): + """test coercion triggered by insert""" + target = original.copy() + res = target.insert(1, value) + tm.assert_index_equal(res, expected) + assert res.dtype == expected_dtype + + @pytest.mark.parametrize( + "insert, coerced_val, coerced_dtype", + [ + (1, 1, object), + (1.1, 1.1, object), + (False, False, object), + ("x", "x", object), + ], + ) + def test_insert_index_object(self, insert, coerced_val, coerced_dtype): + obj = pd.Index(list("abcd"), dtype=object) + assert obj.dtype == object + + exp = pd.Index(["a", coerced_val, "b", "c", "d"], dtype=object) + self._assert_insert_conversion(obj, insert, exp, coerced_dtype) + + @pytest.mark.parametrize( + "insert, coerced_val, coerced_dtype", + [ + (1, 1, None), + (1.1, 1.1, np.float64), + (False, False, object), # GH#36319 + ("x", "x", object), + ], + ) + def test_insert_int_index( + self, any_int_numpy_dtype, insert, coerced_val, coerced_dtype + ): + dtype = any_int_numpy_dtype + obj = pd.Index([1, 2, 3, 4], dtype=dtype) + coerced_dtype = coerced_dtype if coerced_dtype is not None else dtype + + exp = pd.Index([1, coerced_val, 2, 3, 4], dtype=coerced_dtype) + self._assert_insert_conversion(obj, insert, exp, coerced_dtype) + + @pytest.mark.parametrize( + "insert, coerced_val, coerced_dtype", + [ + (1, 1.0, None), + # When float_numpy_dtype=float32, this is not the case + # see the correction below + (1.1, 1.1, np.float64), + (False, False, object), # GH#36319 + ("x", "x", object), + ], + ) + def test_insert_float_index( + self, float_numpy_dtype, insert, coerced_val, coerced_dtype + ): + dtype = float_numpy_dtype + obj = pd.Index([1.0, 2.0, 3.0, 4.0], dtype=dtype) + coerced_dtype = coerced_dtype if coerced_dtype is not None else dtype + + if np_version_gt2 and dtype == "float32" and coerced_val == 1.1: + # Hack, in the 2nd test case, since 1.1 can be losslessly cast to float32 + # the expected dtype will be float32 if the original dtype was float32 + coerced_dtype = np.float32 + exp = pd.Index([1.0, coerced_val, 2.0, 3.0, 4.0], dtype=coerced_dtype) + self._assert_insert_conversion(obj, insert, exp, coerced_dtype) + + @pytest.mark.parametrize( + "fill_val,exp_dtype", + [ + (pd.Timestamp("2012-01-01").as_unit("s"), "datetime64[ns]"), + ( + pd.Timestamp("2012-01-01", tz="US/Eastern").as_unit("s"), + "datetime64[ns, US/Eastern]", + ), + ], + ids=["datetime64", "datetime64tz"], + ) + @pytest.mark.parametrize( + "insert_value", + [ + pd.Timestamp("2012-01-01").as_unit("s"), + pd.Timestamp("2012-01-01", tz="Asia/Tokyo").as_unit("s"), + 1, + ], + ) + def test_insert_index_datetimes(self, fill_val, exp_dtype, insert_value): + obj = pd.DatetimeIndex( + ["2011-01-01", "2011-01-02", "2011-01-03", "2011-01-04"], tz=fill_val.tz + ).as_unit("ns") + assert obj.dtype == exp_dtype + + exp = pd.DatetimeIndex( + ["2011-01-01", fill_val.date(), "2011-01-02", "2011-01-03", "2011-01-04"], + tz=fill_val.tz, + ).as_unit("ns") + self._assert_insert_conversion(obj, fill_val, exp, exp_dtype) + + if fill_val.tz: + # mismatched tzawareness + ts = pd.Timestamp("2012-01-01").as_unit("s") + result = obj.insert(1, ts) + expected = obj.astype(object).insert(1, ts) + assert expected.dtype == object + tm.assert_index_equal(result, expected) + + ts = pd.Timestamp("2012-01-01", tz="Asia/Tokyo").as_unit("s") + result = obj.insert(1, ts) + # once deprecation is enforced: + expected = obj.insert(1, ts.tz_convert(obj.dtype.tz)) + assert expected.dtype == obj.dtype + tm.assert_index_equal(result, expected) + + else: + # mismatched tzawareness + ts = pd.Timestamp("2012-01-01", tz="Asia/Tokyo").as_unit("s") + result = obj.insert(1, ts) + expected = obj.astype(object).insert(1, ts) + assert expected.dtype == object + tm.assert_index_equal(result, expected) + + item = 1 + result = obj.insert(1, item) + expected = obj.astype(object).insert(1, item) + assert expected[1] == item + assert expected.dtype == object + tm.assert_index_equal(result, expected) + + def test_insert_index_timedelta64(self): + obj = pd.TimedeltaIndex(["1 day", "2 day", "3 day", "4 day"]) + assert obj.dtype == "timedelta64[us]" + + # timedelta64 + timedelta64 => timedelta64 + exp = pd.TimedeltaIndex(["1 day", "10 day", "2 day", "3 day", "4 day"]) + self._assert_insert_conversion( + obj, pd.Timedelta("10 day"), exp, "timedelta64[us]" + ) + + for item in [pd.Timestamp("2012-01-01").as_unit("s"), 1]: + result = obj.insert(1, item) + expected = obj.astype(object).insert(1, item) + assert expected.dtype == object + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize( + "insert, coerced_val, coerced_dtype", + [ + (pd.Period("2012-01", freq="M"), "2012-01", "period[M]"), + ( + pd.Timestamp("2012-01-01").as_unit("s"), + pd.Timestamp("2012-01-01").as_unit("s"), + object, + ), + (1, 1, object), + ("x", "x", object), + ], + ) + def test_insert_index_period(self, insert, coerced_val, coerced_dtype): + obj = pd.PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq="M") + assert obj.dtype == "period[M]" + + data = [ + pd.Period("2011-01", freq="M"), + coerced_val, + pd.Period("2011-02", freq="M"), + pd.Period("2011-03", freq="M"), + pd.Period("2011-04", freq="M"), + ] + if isinstance(insert, pd.Period): + exp = pd.PeriodIndex(data, freq="M") + self._assert_insert_conversion(obj, insert, exp, coerced_dtype) + + # string that can be parsed to appropriate PeriodDtype + self._assert_insert_conversion(obj, str(insert), exp, coerced_dtype) + + else: + result = obj.insert(0, insert) + expected = obj.astype(object).insert(0, insert) + tm.assert_index_equal(result, expected) + + # TODO: ATM inserting '2012-01-01 00:00:00' when we have obj.freq=="M" + # casts that string to Period[M], not clear that is desirable + if not isinstance(insert, pd.Timestamp): + # non-castable string + result = obj.insert(0, str(insert)) + expected = obj.astype(object).insert(0, str(insert)) + tm.assert_index_equal(result, expected) + + @pytest.mark.xfail(reason="Test not implemented") + def test_insert_index_complex128(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_insert_index_bool(self): + raise NotImplementedError + + +class TestWhereCoercion(CoercionBase): + method = "where" + _cond = np.array([True, False, True, False]) + + def _assert_where_conversion( + self, original, cond, values, expected, expected_dtype + ): + """test coercion triggered by where""" + target = original.copy() + res = target.where(cond, values) + tm.assert_equal(res, expected) + assert res.dtype == expected_dtype + + def _construct_exp(self, obj, klass, fill_val, exp_dtype): + if fill_val is True: + values = klass([True, False, True, True]) + elif isinstance(fill_val, (datetime, np.datetime64)): + values = pd.date_range(fill_val, periods=4) + else: + values = klass(x * fill_val for x in [5, 6, 7, 8]) + + exp = klass([obj[0], values[1], obj[2], values[3]], dtype=exp_dtype) + return values, exp + + def _run_test(self, obj, fill_val, klass, exp_dtype): + cond = klass(self._cond) + + exp = klass([obj[0], fill_val, obj[2], fill_val], dtype=exp_dtype) + self._assert_where_conversion(obj, cond, fill_val, exp, exp_dtype) + + values, exp = self._construct_exp(obj, klass, fill_val, exp_dtype) + self._assert_where_conversion(obj, cond, values, exp, exp_dtype) + + @pytest.mark.parametrize( + "fill_val,exp_dtype", + [(1, object), (1.1, object), (1 + 1j, object), (True, object)], + ) + def test_where_object(self, index_or_series, fill_val, exp_dtype): + klass = index_or_series + obj = klass(list("abcd"), dtype=object) + assert obj.dtype == object + self._run_test(obj, fill_val, klass, exp_dtype) + + @pytest.mark.parametrize( + "fill_val,exp_dtype", + [(1, np.int64), (1.1, np.float64), (1 + 1j, np.complex128), (True, object)], + ) + def test_where_int64(self, index_or_series, fill_val, exp_dtype, request): + klass = index_or_series + + obj = klass([1, 2, 3, 4]) + assert obj.dtype == np.int64 + self._run_test(obj, fill_val, klass, exp_dtype) + + @pytest.mark.parametrize( + "fill_val, exp_dtype", + [(1, np.float64), (1.1, np.float64), (1 + 1j, np.complex128), (True, object)], + ) + def test_where_float64(self, index_or_series, fill_val, exp_dtype, request): + klass = index_or_series + + obj = klass([1.1, 2.2, 3.3, 4.4]) + assert obj.dtype == np.float64 + self._run_test(obj, fill_val, klass, exp_dtype) + + @pytest.mark.parametrize( + "fill_val,exp_dtype", + [ + (1, np.complex128), + (1.1, np.complex128), + (1 + 1j, np.complex128), + (True, object), + ], + ) + def test_where_complex128(self, index_or_series, fill_val, exp_dtype): + klass = index_or_series + obj = klass([1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=np.complex128) + assert obj.dtype == np.complex128 + self._run_test(obj, fill_val, klass, exp_dtype) + + @pytest.mark.parametrize( + "fill_val,exp_dtype", + [(1, object), (1.1, object), (1 + 1j, object), (True, np.bool_)], + ) + def test_where_series_bool(self, index_or_series, fill_val, exp_dtype): + klass = index_or_series + + obj = klass([True, False, True, False]) + assert obj.dtype == np.bool_ + self._run_test(obj, fill_val, klass, exp_dtype) + + @pytest.mark.parametrize( + "fill_val,exp_dtype", + [ + (pd.Timestamp("2012-01-01").as_unit("s"), "datetime64[ns]"), + (pd.Timestamp("2012-01-01", tz="US/Eastern").as_unit("s"), object), + ], + ids=["datetime64", "datetime64tz"], + ) + def test_where_datetime64(self, index_or_series, fill_val, exp_dtype): + klass = index_or_series + + obj = klass( + pd.date_range("2011-01-01", periods=4, freq="D", unit="ns")._with_freq(None) + ) + assert obj.dtype == "datetime64[ns]" + + fv = fill_val + # do the check with each of the available datetime scalars + if exp_dtype == "datetime64[ns]": + for scalar in [fv, fv.to_pydatetime(), fv.to_datetime64()]: + self._run_test(obj, scalar, klass, exp_dtype) + else: + for scalar in [fv, fv.to_pydatetime()]: + self._run_test(obj, fill_val, klass, exp_dtype) + + @pytest.mark.xfail(reason="Test not implemented") + def test_where_index_complex128(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_where_index_bool(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_where_series_timedelta64(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_where_series_period(self): + raise NotImplementedError + + @pytest.mark.parametrize( + "value", + [pd.Timedelta(days=9).as_unit("us"), timedelta(days=9), np.timedelta64(9, "D")], + ) + def test_where_index_timedelta64(self, value): + tdi = pd.timedelta_range("1 Day", periods=4) + cond = np.array([True, False, False, True]) + + expected = pd.TimedeltaIndex(["1 Day", value, value, "4 Days"]) + result = tdi.where(cond, value) + tm.assert_index_equal(result, expected) + + # wrong-dtyped NaT + dtnat = np.datetime64("NaT", "ns") + expected = pd.Index([tdi[0], dtnat, dtnat, tdi[3]], dtype=object) + assert expected[1] is dtnat + + result = tdi.where(cond, dtnat) + tm.assert_index_equal(result, expected) + + def test_where_index_period(self): + dti = pd.date_range("2016-01-01", periods=3, freq="QS") + pi = dti.to_period("Q") + + cond = np.array([False, True, False]) + + # Passing a valid scalar + value = pi[-1] + pi.freq * 10 + expected = pd.PeriodIndex([value, pi[1], value]) + result = pi.where(cond, value) + tm.assert_index_equal(result, expected) + + # Case passing ndarray[object] of Periods + other = np.asarray(pi + pi.freq * 10, dtype=object) + result = pi.where(cond, other) + expected = pd.PeriodIndex([other[0], pi[1], other[2]]) + tm.assert_index_equal(result, expected) + + # Passing a mismatched scalar -> casts to object + td = pd.Timedelta(days=4) + expected = pd.Index([td, pi[1], td], dtype=object) + result = pi.where(cond, td) + tm.assert_index_equal(result, expected) + + per = pd.Period("2020-04-21", "D") + expected = pd.Index([per, pi[1], per], dtype=object) + result = pi.where(cond, per) + tm.assert_index_equal(result, expected) + + +class TestFillnaSeriesCoercion(CoercionBase): + # not indexing, but place here for consistency + + method = "fillna" + + def _assert_fillna_conversion(self, original, value, expected, expected_dtype): + """test coercion triggered by fillna""" + target = original.copy() + res = target.fillna(value) + tm.assert_equal(res, expected) + assert res.dtype == expected_dtype + + @pytest.mark.parametrize( + "fill_val, fill_dtype", + [(1, object), (1.1, object), (1 + 1j, object), (True, object)], + ) + def test_fillna_object(self, index_or_series, fill_val, fill_dtype): + klass = index_or_series + obj = klass(["a", np.nan, "c", "d"], dtype=object) + assert obj.dtype == object + + exp = klass(["a", fill_val, "c", "d"], dtype=object) + self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype) + + @pytest.mark.parametrize( + "fill_val,fill_dtype", + [(1, np.float64), (1.1, np.float64), (1 + 1j, np.complex128), (True, object)], + ) + def test_fillna_float64(self, index_or_series, fill_val, fill_dtype): + klass = index_or_series + obj = klass([1.1, np.nan, 3.3, 4.4]) + assert obj.dtype == np.float64 + + exp = klass([1.1, fill_val, 3.3, 4.4]) + self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype) + + @pytest.mark.parametrize( + "fill_val,fill_dtype", + [ + (1, np.complex128), + (1.1, np.complex128), + (1 + 1j, np.complex128), + (True, object), + ], + ) + def test_fillna_complex128(self, index_or_series, fill_val, fill_dtype): + klass = index_or_series + obj = klass([1 + 1j, np.nan, 3 + 3j, 4 + 4j], dtype=np.complex128) + assert obj.dtype == np.complex128 + + exp = klass([1 + 1j, fill_val, 3 + 3j, 4 + 4j]) + self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype) + + @pytest.mark.parametrize( + "fill_val,fill_dtype", + [ + (pd.Timestamp("2012-01-01").as_unit("s"), "datetime64[s]"), + (pd.Timestamp("2012-01-01", tz="US/Eastern").as_unit("s"), object), + (1, object), + ("x", object), + ], + ids=["datetime64", "datetime64tz", "object", "object"], + ) + def test_fillna_datetime(self, index_or_series, fill_val, fill_dtype): + klass = index_or_series + obj = klass( + [ + pd.Timestamp("2011-01-01").as_unit("s"), + pd.NaT, + pd.Timestamp("2011-01-03").as_unit("s"), + pd.Timestamp("2011-01-04").as_unit("s"), + ] + ) + assert obj.dtype == "datetime64[s]" + + exp = klass( + [ + pd.Timestamp("2011-01-01").as_unit("s"), + fill_val, + pd.Timestamp("2011-01-03").as_unit("s"), + pd.Timestamp("2011-01-04").as_unit("s"), + ] + ) + self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype) + + @pytest.mark.parametrize( + "fill_val,fill_dtype", + [ + ( + pd.Timestamp("2012-01-01", tz="US/Eastern").as_unit("s"), + "datetime64[s, US/Eastern]", + ), + (pd.Timestamp("2012-01-01").as_unit("s"), object), + # pre-2.0 with a mismatched tz we would get object result + ( + pd.Timestamp("2012-01-01", tz="Asia/Tokyo").as_unit("s"), + "datetime64[s, US/Eastern]", + ), + (1, object), + ("x", object), + ], + ) + def test_fillna_datetime64tz(self, index_or_series, fill_val, fill_dtype): + klass = index_or_series + tz = "US/Eastern" + + obj = klass( + [ + pd.Timestamp("2011-01-01", tz=tz).as_unit("s"), + pd.NaT, + pd.Timestamp("2011-01-03", tz=tz).as_unit("s"), + pd.Timestamp("2011-01-04", tz=tz).as_unit("s"), + ] + ) + assert obj.dtype == "datetime64[s, US/Eastern]" + + if getattr(fill_val, "tz", None) is None: + fv = fill_val + else: + fv = fill_val.tz_convert(tz) + exp = klass( + [ + pd.Timestamp("2011-01-01", tz=tz).as_unit("s"), + fv, + pd.Timestamp("2011-01-03", tz=tz).as_unit("s"), + pd.Timestamp("2011-01-04", tz=tz).as_unit("s"), + ] + ) + self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype) + + @pytest.mark.parametrize( + "fill_val", + [ + 1, + 1.1, + 1 + 1j, + True, + pd.Interval(1, 2, closed="left"), + pd.Timestamp("2012-01-01", tz="US/Eastern").as_unit("s"), + pd.Timestamp("2012-01-01").as_unit("s"), + pd.Timedelta(days=1), + pd.Period("2016-01-01", "D"), + ], + ) + def test_fillna_interval(self, index_or_series, fill_val): + ii = pd.interval_range(1.0, 5.0, closed="right").insert(1, np.nan) + assert isinstance(ii.dtype, pd.IntervalDtype) + obj = index_or_series(ii) + + exp = index_or_series([ii[0], fill_val, ii[2], ii[3], ii[4]], dtype=object) + + fill_dtype = object + self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype) + + @pytest.mark.xfail(reason="Test not implemented") + def test_fillna_series_int64(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_fillna_index_int64(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_fillna_series_bool(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_fillna_index_bool(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_fillna_series_timedelta64(self): + raise NotImplementedError + + @pytest.mark.parametrize( + "fill_val", + [ + 1, + 1.1, + 1 + 1j, + True, + pd.Interval(1, 2, closed="left"), + pd.Timestamp("2012-01-01", tz="US/Eastern").as_unit("s"), + pd.Timestamp("2012-01-01").as_unit("s"), + pd.Timedelta(days=1), + pd.Period("2016-01-01", "W"), + ], + ) + def test_fillna_series_period(self, index_or_series, fill_val): + pi = pd.period_range("2016-01-01", periods=4, freq="D").insert(1, pd.NaT) + assert isinstance(pi.dtype, pd.PeriodDtype) + obj = index_or_series(pi) + + exp = index_or_series([pi[0], fill_val, pi[2], pi[3], pi[4]], dtype=object) + + fill_dtype = object + self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype) + + @pytest.mark.xfail(reason="Test not implemented") + def test_fillna_index_timedelta64(self): + raise NotImplementedError + + @pytest.mark.xfail(reason="Test not implemented") + def test_fillna_index_period(self): + raise NotImplementedError + + +class TestReplaceSeriesCoercion(CoercionBase): + klasses = ["series"] + method = "replace" + + rep: dict[str, list] = {} + rep["object"] = ["a", "b"] + rep["int64"] = [4, 5] + rep["float64"] = [1.1, 2.2] + rep["complex128"] = [1 + 1j, 2 + 2j] + rep["bool"] = [True, False] + rep["datetime64[ns]"] = [ + pd.Timestamp("2011-01-01").as_unit("s"), + pd.Timestamp("2011-01-03").as_unit("s"), + ] + + for tz in ["UTC", "US/Eastern"]: + # to test tz => different tz replacement + key = f"datetime64[ns, {tz}]" + rep[key] = [ + pd.Timestamp("2011-01-01", tz=tz).as_unit("s"), + pd.Timestamp("2011-01-03", tz=tz).as_unit("s"), + ] + + rep["timedelta64[us]"] = [pd.Timedelta("1 day"), pd.Timedelta("2 day")] + + @pytest.fixture(params=["dict", "series"]) + def how(self, request): + return request.param + + @pytest.fixture( + params=[ + "object", + "int64", + "float64", + "complex128", + "bool", + "datetime64[ns]", + "datetime64[ns, UTC]", + "datetime64[ns, US/Eastern]", + "timedelta64[us]", + ] + ) + def from_key(self, request): + return request.param + + @pytest.fixture( + params=[ + "object", + "int64", + "float64", + "complex128", + "bool", + "datetime64[ns]", + "datetime64[ns, UTC]", + "datetime64[ns, US/Eastern]", + "timedelta64[us]", + ], + ids=[ + "object", + "int64", + "float64", + "complex128", + "bool", + "datetime64", + "datetime64tz", + "datetime64tz", + "timedelta64", + ], + ) + def to_key(self, request): + return request.param + + @pytest.fixture + def replacer(self, how, from_key, to_key): + """ + Object we will pass to `Series.replace` + """ + if how == "dict": + replacer = dict(zip(self.rep[from_key], self.rep[to_key])) + elif how == "series": + replacer = pd.Series(self.rep[to_key], index=self.rep[from_key]) + else: + raise ValueError + return replacer + + def test_replace_series(self, to_key, from_key, replacer): + index = pd.Index([3, 4], name="xxx") + obj = pd.Series(self.rep[from_key], index=index, name="yyy") + obj = obj.astype(from_key) + assert obj.dtype == from_key + + if from_key.startswith("datetime") and to_key.startswith("datetime"): + # tested below + return + elif from_key in ["datetime64[ns, US/Eastern]", "datetime64[ns, UTC]"]: + # tested below + return + + if (from_key == "float64" and to_key in ("int64")) or ( + from_key == "complex128" and to_key in ("int64", "float64") + ): + if not IS64 or is_platform_windows(): + pytest.skip(f"32-bit platform buggy: {from_key} -> {to_key}") + + # Expected: do not downcast by replacement + exp = pd.Series(self.rep[to_key], index=index, name="yyy", dtype=from_key) + + else: + exp = pd.Series(self.rep[to_key], index=index, name="yyy") + + result = obj.replace(replacer) + tm.assert_series_equal(result, exp, check_dtype=False) + + @pytest.mark.parametrize( + "to_key", + ["timedelta64[us]", "bool", "object", "complex128", "float64", "int64"], + indirect=True, + ) + @pytest.mark.parametrize( + "from_key", ["datetime64[ns, UTC]", "datetime64[ns, US/Eastern]"], indirect=True + ) + def test_replace_series_datetime_tz( + self, to_key, from_key, replacer, using_infer_string + ): + index = pd.Index([3, 4], name="xyz") + obj = pd.Series(self.rep[from_key], index=index, name="yyy").dt.as_unit("ns") + assert obj.dtype == from_key + + exp = pd.Series(self.rep[to_key], index=index, name="yyy") + if using_infer_string and to_key == "object": + assert exp.dtype == "string" + else: + assert exp.dtype == to_key + + result = obj.replace(replacer) + tm.assert_series_equal(result, exp, check_dtype=False) + + @pytest.mark.parametrize( + "to_key", + ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, US/Eastern]"], + indirect=True, + ) + @pytest.mark.parametrize( + "from_key", + ["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, US/Eastern]"], + indirect=True, + ) + def test_replace_series_datetime_datetime(self, to_key, from_key, replacer): + index = pd.Index([3, 4], name="xyz") + obj = pd.Series(self.rep[from_key], index=index, name="yyy").dt.as_unit("ns") + assert obj.dtype == from_key + + exp = pd.Series(self.rep[to_key], index=index, name="yyy") + if isinstance(obj.dtype, pd.DatetimeTZDtype) and isinstance( + exp.dtype, pd.DatetimeTZDtype + ): + # with mismatched tzs, we retain the original dtype as of 2.0 + exp = exp.astype(obj.dtype) + elif to_key == from_key: + exp = exp.dt.as_unit("ns") + + result = obj.replace(replacer) + tm.assert_series_equal(result, exp, check_dtype=False) + + @pytest.mark.xfail(reason="Test not implemented") + def test_replace_series_period(self): + raise NotImplementedError diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_datetime.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_datetime.py new file mode 100644 index 0000000000000000000000000000000000000000..a890de3b977ea2e739cf6c574f789763e4401c98 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_datetime.py @@ -0,0 +1,191 @@ +import re + +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + Series, + Timestamp, + date_range, +) +import pandas._testing as tm + + +class TestDatetimeIndex: + def test_get_loc_naive_dti_aware_str_deprecated(self): + # GH#46903 + ts = Timestamp("20130101")._value + dti = pd.DatetimeIndex([ts + 50 + i for i in range(100)]) + ser = Series(range(100), index=dti) + + key = "2013-01-01 00:00:00.000000050+0000" + msg = re.escape(repr(key)) + with pytest.raises(KeyError, match=msg): + ser[key] + + with pytest.raises(KeyError, match=msg): + dti.get_loc(key) + + def test_indexing_with_datetime_tz(self): + # GH#8260 + # support datetime64 with tz + + idx = Index(date_range("20130101", periods=3, tz="US/Eastern"), name="foo") + dr = date_range("20130110", periods=3) + df = DataFrame({"A": idx, "B": dr}) + df["C"] = idx + df.iloc[1, 1] = pd.NaT + df.iloc[1, 2] = pd.NaT + + expected = Series( + [Timestamp("2013-01-02 00:00:00-0500", tz="US/Eastern"), pd.NaT, pd.NaT], + index=list("ABC"), + dtype="object", + name=1, + ) + + # indexing + result = df.iloc[1] + tm.assert_series_equal(result, expected) + result = df.loc[1] + tm.assert_series_equal(result, expected) + + def test_indexing_fast_xs(self): + # indexing - fast_xs + df = DataFrame({"a": date_range("2014-01-01", periods=10, tz="UTC", unit="ns")}) + result = df.iloc[5] + expected = Series( + [Timestamp("2014-01-06 00:00:00+0000", tz="UTC")], + index=["a"], + name=5, + dtype="M8[ns, UTC]", + ) + tm.assert_series_equal(result, expected) + + result = df.loc[5] + tm.assert_series_equal(result, expected) + + # indexing - boolean + result = df[df.a > df.a[3]] + expected = df.iloc[4:] + tm.assert_frame_equal(result, expected) + + def test_consistency_with_tz_aware_scalar(self): + # xef gh-12938 + # various ways of indexing the same tz-aware scalar + df = Series([Timestamp("2016-03-30 14:35:25", tz="Europe/Brussels")]).to_frame() + + df = pd.concat([df, df]).reset_index(drop=True) + expected = Timestamp("2016-03-30 14:35:25+0200", tz="Europe/Brussels") + + result = df[0][0] + assert result == expected + + result = df.iloc[0, 0] + assert result == expected + + result = df.loc[0, 0] + assert result == expected + + result = df.iat[0, 0] + assert result == expected + + result = df.at[0, 0] + assert result == expected + + result = df[0].loc[0] + assert result == expected + + result = df[0].at[0] + assert result == expected + + def test_indexing_with_datetimeindex_tz(self, indexer_sl): + # GH 12050 + # indexing on a series with a datetimeindex with tz + index = date_range("2015-01-01", periods=2, tz="utc") + + ser = Series(range(2), index=index, dtype="int64") + + # list-like indexing + + for sel in (index, list(index)): + # getitem + result = indexer_sl(ser)[sel] + expected = ser.copy() + if sel is not index: + expected.index = expected.index._with_freq(None) + tm.assert_series_equal(result, expected) + + # setitem + result = ser.copy() + indexer_sl(result)[sel] = 1 + expected = Series(1, index=index) + tm.assert_series_equal(result, expected) + + # single element indexing + + # getitem + assert indexer_sl(ser)[index[1]] == 1 + + # setitem + result = ser.copy() + indexer_sl(result)[index[1]] = 5 + expected = Series([0, 5], index=index) + tm.assert_series_equal(result, expected) + + def test_nanosecond_getitem_setitem_with_tz(self): + # GH 11679 + data = ["2016-06-28 08:30:00.123456789"] + index = pd.DatetimeIndex(data, dtype="datetime64[ns, America/Chicago]") + df = DataFrame({"a": [10]}, index=index) + result = df.loc[df.index[0]] + expected = Series(10, index=["a"], name=df.index[0]) + tm.assert_series_equal(result, expected) + + result = df.copy() + result.loc[df.index[0], "a"] = -1 + expected = DataFrame(-1, index=index, columns=["a"]) + tm.assert_frame_equal(result, expected) + + def test_getitem_str_slice_millisecond_resolution(self, frame_or_series): + # GH#33589 + + keys = [ + "2017-10-25T16:25:04.151", + "2017-10-25T16:25:04.252", + "2017-10-25T16:50:05.237", + "2017-10-25T16:50:05.238", + ] + obj = frame_or_series( + [1, 2, 3, 4], + index=[Timestamp(x) for x in keys], + ) + result = obj[keys[1] : keys[2]] + expected = frame_or_series( + [2, 3], + index=[ + Timestamp(keys[1]), + Timestamp(keys[2]), + ], + ) + tm.assert_equal(result, expected) + + def test_getitem_pyarrow_index(self, frame_or_series): + # GH 53644 + pytest.importorskip("pyarrow") + obj = frame_or_series( + range(5), + index=date_range("2020", freq="D", periods=5).astype( + "timestamp[us][pyarrow]" + ), + ) + result = obj.loc[obj.index[:-3]] + expected = frame_or_series( + range(2), + index=date_range("2020", freq="D", periods=2).astype( + "timestamp[us][pyarrow]" + ), + ) + tm.assert_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_floats.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_floats.py new file mode 100644 index 0000000000000000000000000000000000000000..f9b9e8a6c7c28760593abe5f7464904fb4a0c56c --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_floats.py @@ -0,0 +1,674 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + RangeIndex, + Series, + date_range, + period_range, + timedelta_range, +) +import pandas._testing as tm + + +def gen_obj(klass, index): + if klass is Series: + obj = Series(np.arange(len(index)), index=index) + else: + obj = DataFrame( + np.random.default_rng(2).standard_normal((len(index), len(index))), + index=index, + columns=index, + ) + return obj + + +class TestFloatIndexers: + def check(self, result, original, indexer, getitem): + """ + comparator for results + we need to take care if we are indexing on a + Series or a frame + """ + if isinstance(original, Series): + expected = original.iloc[indexer] + elif getitem: + expected = original.iloc[:, indexer] + else: + expected = original.iloc[indexer] + + tm.assert_almost_equal(result, expected) + + @pytest.mark.parametrize( + "index", + [ + Index(list("abcde")), + Index(list("abcde"), dtype="category"), + date_range("2020-01-01", periods=5), + timedelta_range("1 day", periods=5), + period_range("2020-01-01", periods=5), + ], + ) + def test_scalar_non_numeric(self, index, frame_or_series, indexer_sl): + # GH 4892 + # float_indexers should raise exceptions + # on appropriate Index types & accessors + + s = gen_obj(frame_or_series, index) + + # getting + with pytest.raises(KeyError, match="^3.0$"): + indexer_sl(s)[3.0] + + # contains + assert 3.0 not in s + + s2 = s.copy() + indexer_sl(s2)[3.0] = 10 + + if indexer_sl is tm.setitem: + assert 3.0 in s2.axes[-1] + elif indexer_sl is tm.loc: + assert 3.0 in s2.axes[0] + else: + assert 3.0 not in s2.axes[0] + assert 3.0 not in s2.axes[-1] + + @pytest.mark.parametrize( + "index", + [ + Index(list("abcde")), + Index(list("abcde"), dtype="category"), + date_range("2020-01-01", periods=5), + timedelta_range("1 day", periods=5), + period_range("2020-01-01", periods=5), + ], + ) + def test_scalar_non_numeric_series_fallback(self, index): + # starting in 3.0, integer keys are always treated as labels, no longer + # fall back to positional. + s = Series(np.arange(len(index)), index=index) + + with pytest.raises(KeyError, match="3"): + s[3] + with pytest.raises(KeyError, match="^3.0$"): + s[3.0] + + def test_scalar_with_mixed(self, indexer_sl): + s2 = Series([1, 2, 3], index=["a", "b", "c"]) + s3 = Series([1, 2, 3], index=["a", "b", 1.5]) + + # lookup in a pure string index with an invalid indexer + + with pytest.raises(KeyError, match="^1.0$"): + indexer_sl(s2)[1.0] + + with pytest.raises(KeyError, match=r"^1\.0$"): + indexer_sl(s2)[1.0] + + result = indexer_sl(s2)["b"] + expected = 2 + assert result == expected + + # mixed index so we have label + # indexing + with pytest.raises(KeyError, match="^1.0$"): + indexer_sl(s3)[1.0] + + if indexer_sl is not tm.loc: + # as of 3.0, __getitem__ no longer falls back to positional + with pytest.raises(KeyError, match="^1$"): + s3[1] + + with pytest.raises(KeyError, match=r"^1\.0$"): + indexer_sl(s3)[1.0] + + result = indexer_sl(s3)[1.5] + expected = 3 + assert result == expected + + @pytest.mark.parametrize( + "index", [Index(np.arange(5), dtype=np.int64), RangeIndex(5)] + ) + def test_scalar_integer(self, index, frame_or_series, indexer_sl): + getitem = indexer_sl is not tm.loc + + # test how scalar float indexers work on int indexes + + # integer index + i = index + obj = gen_obj(frame_or_series, i) + + # coerce to equal int + + result = indexer_sl(obj)[3.0] + self.check(result, obj, 3, getitem) + + if isinstance(obj, Series): + + def compare(x, y): + assert x == y + + expected = 100 + else: + compare = tm.assert_series_equal + if getitem: + expected = Series(100, index=range(len(obj)), name=3) + else: + expected = Series(100.0, index=range(len(obj)), name=3) + + s2 = obj.copy() + indexer_sl(s2)[3.0] = 100 + + result = indexer_sl(s2)[3.0] + compare(result, expected) + + result = indexer_sl(s2)[3] + compare(result, expected) + + @pytest.mark.parametrize( + "index", [Index(np.arange(5), dtype=np.int64), RangeIndex(5)] + ) + def test_scalar_integer_contains_float(self, index, frame_or_series): + # contains + # integer index + obj = gen_obj(frame_or_series, index) + + # coerce to equal int + assert 3.0 in obj + + def test_scalar_float(self, frame_or_series): + # scalar float indexers work on a float index + index = Index(np.arange(5.0)) + s = gen_obj(frame_or_series, index) + + # assert all operations except for iloc are ok + indexer = index[3] + for idxr in [tm.loc, tm.setitem]: + getitem = idxr is not tm.loc + + # getting + result = idxr(s)[indexer] + self.check(result, s, 3, getitem) + + # setting + s2 = s.copy() + + result = idxr(s2)[indexer] + self.check(result, s, 3, getitem) + + # random float is a KeyError + with pytest.raises(KeyError, match=r"^3\.5$"): + idxr(s)[3.5] + + # contains + assert 3.0 in s + + # iloc succeeds with an integer + expected = s.iloc[3] + s2 = s.copy() + + s2.iloc[3] = expected + result = s2.iloc[3] + self.check(result, s, 3, False) + + @pytest.mark.parametrize( + "index", + [ + Index(list("abcde"), dtype=object), + date_range("2020-01-01", periods=5), + timedelta_range("1 day", periods=5), + period_range("2020-01-01", periods=5), + ], + ) + @pytest.mark.parametrize("idx", [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]) + def test_slice_non_numeric(self, index, idx, frame_or_series, indexer_sli): + # GH 4892 + # float_indexers should raise exceptions + # on appropriate Index types & accessors + + s = gen_obj(frame_or_series, index) + + # getitem + if indexer_sli is tm.iloc: + msg = ( + "cannot do positional indexing " + rf"on {type(index).__name__} with these indexers \[(3|4)\.0\] of " + "type float" + ) + else: + msg = ( + "cannot do slice indexing " + rf"on {type(index).__name__} with these indexers " + r"\[(3|4)(\.0)?\] " + r"of type (float|int)" + ) + with pytest.raises(TypeError, match=msg): + indexer_sli(s)[idx] + + # setitem + if indexer_sli is tm.iloc: + # otherwise we keep the same message as above + msg = "slice indices must be integers or None or have an __index__ method" + with pytest.raises(TypeError, match=msg): + indexer_sli(s)[idx] = 0 + + def test_slice_integer(self): + # same as above, but for Integer based indexes + # these coerce to a like integer + # oob indicates if we are out of bounds + # of positional indexing + for index, oob in [ + (Index(np.arange(5, dtype=np.int64)), False), + (RangeIndex(5), False), + (Index(np.arange(5, dtype=np.int64) + 10), True), + ]: + # s is an in-range index + s = Series(range(5), index=index) + + # getitem + for idx in [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]: + result = s.loc[idx] + + # these are all label indexing + # except getitem which is positional + # empty + if oob: + indexer = slice(0, 0) + else: + indexer = slice(3, 5) + self.check(result, s, indexer, False) + + # getitem out-of-bounds + for idx in [slice(-6, 6), slice(-6.0, 6.0)]: + result = s.loc[idx] + + # these are all label indexing + # except getitem which is positional + # empty + if oob: + indexer = slice(0, 0) + else: + indexer = slice(-6, 6) + self.check(result, s, indexer, False) + + # positional indexing + msg = ( + "cannot do slice indexing " + rf"on {type(index).__name__} with these indexers \[-6\.0\] of " + "type float" + ) + with pytest.raises(TypeError, match=msg): + s[slice(-6.0, 6.0)] + + # getitem odd floats + for idx, res1 in [ + (slice(2.5, 4), slice(3, 5)), + (slice(2, 3.5), slice(2, 4)), + (slice(2.5, 3.5), slice(3, 4)), + ]: + result = s.loc[idx] + if oob: + res = slice(0, 0) + else: + res = res1 + + self.check(result, s, res, False) + + # positional indexing + msg = ( + "cannot do slice indexing " + rf"on {type(index).__name__} with these indexers \[(2|3)\.5\] of " + "type float" + ) + with pytest.raises(TypeError, match=msg): + s[idx] + + @pytest.mark.parametrize("idx", [slice(2, 4.0), slice(2.0, 4), slice(2.0, 4.0)]) + def test_integer_positional_indexing(self, idx): + """make sure that we are raising on positional indexing + w.r.t. an integer index + """ + s = Series(range(2, 6), index=range(2, 6)) + + result = s[2:4] + expected = s.iloc[2:4] + tm.assert_series_equal(result, expected) + + klass = RangeIndex + msg = ( + "cannot do (slice|positional) indexing " + rf"on {klass.__name__} with these indexers \[(2|4)\.0\] of " + "type float" + ) + with pytest.raises(TypeError, match=msg): + s[idx] + with pytest.raises(TypeError, match=msg): + s.iloc[idx] + + @pytest.mark.parametrize( + "index", [Index(np.arange(5), dtype=np.int64), RangeIndex(5)] + ) + def test_slice_integer_frame_getitem(self, index): + # similar to above, but on the getitem dim (of a DataFrame) + s = DataFrame(np.random.default_rng(2).standard_normal((5, 2)), index=index) + + # getitem + for idx in [slice(0.0, 1), slice(0, 1.0), slice(0.0, 1.0)]: + result = s.loc[idx] + indexer = slice(0, 2) + self.check(result, s, indexer, False) + + # positional indexing + msg = ( + "cannot do slice indexing " + rf"on {type(index).__name__} with these indexers \[(0|1)\.0\] of " + "type float" + ) + with pytest.raises(TypeError, match=msg): + s[idx] + + # getitem out-of-bounds + for idx in [slice(-10, 10), slice(-10.0, 10.0)]: + result = s.loc[idx] + self.check(result, s, slice(-10, 10), True) + + # positional indexing + msg = ( + "cannot do slice indexing " + rf"on {type(index).__name__} with these indexers \[-10\.0\] of " + "type float" + ) + with pytest.raises(TypeError, match=msg): + s[slice(-10.0, 10.0)] + + # getitem odd floats + for idx, res in [ + (slice(0.5, 1), slice(1, 2)), + (slice(0, 0.5), slice(0, 1)), + (slice(0.5, 1.5), slice(1, 2)), + ]: + result = s.loc[idx] + self.check(result, s, res, False) + + # positional indexing + msg = ( + "cannot do slice indexing " + rf"on {type(index).__name__} with these indexers \[0\.5\] of " + "type float" + ) + with pytest.raises(TypeError, match=msg): + s[idx] + + @pytest.mark.parametrize("idx", [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]) + @pytest.mark.parametrize( + "index", [Index(np.arange(5), dtype=np.int64), RangeIndex(5)] + ) + def test_float_slice_getitem_with_integer_index_raises(self, idx, index): + # similar to above, but on the getitem dim (of a DataFrame) + s = DataFrame(np.random.default_rng(2).standard_normal((5, 2)), index=index) + + # setitem + sc = s.copy() + sc.loc[idx] = 0 + result = sc.loc[idx].values.ravel() + assert (result == 0).all() + + # positional indexing + msg = ( + "cannot do slice indexing " + rf"on {type(index).__name__} with these indexers \[(3|4)\.0\] of " + "type float" + ) + with pytest.raises(TypeError, match=msg): + s[idx] = 0 + + with pytest.raises(TypeError, match=msg): + s[idx] + + @pytest.mark.parametrize("idx", [slice(3.0, 4), slice(3, 4.0), slice(3.0, 4.0)]) + def test_slice_float(self, idx, frame_or_series, indexer_sl): + # same as above, but for floats + index = Index(np.arange(5.0)) + 0.1 + s = gen_obj(frame_or_series, index) + + expected = s.iloc[3:4] + + # getitem + result = indexer_sl(s)[idx] + assert isinstance(result, type(s)) + tm.assert_equal(result, expected) + + # setitem + s2 = s.copy() + indexer_sl(s2)[idx] = 0 + result = indexer_sl(s2)[idx].values.ravel() + assert (result == 0).all() + + def test_floating_index_doc_example(self): + index = Index([1.5, 2, 3, 4.5, 5]) + s = Series(range(5), index=index) + assert s[3] == 2 + assert s.loc[3] == 2 + assert s.iloc[3] == 3 + + def test_floating_misc(self, indexer_sl): + # related 236 + # scalar/slicing of a float index + s = Series(np.arange(5), index=np.arange(5) * 2.5, dtype=np.int64) + + # label based slicing + result = indexer_sl(s)[1.0:3.0] + expected = Series(1, index=[2.5]) + tm.assert_series_equal(result, expected) + + # exact indexing when found + + result = indexer_sl(s)[5.0] + assert result == 2 + + result = indexer_sl(s)[5] + assert result == 2 + + # value not found (and no fallbacking at all) + + # scalar integers + with pytest.raises(KeyError, match=r"^4$"): + indexer_sl(s)[4] + + # fancy floats/integers create the correct entry (as nan) + # fancy tests + expected = Series([2, 0], index=Index([5.0, 0.0], dtype=np.float64)) + for fancy_idx in [[5.0, 0.0], np.array([5.0, 0.0])]: # float + tm.assert_series_equal(indexer_sl(s)[fancy_idx], expected) + + expected = Series([2, 0], index=Index([5, 0], dtype="float64")) + for fancy_idx in [[5, 0], np.array([5, 0])]: + tm.assert_series_equal(indexer_sl(s)[fancy_idx], expected) + + # all should return the same as we are slicing 'the same' + result2 = indexer_sl(s)[2.0:5.0] + result3 = indexer_sl(s)[2.0:5] + result4 = indexer_sl(s)[2.1:5] + tm.assert_series_equal(result2, result3) + tm.assert_series_equal(result2, result4) + + # list selection + result1 = indexer_sl(s)[[0.0, 5, 10]] + result2 = s.iloc[[0, 2, 4]] + tm.assert_series_equal(result1, result2) + + with pytest.raises(KeyError, match="not in index"): + indexer_sl(s)[[1.6, 5, 10]] + + with pytest.raises(KeyError, match="not in index"): + indexer_sl(s)[[0, 1, 2]] + + result = indexer_sl(s)[[2.5, 5]] + tm.assert_series_equal(result, Series([1, 2], index=[2.5, 5.0])) + + result = indexer_sl(s)[[2.5]] + tm.assert_series_equal(result, Series([1], index=[2.5])) + + def test_floatindex_slicing_bug(self, float_numpy_dtype): + # GH 5557, related to slicing a float index + dtype = float_numpy_dtype + ser = { + 256: 2321.0, + 1: 78.0, + 2: 2716.0, + 3: 0.0, + 4: 369.0, + 5: 0.0, + 6: 269.0, + 7: 0.0, + 8: 0.0, + 9: 0.0, + 10: 3536.0, + 11: 0.0, + 12: 24.0, + 13: 0.0, + 14: 931.0, + 15: 0.0, + 16: 101.0, + 17: 78.0, + 18: 9643.0, + 19: 0.0, + 20: 0.0, + 21: 0.0, + 22: 63761.0, + 23: 0.0, + 24: 446.0, + 25: 0.0, + 26: 34773.0, + 27: 0.0, + 28: 729.0, + 29: 78.0, + 30: 0.0, + 31: 0.0, + 32: 3374.0, + 33: 0.0, + 34: 1391.0, + 35: 0.0, + 36: 361.0, + 37: 0.0, + 38: 61808.0, + 39: 0.0, + 40: 0.0, + 41: 0.0, + 42: 6677.0, + 43: 0.0, + 44: 802.0, + 45: 0.0, + 46: 2691.0, + 47: 0.0, + 48: 3582.0, + 49: 0.0, + 50: 734.0, + 51: 0.0, + 52: 627.0, + 53: 70.0, + 54: 2584.0, + 55: 0.0, + 56: 324.0, + 57: 0.0, + 58: 605.0, + 59: 0.0, + 60: 0.0, + 61: 0.0, + 62: 3989.0, + 63: 10.0, + 64: 42.0, + 65: 0.0, + 66: 904.0, + 67: 0.0, + 68: 88.0, + 69: 70.0, + 70: 8172.0, + 71: 0.0, + 72: 0.0, + 73: 0.0, + 74: 64902.0, + 75: 0.0, + 76: 347.0, + 77: 0.0, + 78: 36605.0, + 79: 0.0, + 80: 379.0, + 81: 70.0, + 82: 0.0, + 83: 0.0, + 84: 3001.0, + 85: 0.0, + 86: 1630.0, + 87: 7.0, + 88: 364.0, + 89: 0.0, + 90: 67404.0, + 91: 9.0, + 92: 0.0, + 93: 0.0, + 94: 7685.0, + 95: 0.0, + 96: 1017.0, + 97: 0.0, + 98: 2831.0, + 99: 0.0, + 100: 2963.0, + 101: 0.0, + 102: 854.0, + 103: 0.0, + 104: 0.0, + 105: 0.0, + 106: 0.0, + 107: 0.0, + 108: 0.0, + 109: 0.0, + 110: 0.0, + 111: 0.0, + 112: 0.0, + 113: 0.0, + 114: 0.0, + 115: 0.0, + 116: 0.0, + 117: 0.0, + 118: 0.0, + 119: 0.0, + 120: 0.0, + 121: 0.0, + 122: 0.0, + 123: 0.0, + 124: 0.0, + 125: 0.0, + 126: 67744.0, + 127: 22.0, + 128: 264.0, + 129: 0.0, + 260: 197.0, + 268: 0.0, + 265: 0.0, + 269: 0.0, + 261: 0.0, + 266: 1198.0, + 267: 0.0, + 262: 2629.0, + 258: 775.0, + 257: 0.0, + 263: 0.0, + 259: 0.0, + 264: 163.0, + 250: 10326.0, + 251: 0.0, + 252: 1228.0, + 253: 0.0, + 254: 2769.0, + 255: 0.0, + } + + # smoke test for the repr + s = Series(ser, dtype=dtype) + result = s.value_counts() + assert result.index.dtype == dtype + str(result) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_iat.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_iat.py new file mode 100644 index 0000000000000000000000000000000000000000..bb9252a50eb2aded723170f8bddecf8811c1ee76 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_iat.py @@ -0,0 +1,30 @@ +import numpy as np + +from pandas import ( + DataFrame, + Series, + period_range, +) + + +def test_iat(float_frame): + for i, row in enumerate(float_frame.index): + for j, col in enumerate(float_frame.columns): + result = float_frame.iat[i, j] + expected = float_frame.at[row, col] + assert result == expected + + +def test_iat_duplicate_columns(): + # https://github.com/pandas-dev/pandas/issues/11754 + df = DataFrame([[1, 2]], columns=["x", "x"]) + assert df.iat[0, 0] == 1 + + +def test_iat_getitem_series_with_period_index(): + # GH#4390, iat incorrectly indexing + index = period_range("1/1/2001", periods=10) + ser = Series(np.random.default_rng(2).standard_normal(10), index=index) + expected = ser[index[0]] + result = ser.iat[0] + assert expected == result diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_iloc.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_iloc.py new file mode 100644 index 0000000000000000000000000000000000000000..fb67a2fe64533cf6480543fc5b8d56117e6fc76d --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_iloc.py @@ -0,0 +1,1550 @@ +"""test positional based indexing with iloc""" + +from datetime import datetime +import re + +import numpy as np +import pytest + +from pandas.compat import pa_version_under16p0 +from pandas.errors import IndexingError +import pandas.util._test_decorators as td + +from pandas import ( + NA, + Categorical, + CategoricalDtype, + DataFrame, + Index, + Interval, + NaT, + Series, + Timestamp, + array, + concat, + date_range, + interval_range, + isna, + to_datetime, +) +import pandas._testing as tm +from pandas.api.types import is_scalar +from pandas.tests.indexing.common import check_indexing_smoketest_or_raises + +# We pass through the error message from numpy +_slice_iloc_msg = re.escape( + "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) " + "and integer or boolean arrays are valid indices" +) + + +class TestiLoc: + @pytest.mark.parametrize("key", [2, -1, [0, 1, 2]]) + @pytest.mark.parametrize( + "index", + [ + Index(list("abcd"), dtype=object), + Index([2, 4, "null", 8], dtype=object), + date_range("20130101", periods=4), + Index(range(0, 8, 2), dtype=np.float64), + Index([]), + ], + ) + def test_iloc_getitem_int_and_list_int(self, key, frame_or_series, index, request): + obj = frame_or_series(range(len(index)), index=index) + check_indexing_smoketest_or_raises( + obj, + "iloc", + key, + fails=IndexError, + ) + + # array of ints (GH5006), make sure that a single indexer is returning + # the correct type + + +class TestiLocBaseIndependent: + """Tests Independent Of Base Class""" + + @pytest.mark.parametrize( + "key", + [ + slice(None), + slice(3), + range(3), + [0, 1, 2], + Index(range(3)), + np.asarray([0, 1, 2]), + ], + ) + def test_iloc_setitem_fullcol_categorical(self, indexer_li, key): + frame = DataFrame({0: range(3)}, dtype=object) + + cat = Categorical(["alpha", "beta", "gamma"]) + + assert frame._mgr.blocks[0]._can_hold_element(cat) + + df = frame.copy() + orig_vals = df.values + + indexer_li(df)[key, 0] = cat + + expected = DataFrame({0: cat}).astype(object) + assert np.shares_memory(df[0].values, orig_vals) + + tm.assert_frame_equal(df, expected) + + # check we dont have a view on cat (may be undesired GH#39986) + df.iloc[0, 0] = "gamma" + assert cat[0] != "gamma" + + # pre-2.0 with mixed dataframe ("split" path) we always overwrote the + # column. as of 2.0 we correctly write "into" the column, so + # we retain the object dtype. + frame = DataFrame({0: np.array([0, 1, 2], dtype=object), 1: range(3)}) + df = frame.copy() + indexer_li(df)[key, 0] = cat + expected = DataFrame({0: Series(cat.astype(object), dtype=object), 1: range(3)}) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_iloc_setitem_ea_inplace( + self, frame_or_series, index_or_series_or_array, has_ref + ): + # GH#38952 Case with not setting a full column + # IntegerArray without NAs + arr = array([1, 2, 3, 4]) + obj = frame_or_series(arr.to_numpy("i8")) + if has_ref: + view = obj[:] # noqa: F841 + + if frame_or_series is Series: + values = obj.values + else: + values = obj._mgr.blocks[0].values + + if frame_or_series is Series: + obj.iloc[:2] = index_or_series_or_array(arr[2:]) + else: + obj.iloc[:2, 0] = index_or_series_or_array(arr[2:]) + + expected = frame_or_series(np.array([3, 4, 3, 4], dtype="i8")) + tm.assert_equal(obj, expected) + + # Check that we are actually in-place + if not has_ref: + if frame_or_series is Series: + assert obj.values is not values + assert np.shares_memory(obj.values, values) + else: + assert np.shares_memory(obj[0].values, values) + + def test_is_scalar_access(self): + # GH#32085 index with duplicates doesn't matter for _is_scalar_access + index = Index([1, 2, 1]) + ser = Series(range(3), index=index) + + assert ser.iloc._is_scalar_access((1,)) + + df = ser.to_frame() + assert df.iloc._is_scalar_access((1, 0)) + + @pytest.mark.skipif( + pa_version_under16p0, reason="https://github.com/apache/arrow/issues/40642" + ) + def test_iloc_exceeds_bounds(self): + # GH6296 + # iloc should allow indexers that exceed the bounds + df = DataFrame(np.random.default_rng(2).random((20, 5)), columns=list("ABCDE")) + + # lists of positions should raise IndexError! + msg = "positional indexers are out-of-bounds" + with pytest.raises(IndexError, match=msg): + df.iloc[:, [0, 1, 2, 3, 4, 5]] + with pytest.raises(IndexError, match=msg): + df.iloc[[1, 30]] + with pytest.raises(IndexError, match=msg): + df.iloc[[1, -30]] + with pytest.raises(IndexError, match=msg): + df.iloc[[100]] + + s = df["A"] + with pytest.raises(IndexError, match=msg): + s.iloc[[100]] + with pytest.raises(IndexError, match=msg): + s.iloc[[-100]] + + # still raise on a single indexer + msg = "single positional indexer is out-of-bounds" + with pytest.raises(IndexError, match=msg): + df.iloc[30] + with pytest.raises(IndexError, match=msg): + df.iloc[-30] + + # GH10779 + # single positive/negative indexer exceeding Series bounds should raise + # an IndexError + with pytest.raises(IndexError, match=msg): + s.iloc[30] + with pytest.raises(IndexError, match=msg): + s.iloc[-30] + + # slices are ok + result = df.iloc[:, 4:10] # 0 < start < len < stop + expected = df.iloc[:, 4:] + tm.assert_frame_equal(result, expected) + + result = df.iloc[:, -4:-10] # stop < 0 < start < len + expected = df.iloc[:, :0] + tm.assert_frame_equal(result, expected) + + result = df.iloc[:, 10:4:-1] # 0 < stop < len < start (down) + expected = df.iloc[:, :4:-1] + tm.assert_frame_equal(result, expected) + + result = df.iloc[:, 4:-10:-1] # stop < 0 < start < len (down) + expected = df.iloc[:, 4::-1] + tm.assert_frame_equal(result, expected) + + result = df.iloc[:, -10:4] # start < 0 < stop < len + expected = df.iloc[:, :4] + tm.assert_frame_equal(result, expected) + + result = df.iloc[:, 10:4] # 0 < stop < len < start + expected = df.iloc[:, :0] + tm.assert_frame_equal(result, expected) + + result = df.iloc[:, -10:-11:-1] # stop < start < 0 < len (down) + expected = df.iloc[:, :0] + tm.assert_frame_equal(result, expected) + + result = df.iloc[:, 10:11] # 0 < len < start < stop + expected = df.iloc[:, :0] + tm.assert_frame_equal(result, expected) + + # slice bounds exceeding is ok + result = s.iloc[18:30] + expected = s.iloc[18:] + tm.assert_series_equal(result, expected) + + result = s.iloc[30:] + expected = s.iloc[:0] + tm.assert_series_equal(result, expected) + + result = s.iloc[30::-1] + expected = s.iloc[::-1] + tm.assert_series_equal(result, expected) + + # doc example + dfl = DataFrame( + np.random.default_rng(2).standard_normal((5, 2)), columns=list("AB") + ) + tm.assert_frame_equal( + dfl.iloc[:, 2:3], + DataFrame(index=dfl.index, columns=Index([], dtype=dfl.columns.dtype)), + ) + tm.assert_frame_equal(dfl.iloc[:, 1:3], dfl.iloc[:, [1]]) + tm.assert_frame_equal(dfl.iloc[4:6], dfl.iloc[[4]]) + + msg = "positional indexers are out-of-bounds" + with pytest.raises(IndexError, match=msg): + dfl.iloc[[4, 5, 6]] + msg = "single positional indexer is out-of-bounds" + with pytest.raises(IndexError, match=msg): + dfl.iloc[:, 4] + + @pytest.mark.parametrize("index,columns", [(np.arange(20), list("ABCDE"))]) + @pytest.mark.parametrize( + "index_vals,column_vals", + [ + ([slice(None), ["A", "D"]]), + (["1", "2"], slice(None)), + ([datetime(2019, 1, 1)], slice(None)), + ], + ) + def test_iloc_non_integer_raises(self, index, columns, index_vals, column_vals): + # GH 25753 + df = DataFrame( + np.random.default_rng(2).standard_normal((len(index), len(columns))), + index=index, + columns=columns, + ) + msg = ".iloc requires numeric indexers, got" + with pytest.raises(IndexError, match=msg): + df.iloc[index_vals, column_vals] + + def test_iloc_getitem_invalid_scalar(self, frame_or_series): + # GH 21982 + + obj = DataFrame(np.arange(100).reshape(10, 10)) + obj = tm.get_obj(obj, frame_or_series) + + with pytest.raises(TypeError, match="Cannot index by location index"): + obj.iloc["a"] + + def test_iloc_array_not_mutating_negative_indices(self): + # GH 21867 + array_with_neg_numbers = np.array([1, 2, -1]) + array_copy = array_with_neg_numbers.copy() + df = DataFrame( + {"A": [100, 101, 102], "B": [103, 104, 105], "C": [106, 107, 108]}, + index=[1, 2, 3], + ) + df.iloc[array_with_neg_numbers] + tm.assert_numpy_array_equal(array_with_neg_numbers, array_copy) + df.iloc[:, array_with_neg_numbers] + tm.assert_numpy_array_equal(array_with_neg_numbers, array_copy) + + def test_iloc_getitem_neg_int_can_reach_first_index(self): + # GH10547 and GH10779 + # negative integers should be able to reach index 0 + df = DataFrame({"A": [2, 3, 5], "B": [7, 11, 13]}) + s = df["A"] + + expected = df.iloc[0] + result = df.iloc[-3] + tm.assert_series_equal(result, expected) + + expected = df.iloc[[0]] + result = df.iloc[[-3]] + tm.assert_frame_equal(result, expected) + + expected = s.iloc[0] + result = s.iloc[-3] + assert result == expected + + expected = s.iloc[[0]] + result = s.iloc[[-3]] + tm.assert_series_equal(result, expected) + + # check the length 1 Series case highlighted in GH10547 + expected = Series(["a"], index=["A"]) + result = expected.iloc[[-1]] + tm.assert_series_equal(result, expected) + + def test_iloc_getitem_dups(self): + # GH 6766 + df1 = DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}]) + df2 = DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}]) + df = concat([df1, df2], axis=1) + + # cross-sectional indexing + result = df.iloc[0, 0] + assert isna(result) + + result = df.iloc[0, :] + expected = Series([np.nan, 1, 3, 3], index=["A", "B", "A", "B"], name=0) + tm.assert_series_equal(result, expected) + + def test_iloc_getitem_array(self): + df = DataFrame( + [ + {"A": 1, "B": 2, "C": 3}, + {"A": 100, "B": 200, "C": 300}, + {"A": 1000, "B": 2000, "C": 3000}, + ] + ) + + expected = DataFrame([{"A": 1, "B": 2, "C": 3}]) + tm.assert_frame_equal(df.iloc[[0]], expected) + + expected = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 100, "B": 200, "C": 300}]) + tm.assert_frame_equal(df.iloc[[0, 1]], expected) + + expected = DataFrame([{"B": 2, "C": 3}, {"B": 2000, "C": 3000}], index=[0, 2]) + result = df.iloc[[0, 2], [1, 2]] + tm.assert_frame_equal(result, expected) + + def test_iloc_getitem_bool(self): + df = DataFrame( + [ + {"A": 1, "B": 2, "C": 3}, + {"A": 100, "B": 200, "C": 300}, + {"A": 1000, "B": 2000, "C": 3000}, + ] + ) + + expected = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 100, "B": 200, "C": 300}]) + result = df.iloc[[True, True, False]] + tm.assert_frame_equal(result, expected) + + expected = DataFrame( + [{"A": 1, "B": 2, "C": 3}, {"A": 1000, "B": 2000, "C": 3000}], index=[0, 2] + ) + result = df.iloc[lambda x: x.index % 2 == 0] + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("index", [[True, False], [True, False, True, False]]) + def test_iloc_getitem_bool_diff_len(self, index): + # GH26658 + s = Series([1, 2, 3]) + msg = f"Boolean index has wrong length: {len(index)} instead of {len(s)}" + with pytest.raises(IndexError, match=msg): + s.iloc[index] + + def test_iloc_getitem_slice(self): + df = DataFrame( + [ + {"A": 1, "B": 2, "C": 3}, + {"A": 100, "B": 200, "C": 300}, + {"A": 1000, "B": 2000, "C": 3000}, + ] + ) + + expected = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 100, "B": 200, "C": 300}]) + result = df.iloc[:2] + tm.assert_frame_equal(result, expected) + + expected = DataFrame([{"A": 100, "B": 200}], index=[1]) + result = df.iloc[1:2, 0:2] + tm.assert_frame_equal(result, expected) + + expected = DataFrame( + [{"A": 1, "C": 3}, {"A": 100, "C": 300}, {"A": 1000, "C": 3000}] + ) + result = df.iloc[:, lambda df: [0, 2]] + tm.assert_frame_equal(result, expected) + + def test_iloc_getitem_slice_dups(self): + df1 = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + columns=["A", "A", "B", "B"], + ) + df2 = DataFrame( + np.random.default_rng(2).integers(0, 10, size=20).reshape(10, 2), + columns=["A", "C"], + ) + + # axis=1 + df = concat([df1, df2], axis=1) + tm.assert_frame_equal(df.iloc[:, :4], df1) + tm.assert_frame_equal(df.iloc[:, 4:], df2) + + df = concat([df2, df1], axis=1) + tm.assert_frame_equal(df.iloc[:, :2], df2) + tm.assert_frame_equal(df.iloc[:, 2:], df1) + + exp = concat([df2, df1.iloc[:, [0]]], axis=1) + tm.assert_frame_equal(df.iloc[:, 0:3], exp) + + # axis=0 + df = concat([df, df], axis=0) + tm.assert_frame_equal(df.iloc[0:10, :2], df2) + tm.assert_frame_equal(df.iloc[0:10, 2:], df1) + tm.assert_frame_equal(df.iloc[10:, :2], df2) + tm.assert_frame_equal(df.iloc[10:, 2:], df1) + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_iloc_setitem(sel, has_ref): + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), + index=np.arange(0, 8, 2), + columns=np.arange(0, 12, 3), + ) + if has_ref: + view = df[:] # noqa: F841 + + df.iloc[1, 1] = 1 + result = df.iloc[1, 1] + assert result == 1 + + df.iloc[:, 2:3] = 0 + expected = df.iloc[:, 2:3] + result = df.iloc[:, 2:3] + tm.assert_frame_equal(result, expected) + + # GH5771 + s = Series(0, index=[4, 5, 6]) + s.iloc[1:2] += 1 + expected = Series([0, 1, 0], index=[4, 5, 6]) + tm.assert_series_equal(s, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_iloc_setitem_axis_argument(self, has_ref): + # GH45032 + df = DataFrame([[6, "c", 10], [7, "d", 11], [8, "e", 12]]) + df[1] = df[1].astype(object) + if has_ref: + view = df[:] + expected = DataFrame([[6, "c", 10], [7, "d", 11], [5, 5, 5]]) + expected[1] = expected[1].astype(object) + df.iloc(axis=0)[2] = 5 + tm.assert_frame_equal(df, expected) + + df = DataFrame([[6, "c", 10], [7, "d", 11], [8, "e", 12]]) + df[1] = df[1].astype(object) + if has_ref: + view = df[:] # noqa: F841 + expected = DataFrame([[6, "c", 5], [7, "d", 5], [8, "e", 5]]) + expected[1] = expected[1].astype(object) + df.iloc(axis=1)[2] = 5 + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_iloc_setitem_list(self, has_ref): + # setitem with an iloc list + df = DataFrame( + np.arange(9).reshape((3, 3)), index=["A", "B", "C"], columns=["A", "B", "C"] + ) + if has_ref: + view = df[:] # noqa: F841 + df.iloc[[0, 1], [1, 2]] + df.iloc[[0, 1], [1, 2]] += 100 + + expected = DataFrame( + np.array([0, 101, 102, 3, 104, 105, 6, 7, 8]).reshape((3, 3)), + index=["A", "B", "C"], + columns=["A", "B", "C"], + ) + tm.assert_frame_equal(df, expected) + + def test_iloc_setitem_pandas_object(self): + # GH 17193 + s_orig = Series([0, 1, 2, 3]) + expected = Series([0, -1, -2, 3]) + + s = s_orig.copy() + s.iloc[Series([1, 2])] = [-1, -2] + tm.assert_series_equal(s, expected) + + s = s_orig.copy() + s.iloc[Index([1, 2])] = [-1, -2] + tm.assert_series_equal(s, expected) + + def test_iloc_setitem_dups(self): + # GH 6766 + # iloc with a mask aligning from another iloc + df1 = DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}]) + df2 = DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}]) + df = concat([df1, df2], axis=1) + + expected = df.fillna(3) + inds = np.isnan(df.iloc[:, 0]) + mask = inds[inds].index + df.iloc[mask, 0] = df.iloc[mask, 2] + tm.assert_frame_equal(df, expected) + + # del a dup column across blocks + expected = DataFrame({0: [1, 2], 1: [3, 4]}) + expected.columns = ["B", "B"] + del df["A"] + tm.assert_frame_equal(df, expected) + + # assign back to self + df.iloc[[0, 1], [0, 1]] = df.iloc[[0, 1], [0, 1]] + tm.assert_frame_equal(df, expected) + + # reversed x 2 + df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(drop=True) + df.iloc[[1, 0], [0, 1]] = df.iloc[[1, 0], [0, 1]].reset_index(drop=True) + tm.assert_frame_equal(df, expected) + + def test_iloc_setitem_frame_duplicate_columns_multiple_blocks(self): + # Same as the "assign back to self" check in test_iloc_setitem_dups + # but on a DataFrame with multiple blocks + df = DataFrame([[0, 1], [2, 3]], columns=["B", "B"]) + + # setting float values that can be held by existing integer arrays + # is inplace + df.iloc[:, 0] = df.iloc[:, 0].astype("f8") + assert len(df._mgr.blocks) == 1 + + # if the assigned values cannot be held by existing integer arrays, + # we raise + with pytest.raises(TypeError, match="Invalid value"): + df.iloc[:, 0] = df.iloc[:, 0] + 0.5 + + expected = df.copy() + + # assign back to self + df.iloc[[0, 1], [0, 1]] = df.iloc[[0, 1], [0, 1]] + + tm.assert_frame_equal(df, expected) + + # TODO: GH#27620 this test used to compare iloc against ix; check if this + # is redundant with another test comparing iloc against loc + def test_iloc_getitem_frame(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + index=range(0, 20, 2), + columns=range(0, 8, 2), + ) + + result = df.iloc[2] + exp = df.loc[4] + tm.assert_series_equal(result, exp) + + result = df.iloc[2, 2] + exp = df.loc[4, 4] + assert result == exp + + # slice + result = df.iloc[4:8] + expected = df.loc[8:14] + tm.assert_frame_equal(result, expected) + + result = df.iloc[:, 2:3] + expected = df.loc[:, 4:5] + tm.assert_frame_equal(result, expected) + + # list of integers + result = df.iloc[[0, 1, 3]] + expected = df.loc[[0, 2, 6]] + tm.assert_frame_equal(result, expected) + + result = df.iloc[[0, 1, 3], [0, 1]] + expected = df.loc[[0, 2, 6], [0, 2]] + tm.assert_frame_equal(result, expected) + + # neg indices + result = df.iloc[[-1, 1, 3], [-1, 1]] + expected = df.loc[[18, 2, 6], [6, 2]] + tm.assert_frame_equal(result, expected) + + # dups indices + result = df.iloc[[-1, -1, 1, 3], [-1, 1]] + expected = df.loc[[18, 18, 2, 6], [6, 2]] + tm.assert_frame_equal(result, expected) + + # with index-like + s = Series(index=range(1, 5), dtype=object) + result = df.iloc[s.index] + expected = df.loc[[2, 4, 6, 8]] + tm.assert_frame_equal(result, expected) + + def test_iloc_getitem_labelled_frame(self): + # try with labelled frame + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + index=list("abcdefghij"), + columns=list("ABCD"), + ) + + result = df.iloc[1, 1] + exp = df.loc["b", "B"] + assert result == exp + + result = df.iloc[:, 2:3] + expected = df.loc[:, ["C"]] + tm.assert_frame_equal(result, expected) + + # negative indexing + result = df.iloc[-1, -1] + exp = df.loc["j", "D"] + assert result == exp + + # out-of-bounds exception + msg = "index 5 is out of bounds for axis 0 with size 4|index out of bounds" + with pytest.raises(IndexError, match=msg): + df.iloc[10, 5] + + # trying to use a label + msg = ( + r"Location based indexing can only have \[integer, integer " + r"slice \(START point is INCLUDED, END point is EXCLUDED\), " + r"listlike of integers, boolean array\] types" + ) + with pytest.raises(ValueError, match=msg): + df.iloc["j", "D"] + + def test_iloc_getitem_doc_issue(self): + # multi axis slicing issue with single block + # surfaced in GH 6059 + + arr = np.random.default_rng(2).standard_normal((6, 4)) + index = date_range("20130101", periods=6) + columns = list("ABCD") + df = DataFrame(arr, index=index, columns=columns) + + # defines ref_locs + df.describe() + + result = df.iloc[3:5, 0:2] + + expected = DataFrame(arr[3:5, 0:2], index=index[3:5], columns=columns[0:2]) + tm.assert_frame_equal(result, expected) + + # for dups + df.columns = list("aaaa") + result = df.iloc[3:5, 0:2] + + expected = DataFrame(arr[3:5, 0:2], index=index[3:5], columns=list("aa")) + tm.assert_frame_equal(result, expected) + + # related + arr = np.random.default_rng(2).standard_normal((6, 4)) + index = list(range(0, 12, 2)) + columns = list(range(0, 8, 2)) + df = DataFrame(arr, index=index, columns=columns) + + df._mgr.blocks[0].mgr_locs + result = df.iloc[1:5, 2:4] + expected = DataFrame(arr[1:5, 2:4], index=index[1:5], columns=columns[2:4]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_iloc_setitem_series(self, has_ref): + df = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + index=list("abcdefghij"), + columns=list("ABCD"), + ) + if has_ref: + view = df[:] # noqa: F841 + + df.iloc[1, 1] = 1 + result = df.iloc[1, 1] + assert result == 1 + + df.iloc[:, 2:3] = 0 + expected = df.iloc[:, 2:3] + result = df.iloc[:, 2:3] + tm.assert_frame_equal(result, expected) + + s = Series(np.random.default_rng(2).standard_normal(10), index=range(0, 20, 2)) + + s.iloc[1] = 1 + result = s.iloc[1] + assert result == 1 + + s.iloc[:4] = 0 + expected = s.iloc[:4] + result = s.iloc[:4] + tm.assert_series_equal(result, expected) + + s = Series([-1] * 6) + s.iloc[0::2] = [0, 2, 4] + s.iloc[1::2] = [1, 3, 5] + result = s + expected = Series([0, 1, 2, 3, 4, 5]) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_iloc_setitem_list_of_lists(self, has_ref): + # GH 7551 + # list-of-list is set incorrectly in mixed vs. single dtyped frames + df = DataFrame( + {"A": np.arange(5, dtype="int64"), "B": np.arange(5, 10, dtype="int64")} + ) + if has_ref: + view = df[:] + df.iloc[2:4] = [[10, 11], [12, 13]] + expected = DataFrame({"A": [0, 1, 10, 12, 4], "B": [5, 6, 11, 13, 9]}) + tm.assert_frame_equal(df, expected) + + df = DataFrame( + {"A": ["a", "b", "c", "d", "e"], "B": np.arange(5, 10, dtype="int64")} + ) + if has_ref: + view = df[:] # noqa: F841 + df.iloc[2:4] = [["x", 11], ["y", 13]] + expected = DataFrame({"A": ["a", "b", "x", "y", "e"], "B": [5, 6, 11, 13, 9]}) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + @pytest.mark.parametrize("indexer", [[0], slice(None, 1, None), np.array([0])]) + @pytest.mark.parametrize("value", [["Z"], np.array(["Z"])]) + def test_iloc_setitem_with_scalar_index(self, has_ref, indexer, value): + # GH #19474 + # assigning like "df.iloc[0, [0]] = ['Z']" should be evaluated + # elementwisely, not using "setter('A', ['Z'])". + + # Set object type to avoid upcast when setting "Z" + df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"]).astype({"A": object}) + if has_ref: + view = df[:] # noqa: F841 + df.iloc[0, indexer] = value + result = df.iloc[0, 0] + + assert is_scalar(result) and result == "Z" + + @pytest.mark.filterwarnings("ignore::UserWarning") + def test_iloc_mask(self): + # GH 60994, iloc with a mask (of a series) should return accordingly + df = DataFrame(list(range(5)), index=list("ABCDE"), columns=["a"]) + mask = df.a % 2 == 0 + msg = "iLocation based boolean indexing cannot use an indexable as a mask" + with pytest.raises(ValueError, match=msg): + df.iloc[mask] + + mask.index = range(len(mask)) + msg = "Unalignable boolean Series provided as indexer" + with pytest.raises(IndexingError, match=msg): + df.iloc[mask] + + # ndarray ok + result = df.iloc[np.array([True] * len(mask), dtype=bool)] + tm.assert_frame_equal(result, df) + + # the possibilities + locs = np.arange(4) + nums = 2**locs + reps = [bin(num) for num in nums] + df = DataFrame({"locs": locs, "nums": nums}, reps) + + expected = { + (None, ""): "0b1100", + (None, ".loc"): "0b1100", + (None, ".iloc"): "0b1100", + ("index", ""): "0b11", + ("index", ".loc"): "0b11", + ( + "index", + ".iloc", + ): "iLocation based boolean indexing cannot use an indexable as a mask", + ("locs", ""): "Unalignable boolean Series provided as indexer", + ("locs", ".loc"): "Unalignable boolean Series provided as indexer", + ("locs", ".iloc"): "Unalignable boolean Series provided as indexer", + } + + # UserWarnings from reindex of a boolean mask + for idx in [None, "index", "locs"]: + mask = (df.nums > 2).values + if idx: + mask_index = getattr(df, idx)[::-1] + mask = Series(mask, list(mask_index)) + for method in ["", ".loc", ".iloc"]: + try: + if method: + accessor = getattr(df, method[1:]) + else: + accessor = df + answer = str(bin(accessor[mask]["nums"].sum())) + except (ValueError, IndexingError) as err: + answer = str(err) + + key = ( + idx, + method, + ) + expected_result = expected.get(key) + + # Fix the assertion to check for substring match + if ( + idx is None or (idx == "index" and method != ".iloc") + ) and "0b" in expected_result: + # For successful numeric results, exact match is needed + assert expected_result == answer, ( + f"[{key}] does not match [{answer}]" + ) + else: + # For error messages, substring match is sufficient + assert expected_result in answer, f"[{key}] not found in [{answer}]" + + def test_iloc_with_numpy_bool_array(self): + df = DataFrame(list(range(5)), index=list("ABCDE"), columns=["a"]) + result = df.iloc[np.array([True, False, True, False, True], dtype=bool)] + expected = DataFrame({"a": [0, 2, 4]}, index=["A", "C", "E"]) + tm.assert_frame_equal(result, expected) + + def test_iloc_series_mask_with_index_mismatch_raises(self): + df = DataFrame(list(range(5)), index=list("ABCDE"), columns=["a"]) + mask = df.a % 2 == 0 + msg = "Unalignable boolean Series provided as indexer" + with pytest.raises(IndexingError, match=msg): + df.iloc[Series([True] * len(mask), dtype=bool)] + + def test_iloc_series_mask_all_true(self): + df = DataFrame(list(range(5)), columns=["a"]) + mask = Series([True] * len(df), dtype=bool) + result = df.iloc[mask] + tm.assert_frame_equal(result, df) + + def test_iloc_series_mask_alternate_true(self): + df = DataFrame(list(range(5)), columns=["a"]) + mask = Series([True, False, True, False, True], dtype=bool) + result = df.iloc[mask] + expected = DataFrame({"a": [0, 2, 4]}, index=[0, 2, 4]) + tm.assert_frame_equal(result, expected) + + def test_iloc_non_unique_indexing(self): + # GH 4017, non-unique indexing (on the axis) + df = DataFrame({"A": [0.1] * 3000, "B": [1] * 3000}) + idx = np.arange(30) * 99 + expected = df.iloc[idx] + + df3 = concat([df, 2 * df, 3 * df]) + result = df3.iloc[idx] + + tm.assert_frame_equal(result, expected) + + df2 = DataFrame({"A": [0.1] * 1000, "B": [1] * 1000}) + df2 = concat([df2, 2 * df2, 3 * df2]) + + with pytest.raises(KeyError, match="not in index"): + df2.loc[idx] + + def test_iloc_empty_list_indexer_is_ok(self): + df = DataFrame( + np.ones((5, 2)), + index=Index([f"i-{i}" for i in range(5)], name="a"), + columns=Index([f"i-{i}" for i in range(2)], name="a"), + ) + # vertical empty + tm.assert_frame_equal( + df.iloc[:, []], + df.iloc[:, :0], + check_index_type=True, + check_column_type=True, + ) + # horizontal empty + tm.assert_frame_equal( + df.iloc[[], :], + df.iloc[:0, :], + check_index_type=True, + check_column_type=True, + ) + # horizontal empty + tm.assert_frame_equal( + df.iloc[[]], df.iloc[:0, :], check_index_type=True, check_column_type=True + ) + + def test_identity_slice_returns_new_object(self): + # GH13873 + original_df = DataFrame({"a": [1, 2, 3]}) + sliced_df = original_df.iloc[:] + assert sliced_df is not original_df + + # should be a shallow copy + assert np.shares_memory(original_df["a"], sliced_df["a"]) + + # Setting using .loc[:, "a"] sets inplace so alters both sliced and orig + # depending on CoW + original_df.loc[:, "a"] = [4, 4, 4] + assert (sliced_df["a"] == [1, 2, 3]).all() + + original_series = Series([1, 2, 3, 4, 5, 6]) + sliced_series = original_series.iloc[:] + assert sliced_series is not original_series + + # should also be a shallow copy + original_series[:3] = [7, 8, 9] + # shallow copy not updated (CoW) + assert all(sliced_series[:3] == [1, 2, 3]) + + def test_indexing_zerodim_np_array(self): + # GH24919 + df = DataFrame([[1, 2], [3, 4]]) + result = df.iloc[np.array(0)] + s = Series([1, 2], name=0) + tm.assert_series_equal(result, s) + + def test_series_indexing_zerodim_np_array(self): + # GH24919 + s = Series([1, 2]) + result = s.iloc[np.array(0)] + assert result == 1 + + def test_iloc_setitem_categorical_updates_inplace(self): + # Mixed dtype ensures we go through take_split_path in setitem_with_indexer + cat = Categorical(["A", "B", "C"]) + df = DataFrame({1: cat, 2: [1, 2, 3]}, copy=False) + + assert tm.shares_memory(df[1], cat) + + # With the enforcement of GH#45333 in 2.0, this modifies original + # values inplace + df.iloc[:, 0] = cat[::-1] + + assert tm.shares_memory(df[1], cat) + expected = Categorical(["C", "B", "A"], categories=["A", "B", "C"]) + tm.assert_categorical_equal(cat, expected) + + def test_iloc_with_boolean_operation(self): + # GH 20627 + result = DataFrame([[0, 1], [2, 3], [4, 5], [6, np.nan]]) + result.iloc[result.index <= 2] *= 2 + expected = DataFrame([[0, 2], [4, 6], [8, 10], [6, np.nan]]) + tm.assert_frame_equal(result, expected) + + result.iloc[result.index > 2] *= 2 + expected = DataFrame([[0, 2], [4, 6], [8, 10], [12, np.nan]]) + tm.assert_frame_equal(result, expected) + + result.iloc[[True, True, False, False]] *= 2 + expected = DataFrame([[0, 4], [8, 12], [8, 10], [12, np.nan]]) + tm.assert_frame_equal(result, expected) + + result.iloc[[False, False, True, True]] /= 2 + expected = DataFrame([[0, 4.0], [8, 12.0], [4, 5.0], [6, np.nan]]) + tm.assert_frame_equal(result, expected) + + def test_iloc_getitem_singlerow_slice_categoricaldtype_gives_series(self): + # GH#29521 + df = DataFrame({"x": Categorical("a b c d e".split())}) + result = df.iloc[0] + raw_cat = Categorical(["a"], categories=["a", "b", "c", "d", "e"]) + expected = Series(raw_cat, index=["x"], name=0, dtype="category") + + tm.assert_series_equal(result, expected) + + def test_iloc_getitem_categorical_values(self): + # GH#14580 + # test iloc() on Series with Categorical data + + ser = Series([1, 2, 3]).astype("category") + + # get slice + result = ser.iloc[0:2] + expected = Series([1, 2]).astype(CategoricalDtype([1, 2, 3])) + tm.assert_series_equal(result, expected) + + # get list of indexes + result = ser.iloc[[0, 1]] + expected = Series([1, 2]).astype(CategoricalDtype([1, 2, 3])) + tm.assert_series_equal(result, expected) + + # get boolean array + result = ser.iloc[[True, False, False]] + expected = Series([1]).astype(CategoricalDtype([1, 2, 3])) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("value", [None, NaT, np.nan]) + def test_iloc_setitem_td64_values_cast_na(self, value): + # GH#18586 + series = Series([0, 1, 2], dtype="timedelta64[ns]") + series.iloc[0] = value + expected = Series([NaT, 1, 2], dtype="timedelta64[ns]") + tm.assert_series_equal(series, expected) + + @pytest.mark.parametrize("not_na", [Interval(0, 1), "a", 1.0]) + def test_setitem_mix_of_nan_and_interval(self, not_na, nulls_fixture): + # GH#27937 + dtype = CategoricalDtype(categories=[not_na]) + ser = Series( + [nulls_fixture, nulls_fixture, nulls_fixture, nulls_fixture], dtype=dtype + ) + ser.iloc[:3] = [nulls_fixture, not_na, nulls_fixture] + exp = Series([nulls_fixture, not_na, nulls_fixture, nulls_fixture], dtype=dtype) + tm.assert_series_equal(ser, exp) + + def test_iloc_setitem_empty_frame_raises_with_3d_ndarray(self): + idx = Index([]) + obj = DataFrame( + np.random.default_rng(2).standard_normal((len(idx), len(idx))), + index=idx, + columns=idx, + ) + nd3 = np.random.default_rng(2).integers(5, size=(2, 2, 2)) + + msg = f"Cannot set values with ndim > {obj.ndim}" + with pytest.raises(ValueError, match=msg): + obj.iloc[nd3] = 0 + + def test_iloc_getitem_read_only_values(self, indexer_li): + # GH#10043 this is fundamentally a test for iloc, but test loc while + # we're here + rw_array = np.eye(10) + rw_df = DataFrame(rw_array) + + ro_array = np.eye(10) + ro_array.setflags(write=False) + ro_df = DataFrame(ro_array) + + tm.assert_frame_equal( + indexer_li(rw_df)[[1, 2, 3]], indexer_li(ro_df)[[1, 2, 3]] + ) + tm.assert_frame_equal(indexer_li(rw_df)[[1]], indexer_li(ro_df)[[1]]) + tm.assert_series_equal(indexer_li(rw_df)[1], indexer_li(ro_df)[1]) + tm.assert_frame_equal(indexer_li(rw_df)[1:3], indexer_li(ro_df)[1:3]) + + def test_iloc_getitem_readonly_key(self): + # GH#17192 iloc with read-only array raising TypeError + df = DataFrame({"data": np.ones(100, dtype="float64")}) + indices = np.array([1, 3, 6]) + indices.flags.writeable = False + + result = df.iloc[indices] + expected = df.loc[[1, 3, 6]] + tm.assert_frame_equal(result, expected) + + result = df["data"].iloc[indices] + expected = df["data"].loc[[1, 3, 6]] + tm.assert_series_equal(result, expected) + + def test_iloc_assign_series_to_df_cell(self): + # GH 37593 + df = DataFrame(columns=["a"], index=[0]) + df.iloc[0, 0] = Series([1, 2, 3]) + expected = DataFrame({"a": [Series([1, 2, 3])]}, columns=["a"], index=[0]) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("klass", [list, np.array]) + def test_iloc_setitem_bool_indexer(self, klass): + # GH#36741 + df = DataFrame({"flag": ["x", "y", "z"], "value": [1, 3, 4]}) + indexer = klass([True, False, False]) + df.iloc[indexer, 1] = df.iloc[indexer, 1] * 2 + expected = DataFrame({"flag": ["x", "y", "z"], "value": [2, 3, 4]}) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + @pytest.mark.parametrize("indexer", [[1], slice(1, 2)]) + def test_iloc_setitem_pure_position_based(self, indexer, has_ref): + # GH#22046 + df1 = DataFrame({"a2": [11, 12, 13], "b2": [14, 15, 16]}) + df2 = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]}) + if has_ref: + view = df2[:] # noqa: F841 + df2.iloc[:, indexer] = df1.iloc[:, [0]] + expected = DataFrame({"a": [1, 2, 3], "b": [11, 12, 13], "c": [7, 8, 9]}) + tm.assert_frame_equal(df2, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_iloc_setitem_dictionary_value(self, has_ref): + # GH#37728 + df = DataFrame({"x": [1, 2], "y": [2, 2]}) + if has_ref: + view = df[:] + rhs = {"x": 9, "y": 99} + df.iloc[1] = rhs + expected = DataFrame({"x": [1, 9], "y": [2, 99]}) + tm.assert_frame_equal(df, expected) + + # GH#38335 same thing, mixed dtypes + df = DataFrame({"x": [1, 2], "y": [2.0, 2.0]}) + if has_ref: + view = df[:] # noqa: F841 + df.iloc[1] = rhs + expected = DataFrame({"x": [1, 9], "y": [2.0, 99.0]}) + tm.assert_frame_equal(df, expected) + + def test_iloc_getitem_float_duplicates(self): + df = DataFrame( + np.random.default_rng(2).standard_normal((3, 3)), + index=[0.1, 0.2, 0.2], + columns=list("abc"), + ) + expect = df.iloc[1:] + tm.assert_frame_equal(df.loc[0.2], expect) + + expect = df.iloc[1:, 0] + tm.assert_series_equal(df.loc[0.2, "a"], expect) + + df.index = [1, 0.2, 0.2] + expect = df.iloc[1:] + tm.assert_frame_equal(df.loc[0.2], expect) + + expect = df.iloc[1:, 0] + tm.assert_series_equal(df.loc[0.2, "a"], expect) + + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 3)), + index=[1, 0.2, 0.2, 1], + columns=list("abc"), + ) + expect = df.iloc[1:-1] + tm.assert_frame_equal(df.loc[0.2], expect) + + expect = df.iloc[1:-1, 0] + tm.assert_series_equal(df.loc[0.2, "a"], expect) + + df.index = [0.1, 0.2, 2, 0.2] + expect = df.iloc[[1, -1]] + tm.assert_frame_equal(df.loc[0.2], expect) + + expect = df.iloc[[1, -1], 0] + tm.assert_series_equal(df.loc[0.2, "a"], expect) + + def test_iloc_setitem_custom_object(self): + # iloc with an object + class TO: + def __init__(self, value) -> None: + self.value = value + + def __str__(self) -> str: + return f"[{self.value}]" + + __repr__ = __str__ + + def __eq__(self, other) -> bool: + return self.value == other.value + + def view(self): + return self + + df = DataFrame(index=[0, 1], columns=[0]) + df.iloc[1, 0] = TO(1) + df.iloc[1, 0] = TO(2) + + result = DataFrame(index=[0, 1], columns=[0]) + result.iloc[1, 0] = TO(2) + + tm.assert_frame_equal(result, df) + + # remains object dtype even after setting it back + df = DataFrame(index=[0, 1], columns=[0]) + df.iloc[1, 0] = TO(1) + df.iloc[1, 0] = np.nan + result = DataFrame(index=[0, 1], columns=[0]) + + tm.assert_frame_equal(result, df) + + def test_iloc_getitem_with_duplicates(self): + df = DataFrame( + np.random.default_rng(2).random((3, 3)), + columns=list("ABC"), + index=list("aab"), + ) + + result = df.iloc[0] + assert isinstance(result, Series) + tm.assert_almost_equal(result.values, df.values[0]) + + result = df.T.iloc[:, 0] + assert isinstance(result, Series) + tm.assert_almost_equal(result.values, df.values[0]) + + def test_iloc_getitem_with_duplicates2(self): + # GH#2259 + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=[1, 1, 2]) + result = df.iloc[:, [0]] + expected = df.take([0], axis=1) + tm.assert_frame_equal(result, expected) + + def test_iloc_interval(self): + # GH#17130 + df = DataFrame({Interval(1, 2): [1, 2]}) + + result = df.iloc[0] + expected = Series({Interval(1, 2): 1}, name=0) + tm.assert_series_equal(result, expected) + + result = df.iloc[:, 0] + expected = Series([1, 2], name=Interval(1, 2)) + tm.assert_series_equal(result, expected) + + result = df.copy() + result.iloc[:, 0] += 1 + expected = DataFrame({Interval(1, 2): [2, 3]}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("indexing_func", [list, np.array]) + @pytest.mark.parametrize("rhs_func", [list, np.array]) + def test_loc_setitem_boolean_list(self, rhs_func, indexing_func): + # GH#20438 testing specifically list key, not arraylike + ser = Series([0, 1, 2]) + ser.iloc[indexing_func([True, False, True])] = rhs_func([5, 10]) + expected = Series([5, 1, 10]) + tm.assert_series_equal(ser, expected) + + df = DataFrame({"a": [0, 1, 2]}) + df.iloc[indexing_func([True, False, True])] = rhs_func([[5], [10]]) + expected = DataFrame({"a": [5, 1, 10]}) + tm.assert_frame_equal(df, expected) + + def test_iloc_getitem_slice_negative_step_ea_block(self): + # GH#44551 + df = DataFrame({"A": [1, 2, 3]}, dtype="Int64") + + res = df.iloc[:, ::-1] + tm.assert_frame_equal(res, df) + + df["B"] = "foo" + res = df.iloc[:, ::-1] + expected = DataFrame({"B": df["B"], "A": df["A"]}) + tm.assert_frame_equal(res, expected) + + def test_iloc_setitem_2d_ndarray_into_ea_block(self): + # GH#44703 + df = DataFrame({"status": ["a", "b", "c"]}, dtype="category") + df.iloc[np.array([0, 1]), np.array([0])] = np.array([["a"], ["a"]]) + + expected = DataFrame({"status": ["a", "a", "c"]}, dtype=df["status"].dtype) + tm.assert_frame_equal(df, expected) + + def test_iloc_getitem_int_single_ea_block_view(self): + # GH#45241 + # TODO: make an extension interface test for this? + arr = interval_range(1, 10.0)._values + df = DataFrame(arr, copy=False) + + # ser should be a *view* on the DataFrame data + ser = df.iloc[2] + + # if we have a view, then changing arr[2] should also change ser[0] + assert arr[2] != arr[-1] # otherwise the rest isn't meaningful + arr[2] = arr[-1] + assert ser[0] == arr[-1] + + def test_iloc_setitem_multicolumn_to_datetime(self, using_infer_string): + # GH#20511 + df = DataFrame({"A": ["2022-01-01", "2022-01-02"], "B": ["2021", "2022"]}) + + if using_infer_string: + with pytest.raises(TypeError, match="Invalid value"): + df.iloc[:, [0]] = DataFrame({"A": to_datetime(["2021", "2022"])}) + else: + df.iloc[:, [0]] = DataFrame({"A": to_datetime(["2021", "2022"])}) + expected = DataFrame( + { + "A": [ + Timestamp("2021-01-01 00:00:00"), + Timestamp("2022-01-01 00:00:00"), + ], + "B": ["2021", "2022"], + } + ) + tm.assert_frame_equal(df, expected, check_dtype=False) + + +class TestILocErrors: + # NB: this test should work for _any_ Series we can pass as + # series_with_simple_index + def test_iloc_float_raises(self, series_with_simple_index, frame_or_series): + # GH#4892 + # float_indexers should raise exceptions + # on appropriate Index types & accessors + # this duplicates the code below + # but is specifically testing for the error + # message + + obj = series_with_simple_index + if frame_or_series is DataFrame: + obj = obj.to_frame() + + msg = "Cannot index by location index with a non-integer key" + with pytest.raises(TypeError, match=msg): + obj.iloc[3.0] + + with pytest.raises(IndexError, match=_slice_iloc_msg): + obj.iloc[3.0] = 0 + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_iloc_getitem_setitem_fancy_exceptions(self, float_frame, has_ref): + with pytest.raises(IndexingError, match="Too many indexers"): + float_frame.iloc[:, :, :] + + if has_ref: + view = float_frame[:] # noqa: F841 + with pytest.raises(IndexError, match="too many indices for array"): + # GH#32257 we let numpy do validation, get their exception + float_frame.iloc[:, :, :] = 1 + + def test_iloc_frame_indexer(self): + # GH#39004 + df = DataFrame({"a": [1, 2, 3]}) + indexer = DataFrame({"a": [True, False, True]}) + msg = "DataFrame indexer for .iloc is not supported. Consider using .loc" + with pytest.raises(TypeError, match=msg): + df.iloc[indexer] = 1 + + msg = ( + "DataFrame indexer is not allowed for .iloc\n" + "Consider using .loc for automatic alignment." + ) + with pytest.raises(IndexError, match=msg): + df.iloc[indexer] + + +class TestILocSetItemDuplicateColumns: + def test_iloc_setitem_scalar_duplicate_columns(self): + # GH#15686, duplicate columns and mixed dtype + df1 = DataFrame([{"A": None, "B": 1}, {"A": 2, "B": 2}]) + df2 = DataFrame([{"A": 3, "B": 3}, {"A": 4, "B": 4}]) + df = concat([df1, df2], axis=1) + df.iloc[0, 0] = -1 + + assert df.iloc[0, 0] == -1 + assert df.iloc[0, 2] == 3 + assert df.dtypes.iloc[2] == np.int64 + + def test_iloc_setitem_list_duplicate_columns(self): + # GH#22036 setting with same-sized list + df = DataFrame([[0, "str", "str2"]], columns=["a", "b", "b"]) + + df.iloc[:, 2] = ["str3"] + + expected = DataFrame([[0, "str", "str3"]], columns=["a", "b", "b"]) + tm.assert_frame_equal(df, expected) + + def test_iloc_setitem_series_duplicate_columns(self): + df = DataFrame( + np.arange(8, dtype=np.int64).reshape(2, 4), columns=["A", "B", "A", "B"] + ) + df.iloc[:, 0] = df.iloc[:, 0].astype(np.float64) + assert df.dtypes.iloc[2] == np.int64 + + @pytest.mark.parametrize( + ["dtypes", "init_value", "expected_value"], + [("int64", "0", 0), ("float", "1.2", 1.2)], + ) + def test_iloc_setitem_dtypes_duplicate_columns( + self, dtypes, init_value, expected_value + ): + # GH#22035 + df = DataFrame( + [[init_value, "str", "str2"]], columns=["a", "b", "b"], dtype=object + ) + + # with the enforcement of GH#45333 in 2.0, this sets values inplace, + # so we retain object dtype + df.iloc[:, 0] = df.iloc[:, 0].astype(dtypes) + + expected_df = DataFrame( + [[expected_value, "str", "str2"]], + columns=["a", "b", "b"], + dtype=object, + ) + tm.assert_frame_equal(df, expected_df) + + +class TestILocCallable: + def test_frame_iloc_getitem_callable(self): + # GH#11485 + df = DataFrame({"X": [1, 2, 3, 4], "Y": list("aabb")}, index=list("ABCD")) + + # return location + res = df.iloc[lambda x: [1, 3]] + tm.assert_frame_equal(res, df.iloc[[1, 3]]) + + res = df.iloc[lambda x: [1, 3], :] + tm.assert_frame_equal(res, df.iloc[[1, 3], :]) + + res = df.iloc[lambda x: [1, 3], lambda x: 0] + tm.assert_series_equal(res, df.iloc[[1, 3], 0]) + + res = df.iloc[lambda x: [1, 3], lambda x: [0]] + tm.assert_frame_equal(res, df.iloc[[1, 3], [0]]) + + # mixture + res = df.iloc[[1, 3], lambda x: 0] + tm.assert_series_equal(res, df.iloc[[1, 3], 0]) + + res = df.iloc[[1, 3], lambda x: [0]] + tm.assert_frame_equal(res, df.iloc[[1, 3], [0]]) + + res = df.iloc[lambda x: [1, 3], 0] + tm.assert_series_equal(res, df.iloc[[1, 3], 0]) + + res = df.iloc[lambda x: [1, 3], [0]] + tm.assert_frame_equal(res, df.iloc[[1, 3], [0]]) + + def test_frame_iloc_setitem_callable(self): + # GH#11485 + df = DataFrame( + {"X": [1, 2, 3, 4], "Y": Series(list("aabb"), dtype=object)}, + index=list("ABCD"), + ) + + # return location + res = df.copy() + res.iloc[lambda x: [1, 3]] = 0 + exp = df.copy() + exp.iloc[[1, 3]] = 0 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[lambda x: [1, 3], :] = -1 + exp = df.copy() + exp.iloc[[1, 3], :] = -1 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[lambda x: [1, 3], lambda x: 0] = 5 + exp = df.copy() + exp.iloc[[1, 3], 0] = 5 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[lambda x: [1, 3], lambda x: [0]] = 25 + exp = df.copy() + exp.iloc[[1, 3], [0]] = 25 + tm.assert_frame_equal(res, exp) + + # mixture + res = df.copy() + res.iloc[[1, 3], lambda x: 0] = -3 + exp = df.copy() + exp.iloc[[1, 3], 0] = -3 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[[1, 3], lambda x: [0]] = -5 + exp = df.copy() + exp.iloc[[1, 3], [0]] = -5 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[lambda x: [1, 3], 0] = 10 + exp = df.copy() + exp.iloc[[1, 3], 0] = 10 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.iloc[lambda x: [1, 3], [0]] = [-5, -5] + exp = df.copy() + exp.iloc[[1, 3], [0]] = [-5, -5] + tm.assert_frame_equal(res, exp) + + +class TestILocSeries: + def test_iloc(self): + ser = Series( + np.random.default_rng(2).standard_normal(10), index=list(range(0, 20, 2)) + ) + ser_original = ser.copy() + + for i in range(len(ser)): + result = ser.iloc[i] + exp = ser[ser.index[i]] + tm.assert_almost_equal(result, exp) + + # pass a slice + result = ser.iloc[slice(1, 3)] + expected = ser.loc[2:4] + tm.assert_series_equal(result, expected) + + # test slice is a view + with tm.assert_produces_warning(None): + # GH#45324 make sure we aren't giving a spurious FutureWarning + result[:] = 0 + tm.assert_series_equal(ser, ser_original) + + # list of integers + result = ser.iloc[[0, 2, 3, 4, 5]] + expected = ser.reindex(ser.index[[0, 2, 3, 4, 5]]) + tm.assert_series_equal(result, expected) + + def test_iloc_getitem_nonunique(self): + ser = Series([0, 1, 2], index=[0, 1, 0]) + assert ser.iloc[2] == 2 + + def test_iloc_setitem_pure_position_based(self): + # GH#22046 + ser1 = Series([1, 2, 3]) + ser2 = Series([4, 5, 6], index=[1, 0, 2]) + ser1.iloc[1:3] = ser2.iloc[1:3] + expected = Series([1, 5, 6]) + tm.assert_series_equal(ser1, expected) + + def test_iloc_nullable_int64_size_1_nan(self): + # GH 31861 + result = DataFrame({"a": ["test"], "b": [np.nan]}) + + ser = Series([NA], name="b", dtype="Int64") + with pytest.raises(TypeError, match="Invalid value"): + result.loc[:, "b"] = ser + + def test_iloc_arrow_extension_array(self): + # GH#61311 + pytest.importorskip("pyarrow") + df = DataFrame({"a": [1, 2], "c": [0, 2], "d": ["c", "a"]}) + df_arrow = DataFrame( + {"a": [1, 2], "c": [0, 2], "d": ["c", "a"]} + ).convert_dtypes(dtype_backend="pyarrow") + expected = df.iloc[:, df["c"]] + result = df_arrow.iloc[:, df_arrow["c"]] + tm.assert_frame_equal(result, expected, check_dtype=False) + + @td.skip_if_no("pyarrow") + def test_setitem_pyarrow_int_series(self): + # GH#62462 + ser = Series([1, 2, 3], dtype="int64[pyarrow]") + idx = Index([0, 1]) + vals = Series([7, 8], dtype="int64[pyarrow]") + + ser.iloc[idx] = vals + + expected = Series([7, 8, 3], dtype="int64[pyarrow]") + tm.assert_series_equal(ser, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_indexers.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_indexers.py new file mode 100644 index 0000000000000000000000000000000000000000..ddc5c039160d5ada6c6dccb62514590a4ce9f620 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_indexers.py @@ -0,0 +1,61 @@ +# Tests aimed at pandas.core.indexers +import numpy as np +import pytest + +from pandas.core.indexers import ( + is_scalar_indexer, + length_of_indexer, + validate_indices, +) + + +def test_length_of_indexer(): + arr = np.zeros(4, dtype=bool) + arr[0] = 1 + result = length_of_indexer(arr) + assert result == 1 + + +def test_is_scalar_indexer(): + indexer = (0, 1) + assert is_scalar_indexer(indexer, 2) + assert not is_scalar_indexer(indexer[0], 2) + + indexer = (np.array([2]), 1) + assert not is_scalar_indexer(indexer, 2) + + indexer = (np.array([2]), np.array([3])) + assert not is_scalar_indexer(indexer, 2) + + indexer = (np.array([2]), np.array([3, 4])) + assert not is_scalar_indexer(indexer, 2) + + assert not is_scalar_indexer(slice(None), 1) + + indexer = 0 + assert is_scalar_indexer(indexer, 1) + + indexer = (0,) + assert is_scalar_indexer(indexer, 1) + + +class TestValidateIndices: + def test_validate_indices_ok(self): + indices = np.asarray([0, 1]) + validate_indices(indices, 2) + validate_indices(indices[:0], 0) + validate_indices(np.array([-1, -1]), 0) + + def test_validate_indices_low(self): + indices = np.asarray([0, -2]) + with pytest.raises(ValueError, match="'indices' contains"): + validate_indices(indices, 2) + + def test_validate_indices_high(self): + indices = np.asarray([0, 1, 2]) + with pytest.raises(IndexError, match="indices are out"): + validate_indices(indices, 2) + + def test_validate_indices_empty(self): + with pytest.raises(IndexError, match="indices are out"): + validate_indices(np.array([0, 1]), 0) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_indexing.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..3dffb9b04ea1a359ee0a96eb94f9edfb6067aaf0 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_indexing.py @@ -0,0 +1,1136 @@ +"""test fancy indexing & misc""" + +import array +from datetime import datetime +import re +import weakref + +import numpy as np +import pytest + +from pandas.errors import IndexingError + +from pandas.core.dtypes.common import ( + is_float_dtype, + is_integer_dtype, + is_object_dtype, +) + +import pandas as pd +from pandas import ( + DataFrame, + Index, + NaT, + Series, + date_range, + offsets, + timedelta_range, +) +import pandas._testing as tm +from pandas.tests.indexing.common import _mklbl +from pandas.tests.indexing.test_floats import gen_obj + +# ------------------------------------------------------------------------ +# Indexing test cases + + +class TestFancy: + """pure get/set item & fancy indexing""" + + def test_setitem_ndarray_1d(self): + # GH5508 + + # len of indexer vs length of the 1d ndarray + df = DataFrame(index=Index(np.arange(1, 11), dtype=np.int64)) + df["foo"] = np.zeros(10, dtype=np.float64) + df["bar"] = np.zeros(10, dtype=complex) + + # invalid + msg = "Must have equal len keys and value when setting with an iterable" + with pytest.raises(ValueError, match=msg): + df.loc[df.index[2:5], "bar"] = np.array([2.33j, 1.23 + 0.1j, 2.2, 1.0]) + + # valid + df.loc[df.index[2:6], "bar"] = np.array([2.33j, 1.23 + 0.1j, 2.2, 1.0]) + + result = df.loc[df.index[2:6], "bar"] + expected = Series( + [2.33j, 1.23 + 0.1j, 2.2, 1.0], index=[3, 4, 5, 6], name="bar" + ) + tm.assert_series_equal(result, expected) + + def test_setitem_ndarray_1d_2(self): + # GH5508 + + # dtype getting changed? + df = DataFrame(index=Index(np.arange(1, 11))) + df["foo"] = np.zeros(10, dtype=np.float64) + df["bar"] = np.zeros(10, dtype=complex) + + msg = "Must have equal len keys and value when setting with an iterable" + with pytest.raises(ValueError, match=msg): + df[2:5] = np.arange(1, 4) * 1j + + @pytest.mark.filterwarnings( + "ignore:Series.__getitem__ treating keys as positions is deprecated:" + "FutureWarning" + ) + def test_getitem_ndarray_3d(self, index, frame_or_series, indexer_sli): + # GH 25567 + obj = gen_obj(frame_or_series, index) + idxr = indexer_sli(obj) + nd3 = np.random.default_rng(2).integers(5, size=(2, 2, 2)) + + msgs = [] + if frame_or_series is Series and indexer_sli in [tm.setitem, tm.iloc]: + msgs.append(r"Wrong number of dimensions. values.ndim > ndim \[3 > 1\]") + if frame_or_series is Series or indexer_sli is tm.iloc: + msgs.append(r"Buffer has wrong number of dimensions \(expected 1, got 3\)") + if indexer_sli is tm.loc or ( + frame_or_series is Series and indexer_sli is tm.setitem + ): + msgs.append("Cannot index with multidimensional key") + if frame_or_series is DataFrame and indexer_sli is tm.setitem: + msgs.append("Index data must be 1-dimensional") + if isinstance(index, pd.IntervalIndex) and indexer_sli is tm.iloc: + msgs.append("Index data must be 1-dimensional") + if isinstance(index, (pd.TimedeltaIndex, pd.DatetimeIndex, pd.PeriodIndex)): + msgs.append("Data must be 1-dimensional") + if len(index) == 0 or isinstance(index, pd.MultiIndex): + msgs.append("positional indexers are out-of-bounds") + if type(index) is Index and not isinstance(index._values, np.ndarray): + # e.g. Int64 + msgs.append("values must be a 1D array") + + # string[pyarrow] + msgs.append("only handle 1-dimensional arrays") + + msg = "|".join(msgs) + + potential_errors = (IndexError, ValueError, NotImplementedError) + with pytest.raises(potential_errors, match=msg): + idxr[nd3] + + @pytest.mark.filterwarnings( + "ignore:Series.__setitem__ treating keys as positions is deprecated:" + "FutureWarning" + ) + def test_setitem_ndarray_3d(self, index, frame_or_series, indexer_sli): + # GH 25567 + obj = gen_obj(frame_or_series, index) + idxr = indexer_sli(obj) + nd3 = np.random.default_rng(2).integers(5, size=(2, 2, 2)) + + if indexer_sli is tm.iloc: + err = ValueError + msg = f"Cannot set values with ndim > {obj.ndim}" + else: + err = ValueError + msg = "|".join( + [ + r"Buffer has wrong number of dimensions \(expected 1, got 3\)", + "Cannot set values with ndim > 1", + "Index data must be 1-dimensional", + "Data must be 1-dimensional", + "Array conditional must be same shape as self", + ] + ) + + with pytest.raises(err, match=msg): + idxr[nd3] = 0 + + def test_getitem_ndarray_0d(self): + # GH#24924 + key = np.array(0) + + # dataframe __getitem__ + df = DataFrame([[1, 2], [3, 4]]) + result = df[key] + expected = Series([1, 3], name=0) + tm.assert_series_equal(result, expected) + + # series __getitem__ + ser = Series([1, 2]) + result = ser[key] + assert result == 1 + + def test_inf_upcast(self): + # GH 16957 + # We should be able to use np.inf as a key + # np.inf should cause an index to convert to float + + # Test with np.inf in rows + df = DataFrame(columns=[0]) + df.loc[1] = 1 + df.loc[2] = 2 + df.loc[np.inf] = 3 + + # make sure we can look up the value + assert df.loc[np.inf, 0] == 3 + + result = df.index + expected = Index([1, 2, np.inf], dtype=np.float64) + tm.assert_index_equal(result, expected) + + def test_setitem_dtype_upcast(self): + # GH3216 + df = DataFrame([{"a": 1}, {"a": 3, "b": 2}]) + df["c"] = np.nan + assert df["c"].dtype == np.float64 + + with pytest.raises(TypeError, match="Invalid value"): + df.loc[0, "c"] = "foo" + + @pytest.mark.parametrize("val", [3.14, "wxyz"]) + def test_setitem_dtype_upcast2(self, val): + # GH10280 + df = DataFrame( + np.arange(6, dtype="int64").reshape(2, 3), + index=list("ab"), + columns=["foo", "bar", "baz"], + ) + + left = df.copy() + with pytest.raises(TypeError, match="Invalid value"): + left.loc["a", "bar"] = val + + def test_setitem_dtype_upcast3(self): + left = DataFrame( + np.arange(6, dtype="int64").reshape(2, 3) / 10.0, + index=list("ab"), + columns=["foo", "bar", "baz"], + ) + with pytest.raises(TypeError, match="Invalid value"): + left.loc["a", "bar"] = "wxyz" + + def test_dups_fancy_indexing(self): + # GH 3455 + + df = DataFrame(np.eye(3), columns=["a", "a", "b"]) + result = df[["b", "a"]].columns + expected = Index(["b", "a", "a"]) + tm.assert_index_equal(result, expected) + + def test_dups_fancy_indexing_across_dtypes(self): + # across dtypes + df = DataFrame([[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]], columns=list("aaaaaaa")) + result = DataFrame([[1, 2, 1.0, 2.0, 3.0, "foo", "bar"]]) + result.columns = list("aaaaaaa") # GH#3468 + + # GH#3509 smoke tests for indexing with duplicate columns + df.iloc[:, 4] + result.iloc[:, 4] + + tm.assert_frame_equal(df, result) + + def test_dups_fancy_indexing_not_in_order(self): + # GH 3561, dups not in selected order + df = DataFrame( + {"test": [5, 7, 9, 11], "test1": [4.0, 5, 6, 7], "other": list("abcd")}, + index=["A", "A", "B", "C"], + ) + rows = ["C", "B"] + expected = DataFrame( + {"test": [11, 9], "test1": [7.0, 6], "other": ["d", "c"]}, index=rows + ) + result = df.loc[rows] + tm.assert_frame_equal(result, expected) + + result = df.loc[Index(rows)] + tm.assert_frame_equal(result, expected) + + rows = ["C", "B", "E"] + with pytest.raises(KeyError, match="not in index"): + df.loc[rows] + + # see GH5553, make sure we use the right indexer + rows = ["F", "G", "H", "C", "B", "E"] + with pytest.raises(KeyError, match="not in index"): + df.loc[rows] + + def test_dups_fancy_indexing_only_missing_label(self, using_infer_string): + # List containing only missing label + dfnu = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), index=list("AABCD") + ) + if using_infer_string: + with pytest.raises( + KeyError, + match=re.escape( + "\"None of [Index(['E'], dtype='str')] are in the [index]\"" + ), + ): + dfnu.loc[["E"]] + else: + with pytest.raises( + KeyError, + match=re.escape( + "\"None of [Index(['E'], dtype='object')] are in the [index]\"" + ), + ): + dfnu.loc[["E"]] + + @pytest.mark.parametrize("vals", [[0, 1, 2], list("abc")]) + def test_dups_fancy_indexing_missing_label(self, vals): + # GH 4619; duplicate indexer with missing label + df = DataFrame({"A": vals}) + with pytest.raises(KeyError, match="not in index"): + df.loc[[0, 8, 0]] + + def test_dups_fancy_indexing_non_unique(self): + # non unique with non unique selector + df = DataFrame({"test": [5, 7, 9, 11]}, index=["A", "A", "B", "C"]) + with pytest.raises(KeyError, match="not in index"): + df.loc[["A", "A", "E"]] + + def test_dups_fancy_indexing2(self): + # GH 5835 + # dups on index and missing values + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 5)), + columns=["A", "B", "B", "B", "A"], + ) + + with pytest.raises(KeyError, match="not in index"): + df.loc[:, ["A", "B", "C"]] + + def test_dups_fancy_indexing3(self): + # GH 6504, multi-axis indexing + df = DataFrame( + np.random.default_rng(2).standard_normal((9, 2)), + index=[1, 1, 1, 2, 2, 2, 3, 3, 3], + columns=["a", "b"], + ) + + expected = df.iloc[0:6] + result = df.loc[[1, 2]] + tm.assert_frame_equal(result, expected) + + expected = df + result = df.loc[:, ["a", "b"]] + tm.assert_frame_equal(result, expected) + + expected = df.iloc[0:6, :] + result = df.loc[[1, 2], ["a", "b"]] + tm.assert_frame_equal(result, expected) + + def test_duplicate_int_indexing(self, indexer_sl): + # GH 17347 + ser = Series(range(3), index=[1, 1, 3]) + expected = Series(range(2), index=[1, 1]) + result = indexer_sl(ser)[[1]] + tm.assert_series_equal(result, expected) + + def test_indexing_mixed_frame_bug(self): + # GH3492 + df = DataFrame( + {"a": {1: "aaa", 2: "bbb", 3: "ccc"}, "b": {1: 111, 2: 222, 3: 333}} + ) + + # this works, new column is created correctly + df["test"] = df["a"].apply(lambda x: "_" if x == "aaa" else x) + + # this does not work, ie column test is not changed + idx = df["test"] == "_" + temp = df.loc[idx, "a"].apply(lambda x: "-----" if x == "aaa" else x) + df.loc[idx, "test"] = temp + assert df.iloc[0, 2] == "-----" + + def test_multitype_list_index_access(self): + # GH 10610 + df = DataFrame( + np.random.default_rng(2).random((10, 5)), columns=["a", 20, 21, 22, 23] + ) + + with pytest.raises(KeyError, match=re.escape("'[26, -8] not in index'")): + df[[22, 26, -8]] + assert df[21].shape[0] == df.shape[0] + + def test_set_index_nan(self): + # GH 3586 + df = DataFrame( + { + "PRuid": { + 17: "nonQC", + 18: "nonQC", + 19: "nonQC", + 20: "10", + 21: "11", + 22: "12", + 23: "13", + 24: "24", + 25: "35", + 26: "46", + 27: "47", + 28: "48", + 29: "59", + 30: "10", + }, + "QC": { + 17: 0.0, + 18: 0.0, + 19: 0.0, + 20: np.nan, + 21: np.nan, + 22: np.nan, + 23: np.nan, + 24: 1.0, + 25: np.nan, + 26: np.nan, + 27: np.nan, + 28: np.nan, + 29: np.nan, + 30: np.nan, + }, + "data": { + 17: 7.9544899999999998, + 18: 8.0142609999999994, + 19: 7.8591520000000008, + 20: 0.86140349999999999, + 21: 0.87853110000000001, + 22: 0.8427041999999999, + 23: 0.78587700000000005, + 24: 0.73062459999999996, + 25: 0.81668560000000001, + 26: 0.81927080000000008, + 27: 0.80705009999999999, + 28: 0.81440240000000008, + 29: 0.80140849999999997, + 30: 0.81307740000000006, + }, + "year": { + 17: 2006, + 18: 2007, + 19: 2008, + 20: 1985, + 21: 1985, + 22: 1985, + 23: 1985, + 24: 1985, + 25: 1985, + 26: 1985, + 27: 1985, + 28: 1985, + 29: 1985, + 30: 1986, + }, + } + ).reset_index() + + result = ( + df.set_index(["year", "PRuid", "QC"]) + .reset_index() + .reindex(columns=df.columns) + ) + tm.assert_frame_equal(result, df) + + def test_multi_assign(self): + # GH 3626, an assignment of a sub-df to a df + # set float64 to avoid upcast when setting nan + df = DataFrame( + { + "FC": ["a", "b", "a", "b", "a", "b"], + "PF": [0, 0, 0, 0, 1, 1], + "col1": list(range(6)), + "col2": list(range(6, 12)), + } + ).astype({"col2": "float64"}) + df.iloc[1, 0] = np.nan + df2 = df.copy() + + mask = ~df2.FC.isna() + cols = ["col1", "col2"] + + dft = df2 * 2 + dft.iloc[3, 3] = np.nan + + expected = DataFrame( + { + "FC": ["a", np.nan, "a", "b", "a", "b"], + "PF": [0, 0, 0, 0, 1, 1], + "col1": Series([0, 1, 4, 6, 8, 10]), + "col2": [12, 7, 16, np.nan, 20, 22], + } + ) + + # frame on rhs + df2.loc[mask, cols] = dft.loc[mask, cols] + tm.assert_frame_equal(df2, expected) + + # with an ndarray on rhs + # coerces to float64 because values has float64 dtype + # GH 14001 + expected = DataFrame( + { + "FC": ["a", np.nan, "a", "b", "a", "b"], + "PF": [0, 0, 0, 0, 1, 1], + "col1": [0, 1, 4, 6, 8, 10], + "col2": [12, 7, 16, np.nan, 20, 22], + } + ) + df2 = df.copy() + df2.loc[mask, cols] = dft.loc[mask, cols].values + tm.assert_frame_equal(df2, expected) + + def test_multi_assign_broadcasting_rhs(self): + # broadcasting on the rhs is required + df = DataFrame( + { + "A": [1, 2, 0, 0, 0], + "B": [0, 0, 0, 10, 11], + "C": [0, 0, 0, 10, 11], + "D": [3, 4, 5, 6, 7], + } + ) + + expected = df.copy() + mask = expected["A"] == 0 + for col in ["A", "B"]: + expected.loc[mask, col] = df["D"] + + df.loc[df["A"] == 0, ["A", "B"]] = df["D"].copy() + tm.assert_frame_equal(df, expected) + + def test_setitem_list(self): + # GH 6043 + # iloc with a list + df = DataFrame(index=[0, 1], columns=[0]) + df.iloc[1, 0] = [1, 2, 3] + df.iloc[1, 0] = [1, 2] + + result = DataFrame(index=[0, 1], columns=[0]) + result.iloc[1, 0] = [1, 2] + + tm.assert_frame_equal(result, df) + + def test_string_slice(self): + # GH 14424 + # string indexing against datetimelike with object + # dtype should properly raises KeyError + df = DataFrame([1], Index([pd.Timestamp("2011-01-01")], dtype=object)) + assert df.index._is_all_dates + with pytest.raises(KeyError, match="'2011'"): + df["2011"] + + with pytest.raises(KeyError, match="'2011'"): + df.loc["2011", 0] + + def test_string_slice_empty(self): + # GH 14424 + + df = DataFrame() + assert not df.index._is_all_dates + with pytest.raises(KeyError, match="'2011'"): + df["2011"] + + with pytest.raises(KeyError, match="^0$"): + df.loc["2011", 0] + + def test_astype_assignment(self, using_infer_string): + # GH4312 (iloc) + df_orig = DataFrame( + [["1", "2", "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG") + ) + df_orig[list("ABCDG")] = df_orig[list("ABCDG")].astype(object) + + df = df_orig.copy() + + # with the enforcement of GH#45333 in 2.0, this setting is attempted inplace, + # so object dtype is retained + df.iloc[:, 0:2] = df.iloc[:, 0:2].astype(np.int64) + expected = DataFrame( + [[1, 2, "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG") + ) + expected[list("CDG")] = expected[list("CDG")].astype(object) + expected["A"] = expected["A"].astype(object) + expected["B"] = expected["B"].astype(object) + tm.assert_frame_equal(df, expected) + + # GH5702 (loc) + df = df_orig.copy() + df.loc[:, "A"] = df.loc[:, "A"].astype(np.int64) + expected = DataFrame( + [[1, "2", "3", ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG") + ) + expected[list("ABCDG")] = expected[list("ABCDG")].astype(object) + tm.assert_frame_equal(df, expected) + + df = df_orig.copy() + + df.loc[:, ["B", "C"]] = df.loc[:, ["B", "C"]].astype(np.int64) + expected = DataFrame( + [["1", 2, 3, ".4", 5, 6.0, "foo"]], columns=list("ABCDEFG") + ) + expected[list("ABCDG")] = expected[list("ABCDG")].astype(object) + tm.assert_frame_equal(df, expected) + + def test_astype_assignment_full_replacements(self): + # full replacements / no nans + df = DataFrame({"A": [1.0, 2.0, 3.0, 4.0]}) + + # With the enforcement of GH#45333 in 2.0, this assignment occurs inplace, + # so float64 is retained + df.iloc[:, 0] = df["A"].astype(np.int64) + expected = DataFrame({"A": [1.0, 2.0, 3.0, 4.0]}) + tm.assert_frame_equal(df, expected) + + df = DataFrame({"A": [1.0, 2.0, 3.0, 4.0]}) + df.loc[:, "A"] = df["A"].astype(np.int64) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("indexer", [tm.getitem, tm.loc]) + def test_index_type_coercion(self, indexer): + # GH 11836 + # if we have an index type and set it with something that looks + # to numpy like the same, but is actually, not + # (e.g. setting with a float or string '0') + # then we need to coerce to object + + # integer indexes + for s in [Series(range(5)), Series(range(5), index=range(1, 6))]: + assert is_integer_dtype(s.index) + + s2 = s.copy() + indexer(s2)[0.1] = 0 + assert is_float_dtype(s2.index) + assert indexer(s2)[0.1] == 0 + + s2 = s.copy() + indexer(s2)[0.0] = 0 + exp = s.index + if 0 not in s: + exp = Index([*s.index.tolist(), 0]) + tm.assert_index_equal(s2.index, exp) + + s2 = s.copy() + indexer(s2)["0"] = 0 + assert is_object_dtype(s2.index) + + for s in [Series(range(5), index=np.arange(5.0))]: + assert is_float_dtype(s.index) + + s2 = s.copy() + indexer(s2)[0.1] = 0 + assert is_float_dtype(s2.index) + assert indexer(s2)[0.1] == 0 + + s2 = s.copy() + indexer(s2)[0.0] = 0 + tm.assert_index_equal(s2.index, s.index) + + s2 = s.copy() + indexer(s2)["0"] = 0 + assert is_object_dtype(s2.index) + + +class TestMisc: + def test_float_index_to_mixed(self): + df = DataFrame( + { + 0.0: np.random.default_rng(2).random(10), + 1.0: np.random.default_rng(2).random(10), + } + ) + df["a"] = 10 + + expected = DataFrame({0.0: df[0.0], 1.0: df[1.0], "a": [10] * 10}) + tm.assert_frame_equal(expected, df) + + def test_float_index_non_scalar_assignment(self): + df = DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]}, index=[1.0, 2.0, 3.0]) + df.loc[df.index[:2]] = 1 + expected = DataFrame({"a": [1, 1, 3], "b": [1, 1, 5]}, index=df.index) + tm.assert_frame_equal(expected, df) + + def test_loc_setitem_fullindex_views(self): + df = DataFrame({"a": [1, 2, 3], "b": [3, 4, 5]}, index=[1.0, 2.0, 3.0]) + df2 = df.copy() + df.loc[df.index] = df.loc[df.index] + tm.assert_frame_equal(df, df2) + + def test_rhs_alignment(self): + # GH8258, tests that both rows & columns are aligned to what is + # assigned to. covers both uniform data-type & multi-type cases + def run_tests(df, rhs, right_loc, right_iloc): + # label, index, slice + lbl_one, idx_one, slice_one = list("bcd"), [1, 2, 3], slice(1, 4) + lbl_two, idx_two, slice_two = ["joe", "jolie"], [1, 2], slice(1, 3) + + left = df.copy() + left.loc[lbl_one, lbl_two] = rhs + tm.assert_frame_equal(left, right_loc) + + left = df.copy() + left.iloc[idx_one, idx_two] = rhs + tm.assert_frame_equal(left, right_iloc) + + left = df.copy() + left.iloc[slice_one, slice_two] = rhs + tm.assert_frame_equal(left, right_iloc) + + xs = np.arange(20).reshape(5, 4) + cols = ["jim", "joe", "jolie", "joline"] + df = DataFrame(xs, columns=cols, index=list("abcde"), dtype="int64") + + # right hand side; permute the indices and multiply by -2 + rhs = -2 * df.iloc[3:0:-1, 2:0:-1] + + # expected `right` result; just multiply by -2 + right_iloc = df.copy() + right_iloc["joe"] = [1, 14, 10, 6, 17] + right_iloc["jolie"] = [2, 13, 9, 5, 18] + right_iloc.iloc[1:4, 1:3] *= -2 + right_loc = df.copy() + right_loc.iloc[1:4, 1:3] *= -2 + + # run tests with uniform dtypes + run_tests(df, rhs, right_loc, right_iloc) + + # make frames multi-type & re-run tests + for frame in [df, rhs, right_loc, right_iloc]: + frame["joe"] = frame["joe"].astype("float64") + frame["jolie"] = frame["jolie"].map(lambda x: f"@{x}") + right_iloc["joe"] = [1.0, "@-28", "@-20", "@-12", 17.0] + right_iloc["jolie"] = ["@2", -26.0, -18.0, -10.0, "@18"] + with pytest.raises(TypeError, match="Invalid value"): + run_tests(df, rhs, right_loc, right_iloc) + + @pytest.mark.parametrize( + "idx", [_mklbl("A", 20), np.arange(20) + 100, np.linspace(100, 150, 20)] + ) + def test_str_label_slicing_with_negative_step(self, idx): + SLC = pd.IndexSlice + + idx = Index(idx) + ser = Series(np.arange(20), index=idx) + tm.assert_indexing_slices_equivalent(ser, SLC[idx[9] :: -1], SLC[9::-1]) + tm.assert_indexing_slices_equivalent(ser, SLC[: idx[9] : -1], SLC[:8:-1]) + tm.assert_indexing_slices_equivalent( + ser, SLC[idx[13] : idx[9] : -1], SLC[13:8:-1] + ) + tm.assert_indexing_slices_equivalent(ser, SLC[idx[9] : idx[13] : -1], SLC[:0]) + + def test_slice_with_zero_step_raises(self, index, indexer_sl, frame_or_series): + obj = frame_or_series(np.arange(len(index)), index=index) + with pytest.raises(ValueError, match="slice step cannot be zero"): + indexer_sl(obj)[::0] + + def test_loc_setitem_indexing_assignment_dict_already_exists(self): + index = Index([-5, 0, 5], name="z") + df = DataFrame({"x": [1, 2, 6], "y": [2, 2, 8]}, index=index) + expected = df.copy() + rhs = {"x": 9, "y": 99} + df.loc[5] = rhs + expected.loc[5] = [9, 99] + tm.assert_frame_equal(df, expected) + + # GH#38335 same thing, mixed dtypes + df = DataFrame({"x": [1, 2, 6], "y": [2.0, 2.0, 8.0]}, index=index) + df.loc[5] = rhs + expected = DataFrame({"x": [1, 2, 9], "y": [2.0, 2.0, 99.0]}, index=index) + tm.assert_frame_equal(df, expected) + + def test_iloc_getitem_indexing_dtypes_on_empty(self): + # Check that .iloc returns correct dtypes GH9983 + df = DataFrame({"a": [1, 2, 3], "b": ["b", "b2", "b3"]}) + df2 = df.iloc[[], :] + + assert df2.loc[:, "a"].dtype == np.int64 + tm.assert_series_equal(df2.loc[:, "a"], df2.iloc[:, 0]) + + @pytest.mark.parametrize("size", [5, 999999, 1000000]) + def test_loc_range_in_series_indexing(self, size): + # range can cause an indexing error + # GH 11652 + s = Series(index=range(size), dtype=np.float64) + s.loc[range(1)] = 42 + tm.assert_series_equal(s.loc[range(1)], Series(42.0, index=range(1))) + + s.loc[range(2)] = 43 + tm.assert_series_equal(s.loc[range(2)], Series(43.0, index=range(2))) + + def test_partial_boolean_frame_indexing(self): + # GH 17170 + df = DataFrame( + np.arange(9.0).reshape(3, 3), index=list("abc"), columns=list("ABC") + ) + index_df = DataFrame(1, index=list("ab"), columns=list("AB")) + result = df[index_df.notnull()] + expected = DataFrame( + np.array([[0.0, 1.0, np.nan], [3.0, 4.0, np.nan], [np.nan] * 3]), + index=list("abc"), + columns=list("ABC"), + ) + tm.assert_frame_equal(result, expected) + + def test_period_column_slicing(self): + # GH#60273 The transpose operation creates a single 5x1 block of PeriodDtype + # Make sure it is reindexed correctly + df = DataFrame( + pd.period_range("2021-01-01", periods=5, freq="D"), + columns=["A"], + ).T + result = df[[0, 1, 2]] + expected = DataFrame( + [ + [ + pd.Period("2021-01-01", freq="D"), + pd.Period("2021-01-02", freq="D"), + pd.Period("2021-01-03", freq="D"), + ] + ], + index=["A"], + columns=[0, 1, 2], + ) + tm.assert_frame_equal(result, expected) + + def test_no_reference_cycle(self): + df = DataFrame({"a": [0, 1], "b": [2, 3]}) + for name in ("loc", "iloc", "at", "iat"): + getattr(df, name) + wr = weakref.ref(df) + del df + assert wr() is None + + def test_label_indexing_on_nan(self, nulls_fixture): + # GH 32431 + df = Series([1, "{1,2}", 1, nulls_fixture]) + vc = df.value_counts(dropna=False) + result1 = vc.loc[nulls_fixture] + result2 = vc[nulls_fixture] + + expected = 1 + assert result1 == expected + assert result2 == expected + + +class TestDataframeNoneCoercion: + EXPECTED_SINGLE_ROW_RESULTS = [ + # For numeric series, we should coerce to NaN. + ([1, 2, 3], [np.nan, 2, 3], FutureWarning), + ([1.0, 2.0, 3.0], [np.nan, 2.0, 3.0], None), + # For datetime series, we should coerce to NaT. + ( + [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + [NaT, datetime(2000, 1, 2), datetime(2000, 1, 3)], + None, + ), + # For objects, we should preserve the None value. + (["foo", "bar", "baz"], [None, "bar", "baz"], None), + ] + + @pytest.mark.parametrize("expected", EXPECTED_SINGLE_ROW_RESULTS) + def test_coercion_with_loc(self, expected): + start_data, expected_result, warn = expected + + start_dataframe = DataFrame({"foo": start_data}) + start_dataframe.loc[0, ["foo"]] = None + + expected_dataframe = DataFrame({"foo": expected_result}) + tm.assert_frame_equal(start_dataframe, expected_dataframe) + + @pytest.mark.parametrize("expected", EXPECTED_SINGLE_ROW_RESULTS) + def test_coercion_with_setitem_and_dataframe(self, expected): + start_data, expected_result, warn = expected + + start_dataframe = DataFrame({"foo": start_data}) + start_dataframe[start_dataframe["foo"] == start_dataframe["foo"][0]] = None + + expected_dataframe = DataFrame({"foo": expected_result}) + tm.assert_frame_equal(start_dataframe, expected_dataframe) + + @pytest.mark.parametrize("expected", EXPECTED_SINGLE_ROW_RESULTS) + def test_none_coercion_loc_and_dataframe(self, expected): + start_data, expected_result, warn = expected + + start_dataframe = DataFrame({"foo": start_data}) + start_dataframe.loc[start_dataframe["foo"] == start_dataframe["foo"][0]] = None + + expected_dataframe = DataFrame({"foo": expected_result}) + tm.assert_frame_equal(start_dataframe, expected_dataframe) + + def test_none_coercion_mixed_dtypes(self): + start_dataframe = DataFrame( + { + "a": [1, 2, 3], + "b": [1.0, 2.0, 3.0], + "c": [datetime(2000, 1, 1), datetime(2000, 1, 2), datetime(2000, 1, 3)], + "d": ["a", "b", "c"], + } + ) + start_dataframe.iloc[0] = None + + exp = DataFrame( + { + "a": [np.nan, 2, 3], + "b": [np.nan, 2.0, 3.0], + "c": [NaT, datetime(2000, 1, 2), datetime(2000, 1, 3)], + "d": [None, "b", "c"], + } + ) + tm.assert_frame_equal(start_dataframe, exp) + + +class TestDatetimelikeCoercion: + def test_setitem_dt64_string_scalar(self, tz_naive_fixture, indexer_sli): + # dispatching _can_hold_element to underlying DatetimeArray + tz = tz_naive_fixture + + dti = date_range("2016-01-01", periods=3, tz=tz) + ser = Series(dti.copy(deep=True)) + + values = ser._values + + newval = "2018-01-01" + values._validate_setitem_value(newval) + + indexer_sli(ser)[0] = newval + + if tz is None: + # TODO(EA2D): we can make this no-copy in tz-naive case too + assert ser.dtype == dti.dtype + assert ser._values._ndarray is values._ndarray + else: + assert ser._values is values + + @pytest.mark.parametrize("box", [list, np.array, pd.array, pd.Categorical, Index]) + @pytest.mark.parametrize( + "key", [[0, 1], slice(0, 2), np.array([True, True, False])] + ) + def test_setitem_dt64_string_values(self, tz_naive_fixture, indexer_sli, key, box): + # dispatching _can_hold_element to underling DatetimeArray + tz = tz_naive_fixture + + if isinstance(key, slice) and indexer_sli is tm.loc: + key = slice(0, 1) + + dti = date_range("2016-01-01", periods=3, tz=tz) + ser = Series(dti.copy(deep=True)) + + values = ser._values + + newvals = box(["2019-01-01", "2010-01-02"]) + values._validate_setitem_value(newvals) + + indexer_sli(ser)[key] = newvals + + if tz is None: + # TODO(EA2D): we can make this no-copy in tz-naive case too + assert ser.dtype == dti.dtype + assert ser._values._ndarray is values._ndarray + else: + assert ser._values is values + + @pytest.mark.parametrize("scalar", ["3 Days", offsets.Hour(4)]) + def test_setitem_td64_scalar(self, indexer_sli, scalar): + # dispatching _can_hold_element to underling TimedeltaArray + tdi = timedelta_range("1 Day", periods=3) + ser = Series(tdi.copy(deep=True)) + + values = ser._values + values._validate_setitem_value(scalar) + + indexer_sli(ser)[0] = scalar + assert ser._values._ndarray is values._ndarray + + @pytest.mark.parametrize("box", [list, np.array, pd.array, pd.Categorical, Index]) + @pytest.mark.parametrize( + "key", [[0, 1], slice(0, 2), np.array([True, True, False])] + ) + def test_setitem_td64_string_values(self, indexer_sli, key, box): + # dispatching _can_hold_element to underling TimedeltaArray + if isinstance(key, slice) and indexer_sli is tm.loc: + key = slice(0, 1) + + tdi = timedelta_range("1 Day", periods=3) + ser = Series(tdi.copy(deep=True)) + + values = ser._values + + newvals = box(["10 Days", "44 hours"]) + values._validate_setitem_value(newvals) + + indexer_sli(ser)[key] = newvals + assert ser._values._ndarray is values._ndarray + + +def test_extension_array_cross_section(): + # A cross-section of a homogeneous EA should be an EA + df = DataFrame( + { + "A": pd.array([1, 2], dtype="Int64"), + "B": pd.array([3, 4], dtype="Int64"), + }, + index=["a", "b"], + ) + expected = Series(pd.array([1, 3], dtype="Int64"), index=["A", "B"], name="a") + result = df.loc["a"] + tm.assert_series_equal(result, expected) + + result = df.iloc[0] + tm.assert_series_equal(result, expected) + + +def test_extension_array_cross_section_converts(): + # all numeric columns -> numeric series + df = DataFrame( + { + "A": pd.array([1, 2], dtype="Int64"), + "B": np.array([1, 2], dtype="int64"), + }, + index=["a", "b"], + ) + result = df.loc["a"] + expected = Series([1, 1], dtype="Int64", index=["A", "B"], name="a") + tm.assert_series_equal(result, expected) + + result = df.iloc[0] + tm.assert_series_equal(result, expected) + + # mixed columns -> object series + df = DataFrame( + {"A": pd.array([1, 2], dtype="Int64"), "B": np.array(["a", "b"])}, + index=["a", "b"], + ) + result = df.loc["a"] + expected = Series([1, "a"], dtype=object, index=["A", "B"], name="a") + tm.assert_series_equal(result, expected) + + result = df.iloc[0] + tm.assert_series_equal(result, expected) + + +@pytest.mark.parametrize( + "ser, keys", + [(Series([10]), (0, 0)), (Series([1, 2, 3], index=list("abc")), (0, 1))], +) +def test_ser_tup_indexer_exceeds_dimensions(ser, keys, indexer_li): + # GH#13831 + exp_err, exp_msg = IndexingError, "Too many indexers" + with pytest.raises(exp_err, match=exp_msg): + indexer_li(ser)[keys] + + if indexer_li == tm.iloc: + # For iloc.__setitem__ we let numpy handle the error reporting. + exp_err, exp_msg = IndexError, "too many indices for array" + + with pytest.raises(exp_err, match=exp_msg): + indexer_li(ser)[keys] = 0 + + +def test_ser_list_indexer_exceeds_dimensions(indexer_li): + # GH#13831 + # Make sure an exception is raised when a tuple exceeds the dimension of the series, + # but not list when a list is used. + ser = Series([10]) + res = indexer_li(ser)[[0, 0]] + exp = Series([10, 10], index=Index([0, 0])) + tm.assert_series_equal(res, exp) + + +@pytest.mark.parametrize( + "value", [(0, 1), [0, 1], np.array([0, 1]), array.array("b", [0, 1])] +) +def test_scalar_setitem_with_nested_value(value): + # For numeric data, we try to unpack and thus raise for mismatching length + df = DataFrame({"A": [1, 2, 3]}) + msg = "|".join( + [ + "Must have equal len keys and value", + "setting an array element with a sequence", + ] + ) + with pytest.raises(ValueError, match=msg): + df.loc[0, "B"] = value + + # TODO For object dtype this happens as well, but should we rather preserve + # the nested data and set as such? + df = DataFrame({"A": [1, 2, 3], "B": np.array([1, "a", "b"], dtype=object)}) + with pytest.raises(ValueError, match="Must have equal len keys and value"): + df.loc[0, "B"] = value + # if isinstance(value, np.ndarray): + # assert (df.loc[0, "B"] == value).all() + # else: + # assert df.loc[0, "B"] == value + + +@pytest.mark.parametrize( + "value", [(0, 1), [0, 1], np.array([0, 1]), array.array("b", [0, 1])] +) +def test_scalar_setitem_series_with_nested_value(value, indexer_sli): + # For numeric data, we try to unpack and thus raise for mismatching length + ser = Series([1, 2, 3]) + with pytest.raises(ValueError, match="setting an array element with a sequence"): + indexer_sli(ser)[0] = value + + # but for object dtype we preserve the nested data and set as such + ser = Series([1, "a", "b"], dtype=object) + indexer_sli(ser)[0] = value + if isinstance(value, np.ndarray): + assert (ser.loc[0] == value).all() + else: + assert ser.loc[0] == value + + +@pytest.mark.parametrize( + "value", [(0.0,), [0.0], np.array([0.0]), array.array("d", [0.0])] +) +def test_scalar_setitem_with_nested_value_length1(value): + # https://github.com/pandas-dev/pandas/issues/46268 + + # For numeric data, assigning length-1 array to scalar position gets unpacked + df = DataFrame({"A": [1, 2, 3]}) + df.loc[0, "B"] = value + expected = DataFrame({"A": [1, 2, 3], "B": [0.0, np.nan, np.nan]}) + tm.assert_frame_equal(df, expected) + + # but for object dtype we preserve the nested data + df = DataFrame({"A": [1, 2, 3], "B": np.array([1, "a", "b"], dtype=object)}) + df.loc[0, "B"] = value + if isinstance(value, np.ndarray): + assert (df.loc[0, "B"] == value).all() + else: + assert df.loc[0, "B"] == value + + +@pytest.mark.parametrize( + "value", [(0.0,), [0.0], np.array([0.0]), array.array("d", [0.0])] +) +def test_scalar_setitem_series_with_nested_value_length1(value, indexer_sli): + # For numeric data, assigning length-1 array to scalar position gets unpacked + # TODO this only happens in case of ndarray, should we make this consistent + # for all list-likes? (as happens for DataFrame.(i)loc, see test above) + ser = Series([1.0, 2.0, 3.0]) + if isinstance(value, np.ndarray): + indexer_sli(ser)[0] = value + expected = Series([0.0, 2.0, 3.0]) + tm.assert_series_equal(ser, expected) + else: + with pytest.raises( + ValueError, match="setting an array element with a sequence" + ): + indexer_sli(ser)[0] = value + + # but for object dtype we preserve the nested data + ser = Series([1, "a", "b"], dtype=object) + indexer_sli(ser)[0] = value + if isinstance(value, np.ndarray): + assert (ser.loc[0] == value).all() + else: + assert ser.loc[0] == value + + +def test_object_dtype_series_set_series_element(): + # GH 48933 + s1 = Series(dtype="O", index=["a", "b"]) + + s1["a"] = Series() + s1.loc["b"] = Series() + + tm.assert_series_equal(s1.loc["a"], Series()) + tm.assert_series_equal(s1.loc["b"], Series()) + + s2 = Series(dtype="O", index=["a", "b"]) + + s2.iloc[1] = Series() + tm.assert_series_equal(s2.iloc[1], Series()) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_loc.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_loc.py new file mode 100644 index 0000000000000000000000000000000000000000..9559b610452a57c49cf3d72cf708c001cbc9614c --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_loc.py @@ -0,0 +1,3480 @@ +"""test label based indexing with loc""" + +from collections import namedtuple +import contextlib +from datetime import ( + date, + datetime, + time, + timedelta, +) +import re + +from dateutil.tz import gettz +import numpy as np +import pytest + +from pandas._libs import index as libindex +from pandas.errors import IndexingError +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + Categorical, + CategoricalDtype, + CategoricalIndex, + DataFrame, + DatetimeIndex, + Index, + IndexSlice, + MultiIndex, + Period, + PeriodIndex, + Series, + SparseDtype, + Timedelta, + Timestamp, + date_range, + timedelta_range, + to_datetime, + to_timedelta, +) +import pandas._testing as tm +from pandas.api.types import is_scalar +from pandas.core.indexing import _one_ellipsis_message +from pandas.tests.indexing.common import check_indexing_smoketest_or_raises + + +@pytest.mark.parametrize( + "series, new_series, expected_ser", + [ + [[np.nan, np.nan, "b"], ["a", np.nan, np.nan], [False, True, True]], + [[np.nan, "b"], ["a", np.nan], [False, True]], + ], +) +def test_not_change_nan_loc(series, new_series, expected_ser): + # GH 28403 + df = DataFrame({"A": series}) + df.loc[:, "A"] = new_series + expected = DataFrame({"A": expected_ser}) + tm.assert_frame_equal(df.isna(), expected) + tm.assert_frame_equal(df.notna(), ~expected) + + +def test_loc_dtype(): + # GH 60600 + df = DataFrame([["a", 1.0, 2.0], ["b", 3.0, 4.0]]) + result = df.loc[0, [1, 2]] + expected = Series([1.0, 2.0], index=[1, 2], dtype=float, name=0) + tm.assert_series_equal(result, expected) + + +class TestLoc: + def test_none_values_on_string_columns(self, using_infer_string): + # Issue #32218 + df = DataFrame(["1", "2", None], columns=["a"], dtype=object) + assert df.loc[2, "a"] is None + + df = DataFrame(["1", "2", None], columns=["a"], dtype="str") + if using_infer_string: + assert np.isnan(df.loc[2, "a"]) + else: + assert df.loc[2, "a"] is None + + def test_loc_getitem_int(self, frame_or_series): + # int label + obj = frame_or_series(range(3), index=Index(list("abc"), dtype=object)) + check_indexing_smoketest_or_raises(obj, "loc", 2, fails=KeyError) + + def test_loc_getitem_label(self, frame_or_series): + # label + obj = frame_or_series() + check_indexing_smoketest_or_raises(obj, "loc", "c", fails=KeyError) + + @pytest.mark.parametrize("key", ["f", 20]) + @pytest.mark.parametrize( + "index", + [ + Index(list("abcd"), dtype=object), + Index([2, 4, "null", 8], dtype=object), + date_range("20130101", periods=4), + Index(range(0, 8, 2), dtype=np.float64), + Index([]), + ], + ) + def test_loc_getitem_label_out_of_range(self, key, index, frame_or_series): + obj = frame_or_series(range(len(index)), index=index) + # out of range label + check_indexing_smoketest_or_raises(obj, "loc", key, fails=KeyError) + + @pytest.mark.parametrize("key", [[0, 1, 2], [1, 3.0, "A"]]) + @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) + def test_loc_getitem_label_list(self, key, dtype, frame_or_series): + obj = frame_or_series(range(3), index=Index([0, 1, 2], dtype=dtype)) + # list of labels + check_indexing_smoketest_or_raises(obj, "loc", key, fails=KeyError) + + @pytest.mark.parametrize( + "index", + [ + None, + Index([0, 1, 2], dtype=np.int64), + Index([0, 1, 2], dtype=np.uint64), + Index([0, 1, 2], dtype=np.float64), + MultiIndex.from_arrays([range(3), range(3)]), + ], + ) + @pytest.mark.parametrize( + "key", [[0, 1, 2], [0, 2, 10], [3, 6, 7], [(1, 3), (1, 4), (2, 5)]] + ) + def test_loc_getitem_label_list_with_missing(self, key, index, frame_or_series): + if index is None: + obj = frame_or_series() + else: + obj = frame_or_series(range(len(index)), index=index) + check_indexing_smoketest_or_raises(obj, "loc", key, fails=KeyError) + + @pytest.mark.parametrize("dtype", [np.int64, np.uint64]) + def test_loc_getitem_label_list_fails(self, dtype, frame_or_series): + # fails + obj = frame_or_series(range(3), Index([0, 1, 2], dtype=dtype)) + check_indexing_smoketest_or_raises( + obj, "loc", [20, 30, 40], axes=1, fails=KeyError + ) + + def test_loc_getitem_bool(self, frame_or_series): + obj = frame_or_series() + # boolean indexers + b = [True, False, True, False] + + check_indexing_smoketest_or_raises(obj, "loc", b, fails=IndexError) + + @pytest.mark.parametrize( + "slc, indexes, axes, fails", + [ + [ + slice(1, 3), + [ + Index(list("abcd"), dtype=object), + Index([2, 4, "null", 8], dtype=object), + None, + date_range("20130101", periods=4), + Index(range(0, 12, 3), dtype=np.float64), + ], + None, + TypeError, + ], + [ + slice("20130102", "20130104"), + [date_range("20130101", periods=4)], + 1, + TypeError, + ], + [slice(2, 8), [Index([2, 4, "null", 8], dtype=object)], 0, TypeError], + [slice(2, 8), [Index([2, 4, "null", 8], dtype=object)], 1, KeyError], + [slice(2, 4, 2), [Index([2, 4, "null", 8], dtype=object)], 0, TypeError], + ], + ) + def test_loc_getitem_label_slice(self, slc, indexes, axes, fails, frame_or_series): + # label slices (with ints) + + # real label slices + + # GH 14316 + for index in indexes: + if index is None: + obj = frame_or_series() + else: + obj = frame_or_series(range(len(index)), index=index) + check_indexing_smoketest_or_raises( + obj, + "loc", + slc, + axes=axes, + fails=fails, + ) + + def test_setitem_from_duplicate_axis(self): + # GH#34034 + df = DataFrame( + [[20, "a"], [200, "a"], [200, "a"]], + columns=["col1", "col2"], + index=[10, 1, 1], + ) + df.loc[1, "col1"] = np.arange(2) + expected = DataFrame( + [[20, "a"], [0, "a"], [1, "a"]], columns=["col1", "col2"], index=[10, 1, 1] + ) + tm.assert_frame_equal(df, expected) + + def test_column_types_consistent(self): + # GH 26779 + df = DataFrame( + data={ + "channel": [1, 2, 3], + "A": ["String 1", np.nan, "String 2"], + "B": [ + Timestamp("2019-06-11 11:00:00"), + pd.NaT, + Timestamp("2019-06-11 12:00:00"), + ], + } + ) + df2 = DataFrame( + data={"A": ["String 3"], "B": [Timestamp("2019-06-11 12:00:00")]} + ) + # Change Columns A and B to df2.values wherever Column A is NaN + df.loc[df["A"].isna(), ["A", "B"]] = df2.values + expected = DataFrame( + data={ + "channel": [1, 2, 3], + "A": ["String 1", "String 3", "String 2"], + "B": [ + Timestamp("2019-06-11 11:00:00"), + Timestamp("2019-06-11 12:00:00"), + Timestamp("2019-06-11 12:00:00"), + ], + } + ) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "obj, key, exp", + [ + ( + DataFrame([[1]], columns=Index([False])), + IndexSlice[:, False], + Series([1], name=False), + ), + (Series([1], index=Index([False])), False, [1]), + (DataFrame([[1]], index=Index([False])), False, Series([1], name=False)), + ], + ) + def test_loc_getitem_single_boolean_arg(self, obj, key, exp): + # GH 44322 + res = obj.loc[key] + if isinstance(exp, (DataFrame, Series)): + tm.assert_equal(res, exp) + else: + assert res == exp + + @pytest.mark.parametrize( + "data", + [ + [datetime(2025, 10, 30)], + [Timestamp(2025, 10, 30)] * 2, + [Timedelta(1)], + [Timedelta(1), Timedelta(2)], + ], + ) + def test_loc_empty_slice_assignment_with_datetime(self, data): + # issue #50942 + # empty slice assignment with datetime or timedelta should not raise exceptions + mask = [False] * len(data) + + df = DataFrame(data=data, columns=["A"]) + expected = df.copy() + df.loc[mask] = df + tm.assert_frame_equal(df, expected) + + +class TestLocBaseIndependent: + # Tests for loc that do not depend on subclassing Base + def test_loc_npstr(self): + # GH#45580 + df = DataFrame(index=date_range("2021", "2022")) + result = df.loc[np.array(["2021/6/1"])[0] :] + expected = df.iloc[151:] + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "msg, key", + [ + (r"Period\('2019', 'Y-DEC'\), 'foo', 'bar'", (Period(2019), "foo", "bar")), + (r"Period\('2019', 'Y-DEC'\), 'y1', 'bar'", (Period(2019), "y1", "bar")), + (r"Period\('2019', 'Y-DEC'\), 'foo', 'z1'", (Period(2019), "foo", "z1")), + ( + r"Period\('2018', 'Y-DEC'\), Period\('2016', 'Y-DEC'\), 'bar'", + (Period(2018), Period(2016), "bar"), + ), + (r"Period\('2018', 'Y-DEC'\), 'foo', 'y1'", (Period(2018), "foo", "y1")), + ( + r"Period\('2017', 'Y-DEC'\), 'foo', Period\('2015', 'Y-DEC'\)", + (Period(2017), "foo", Period(2015)), + ), + (r"Period\('2017', 'Y-DEC'\), 'z1', 'bar'", (Period(2017), "z1", "bar")), + ], + ) + def test_contains_raise_error_if_period_index_is_in_multi_index(self, msg, key): + # GH#20684 + """ + parse_datetime_string_with_reso return parameter if type not matched. + PeriodIndex.get_loc takes returned value from parse_datetime_string_with_reso + as a tuple. + If first argument is Period and a tuple has 3 items, + process go on not raise exception + """ + df = DataFrame( + { + "A": [Period(2019), "x1", "x2"], + "B": [Period(2018), Period(2016), "y1"], + "C": [Period(2017), "z1", Period(2015)], + "V1": [1, 2, 3], + "V2": [10, 20, 30], + } + ).set_index(["A", "B", "C"]) + with pytest.raises(KeyError, match=msg): + df.loc[key] + + def test_loc_getitem_missing_unicode_key(self): + df = DataFrame({"a": [1]}) + with pytest.raises(KeyError, match="\u05d0"): + df.loc[:, "\u05d0"] # should not raise UnicodeEncodeError + + def test_loc_getitem_dups(self): + # GH 5678 + # repeated getitems on a dup index returning an ndarray + df = DataFrame( + np.random.default_rng(2).random((20, 5)), + index=["ABCDE"[x % 5] for x in range(20)], + ) + expected = df.loc["A", 0] + result = df.loc[:, 0].loc["A"] + tm.assert_series_equal(result, expected) + + def test_loc_getitem_dups2(self): + # GH4726 + # dup indexing with iloc/loc + df = DataFrame( + [[1, 2, "foo", "bar", Timestamp("20130101")]], + columns=["a", "a", "a", "a", "a"], + index=[1], + ) + expected = Series( + [1, 2, "foo", "bar", Timestamp("20130101")], + index=["a", "a", "a", "a", "a"], + name=1, + ) + + result = df.iloc[0] + tm.assert_series_equal(result, expected) + + result = df.loc[1] + tm.assert_series_equal(result, expected) + + def test_loc_setitem_dups(self): + # GH 6541 + df_orig = DataFrame( + { + "me": list("rttti"), + "foo": list("aaade"), + "bar": np.arange(5, dtype="float64") * 1.34 + 2, + "bar2": np.arange(5, dtype="float64") * -0.34 + 2, + } + ).set_index("me") + + indexer = ( + "r", + ["bar", "bar2"], + ) + df = df_orig.copy() + df.loc[indexer] *= 2.0 + tm.assert_series_equal(df.loc[indexer], 2.0 * df_orig.loc[indexer]) + + indexer = ( + "r", + "bar", + ) + df = df_orig.copy() + df.loc[indexer] *= 2.0 + assert df.loc[indexer] == 2.0 * df_orig.loc[indexer] + + indexer = ( + "t", + ["bar", "bar2"], + ) + df = df_orig.copy() + df.loc[indexer] *= 2.0 + tm.assert_frame_equal(df.loc[indexer], 2.0 * df_orig.loc[indexer]) + + def test_loc_setitem_slice(self): + # GH10503 + + # assigning the same type should not change the type + df1 = DataFrame({"a": [0, 1, 1], "b": Series([100, 200, 300], dtype="uint32")}) + ix = df1["a"] == 1 + newb1 = df1.loc[ix, "b"] + 1 + df1.loc[ix, "b"] = newb1 + expected = DataFrame( + {"a": [0, 1, 1], "b": Series([100, 201, 301], dtype="uint32")} + ) + tm.assert_frame_equal(df1, expected) + + # assigning a new type should get the inferred type + df2 = DataFrame({"a": [0, 1, 1], "b": [100, 200, 300]}, dtype="uint64") + ix = df1["a"] == 1 + newb2 = df2.loc[ix, "b"] + with pytest.raises(TypeError, match="Invalid value"): + df1.loc[ix, "b"] = newb2 + + def test_loc_setitem_dtype(self): + # GH31340 + df = DataFrame({"id": ["A"], "a": [1.2], "b": [0.0], "c": [-2.5]}) + cols = ["a", "b", "c"] + df.loc[:, cols] = df.loc[:, cols].astype("float32") + + # pre-2.0 this setting would swap in new arrays, in 2.0 it is correctly + # in-place, consistent with non-split-path + expected = DataFrame( + { + "id": ["A"], + "a": np.array([1.2], dtype="float64"), + "b": np.array([0.0], dtype="float64"), + "c": np.array([-2.5], dtype="float64"), + } + ) # id is inferred as object + + tm.assert_frame_equal(df, expected) + + def test_getitem_label_list_with_missing(self): + s = Series(range(3), index=["a", "b", "c"]) + + # consistency + with pytest.raises(KeyError, match="not in index"): + s[["a", "d"]] + + s = Series(range(3)) + with pytest.raises(KeyError, match="not in index"): + s[[0, 3]] + + @pytest.mark.parametrize("index", [[True, False], [True, False, True, False]]) + def test_loc_getitem_bool_diff_len(self, index): + # GH26658 + s = Series([1, 2, 3]) + msg = f"Boolean index has wrong length: {len(index)} instead of {len(s)}" + with pytest.raises(IndexError, match=msg): + s.loc[index] + + def test_loc_getitem_int_slice(self): + # TODO: test something here? + pass + + def test_loc_to_fail(self): + # GH3449 + df = DataFrame( + np.random.default_rng(2).random((3, 3)), + index=["a", "b", "c"], + columns=["e", "f", "g"], + ) + + msg = ( + rf"\"None of \[Index\(\[1, 2\], dtype='{np.dtype(int)}'\)\] are " + r"in the \[columns\]\"" + ) + with pytest.raises(KeyError, match=msg): + df.loc[[1, 2], [1, 2]] + + def test_loc_to_fail2(self): + # GH 7496 + # loc should not fallback + + s = Series(dtype=object) + s.loc[1] = 1 + s.loc["a"] = 2 + + with pytest.raises(KeyError, match=r"^-1$"): + s.loc[-1] + + msg = ( + rf"\"None of \[Index\(\[-1, -2\], dtype='{np.dtype(int)}'\)\] are " + r"in the \[index\]\"" + ) + with pytest.raises(KeyError, match=msg): + s.loc[[-1, -2]] + + msg = r"\"None of \[Index\(\['4'\], dtype='object'\)\] are in the \[index\]\"" + with pytest.raises(KeyError, match=msg): + s.loc[Index(["4"], dtype=object)] + + s.loc[-1] = 3 + with pytest.raises(KeyError, match="not in index"): + s.loc[[-1, -2]] + + s["a"] = 2 + msg = ( + rf"\"None of \[Index\(\[-2\], dtype='{np.dtype(int)}'\)\] are " + r"in the \[index\]\"" + ) + with pytest.raises(KeyError, match=msg): + s.loc[[-2]] + + del s["a"] + + with pytest.raises(KeyError, match=msg): + s.loc[[-2]] = 0 + + def test_loc_to_fail3(self): + # inconsistency between .loc[values] and .loc[values,:] + # GH 7999 + df = DataFrame([["a"], ["b"]], index=[1, 2], columns=["value"]) + + msg = ( + rf"\"None of \[Index\(\[3\], dtype='{np.dtype(int)}'\)\] are " + r"in the \[index\]\"" + ) + with pytest.raises(KeyError, match=msg): + df.loc[[3], :] + + with pytest.raises(KeyError, match=msg): + df.loc[[3]] + + def test_loc_getitem_list_with_fail(self): + # 15747 + # should KeyError if *any* missing labels + + s = Series([1, 2, 3]) + + s.loc[[2]] + + msg = "None of [RangeIndex(start=3, stop=4, step=1)] are in the [index]" + with pytest.raises(KeyError, match=re.escape(msg)): + s.loc[[3]] + + # a non-match and a match + with pytest.raises(KeyError, match="not in index"): + s.loc[[2, 3]] + + def test_loc_index(self): + # gh-17131 + # a boolean index should index like a boolean numpy array + + df = DataFrame( + np.random.default_rng(2).random(size=(5, 10)), + index=["alpha_0", "alpha_1", "alpha_2", "beta_0", "beta_1"], + ) + + mask = df.index.map(lambda x: "alpha" in x) + expected = df.loc[np.array(mask)] + + result = df.loc[mask] + tm.assert_frame_equal(result, expected) + + result = df.loc[mask.values] + tm.assert_frame_equal(result, expected) + + result = df.loc[pd.array(mask, dtype="boolean")] + tm.assert_frame_equal(result, expected) + + def test_loc_general(self): + df = DataFrame( + np.random.default_rng(2).random((4, 4)), + columns=["A", "B", "C", "D"], + index=["A", "B", "C", "D"], + ) + + # want this to work + result = df.loc[:, "A":"B"].iloc[0:2, :] + assert (result.columns == ["A", "B"]).all() + assert (result.index == ["A", "B"]).all() + + # mixed type + result = DataFrame({"a": [Timestamp("20130101")], "b": [1]}).iloc[0] + expected = Series([Timestamp("20130101"), 1], index=["a", "b"], name=0) + tm.assert_series_equal(result, expected) + assert result.dtype == object + + @pytest.fixture + def frame_for_consistency(self): + return DataFrame( + { + "date": date_range("2000-01-01", "2000-01-5"), + "val": Series(range(5), dtype=np.int64), + } + ) + + @pytest.mark.parametrize( + "val", + [0, np.array(0, dtype=np.int64), np.array([0, 0, 0, 0, 0], dtype=np.int64)], + ) + def test_loc_setitem_consistency(self, frame_for_consistency, val): + # GH 6149 + # coerce similarly for setitem and loc when rows have a null-slice + df = frame_for_consistency.copy() + with pytest.raises(TypeError, match="Invalid value"): + df.loc[:, "date"] = val + + def test_loc_setitem_consistency_dt64_to_str(self, frame_for_consistency): + # GH 6149 + # coerce similarly for setitem and loc when rows have a null-slice + + df = frame_for_consistency.copy() + with pytest.raises(TypeError, match="Invalid value"): + df.loc[:, "date"] = "foo" + + def test_loc_setitem_consistency_dt64_to_float(self, frame_for_consistency): + # GH 6149 + # coerce similarly for setitem and loc when rows have a null-slice + df = frame_for_consistency.copy() + with pytest.raises(TypeError, match="Invalid value"): + df.loc[:, "date"] = 1.0 + + def test_loc_setitem_consistency_single_row(self): + # GH 15494 + # setting on frame with single row + df = DataFrame({"date": Series([Timestamp("20180101")])}) + with pytest.raises(TypeError, match="Invalid value"): + df.loc[:, "date"] = "string" + + def test_loc_setitem_consistency_empty(self): + # empty (essentially noops) + # before the enforcement of #45333 in 2.0, the loc.setitem here would + # change the dtype of df.x to int64 + expected = DataFrame(columns=["x", "y"]) + df = DataFrame(columns=["x", "y"]) + with tm.assert_produces_warning(None): + df.loc[:, "x"] = 1 + tm.assert_frame_equal(df, expected) + + # setting with setitem swaps in a new array, so changes the dtype + df = DataFrame(columns=["x", "y"]) + df["x"] = 1 + expected["x"] = expected["x"].astype(np.int64) + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_consistency_slice_column_len(self, using_infer_string): + # .loc[:,column] setting with slice == len of the column + # GH10408 + levels = [ + ["Region_1"] * 4, + ["Site_1", "Site_1", "Site_2", "Site_2"], + [3987227376, 3980680971, 3977723249, 3977723089], + ] + mi = MultiIndex.from_arrays(levels, names=["Region", "Site", "RespondentID"]) + + clevels = [ + ["Respondent", "Respondent", "Respondent", "OtherCat", "OtherCat"], + ["Something", "StartDate", "EndDate", "Yes/No", "SomethingElse"], + ] + cols = MultiIndex.from_arrays(clevels, names=["Level_0", "Level_1"]) + + values = [ + ["A", "5/25/2015 10:59", "5/25/2015 11:22", "Yes", np.nan], + ["A", "5/21/2015 9:40", "5/21/2015 9:52", "Yes", "Yes"], + ["A", "5/20/2015 8:27", "5/20/2015 8:41", "Yes", np.nan], + ["A", "5/20/2015 8:33", "5/20/2015 9:09", "Yes", "No"], + ] + df = DataFrame(values, index=mi, columns=cols) + + ctx = contextlib.nullcontext() + if using_infer_string: + ctx = pytest.raises(TypeError, match="Invalid value") + + with ctx: + df.loc[:, ("Respondent", "StartDate")] = to_datetime( + df.loc[:, ("Respondent", "StartDate")] + ) + with ctx: + df.loc[:, ("Respondent", "EndDate")] = to_datetime( + df.loc[:, ("Respondent", "EndDate")] + ) + + if using_infer_string: + # infer-objects won't infer stuff anymore + return + + df = df.infer_objects() + + # Adding a new key + df.loc[:, ("Respondent", "Duration")] = ( + df.loc[:, ("Respondent", "EndDate")] + - df.loc[:, ("Respondent", "StartDate")] + ) + + # timedelta64[m] -> float, so this cannot be done inplace, so + # no warning + with pytest.raises(TypeError, match="Invalid value"): + df.loc[:, ("Respondent", "Duration")] = df.loc[ + :, ("Respondent", "Duration") + ] / Timedelta(60_000_000_000) + + @pytest.mark.parametrize("unit", ["Y", "M", "D", "h", "m", "s", "ms", "us"]) + def test_loc_assign_non_ns_datetime(self, unit): + # GH 27395, non-ns dtype assignment via .loc should work + # and return the same result when using simple assignment + df = DataFrame( + { + "timestamp": [ + np.datetime64("2017-02-11 12:41:29"), + np.datetime64("1991-11-07 04:22:37"), + ] + } + ) + + df.loc[:, unit] = df.loc[:, "timestamp"].values.astype(f"datetime64[{unit}]") + df["expected"] = df.loc[:, "timestamp"].values.astype(f"datetime64[{unit}]") + expected = Series(df.loc[:, "expected"], name=unit) + tm.assert_series_equal(df.loc[:, unit], expected) + + def test_loc_modify_datetime(self): + # see gh-28837 + df = DataFrame.from_dict( + {"date": [1485264372711, 1485265925110, 1540215845888, 1540282121025]} + ) + + df["date_dt"] = to_datetime(df["date"], unit="ms", cache=True).dt.as_unit("us") + + df.loc[:, "date_dt_cp"] = df.loc[:, "date_dt"] + df.loc[[2, 3], "date_dt_cp"] = df.loc[[2, 3], "date_dt"] + + expected = DataFrame( + [ + [1485264372711, "2017-01-24 13:26:12.711", "2017-01-24 13:26:12.711"], + [1485265925110, "2017-01-24 13:52:05.110", "2017-01-24 13:52:05.110"], + [1540215845888, "2018-10-22 13:44:05.888", "2018-10-22 13:44:05.888"], + [1540282121025, "2018-10-23 08:08:41.025", "2018-10-23 08:08:41.025"], + ], + columns=["date", "date_dt", "date_dt_cp"], + ) + + columns = ["date_dt", "date_dt_cp"] + expected[columns] = expected[columns].apply(to_datetime) + + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_loc_setitem_frame_with_reindex(self, has_ref): + # GH#6254 setting issue + df = DataFrame(index=[3, 5, 4], columns=["A"], dtype=float) + if has_ref: + view = df[:] # noqa: F841 + df.loc[[4, 3, 5], "A"] = np.array([1, 2, 3], dtype="int64") + + # setting integer values into a float dataframe with loc is inplace, + # so we retain float dtype + ser = Series([2, 3, 1], index=[3, 5, 4], dtype=float) + expected = DataFrame({"A": ser}) + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_frame_with_reindex_mixed(self): + # GH#40480 + df = DataFrame(index=[3, 5, 4], columns=["A", "B"], dtype=float) + df["B"] = "string" + df.loc[[4, 3, 5], "A"] = np.array([1, 2, 3], dtype="int64") + ser = Series([2, 3, 1], index=[3, 5, 4], dtype="int64") + # pre-2.0 this setting swapped in a new array, now it is inplace + # consistent with non-split-path + expected = DataFrame({"A": ser.astype(float)}) + expected["B"] = "string" + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_frame_with_inverted_slice(self): + # GH#40480 + df = DataFrame(index=[1, 2, 3], columns=["A", "B"], dtype=float) + df["B"] = "string" + df.loc[slice(3, 0, -1), "A"] = np.array([1, 2, 3], dtype="int64") + # pre-2.0 this setting swapped in a new array, now it is inplace + # consistent with non-split-path + expected = DataFrame({"A": [3.0, 2.0, 1.0], "B": "string"}, index=[1, 2, 3]) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_loc_setitem_empty_frame(self, has_ref): + # GH#6252 setting with an empty frame + keys1 = ["@" + str(i) for i in range(5)] + val1 = np.arange(5, dtype="int64") + + keys2 = ["@" + str(i) for i in range(4)] + val2 = np.arange(4, dtype="int64") + + index = list(set(keys1).union(keys2)) + df = DataFrame(index=index) + df["A"] = np.nan + if has_ref: + view = df[:] # noqa: F841 + df.loc[keys1, "A"] = val1 + + df["B"] = np.nan + df.loc[keys2, "B"] = val2 + + # Because df["A"] was initialized as float64, setting values into it + # is inplace, so that dtype is retained + sera = Series(val1, index=keys1, dtype=np.float64) + serb = Series(val2, index=keys2) + expected = DataFrame({"A": sera, "B": serb}, columns=Index(["A", "B"])).reindex( + index=index + ) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize("has_ref", [True, False]) + def test_loc_setitem_frame(self, has_ref): + df = DataFrame( + np.random.default_rng(2).standard_normal((4, 4)), + index=list("abcd"), + columns=list("ABCD"), + ) + if has_ref: + view = df[:] # noqa: F841 + + result = df.iloc[0, 0] + + df.loc["a", "A"] = 1 + result = df.loc["a", "A"] + assert result == 1 + + result = df.iloc[0, 0] + assert result == 1 + + df.loc[:, "B":"D"] = 0 + expected = df.loc[:, "B":"D"] + result = df.iloc[:, 1:] + tm.assert_frame_equal(result, expected) + + def test_loc_setitem_frame_nan_int_coercion_invalid(self): + # GH 8669 + # invalid coercion of nan -> int + df = DataFrame({"A": [1, 2, 3], "B": np.nan}) + df.loc[df.B > df.A, "B"] = df.A + expected = DataFrame({"A": [1, 2, 3], "B": np.nan}) + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_frame_mixed_labels(self): + # GH 6546 + # setting with mixed labels + df = DataFrame({1: [1, 2], 2: [3, 4], "a": ["a", "b"]}) + + result = df.loc[0, [1, 2]] + expected = Series( + [1, 3], index=Index([1, 2], dtype=object), dtype="int64", name=0 + ) + tm.assert_series_equal(result, expected) + + expected = DataFrame({1: [5, 2], 2: [6, 4], "a": ["a", "b"]}) + df.loc[0, [1, 2]] = [5, 6] + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_frame_multiples(self): + # multiple setting + df = DataFrame( + {"A": ["foo", "bar", "baz"], "B": Series(range(3), dtype=np.int64)} + ) + rhs = df.loc[1:2] + rhs.index = df.index[0:2] + df.loc[0:1] = rhs + expected = DataFrame( + {"A": ["bar", "baz", "baz"], "B": Series([1, 2, 2], dtype=np.int64)} + ) + tm.assert_frame_equal(df, expected) + + # multiple setting with frame on rhs (with M8) + df = DataFrame( + { + "date": date_range("2000-01-01", "2000-01-5", unit="ns"), + "val": Series(range(5), dtype=np.int64), + } + ) + expected = DataFrame( + { + "date": [ + Timestamp("20000101"), + Timestamp("20000102"), + Timestamp("20000101"), + Timestamp("20000102"), + Timestamp("20000103"), + ], + "val": Series([0, 1, 0, 1, 2], dtype=np.int64), + } + ) + expected["date"] = expected["date"].astype("M8[ns]") + rhs = df.loc[0:2] + rhs.index = df.index[2:5] + df.loc[2:4] = rhs + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "indexer", [["A"], slice(None, "A", None), np.array(["A"])] + ) + @pytest.mark.parametrize("value", [["Z"], np.array(["Z"])]) + def test_loc_setitem_with_scalar_index(self, indexer, value): + # GH #19474 + # assigning like "df.loc[0, ['A']] = ['Z']" should be evaluated + # elementwisely, not using "setter('A', ['Z'])". + + # Set object dtype to avoid upcast when setting 'Z' + df = DataFrame([[1, 2], [3, 4]], columns=["A", "B"]).astype({"A": object}) + df.loc[0, indexer] = value + result = df.loc[0, "A"] + + assert is_scalar(result) and result == "Z" + + @pytest.mark.parametrize( + "index,box,expected", + [ + ( + ([0, 2], ["A", "B", "C", "D"]), + 7, + DataFrame( + [[7, 7, 7, 7], [3, 4, np.nan, np.nan], [7, 7, 7, 7]], + columns=["A", "B", "C", "D"], + ), + ), + ( + (1, ["C", "D"]), + [7, 8], + DataFrame( + [[1, 2, np.nan, np.nan], [3, 4, 7, 8], [5, 6, np.nan, np.nan]], + columns=["A", "B", "C", "D"], + ), + ), + ( + (1, ["A", "B", "C"]), + np.array([7, 8, 9], dtype=np.int64), + DataFrame( + [[1, 2, np.nan], [7, 8, 9], [5, 6, np.nan]], columns=["A", "B", "C"] + ), + ), + ( + (slice(1, 3, None), ["B", "C", "D"]), + [[7, 8, 9], [10, 11, 12]], + DataFrame( + [[1, 2, np.nan, np.nan], [3, 7, 8, 9], [5, 10, 11, 12]], + columns=["A", "B", "C", "D"], + ), + ), + ( + (slice(1, 3, None), ["C", "A", "D"]), + np.array([[7, 8, 9], [10, 11, 12]], dtype=np.int64), + DataFrame( + [[1, 2, np.nan, np.nan], [8, 4, 7, 9], [11, 6, 10, 12]], + columns=["A", "B", "C", "D"], + ), + ), + ( + (slice(None, None, None), ["A", "C"]), + DataFrame([[7, 8], [9, 10], [11, 12]], columns=["A", "C"]), + DataFrame( + [[7, 2, 8], [9, 4, 10], [11, 6, 12]], columns=["A", "B", "C"] + ), + ), + ], + ) + def test_loc_setitem_missing_columns(self, index, box, expected): + # GH 29334 + df = DataFrame([[1, 2], [3, 4], [5, 6]], columns=["A", "B"]) + + df.loc[index] = box + tm.assert_frame_equal(df, expected) + + def test_loc_coercion(self): + # GH#12411 + df = DataFrame({"date": [Timestamp("20130101").tz_localize("UTC"), pd.NaT]}) + expected = df.dtypes + + result = df.iloc[[0]] + tm.assert_series_equal(result.dtypes, expected) + + result = df.iloc[[1]] + tm.assert_series_equal(result.dtypes, expected) + + def test_loc_coercion2(self): + # GH#12045 + df = DataFrame({"date": [datetime(2012, 1, 1), datetime(1012, 1, 2)]}) + expected = df.dtypes + + result = df.iloc[[0]] + tm.assert_series_equal(result.dtypes, expected) + + result = df.iloc[[1]] + tm.assert_series_equal(result.dtypes, expected) + + def test_loc_coercion3(self): + # GH#11594 + df = DataFrame({"text": ["some words"] + [None] * 9}) + expected = df.dtypes + + result = df.iloc[0:2] + tm.assert_series_equal(result.dtypes, expected) + + result = df.iloc[3:] + tm.assert_series_equal(result.dtypes, expected) + + def test_setitem_new_key_tz(self, indexer_sl): + # GH#12862 should not raise on assigning the second value + vals = [ + to_datetime(42).tz_localize("UTC"), + to_datetime(666).tz_localize("UTC"), + ] + expected = Series(vals, index=Index(["foo", "bar"])) + + ser = Series(dtype=object) + indexer_sl(ser)["foo"] = vals[0] + indexer_sl(ser)["bar"] = vals[1] + + tm.assert_series_equal(ser, expected) + + def test_loc_non_unique(self): + # GH3659 + # non-unique indexer with loc slice + # https://groups.google.com/forum/?fromgroups#!topic/pydata/zTm2No0crYs + + # these are going to raise because the we are non monotonic + df = DataFrame( + {"A": [1, 2, 3, 4, 5, 6], "B": [3, 4, 5, 6, 7, 8]}, index=[0, 1, 0, 1, 2, 3] + ) + msg = "'Cannot get left slice bound for non-unique label: 1'" + with pytest.raises(KeyError, match=msg): + df.loc[1:] + msg = "'Cannot get left slice bound for non-unique label: 0'" + with pytest.raises(KeyError, match=msg): + df.loc[0:] + msg = "'Cannot get left slice bound for non-unique label: 1'" + with pytest.raises(KeyError, match=msg): + df.loc[1:2] + + # monotonic are ok + df = DataFrame( + {"A": [1, 2, 3, 4, 5, 6], "B": [3, 4, 5, 6, 7, 8]}, index=[0, 1, 0, 1, 2, 3] + ).sort_index(axis=0) + result = df.loc[1:] + expected = DataFrame({"A": [2, 4, 5, 6], "B": [4, 6, 7, 8]}, index=[1, 1, 2, 3]) + tm.assert_frame_equal(result, expected) + + result = df.loc[0:] + tm.assert_frame_equal(result, df) + + result = df.loc[1:2] + expected = DataFrame({"A": [2, 4, 5], "B": [4, 6, 7]}, index=[1, 1, 2]) + tm.assert_frame_equal(result, expected) + + @pytest.mark.arm_slow + @pytest.mark.slow + @pytest.mark.parametrize("length, l2", [[900, 100], [900000, 100000]]) + def test_loc_non_unique_memory_error(self, length, l2): + # GH 4280 + # non_unique index with a large selection triggers a memory error + + columns = list("ABCDEFG") + + df = pd.concat( + [ + DataFrame( + np.random.default_rng(2).standard_normal((length, len(columns))), + index=np.arange(length), + columns=columns, + ), + DataFrame(np.ones((l2, len(columns))), index=[0] * l2, columns=columns), + ] + ) + + assert df.index.is_unique is False + + mask = np.arange(l2) + result = df.loc[mask] + expected = pd.concat( + [ + df.take([0]), + DataFrame( + np.ones((len(mask), len(columns))), + index=[0] * len(mask), + columns=columns, + ), + df.take(mask[1:]), + ] + ) + tm.assert_frame_equal(result, expected) + + def test_loc_name(self): + # GH 3880 + df = DataFrame([[1, 1], [1, 1]]) + df.index.name = "index_name" + result = df.iloc[[0, 1]].index.name + assert result == "index_name" + + result = df.loc[[0, 1]].index.name + assert result == "index_name" + + def test_loc_empty_list_indexer_is_ok(self): + df = DataFrame( + np.ones((5, 2)), + index=Index([f"i-{i}" for i in range(5)], name="a"), + columns=Index([f"i-{i}" for i in range(2)], name="a"), + ) + # vertical empty + tm.assert_frame_equal( + df.loc[:, []], df.iloc[:, :0], check_index_type=True, check_column_type=True + ) + # horizontal empty + tm.assert_frame_equal( + df.loc[[], :], df.iloc[:0, :], check_index_type=True, check_column_type=True + ) + # horizontal empty + tm.assert_frame_equal( + df.loc[[]], df.iloc[:0, :], check_index_type=True, check_column_type=True + ) + + def test_identity_slice_returns_new_object(self): + # GH13873 + + original_df = DataFrame({"a": [1, 2, 3]}) + sliced_df = original_df.loc[:] + assert sliced_df is not original_df + assert original_df[:] is not original_df + assert original_df.loc[:, :] is not original_df + + # should be a shallow copy + assert np.shares_memory(original_df["a"]._values, sliced_df["a"]._values) + + # Setting using .loc[:, "a"] sets inplace so alters both sliced and orig + # depending on CoW + original_df.loc[:, "a"] = [4, 4, 4] + assert (sliced_df["a"] == [1, 2, 3]).all() + + # These should not return copies + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))) + assert df[0] is not df.loc[:, 0] + + # Same tests for Series + original_series = Series([1, 2, 3, 4, 5, 6]) + sliced_series = original_series.loc[:] + assert sliced_series is not original_series + assert original_series[:] is not original_series + + original_series[:3] = [7, 8, 9] + assert all(sliced_series[:3] == [1, 2, 3]) + + def test_loc_copy_vs_view(self, request): + # GH 15631 + x = DataFrame(zip(range(3), range(3)), columns=["a", "b"]) + + y = x.copy() + q = y.loc[:, "a"] + q += 2 + + tm.assert_frame_equal(x, y) + + z = x.copy() + q = z.loc[x.index, "a"] + q += 2 + + tm.assert_frame_equal(x, z) + + def test_loc_uint64(self): + # GH20722 + # Test whether loc accept uint64 max value as index. + umax = np.iinfo("uint64").max + ser = Series([1, 2], index=[umax - 1, umax]) + + result = ser.loc[umax - 1] + expected = ser.iloc[0] + assert result == expected + + result = ser.loc[[umax - 1]] + expected = ser.iloc[[0]] + tm.assert_series_equal(result, expected) + + result = ser.loc[[umax - 1, umax]] + tm.assert_series_equal(result, ser) + + def test_loc_uint64_disallow_negative(self): + # GH#41775 + umax = np.iinfo("uint64").max + ser = Series([1, 2], index=[umax - 1, umax]) + + with pytest.raises(KeyError, match="-1"): + # don't wrap around + ser.loc[-1] + + with pytest.raises(KeyError, match="-1"): + # don't wrap around + ser.loc[[-1]] + + def test_loc_setitem_empty_append_expands_rows(self): + # GH6173, various appends to an empty dataframe + + data = [1, 2, 3] + expected = DataFrame( + {"x": data, "y": np.array([np.nan] * len(data), dtype=object)} + ) + + # appends to fit length of data + df = DataFrame(columns=["x", "y"]) + df.loc[:, "x"] = data + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_empty_append_expands_rows_mixed_dtype(self): + # GH#37932 same as test_loc_setitem_empty_append_expands_rows + # but with mixed dtype so we go through take_split_path + data = [1, 2, 3] + expected = DataFrame( + {"x": data, "y": np.array([np.nan] * len(data), dtype=object)} + ) + + df = DataFrame(columns=["x", "y"]) + df["x"] = df["x"].astype(np.int64) + df.loc[:, "x"] = data + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_empty_append_single_value(self): + # only appends one value + expected = DataFrame({"x": [1.0], "y": [np.nan]}) + df = DataFrame(columns=["x", "y"], dtype=float) + df.loc[0, "x"] = expected.loc[0, "x"] + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_empty_append_raises(self): + # GH6173, various appends to an empty dataframe + + data = [1, 2] + df = DataFrame(columns=["x", "y"]) + df.index = df.index.astype(np.int64) + msg = r"None of .*Index.* are in the \[index\]" + with pytest.raises(KeyError, match=msg): + df.loc[[0, 1], "x"] = data + + msg = "setting an array element with a sequence." + with pytest.raises(ValueError, match=msg): + df.loc[0:2, "x"] = data + + def test_indexing_zerodim_np_array(self): + # GH24924 + df = DataFrame([[1, 2], [3, 4]]) + result = df.loc[np.array(0)] + s = Series([1, 2], name=0) + tm.assert_series_equal(result, s) + + def test_series_indexing_zerodim_np_array(self): + # GH24924 + s = Series([1, 2]) + result = s.loc[np.array(0)] + assert result == 1 + + def test_loc_reverse_assignment(self): + # GH26939 + data = [1, 2, 3, 4, 5, 6] + [None] * 4 + expected = Series(data, index=range(2010, 2020)) + + result = Series(index=range(2010, 2020), dtype=np.float64) + result.loc[2015:2010:-1] = [6, 5, 4, 3, 2, 1] + + tm.assert_series_equal(result, expected) + + def test_loc_setitem_str_to_small_float_conversion_type(self, using_infer_string): + # GH#20388 + + col_data = [str(np.random.default_rng(2).random() * 1e-12) for _ in range(5)] + result = DataFrame(col_data, columns=["A"]) + expected = DataFrame(col_data, columns=["A"]) + tm.assert_frame_equal(result, expected) + + # assigning with loc/iloc attempts to set the values inplace, which + # in this case is successful + if using_infer_string: + with pytest.raises(TypeError, match="Invalid value"): + result.loc[result.index, "A"] = [float(x) for x in col_data] + else: + result.loc[result.index, "A"] = [float(x) for x in col_data] + expected = DataFrame(col_data, columns=["A"], dtype=float).astype(object) + tm.assert_frame_equal(result, expected) + + # assigning the entire column using __setitem__ swaps in the new array + # GH#??? + result["A"] = [float(x) for x in col_data] + expected = DataFrame(col_data, columns=["A"], dtype=float) + tm.assert_frame_equal(result, expected) + + def test_loc_getitem_time_object(self, frame_or_series): + rng = date_range("1/1/2000", "1/5/2000", freq="5min") + mask = (rng.hour == 9) & (rng.minute == 30) + + obj = DataFrame( + np.random.default_rng(2).standard_normal((len(rng), 3)), index=rng + ) + obj = tm.get_obj(obj, frame_or_series) + + result = obj.loc[time(9, 30)] + exp = obj.loc[mask] + tm.assert_equal(result, exp) + + chunk = obj.loc["1/4/2000":] + result = chunk.loc[time(9, 30)] + expected = result[-1:] + + # Without resetting the freqs, these are 5 min and 1440 min, respectively + result.index = result.index._with_freq(None) + expected.index = expected.index._with_freq(None) + tm.assert_equal(result, expected) + + @pytest.mark.parametrize("spmatrix_t", ["coo_matrix", "csc_matrix", "csr_matrix"]) + @pytest.mark.parametrize("dtype", [np.complex128, np.float64, np.int64, bool]) + def test_loc_getitem_range_from_spmatrix(self, spmatrix_t, dtype): + sp_sparse = pytest.importorskip("scipy.sparse") + + spmatrix_t = getattr(sp_sparse, spmatrix_t) + + # The bug is triggered by a sparse matrix with purely sparse columns. So the + # recipe below generates a rectangular matrix of dimension (5, 7) where all the + # diagonal cells are ones, meaning the last two columns are purely sparse. + rows, cols = 5, 7 + spmatrix = spmatrix_t(np.eye(rows, cols, dtype=dtype), dtype=dtype) + df = DataFrame.sparse.from_spmatrix(spmatrix) + + # regression test for GH#34526 + itr_idx = range(2, rows) + result = np.nan_to_num(df.loc[itr_idx].values) + expected = spmatrix.toarray()[itr_idx] + tm.assert_numpy_array_equal(result, expected) + + # regression test for GH#34540 + result = df.loc[itr_idx].dtypes.values + expected = np.full(cols, SparseDtype(dtype)) + tm.assert_numpy_array_equal(result, expected) + + def test_loc_getitem_listlike_all_retains_sparse(self): + df = DataFrame({"A": pd.array([0, 0], dtype=SparseDtype("int64"))}) + result = df.loc[[0, 1]] + tm.assert_frame_equal(result, df) + + def test_loc_getitem_sparse_frame(self): + # GH34687 + sp_sparse = pytest.importorskip("scipy.sparse") + + df = DataFrame.sparse.from_spmatrix(sp_sparse.eye(5, dtype=np.int64)) + result = df.loc[range(2)] + expected = DataFrame( + [[1, 0, 0, 0, 0], [0, 1, 0, 0, 0]], + dtype=SparseDtype(np.int64), + ) + tm.assert_frame_equal(result, expected) + + result = df.loc[range(2)].loc[range(1)] + expected = DataFrame([[1, 0, 0, 0, 0]], dtype=SparseDtype(np.int64)) + tm.assert_frame_equal(result, expected) + + def test_loc_getitem_sparse_series(self): + # GH34687 + s = Series([1.0, 0.0, 0.0, 0.0, 0.0], dtype=SparseDtype("float64", 0.0)) + + result = s.loc[range(2)] + expected = Series([1.0, 0.0], dtype=SparseDtype("float64", 0.0)) + tm.assert_series_equal(result, expected) + + result = s.loc[range(3)].loc[range(2)] + expected = Series([1.0, 0.0], dtype=SparseDtype("float64", 0.0)) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("indexer", ["loc", "iloc"]) + def test_getitem_single_row_sparse_df(self, indexer): + # GH#46406 + df = DataFrame([[1.0, 0.0, 1.5], [0.0, 2.0, 0.0]], dtype=SparseDtype(float)) + result = getattr(df, indexer)[0] + expected = Series([1.0, 0.0, 1.5], dtype=SparseDtype(float), name=0) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("key_type", [iter, np.array, Series, Index]) + def test_loc_getitem_iterable(self, float_frame, key_type): + idx = key_type(["A", "B", "C"]) + result = float_frame.loc[:, idx] + expected = float_frame.loc[:, ["A", "B", "C"]] + tm.assert_frame_equal(result, expected) + + def test_loc_getitem_timedelta_0seconds(self): + # GH#10583 + df = DataFrame(np.random.default_rng(2).normal(size=(10, 4))) + df.index = timedelta_range(start="0s", periods=10, freq="s") + expected = df.loc[Timedelta("0s") :, :] + result = df.loc["0s":, :] + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("val,expected", [(2**63 - 1, 1), (2**63, 2)]) + def test_loc_getitem_uint64_scalar(self, val, expected): + # see GH#19399 + df = DataFrame([1, 2], index=[2**63 - 1, 2**63]) + result = df.loc[val] + expected = Series([expected]) + expected.name = val + tm.assert_series_equal(result, expected) + + def test_loc_setitem_int_label_with_float_index(self, float_numpy_dtype): + # note labels are floats + dtype = float_numpy_dtype + ser = Series(["a", "b", "c"], index=Index([0, 0.5, 1], dtype=dtype)) + expected = ser.copy() + + ser.loc[1] = "zoo" + expected.iloc[2] = "zoo" + + tm.assert_series_equal(ser, expected) + + @pytest.mark.parametrize( + "indexer, expected", + [ + # The test name is a misnomer in the 0 case as df.index[indexer] + # is a scalar. + (0, [20, 1, 2, 3, 4, 5, 6, 7, 8, 9]), + (slice(4, 8), [0, 1, 2, 3, 20, 20, 20, 20, 8, 9]), + ([3, 5], [0, 1, 2, 20, 4, 20, 6, 7, 8, 9]), + ], + ) + def test_loc_setitem_listlike_with_timedelta64index(self, indexer, expected): + # GH#16637 + tdi = to_timedelta(range(10), unit="s") + df = DataFrame({"x": range(10)}, dtype="int64", index=tdi) + + df.loc[df.index[indexer], "x"] = 20 + + expected = DataFrame( + expected, + index=tdi, + columns=["x"], + dtype="int64", + ) + + tm.assert_frame_equal(expected, df) + + def test_loc_setitem_categorical_values_partial_column_slice(self): + # Assigning a Category to parts of an int/... column uses the values of + # the Categorical + df = DataFrame({"a": [1, 1, 1, 1, 1], "b": list("aaaaa")}) + with pytest.raises(TypeError, match="Invalid value"): + df.loc[1:2, "a"] = Categorical(["b", "b"], categories=["a", "b"]) + df.loc[2:3, "b"] = Categorical(["b", "b"], categories=["a", "b"]) + + def test_loc_setitem_single_row_categorical(self, using_infer_string): + # GH#25495 + df = DataFrame({"Alpha": ["a"], "Numeric": [0]}) + categories = Categorical(df["Alpha"], categories=["a", "b", "c"]) + + # pre-2.0 this swapped in a new array, in 2.0 it operates inplace, + # consistent with non-split-path + df.loc[:, "Alpha"] = categories + + result = df["Alpha"] + expected = Series(categories, index=df.index, name="Alpha").astype( + object if not using_infer_string else "str" + ) + tm.assert_series_equal(result, expected) + + # double-check that the non-loc setting retains categoricalness + df["Alpha"] = categories + tm.assert_series_equal(df["Alpha"], Series(categories, name="Alpha")) + + def test_loc_setitem_datetime_coercion(self): + # GH#1048 + df = DataFrame({"c": [Timestamp("2010-10-01")] * 3}) + df.loc[0:1, "c"] = np.datetime64("2008-08-08") + assert Timestamp("2008-08-08") == df.loc[0, "c"] + assert Timestamp("2008-08-08") == df.loc[1, "c"] + with pytest.raises(TypeError, match="Invalid value"): + df.loc[2, "c"] = date(2005, 5, 5) + + @pytest.mark.parametrize("idxer", ["var", ["var"]]) + def test_loc_setitem_datetimeindex_tz(self, idxer, tz_naive_fixture): + # GH#11365 + tz = tz_naive_fixture + idx = date_range(start="2015-07-12", periods=3, freq="h", tz=tz) + expected = DataFrame(1.2, index=idx, columns=["var"]) + # if result started off with object dtype, then the .loc.__setitem__ + # below would retain object dtype + result = DataFrame(index=idx, columns=["var"], dtype=np.float64) + if idxer == "var": + with pytest.raises(TypeError, match="Invalid value"): + result.loc[:, idxer] = expected + else: + # See https://github.com/pandas-dev/pandas/issues/56223 + result.loc[:, idxer] = expected + tm.assert_frame_equal(result, expected) + + def test_loc_setitem_time_key(self): + index = date_range("2012-01-01", "2012-01-05", freq="30min") + df = DataFrame( + np.random.default_rng(2).standard_normal((len(index), 5)), index=index + ) + akey = time(12, 0, 0) + bkey = slice(time(13, 0, 0), time(14, 0, 0)) + ainds = [24, 72, 120, 168] + binds = [26, 27, 28, 74, 75, 76, 122, 123, 124, 170, 171, 172] + + result = df.copy() + result.loc[akey] = 0 + result = result.loc[akey] + expected = df.loc[akey].copy() + expected.loc[:] = 0 + tm.assert_frame_equal(result, expected) + + result = df.copy() + result.loc[akey] = 0 + result.loc[akey] = df.iloc[ainds] + tm.assert_frame_equal(result, df) + + result = df.copy() + result.loc[bkey] = 0 + result = result.loc[bkey] + expected = df.loc[bkey].copy() + expected.loc[:] = 0 + tm.assert_frame_equal(result, expected) + + result = df.copy() + result.loc[bkey] = 0 + result.loc[bkey] = df.iloc[binds] + tm.assert_frame_equal(result, df) + + @pytest.mark.parametrize("key", ["A", ["A"], ("A", slice(None))]) + def test_loc_setitem_unsorted_multiindex_columns(self, key): + # GH#38601 + mi = MultiIndex.from_tuples([("A", 4), ("B", "3"), ("A", "2")]) + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=mi) + obj = df.copy() + obj.loc[:, key] = np.zeros((2, 2), dtype="int64") + expected = DataFrame([[0, 2, 0], [0, 5, 0]], columns=mi) + tm.assert_frame_equal(obj, expected) + + df = df.sort_index(axis=1) + df.loc[:, key] = np.zeros((2, 2), dtype="int64") + expected = expected.sort_index(axis=1) + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_uint_drop(self, any_int_numpy_dtype): + # see GH#18311 + # assigning series.loc[0] = 4 changed series.dtype to int + series = Series([1, 2, 3], dtype=any_int_numpy_dtype) + series.loc[0] = 4 + expected = Series([4, 2, 3], dtype=any_int_numpy_dtype) + tm.assert_series_equal(series, expected) + + def test_loc_setitem_td64_non_nano(self): + # GH#14155 + ser = Series(10 * [np.timedelta64(10, "m")]) + ser.loc[[1, 2, 3]] = np.timedelta64(20, "m") + expected = Series(10 * [np.timedelta64(10, "m")]) + expected.loc[[1, 2, 3]] = Timedelta(np.timedelta64(20, "m")) + tm.assert_series_equal(ser, expected) + + def test_loc_setitem_2d_to_1d_raises(self): + data = np.random.default_rng(2).standard_normal((2, 2)) + # float64 dtype to avoid upcast when trying to set float data + ser = Series(range(2), dtype="float64") + + msg = "setting an array element with a sequence." + with pytest.raises(ValueError, match=msg): + ser.loc[range(2)] = data + + with pytest.raises(ValueError, match=msg): + ser.loc[:] = data + + def test_loc_getitem_interval_index(self): + # GH#19977 + index = pd.interval_range(start=0, periods=3) + df = DataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=index, columns=["A", "B", "C"] + ) + + expected = 1 + result = df.loc[0.5, "A"] + tm.assert_almost_equal(result, expected) + + def test_loc_getitem_interval_index2(self): + # GH#19977 + index = pd.interval_range(start=0, periods=3, closed="both") + df = DataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=index, columns=["A", "B", "C"] + ) + + index_exp = pd.interval_range(start=0, periods=2, freq=1, closed="both") + expected = Series([1, 4], index=index_exp, name="A") + result = df.loc[1, "A"] + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("tpl", [(1,), (1, 2)]) + def test_loc_getitem_index_single_double_tuples(self, tpl): + # GH#20991 + idx = Index( + [(1,), (1, 2)], + name="A", + tupleize_cols=False, + ) + df = DataFrame(index=idx) + + result = df.loc[[tpl]] + idx = Index([tpl], name="A", tupleize_cols=False) + expected = DataFrame(index=idx) + tm.assert_frame_equal(result, expected) + + def test_loc_getitem_index_namedtuple(self): + IndexType = namedtuple("IndexType", ["a", "b"]) + idx1 = IndexType("foo", "bar") + idx2 = IndexType("baz", "bof") + index = Index([idx1, idx2], name="composite_index", tupleize_cols=False) + df = DataFrame([(1, 2), (3, 4)], index=index, columns=["A", "B"]) + + result = df.loc[IndexType("foo", "bar")]["A"] + assert result == 1 + + def test_loc_setitem_single_column_mixed(self, using_infer_string): + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + index=["a", "b", "c", "d", "e"], + columns=["foo", "bar", "baz"], + ) + df["str"] = "qux" + df.loc[df.index[::2], "str"] = np.nan + expected = Series( + [np.nan, "qux", np.nan, "qux", np.nan], + dtype=object if not using_infer_string else "str", + ).values + tm.assert_almost_equal(df["str"].values, expected) + + def test_loc_setitem_cast2(self): + # GH#7704 + # dtype conversion on setting + df = DataFrame(np.random.default_rng(2).random((30, 3)), columns=tuple("ABC")) + df["event"] = np.nan + with pytest.raises(TypeError, match="Invalid value"): + df.loc[10, "event"] = "foo" + + def test_loc_setitem_cast3(self): + # Test that data type is preserved . GH#5782 + df = DataFrame({"one": np.arange(6, dtype=np.int8)}) + df.loc[1, "one"] = 6 + assert df.dtypes.one == np.dtype(np.int8) + df.one = np.int8(7) + assert df.dtypes.one == np.dtype(np.int8) + + def test_loc_setitem_range_key(self, frame_or_series): + # GH#45479 don't treat range key as positional + obj = frame_or_series(range(5), index=[3, 4, 1, 0, 2]) + + values = [9, 10, 11] + if obj.ndim == 2: + values = [[9], [10], [11]] + + obj.loc[range(3)] = values + + expected = frame_or_series([0, 1, 10, 9, 11], index=obj.index) + tm.assert_equal(obj, expected) + + def test_loc_setitem_numpy_frame_categorical_value(self): + # GH#52927 + df = DataFrame({"a": [1, 1, 1, 1, 1], "b": ["a", "a", "a", "a", "a"]}) + df.loc[1:2, "a"] = Categorical([2, 2], categories=[1, 2]) + + expected = DataFrame({"a": [1, 2, 2, 1, 1], "b": ["a", "a", "a", "a", "a"]}) + tm.assert_frame_equal(df, expected) + + def test_loc_with_nat_in_tzaware_index(self): + # GH#54409 + timestamp = to_datetime("2023-01-01", utc=True) + df = DataFrame( + { + "index": Series([pd.NaT, timestamp]), + "value": Series([0, 1]), + } + ).set_index("index") + + # Works fine when mixing NaT and valid values + result = df.loc[ + Series([pd.NaT, timestamp, timestamp], dtype=df.index.dtype), + "value", + ] + expected = [0, 1, 1] + assert result.tolist() == expected + + # Regression check: all-NaT lookup should return [0], not raise + result = df.loc[ + Series([pd.NaT], dtype=df.index.dtype), + "value", + ] + assert result.tolist() == [0] + + +class TestLocWithEllipsis: + @pytest.fixture + def indexer(self, indexer_li): + # Test iloc while we're here + return indexer_li + + @pytest.fixture + def obj(self, series_with_simple_index, frame_or_series): + obj = series_with_simple_index + if frame_or_series is not Series: + obj = obj.to_frame() + return obj + + def test_loc_iloc_getitem_ellipsis(self, obj, indexer): + result = indexer(obj)[...] + tm.assert_equal(result, obj) + + @pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning") + def test_loc_iloc_getitem_leading_ellipses(self, series_with_simple_index, indexer): + obj = series_with_simple_index + key = 0 if (indexer is tm.iloc or len(obj) == 0) else obj.index[0] + + if indexer is tm.loc and obj.index.inferred_type == "boolean": + # passing [False] will get interpreted as a boolean mask + # TODO: should it? unambiguous when lengths dont match? + return + if indexer is tm.loc and isinstance(obj.index, MultiIndex): + msg = "MultiIndex does not support indexing with Ellipsis" + with pytest.raises(NotImplementedError, match=msg): + result = indexer(obj)[..., [key]] + + elif len(obj) != 0: + result = indexer(obj)[..., [key]] + expected = indexer(obj)[[key]] + tm.assert_series_equal(result, expected) + + key2 = 0 if indexer is tm.iloc else obj.name + df = obj.to_frame() + result = indexer(df)[..., [key2]] + expected = indexer(df)[:, [key2]] + tm.assert_frame_equal(result, expected) + + def test_loc_iloc_getitem_ellipses_only_one_ellipsis(self, obj, indexer): + # GH37750 + key = 0 if (indexer is tm.iloc or len(obj) == 0) else obj.index[0] + + with pytest.raises(IndexingError, match=_one_ellipsis_message): + indexer(obj)[..., ...] + + with pytest.raises(IndexingError, match=_one_ellipsis_message): + indexer(obj)[..., [key], ...] + + with pytest.raises(IndexingError, match=_one_ellipsis_message): + indexer(obj)[..., ..., key] + + # one_ellipsis_message takes precedence over "Too many indexers" + # only when the first key is Ellipsis + with pytest.raises(IndexingError, match="Too many indexers"): + indexer(obj)[key, ..., ...] + + +class TestLocWithMultiIndex: + @pytest.mark.parametrize( + "keys, expected", + [ + (["b", "a"], [["b", "b", "a", "a"], [1, 2, 1, 2]]), + (["a", "b"], [["a", "a", "b", "b"], [1, 2, 1, 2]]), + ((["a", "b"], [1, 2]), [["a", "a", "b", "b"], [1, 2, 1, 2]]), + ((["a", "b"], [2, 1]), [["a", "a", "b", "b"], [2, 1, 2, 1]]), + ((["b", "a"], [2, 1]), [["b", "b", "a", "a"], [2, 1, 2, 1]]), + ((["b", "a"], [1, 2]), [["b", "b", "a", "a"], [1, 2, 1, 2]]), + ((["c", "a"], [2, 1]), [["c", "a", "a"], [1, 2, 1]]), + ], + ) + @pytest.mark.parametrize("dim", ["index", "columns"]) + def test_loc_getitem_multilevel_index_order(self, dim, keys, expected): + # GH#22797 + # Try to respect order of keys given for MultiIndex.loc + kwargs = {dim: [["c", "a", "a", "b", "b"], [1, 1, 2, 1, 2]]} + df = DataFrame(np.arange(25).reshape(5, 5), **kwargs) + exp_index = MultiIndex.from_arrays(expected) + if dim == "index": + res = df.loc[keys, :] + tm.assert_index_equal(res.index, exp_index) + elif dim == "columns": + res = df.loc[:, keys] + tm.assert_index_equal(res.columns, exp_index) + + def test_loc_preserve_names(self, multiindex_year_month_day_dataframe_random_data): + ymd = multiindex_year_month_day_dataframe_random_data + + result = ymd.loc[2000] + result2 = ymd["A"].loc[2000] + assert result.index.names == ymd.index.names[1:] + assert result2.index.names == ymd.index.names[1:] + + result = ymd.loc[2000, 2] + result2 = ymd["A"].loc[2000, 2] + assert result.index.name == ymd.index.names[2] + assert result2.index.name == ymd.index.names[2] + + def test_loc_getitem_multiindex_nonunique_len_zero(self): + # GH#13691 + mi = MultiIndex.from_product([[0], [1, 1]]) + ser = Series(0, index=mi) + + res = ser.loc[[]] + + expected = ser[:0] + tm.assert_series_equal(res, expected) + + res2 = ser.loc[ser.iloc[0:0]] + tm.assert_series_equal(res2, expected) + + def test_loc_getitem_access_none_value_in_multiindex(self): + # GH#34318: test that you can access a None value using .loc + # through a Multiindex + + ser = Series([None], MultiIndex.from_arrays([["Level1"], ["Level2"]])) + result = ser.loc[("Level1", "Level2")] + assert result is None + + midx = MultiIndex.from_product([["Level1"], ["Level2_a", "Level2_b"]]) + ser = Series([None] * len(midx), dtype=object, index=midx) + result = ser.loc[("Level1", "Level2_a")] + assert result is None + + ser = Series([1] * len(midx), dtype=object, index=midx) + result = ser.loc[("Level1", "Level2_a")] + assert result == 1 + + def test_loc_setitem_multiindex_slice(self): + # GH 34870 + + index = MultiIndex.from_tuples( + zip( + ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"], + ["one", "two", "one", "two", "one", "two", "one", "two"], + ), + names=["first", "second"], + ) + + result = Series([1, 1, 1, 1, 1, 1, 1, 1], index=index) + result.loc[("baz", "one") : ("foo", "two")] = 100 + + expected = Series([1, 1, 100, 100, 100, 100, 1, 1], index=index) + + tm.assert_series_equal(result, expected) + + def test_loc_getitem_slice_datetime_objs_with_datetimeindex(self): + times = date_range("2000-01-01", freq="10min", periods=100000) + ser = Series(range(100000), times) + result = ser.loc[datetime(1900, 1, 1) : datetime(2100, 1, 1)] + tm.assert_series_equal(result, ser) + + def test_loc_getitem_datetime_string_with_datetimeindex(self): + # GH 16710 + df = DataFrame( + {"a": range(10), "b": range(10)}, + index=date_range("2010-01-01", "2010-01-10", unit="ns"), + ) + result = df.loc[["2010-01-01", "2010-01-05"], ["a", "b"]] + expected = DataFrame( + {"a": [0, 4], "b": [0, 4]}, + index=DatetimeIndex(["2010-01-01", "2010-01-05"]).as_unit("ns"), + ) + tm.assert_frame_equal(result, expected) + + def test_loc_getitem_sorted_index_level_with_duplicates(self): + # GH#4516 sorting a MultiIndex with duplicates and multiple dtypes + mi = MultiIndex.from_tuples( + [ + ("foo", "bar"), + ("foo", "bar"), + ("bah", "bam"), + ("bah", "bam"), + ("foo", "bar"), + ("bah", "bam"), + ], + names=["A", "B"], + ) + df = DataFrame( + [ + [1.0, 1], + [2.0, 2], + [3.0, 3], + [4.0, 4], + [5.0, 5], + [6.0, 6], + ], + index=mi, + columns=["C", "D"], + ) + df = df.sort_index(level=0) + + expected = DataFrame( + [[1.0, 1], [2.0, 2], [5.0, 5]], columns=["C", "D"], index=mi.take([0, 1, 4]) + ) + + result = df.loc[("foo", "bar")] + tm.assert_frame_equal(result, expected) + + def test_additional_element_to_categorical_series_loc(self): + # GH#47677 + result = Series(["a", "b", "c"], dtype="category") + result.loc[3] = 0 + expected = Series(["a", "b", "c", 0], dtype="object") + tm.assert_series_equal(result, expected) + + def test_additional_categorical_element_loc(self): + # GH#47677 + result = Series(["a", "b", "c"], dtype="category") + result.loc[3] = "a" + expected = Series(["a", "b", "c", "a"], dtype="category") + tm.assert_series_equal(result, expected) + + def test_loc_set_nan_in_categorical_series(self, any_numeric_ea_dtype): + # GH#47677 + srs = Series( + [1, 2, 3], + dtype=CategoricalDtype(Index([1, 2, 3], dtype=any_numeric_ea_dtype)), + ) + # enlarge + srs.loc[3] = np.nan + expected = Series( + [1, 2, 3, np.nan], + dtype=CategoricalDtype(Index([1, 2, 3], dtype=any_numeric_ea_dtype)), + ) + tm.assert_series_equal(srs, expected) + # set into + srs.loc[1] = np.nan + expected = Series( + [1, np.nan, 3, np.nan], + dtype=CategoricalDtype(Index([1, 2, 3], dtype=any_numeric_ea_dtype)), + ) + tm.assert_series_equal(srs, expected) + + @pytest.mark.parametrize("na", (np.nan, pd.NA, None, pd.NaT)) + def test_loc_consistency_series_enlarge_set_into(self, na): + # GH#47677 + srs_enlarge = Series(["a", "b", "c"], dtype="category") + srs_enlarge.loc[3] = na + + srs_setinto = Series(["a", "b", "c", "a"], dtype="category") + srs_setinto.loc[3] = na + + tm.assert_series_equal(srs_enlarge, srs_setinto) + expected = Series(["a", "b", "c", na], dtype="category") + tm.assert_series_equal(srs_enlarge, expected) + + def test_loc_getitem_preserves_index_level_category_dtype(self): + # GH#15166 + df = DataFrame( + data=np.arange(2, 22, 2), + index=MultiIndex( + levels=[CategoricalIndex(["a", "b"]), range(10)], + codes=[[0] * 5 + [1] * 5, range(10)], + names=["Index1", "Index2"], + ), + ) + + expected = CategoricalIndex( + ["a", "b"], + categories=["a", "b"], + ordered=False, + name="Index1", + dtype="category", + ) + + result = df.index.levels[0] + tm.assert_index_equal(result, expected) + + result = df.loc[["a"]].index.levels[0] + tm.assert_index_equal(result, expected) + + @pytest.mark.parametrize("lt_value", [30, 10]) + def test_loc_multiindex_levels_contain_values_not_in_index_anymore(self, lt_value): + # GH#41170 + df = DataFrame({"a": [12, 23, 34, 45]}, index=[list("aabb"), [0, 1, 2, 3]]) + with pytest.raises(KeyError, match=r"\['b'\] not in index"): + df.loc[df["a"] < lt_value, :].loc[["b"], :] + + def test_loc_multiindex_null_slice_na_level(self): + # GH#42055 + lev1 = np.array([np.nan, np.nan]) + lev2 = ["bar", "baz"] + mi = MultiIndex.from_arrays([lev1, lev2]) + ser = Series([0, 1], index=mi) + result = ser.loc[:, "bar"] + + # TODO: should we have name="bar"? + expected = Series([0], index=[np.nan]) + tm.assert_series_equal(result, expected) + + def test_loc_drops_level(self): + # Based on test_series_varied_multiindex_alignment, where + # this used to fail to drop the first level + mi = MultiIndex.from_product( + [list("ab"), list("xy"), [1, 2]], names=["ab", "xy", "num"] + ) + ser = Series(range(8), index=mi) + + loc_result = ser.loc["a", :, :] + expected = ser.index.droplevel(0)[:4] + tm.assert_index_equal(loc_result.index, expected) + + +class TestLocSetitemWithExpansion: + @td.skip_if_no("pyarrow") + def test_loc_setitem_with_expansion_preserves_ea_dtype(self): + # GH#41626 retain index.dtype in setitem-with-expansion + idx = Index([Timestamp(0).date()], dtype="date32[pyarrow]") + df = DataFrame({"A": range(1)}, index=idx) + item = Timestamp("1970-01-02").date() + + df.loc[item] = 1 + + exp_index = Index([idx[0], item], dtype=idx.dtype) + tm.assert_index_equal(df.index, exp_index) + + ser = df["A"].iloc[:-1] + ser.loc[item] = 1 + tm.assert_index_equal(ser.index, exp_index) + + def test_loc_setitem_with_expansion_large_dataframe(self, monkeypatch): + # GH#10692 + size_cutoff = 50 + with monkeypatch.context(): + monkeypatch.setattr(libindex, "_SIZE_CUTOFF", size_cutoff) + result = DataFrame({"x": range(size_cutoff)}, dtype="int64") + result.loc[size_cutoff] = size_cutoff + expected = DataFrame({"x": range(size_cutoff + 1)}, dtype="int64") + tm.assert_frame_equal(result, expected) + + def test_loc_setitem_empty_series(self): + # GH#5226 + + # partially set with an empty object series + ser = Series(dtype=object) + ser.loc[1] = 1 + tm.assert_series_equal(ser, Series([1], index=range(1, 2))) + ser.loc[3] = 3 + tm.assert_series_equal(ser, Series([1, 3], index=[1, 3])) + + def test_loc_setitem_empty_series_float(self): + # GH#5226 + + # partially set with an empty object series + ser = Series(dtype=object) + ser.loc[1] = 1.0 + tm.assert_series_equal(ser, Series([1.0], index=range(1, 2))) + ser.loc[3] = 3.0 + tm.assert_series_equal(ser, Series([1.0, 3.0], index=[1, 3])) + + def test_loc_setitem_empty_series_str_idx(self): + # GH#5226 + + # partially set with an empty object series + ser = Series(dtype=object) + ser.loc["foo"] = 1 + tm.assert_series_equal(ser, Series([1], index=Index(["foo"]))) + ser.loc["bar"] = 3 + tm.assert_series_equal(ser, Series([1, 3], index=Index(["foo", "bar"]))) + ser.loc[3] = 4 + tm.assert_series_equal(ser, Series([1, 3, 4], index=Index(["foo", "bar", 3]))) + + def test_loc_setitem_incremental_with_dst(self): + # GH#20724 + base = datetime(2015, 11, 1, tzinfo=gettz("US/Pacific")) + idxs = [base + timedelta(seconds=i * 900) for i in range(16)] + result = Series([0], index=[idxs[0]]) + for ts in idxs: + result.loc[ts] = 1 + expected = Series(1, index=idxs) + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize( + "conv", + [ + lambda x: x, + lambda x: x.to_datetime64(), + lambda x: x.to_pydatetime(), + lambda x: np.datetime64(x), + ], + ids=["self", "to_datetime64", "to_pydatetime", "np.datetime64"], + ) + def test_loc_setitem_datetime_keys_cast(self, conv, using_infer_string): + # GH#9516, GH#51363 changed in 3.0 to not cast on Index.insert + dt1 = Timestamp("20130101 09:00:00") + dt2 = Timestamp("20130101 10:00:00") + df = DataFrame() + df.loc[conv(dt1), "one"] = 100 + df.loc[conv(dt2), "one"] = 200 + + expected = DataFrame( + {"one": [100.0, 200.0]}, + index=Index( + [conv(dt1), conv(dt2)], dtype=None if using_infer_string else object + ), + columns=Index(["one"]), + ) + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_categorical_column_retains_dtype(self, ordered): + # GH16360 + result = DataFrame({"A": [1]}) + result.loc[:, "B"] = Categorical(["b"], ordered=ordered) + expected = DataFrame({"A": [1], "B": Categorical(["b"], ordered=ordered)}) + tm.assert_frame_equal(result, expected) + + def test_loc_setitem_with_expansion_and_existing_dst(self): + # GH#18308 + start = Timestamp("2017-10-29 00:00:00+0200", tz="Europe/Madrid") + end = Timestamp("2017-10-29 03:00:00+0100", tz="Europe/Madrid") + ts = Timestamp("2016-10-10 03:00:00", tz="Europe/Madrid") + idx = date_range(start, end, inclusive="left", freq="h") + assert ts not in idx # i.e. result.loc setitem is with-expansion + + result = DataFrame(index=idx, columns=["value"]) + result.loc[ts, "value"] = 12 + expected = DataFrame( + [np.nan] * len(idx) + [12], + index=idx.append(DatetimeIndex([ts])), + columns=["value"], + dtype=object, + ) + tm.assert_frame_equal(result, expected) + + def test_setitem_with_expansion(self): + # indexing - setting an element + df = DataFrame( + data=to_datetime(["2015-03-30 20:12:32", "2015-03-12 00:11:11"]), + columns=["time"], + ) + df["new_col"] = ["new", "old"] + df.time = df.set_index("time").index.tz_localize("UTC") + v = df[df.new_col == "new"].set_index("time").index.tz_convert("US/Pacific") + + # pre-2.0 trying to set a single element on a part of a different + # timezone converted to object; in 2.0 it retains dtype + df2 = df.copy() + df2.loc[df2.new_col == "new", "time"] = v + + expected = Series([v[0].tz_convert("UTC"), df.loc[1, "time"]], name="time") + tm.assert_series_equal(df2.time, expected) + + v = df.loc[df.new_col == "new", "time"] + Timedelta("1s").as_unit("s") + df.loc[df.new_col == "new", "time"] = v + tm.assert_series_equal(df.loc[df.new_col == "new", "time"], v) + + def test_loc_setitem_with_expansion_inf_upcast_empty(self): + # Test with np.inf in columns + df = DataFrame() + df.loc[0, 0] = 1 + df.loc[1, 1] = 2 + df.loc[0, np.inf] = 3 + + result = df.columns + expected = Index([0, 1, np.inf], dtype=np.float64) + tm.assert_index_equal(result, expected) + + @pytest.mark.filterwarnings("ignore:indexing past lexsort depth") + @pytest.mark.parametrize("has_ref", [True, False]) + def test_loc_setitem_with_expansion_nonunique_index(self, index, has_ref): + # GH#40096 + if not len(index): + pytest.skip("Not relevant for empty Index") + + index = index.repeat(2) # ensure non-unique + N = len(index) + arr = np.arange(N).astype(np.int64) + + orig = DataFrame(arr, index=index) + + # key that will requiring object-dtype casting in the index + key = "kapow" + assert key not in index # otherwise test is invalid + # TODO: using a tuple key breaks here in many cases + + exp_index = index.insert(len(index), key) + if isinstance(index, MultiIndex): + assert exp_index[-1][0] == key + else: + assert exp_index[-1] == key + exp_data = np.arange(N + 1).astype(np.float64) + expected = DataFrame(exp_data, index=exp_index) + + # Add new row, but no new columns + df = orig.copy() + if has_ref: + view = df[:] + df.loc[key, 0] = N + tm.assert_frame_equal(df, expected) + + # add new row on a Series + ser = orig.copy()[0] + if has_ref: + view = ser[:] + ser.loc[key] = N + # the series machinery lets us preserve int dtype instead of float + expected = expected[0].astype(np.int64) + tm.assert_series_equal(ser, expected) + + # add new row and new column + df = orig.copy() + if has_ref: + view = df[:] # noqa: F841 + df.loc[key, 1] = N + expected = DataFrame( + {0: [*list(arr), np.nan], 1: [np.nan] * N + [float(N)]}, + index=exp_index, + ) + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_with_expansion_preserves_nullable_int( + self, any_numeric_ea_dtype + ): + # GH#42099 + ser = Series([0, 1, 2, 3], dtype=any_numeric_ea_dtype) + df = DataFrame({"data": ser}) + + result = DataFrame(index=df.index) + result.loc[df.index, "data"] = ser + + tm.assert_frame_equal(result, df, check_column_type=False) + + result = DataFrame(index=df.index) + result.loc[df.index, "data"] = ser._values + tm.assert_frame_equal(result, df, check_column_type=False) + + def test_loc_setitem_ea_not_full_column(self): + # GH#39163 + df = DataFrame({"A": range(5)}) + + val = date_range("2016-01-01", periods=3, tz="US/Pacific") + + df.loc[[0, 1, 2], "B"] = val + + bex = val.append(DatetimeIndex([pd.NaT, pd.NaT], dtype=val.dtype)) + expected = DataFrame({"A": range(5), "B": bex}) + assert expected.dtypes["B"] == val.dtype + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_with_expansion_new_row_and_new_columns(self): + # GH#58316 + df = DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + df.loc["x", ["C", "D"]] = 91 + expected = DataFrame( + { + "A": [1.0, 2.0, 3.0, np.nan], + "B": [4.0, 5.0, 6.0, np.nan], + "C": [np.nan, np.nan, np.nan, 91.0], + "D": [np.nan, np.nan, np.nan, 91.0], + }, + index=Index([0, 1, 2, "x"]), + ) + tm.assert_frame_equal(df, expected) + + +class TestLocCallable: + def test_frame_loc_getitem_callable(self): + # GH#11485 + df = DataFrame({"A": [1, 2, 3, 4], "B": list("aabb"), "C": [1, 2, 3, 4]}) + # iloc cannot use boolean Series (see GH3635) + + # return bool indexer + res = df.loc[lambda x: x.A > 2] + tm.assert_frame_equal(res, df.loc[df.A > 2]) + + res = df.loc[lambda x: x.B == "b", :] + tm.assert_frame_equal(res, df.loc[df.B == "b", :]) + + res = df.loc[lambda x: x.A > 2, lambda x: x.columns == "B"] + tm.assert_frame_equal(res, df.loc[df.A > 2, [False, True, False]]) + + res = df.loc[lambda x: x.A > 2, lambda x: "B"] + tm.assert_series_equal(res, df.loc[df.A > 2, "B"]) + + res = df.loc[lambda x: x.A > 2, lambda x: ["A", "B"]] + tm.assert_frame_equal(res, df.loc[df.A > 2, ["A", "B"]]) + + res = df.loc[lambda x: x.A == 2, lambda x: ["A", "B"]] + tm.assert_frame_equal(res, df.loc[df.A == 2, ["A", "B"]]) + + # scalar + res = df.loc[lambda x: 1, lambda x: "A"] + assert res == df.loc[1, "A"] + + def test_frame_loc_getitem_callable_mixture(self): + # GH#11485 + df = DataFrame({"A": [1, 2, 3, 4], "B": list("aabb"), "C": [1, 2, 3, 4]}) + + res = df.loc[lambda x: x.A > 2, ["A", "B"]] + tm.assert_frame_equal(res, df.loc[df.A > 2, ["A", "B"]]) + + res = df.loc[[2, 3], lambda x: ["A", "B"]] + tm.assert_frame_equal(res, df.loc[[2, 3], ["A", "B"]]) + + res = df.loc[3, lambda x: ["A", "B"]] + tm.assert_series_equal(res, df.loc[3, ["A", "B"]]) + + def test_frame_loc_getitem_callable_labels(self): + # GH#11485 + df = DataFrame({"X": [1, 2, 3, 4], "Y": list("aabb")}, index=list("ABCD")) + + # return label + res = df.loc[lambda x: ["A", "C"]] + tm.assert_frame_equal(res, df.loc[["A", "C"]]) + + res = df.loc[lambda x: ["A", "C"], :] + tm.assert_frame_equal(res, df.loc[["A", "C"], :]) + + res = df.loc[lambda x: ["A", "C"], lambda x: "X"] + tm.assert_series_equal(res, df.loc[["A", "C"], "X"]) + + res = df.loc[lambda x: ["A", "C"], lambda x: ["X"]] + tm.assert_frame_equal(res, df.loc[["A", "C"], ["X"]]) + + # mixture + res = df.loc[["A", "C"], lambda x: "X"] + tm.assert_series_equal(res, df.loc[["A", "C"], "X"]) + + res = df.loc[["A", "C"], lambda x: ["X"]] + tm.assert_frame_equal(res, df.loc[["A", "C"], ["X"]]) + + res = df.loc[lambda x: ["A", "C"], "X"] + tm.assert_series_equal(res, df.loc[["A", "C"], "X"]) + + res = df.loc[lambda x: ["A", "C"], ["X"]] + tm.assert_frame_equal(res, df.loc[["A", "C"], ["X"]]) + + def test_frame_loc_setitem_callable(self): + # GH#11485 + df = DataFrame( + {"X": [1, 2, 3, 4], "Y": Series(list("aabb"), dtype=object)}, + index=list("ABCD"), + ) + + # return label + res = df.copy() + res.loc[lambda x: ["A", "C"]] = -20 + exp = df.copy() + exp.loc[["A", "C"]] = -20 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[lambda x: ["A", "C"], :] = 20 + exp = df.copy() + exp.loc[["A", "C"], :] = 20 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[lambda x: ["A", "C"], lambda x: "X"] = -1 + exp = df.copy() + exp.loc[["A", "C"], "X"] = -1 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[lambda x: ["A", "C"], lambda x: ["X"]] = [5, 10] + exp = df.copy() + exp.loc[["A", "C"], ["X"]] = [5, 10] + tm.assert_frame_equal(res, exp) + + # mixture + res = df.copy() + res.loc[["A", "C"], lambda x: "X"] = np.array([-1, -2]) + exp = df.copy() + exp.loc[["A", "C"], "X"] = np.array([-1, -2]) + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[["A", "C"], lambda x: ["X"]] = 10 + exp = df.copy() + exp.loc[["A", "C"], ["X"]] = 10 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[lambda x: ["A", "C"], "X"] = -2 + exp = df.copy() + exp.loc[["A", "C"], "X"] = -2 + tm.assert_frame_equal(res, exp) + + res = df.copy() + res.loc[lambda x: ["A", "C"], ["X"]] = -4 + exp = df.copy() + exp.loc[["A", "C"], ["X"]] = -4 + tm.assert_frame_equal(res, exp) + + +class TestPartialStringSlicing: + def test_loc_getitem_partial_string_slicing_datetimeindex(self): + # GH#35509 + df = DataFrame( + {"col1": ["a", "b", "c"], "col2": [1, 2, 3]}, + index=to_datetime(["2020-08-01", "2020-07-02", "2020-08-05"]), + ) + expected = DataFrame( + {"col1": ["a", "c"], "col2": [1, 3]}, + index=to_datetime(["2020-08-01", "2020-08-05"]), + ) + result = df.loc["2020-08"] + tm.assert_frame_equal(result, expected) + + def test_loc_getitem_partial_string_slicing_with_periodindex(self): + pi = pd.period_range(start="2017-01-01", end="2018-01-01", freq="M") + ser = pi.to_series() + result = ser.loc[:"2017-12"] + expected = ser.iloc[:-1] + + tm.assert_series_equal(result, expected) + + def test_loc_getitem_partial_string_slicing_with_timedeltaindex(self): + ix = timedelta_range(start="1 day", end="2 days", freq="1h") + ser = ix.to_series() + result = ser.loc[:"1 days"] + expected = ser.iloc[:-1] + + tm.assert_series_equal(result, expected) + + def test_loc_getitem_str_timedeltaindex(self): + # GH#16896 + df = DataFrame({"x": range(3)}, index=to_timedelta(range(3), unit="days")) + expected = df.iloc[0] + sliced = df.loc["0 days"] + tm.assert_series_equal(sliced, expected) + + @pytest.mark.parametrize("indexer_end", [None, "2020-01-02 23:59:59.999999999"]) + def test_loc_getitem_partial_slice_non_monotonicity( + self, tz_aware_fixture, indexer_end, frame_or_series + ): + # GH#33146 + obj = frame_or_series( + [1] * 5, + index=DatetimeIndex( + [ + Timestamp("2019-12-30"), + Timestamp("2020-01-01"), + Timestamp("2019-12-25"), + Timestamp("2020-01-02 23:59:59.999999999"), + Timestamp("2019-12-19"), + ], + tz=tz_aware_fixture, + ), + ) + expected = frame_or_series( + [1] * 2, + index=DatetimeIndex( + [ + Timestamp("2020-01-01"), + Timestamp("2020-01-02 23:59:59.999999999"), + ], + tz=tz_aware_fixture, + ), + ) + indexer = slice("2020-01-01", indexer_end) + + result = obj[indexer] + tm.assert_equal(result, expected) + + result = obj.loc[indexer] + tm.assert_equal(result, expected) + + +class TestLabelSlicing: + def test_loc_getitem_slicing_datetimes_frame(self): + # GH#7523 + + # unique + df_unique = DataFrame( + np.arange(4.0, dtype="float64"), + index=[datetime(2001, 1, i, 10, 00) for i in [1, 2, 3, 4]], + ) + + # duplicates + df_dups = DataFrame( + np.arange(5.0, dtype="float64"), + index=[datetime(2001, 1, i, 10, 00) for i in [1, 2, 2, 3, 4]], + ) + + for df in [df_unique, df_dups]: + result = df.loc[datetime(2001, 1, 1, 10) :] + tm.assert_frame_equal(result, df) + result = df.loc[: datetime(2001, 1, 4, 10)] + tm.assert_frame_equal(result, df) + result = df.loc[datetime(2001, 1, 1, 10) : datetime(2001, 1, 4, 10)] + tm.assert_frame_equal(result, df) + + result = df.loc[datetime(2001, 1, 1, 11) :] + expected = df.iloc[1:] + tm.assert_frame_equal(result, expected) + result = df.loc["20010101 11":] + tm.assert_frame_equal(result, expected) + + def test_loc_getitem_label_slice_across_dst(self): + # GH#21846 + idx = date_range( + "2017-10-29 01:30:00", tz="Europe/Berlin", periods=5, freq="30 min" + ) + series2 = Series([0, 1, 2, 3, 4], index=idx) + + t_1 = Timestamp("2017-10-29 02:30:00+02:00", tz="Europe/Berlin") + t_2 = Timestamp("2017-10-29 02:00:00+01:00", tz="Europe/Berlin") + result = series2.loc[t_1:t_2] + expected = Series([2, 3], index=idx[2:4]) + tm.assert_series_equal(result, expected) + + result = series2[t_1] + expected = 2 + assert result == expected + + @pytest.mark.parametrize( + "index", + [ + pd.period_range(start="2017-01-01", end="2018-01-01", freq="M"), + timedelta_range(start="1 day", end="2 days", freq="1h"), + ], + ) + def test_loc_getitem_label_slice_period_timedelta(self, index): + ser = index.to_series() + result = ser.loc[: index[-2]] + expected = ser.iloc[:-1] + + tm.assert_series_equal(result, expected) + + def test_loc_getitem_slice_floats_inexact(self): + index = [52195.504153, 52196.303147, 52198.369883] + df = DataFrame(np.random.default_rng(2).random((3, 2)), index=index) + + s1 = df.loc[52195.1:52196.5] + assert len(s1) == 2 + + s1 = df.loc[52195.1:52196.6] + assert len(s1) == 2 + + s1 = df.loc[52195.1:52198.9] + assert len(s1) == 3 + + def test_loc_getitem_float_slice_floatindex(self, float_numpy_dtype): + dtype = float_numpy_dtype + ser = Series( + np.random.default_rng(2).random(10), index=np.arange(10, 20, dtype=dtype) + ) + + assert len(ser.loc[12.0:]) == 8 + assert len(ser.loc[12.5:]) == 7 + + idx = np.arange(10, 20, dtype=dtype) + idx[2] = 12.2 + ser.index = idx + assert len(ser.loc[12.0:]) == 8 + assert len(ser.loc[12.5:]) == 7 + + @pytest.mark.parametrize( + "start,stop, expected_slice", + [ + [np.timedelta64(0, "ns"), None, slice(0, 11)], + [np.timedelta64(1, "D"), np.timedelta64(6, "D"), slice(1, 7)], + [None, np.timedelta64(4, "D"), slice(0, 5)], + ], + ) + def test_loc_getitem_slice_label_td64obj(self, start, stop, expected_slice): + # GH#20393 + ser = Series(range(11), timedelta_range("0 days", "10 days")) + result = ser.loc[slice(start, stop)] + expected = ser.iloc[expected_slice] + tm.assert_series_equal(result, expected) + + @pytest.mark.parametrize("start", ["2018", "2020"]) + def test_loc_getitem_slice_unordered_dt_index(self, frame_or_series, start): + obj = frame_or_series( + [1, 2, 3], + index=[Timestamp("2016"), Timestamp("2019"), Timestamp("2017")], + ) + with pytest.raises( + KeyError, match="Value based partial slicing on non-monotonic" + ): + obj.loc[start:"2022"] + + @pytest.mark.parametrize("value", [1, 1.5]) + def test_loc_getitem_slice_labels_int_in_object_index(self, frame_or_series, value): + # GH: 26491 + obj = frame_or_series(range(4), index=[value, "first", 2, "third"]) + result = obj.loc[value:"third"] + expected = frame_or_series(range(4), index=[value, "first", 2, "third"]) + tm.assert_equal(result, expected) + + def test_loc_getitem_slice_columns_mixed_dtype(self): + # GH: 20975 + df = DataFrame({"test": 1, 1: 2, 2: 3}, index=[0]) + expected = DataFrame( + data=[[2, 3]], index=[0], columns=Index([1, 2], dtype=object) + ) + tm.assert_frame_equal(df.loc[:, 1:], expected) + + +class TestLocBooleanLabelsAndSlices: + @pytest.mark.parametrize("bool_value", [True, False]) + def test_loc_bool_incompatible_index_raises( + self, index, frame_or_series, bool_value + ): + # GH20432 + message = f"{bool_value}: boolean label can not be used without a boolean index" + if index.inferred_type != "boolean": + obj = frame_or_series(index=index, dtype="object") + with pytest.raises(KeyError, match=message): + obj.loc[bool_value] + + @pytest.mark.parametrize("bool_value", [True, False]) + def test_loc_bool_should_not_raise(self, frame_or_series, bool_value): + obj = frame_or_series( + index=Index([True, False], dtype="boolean"), dtype="object" + ) + obj.loc[bool_value] + + def test_loc_bool_slice_raises(self, index, frame_or_series): + # GH20432 + message = ( + r"slice\(True, False, None\): boolean values can not be used in a slice" + ) + obj = frame_or_series(index=index, dtype="object") + with pytest.raises(TypeError, match=message): + obj.loc[True:False] + + +class TestLocBooleanMask: + def test_loc_setitem_bool_mask_timedeltaindex(self): + # GH#14946 + df = DataFrame({"x": range(10)}) + df.index = to_timedelta(range(10), unit="s") + conditions = [df["x"] > 3, df["x"] == 3, df["x"] < 3] + expected_data = [ + [0, 1, 2, 3, 10, 10, 10, 10, 10, 10], + [0, 1, 2, 10, 4, 5, 6, 7, 8, 9], + [10, 10, 10, 3, 4, 5, 6, 7, 8, 9], + ] + for cond, data in zip(conditions, expected_data): + result = df.copy() + result.loc[cond, "x"] = 10 + + expected = DataFrame( + data, + index=to_timedelta(range(10), unit="s"), + columns=["x"], + dtype="int64", + ) + tm.assert_frame_equal(expected, result) + + @pytest.mark.parametrize("tz", [None, "UTC"]) + def test_loc_setitem_mask_with_datetimeindex_tz(self, tz): + # GH#16889 + # support .loc with alignment and tz-aware DatetimeIndex + mask = np.array([True, False, True, False]) + + idx = date_range("20010101", periods=4, tz=tz) + df = DataFrame({"a": np.arange(4)}, index=idx).astype("float64") + + result = df.copy() + result.loc[mask, :] = df.loc[mask, :] + tm.assert_frame_equal(result, df) + + result = df.copy() + result.loc[mask] = df.loc[mask] + tm.assert_frame_equal(result, df) + + def test_loc_setitem_mask_and_label_with_datetimeindex(self): + # GH#9478 + # a datetimeindex alignment issue with partial setting + df = DataFrame( + np.arange(6.0).reshape(3, 2), + columns=list("AB"), + index=date_range("1/1/2000", periods=3, freq="1h"), + ) + expected = df.copy() + expected["C"] = [expected.index[0], pd.NaT, pd.NaT] + + mask = df.A < 1 + df.loc[mask, "C"] = df.loc[mask].index + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_mask_td64_series_value(self): + # GH#23462 key list of bools, value is a Series + td1 = Timedelta(0) + td2 = Timedelta(28767471428571405) + df = DataFrame({"col": Series([td1, td2])}) + df_copy = df.copy() + ser = Series([td1]) + + expected = df["col"].iloc[1]._value + df.loc[[True, False]] = ser + result = df["col"].iloc[1]._value + + assert expected == result + tm.assert_frame_equal(df, df_copy) + + def test_loc_setitem_boolean_and_column(self, float_frame): + expected = float_frame.copy() + mask = float_frame["A"] > 0 + + float_frame.loc[mask, "B"] = 0 + + values = expected.values.copy() + values[mask.values, 1] = 0 + expected = DataFrame(values, index=expected.index, columns=expected.columns) + tm.assert_frame_equal(float_frame, expected) + + def test_loc_setitem_ndframe_values_alignment(self): + # GH#45501 + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + df.loc[[False, False, True], ["a"]] = DataFrame( + {"a": [10, 20, 30]}, index=[2, 1, 0] + ) + + expected = DataFrame({"a": [1, 2, 10], "b": [4, 5, 6]}) + tm.assert_frame_equal(df, expected) + + # same thing with Series RHS + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + df.loc[[False, False, True], ["a"]] = Series([10, 11, 12], index=[2, 1, 0]) + tm.assert_frame_equal(df, expected) + + # same thing but setting "a" instead of ["a"] + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + df.loc[[False, False, True], "a"] = Series([10, 11, 12], index=[2, 1, 0]) + tm.assert_frame_equal(df, expected) + + df = DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}) + df_orig = df.copy() + ser = df["a"] + ser.loc[[False, False, True]] = Series([10, 11, 12], index=[2, 1, 0]) + tm.assert_frame_equal(df, df_orig) + + def test_loc_indexer_empty_broadcast(self): + # GH#51450 + df = DataFrame({"a": [], "b": []}, dtype=object) + expected = df.copy() + df.loc[np.array([], dtype=np.bool_), ["a"]] = df["a"].copy() + tm.assert_frame_equal(df, expected) + + def test_loc_indexer_all_false_broadcast(self): + # GH#51450 + df = DataFrame({"a": ["x"], "b": ["y"]}, dtype=object) + expected = df.copy() + df.loc[np.array([False], dtype=np.bool_), ["a"]] = df["b"].copy() + tm.assert_frame_equal(df, expected) + + def test_loc_indexer_length_one(self): + # GH#51435 + df = DataFrame({"a": ["x"], "b": ["y"]}, dtype=object) + expected = DataFrame({"a": ["y"], "b": ["y"]}, dtype=object) + df.loc[np.array([True], dtype=np.bool_), ["a"]] = df["b"].copy() + tm.assert_frame_equal(df, expected) + + +class TestLocListlike: + @pytest.mark.parametrize("box", [lambda x: x, np.asarray, list]) + def test_loc_getitem_list_of_labels_categoricalindex_with_na(self, box): + # passing a list can include valid categories _or_ NA values + ci = CategoricalIndex(["A", "B", np.nan]) + ser = Series(range(3), index=ci) + + result = ser.loc[box(ci)] + tm.assert_series_equal(result, ser) + + result = ser[box(ci)] + tm.assert_series_equal(result, ser) + + result = ser.to_frame().loc[box(ci)] + tm.assert_frame_equal(result, ser.to_frame()) + + ser2 = ser[:-1] + ci2 = ci[1:] + # but if there are no NAs present, this should raise KeyError + msg = "not in index" + with pytest.raises(KeyError, match=msg): + ser2.loc[box(ci2)] + + with pytest.raises(KeyError, match=msg): + ser2[box(ci2)] + + with pytest.raises(KeyError, match=msg): + ser2.to_frame().loc[box(ci2)] + + def test_loc_getitem_series_label_list_missing_values(self): + # gh-11428 + key = np.array( + ["2001-01-04", "2001-01-02", "2001-01-04", "2001-01-14"], dtype="datetime64" + ) + ser = Series([2, 5, 8, 11], date_range("2001-01-01", freq="D", periods=4)) + with pytest.raises(KeyError, match="not in index"): + ser.loc[key] + + def test_loc_getitem_series_label_list_missing_integer_values(self): + # GH: 25927 + ser = Series( + index=np.array([9730701000001104, 10049011000001109]), + data=np.array([999000011000001104, 999000011000001104]), + ) + with pytest.raises(KeyError, match="not in index"): + ser.loc[np.array([9730701000001104, 10047311000001102])] + + @pytest.mark.parametrize("to_period", [True, False]) + def test_loc_getitem_listlike_of_datetimelike_keys(self, to_period): + # GH#11497 + + idx = date_range("2011-01-01", "2011-01-02", freq="D", name="idx") + if to_period: + idx = idx.to_period("D") + ser = Series([0.1, 0.2], index=idx, name="s") + + keys = [Timestamp("2011-01-01"), Timestamp("2011-01-02")] + if to_period: + keys = [x.to_period("D") for x in keys] + result = ser.loc[keys] + exp = Series([0.1, 0.2], index=idx, name="s") + if not to_period: + exp.index = exp.index._with_freq(None) + tm.assert_series_equal(result, exp, check_index_type=True) + + keys = [ + Timestamp("2011-01-02"), + Timestamp("2011-01-02"), + Timestamp("2011-01-01"), + ] + if to_period: + keys = [x.to_period("D") for x in keys] + exp = Series( + [0.2, 0.2, 0.1], index=Index(keys, name="idx", dtype=idx.dtype), name="s" + ) + result = ser.loc[keys] + tm.assert_series_equal(result, exp, check_index_type=True) + + keys = [ + Timestamp("2011-01-03"), + Timestamp("2011-01-02"), + Timestamp("2011-01-03"), + ] + if to_period: + keys = [x.to_period("D") for x in keys] + + with pytest.raises(KeyError, match="not in index"): + ser.loc[keys] + + def test_loc_named_index(self): + # GH 42790 + df = DataFrame( + [[1, 2], [4, 5], [7, 8]], + index=["cobra", "viper", "sidewinder"], + columns=["max_speed", "shield"], + ) + expected = df.iloc[:2] + expected.index.name = "foo" + result = df.loc[Index(["cobra", "viper"], name="foo")] + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "columns, column_key, expected_columns", + [ + ([2011, 2012, 2013], [2011, 2012], [0, 1]), + ([2011, 2012, "All"], [2011, 2012], [0, 1]), + ([2011, 2012, "All"], [2011, "All"], [0, 2]), + ], +) +def test_loc_getitem_label_list_integer_labels(columns, column_key, expected_columns): + # gh-14836 + df = DataFrame( + np.random.default_rng(2).random((3, 3)), columns=columns, index=list("ABC") + ) + expected = df.iloc[:, expected_columns] + result = df.loc[["A", "B", "C"], column_key] + + tm.assert_frame_equal(result, expected, check_column_type=True) + + +def test_loc_setitem_float_intindex(): + # GH 8720 + rand_data = np.random.default_rng(2).standard_normal((8, 4)) + result = DataFrame(rand_data) + result.loc[:, 0.5] = np.nan + expected_data = np.hstack((rand_data, np.array([np.nan] * 8).reshape(8, 1))) + expected = DataFrame(expected_data, columns=[0.0, 1.0, 2.0, 3.0, 0.5]) + tm.assert_frame_equal(result, expected) + + result = DataFrame(rand_data) + result.loc[:, 0.5] = np.nan + tm.assert_frame_equal(result, expected) + + +def test_loc_axis_1_slice(): + # GH 10586 + cols = [(yr, m) for yr in [2014, 2015] for m in [7, 8, 9, 10]] + df = DataFrame( + np.ones((10, 8)), + index=tuple("ABCDEFGHIJ"), + columns=MultiIndex.from_tuples(cols), + ) + result = df.loc(axis=1)[(2014, 9) : (2015, 8)] + expected = DataFrame( + np.ones((10, 4)), + index=tuple("ABCDEFGHIJ"), + columns=MultiIndex.from_tuples([(2014, 9), (2014, 10), (2015, 7), (2015, 8)]), + ) + tm.assert_frame_equal(result, expected) + + +def test_loc_set_dataframe_multiindex(): + # GH 14592 + expected = DataFrame( + "a", index=range(2), columns=MultiIndex.from_product([range(2), range(2)]) + ) + result = expected.copy() + result.loc[0, [(0, 1)]] = result.loc[0, [(0, 1)]] + tm.assert_frame_equal(result, expected) + + +def test_loc_mixed_int_float(): + # GH#19456 + ser = Series(range(2), Index([1, 2.0], dtype=object)) + + result = ser.loc[1] + assert result == 0 + + +def test_loc_with_positional_slice_raises(): + # GH#31840 + ser = Series(range(4), index=["A", "B", "C", "D"]) + + with pytest.raises(TypeError, match="Slicing a positional slice with .loc"): + ser.loc[:3] = 2 + + +def test_loc_slice_disallows_positional(): + # GH#16121, GH#24612, GH#31810 + dti = date_range("2016-01-01", periods=3) + df = DataFrame(np.random.default_rng(2).random((3, 2)), index=dti) + + ser = df[0] + + msg = ( + "cannot do slice indexing on DatetimeIndex with these " + r"indexers \[1\] of type int" + ) + + for obj in [df, ser]: + with pytest.raises(TypeError, match=msg): + obj.loc[1:3] + + with pytest.raises(TypeError, match="Slicing a positional slice with .loc"): + # GH#31840 enforce incorrect behavior + obj.loc[1:3] = 1 + + with pytest.raises(TypeError, match=msg): + df.loc[1:3, 1] + + with pytest.raises(TypeError, match="Slicing a positional slice with .loc"): + # GH#31840 enforce incorrect behavior + df.loc[1:3, 1] = 2 + + +def test_loc_datetimelike_mismatched_dtypes(): + # GH#32650 dont mix and match datetime/timedelta/period dtypes + + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 3)), + columns=["a", "b", "c"], + index=date_range("2012", freq="h", periods=5), + ) + # create dataframe with non-unique DatetimeIndex + df = df.iloc[[0, 2, 2, 3]].copy() + + dti = df.index + tdi = pd.TimedeltaIndex(dti.asi8) # matching i8 values + + msg = r"None of \[TimedeltaIndex.* are in the \[index\]" + with pytest.raises(KeyError, match=msg): + df.loc[tdi] + + with pytest.raises(KeyError, match=msg): + df["a"].loc[tdi] + + +def test_loc_with_period_index_indexer(): + # GH#4125 + idx = pd.period_range("2002-01", "2003-12", freq="M") + df = DataFrame(np.random.default_rng(2).standard_normal((24, 10)), index=idx) + tm.assert_frame_equal(df, df.loc[idx]) + tm.assert_frame_equal(df, df.loc[list(idx)]) + tm.assert_frame_equal(df, df.loc[list(idx)]) + tm.assert_frame_equal(df.iloc[0:5], df.loc[idx[0:5]]) + tm.assert_frame_equal(df, df.loc[list(idx)]) + + +def test_loc_setitem_multiindex_timestamp(): + # GH#13831 + vals = np.random.default_rng(2).standard_normal((8, 6)) + idx = date_range("1/1/2000", periods=8) + cols = ["A", "B", "C", "D", "E", "F"] + exp = DataFrame(vals, index=idx, columns=cols) + exp.loc[exp.index[1], ("A", "B")] = np.nan + vals[1][0:2] = np.nan + res = DataFrame(vals, index=idx, columns=cols) + tm.assert_frame_equal(res, exp) + + +def test_loc_getitem_multiindex_tuple_level(): + # GH#27591 + lev1 = ["a", "b", "c"] + lev2 = [(0, 1), (1, 0)] + lev3 = [0, 1] + cols = MultiIndex.from_product([lev1, lev2, lev3], names=["x", "y", "z"]) + df = DataFrame(6, index=range(5), columns=cols) + + # the lev2[0] here should be treated as a single label, not as a sequence + # of labels + result = df.loc[:, (lev1[0], lev2[0], lev3[0])] + + # TODO: i think this actually should drop levels + expected = df.iloc[:, :1] + tm.assert_frame_equal(result, expected) + + alt = df.xs((lev1[0], lev2[0], lev3[0]), level=[0, 1, 2], axis=1) + tm.assert_frame_equal(alt, expected) + + # same thing on a Series + ser = df.iloc[0] + expected2 = ser.iloc[:1] + + alt2 = ser.xs((lev1[0], lev2[0], lev3[0]), level=[0, 1, 2], axis=0) + tm.assert_series_equal(alt2, expected2) + + result2 = ser.loc[lev1[0], lev2[0], lev3[0]] + assert result2 == 6 + + +def test_loc_getitem_nullable_index_with_duplicates(): + # GH#34497 + df = DataFrame( + data=np.array([[1, 2, 3, 4], [5, 6, 7, 8], [1, 2, pd.NA, pd.NA]]).T, + columns=["a", "b", "c"], + dtype="Int64", + ) + df2 = df.set_index("c") + assert df2.index.dtype == "Int64" + + res = df2.loc[1] + expected = Series([1, 5], index=df2.columns, dtype="Int64", name=1) + tm.assert_series_equal(res, expected) + + # pd.NA and duplicates in an object-dtype Index + df2.index = df2.index.astype(object) + res = df2.loc[1] + tm.assert_series_equal(res, expected) + + +@pytest.mark.parametrize("value", [300, np.uint16(300), np.int16(300)]) +def test_loc_setitem_uint8_upcast(value): + # GH#26049 + + df = DataFrame([1, 2, 3, 4], columns=["col1"], dtype="uint8") + with pytest.raises(TypeError, match="Invalid value"): + df.loc[2, "col1"] = value # value that can't be held in uint8 + + +@pytest.mark.parametrize( + "fill_val,exp_dtype", + [ + (Timestamp("2022-01-06"), "datetime64[ns]"), + (Timestamp("2022-01-07", tz="US/Eastern"), "datetime64[ns, US/Eastern]"), + ], +) +def test_loc_setitem_using_datetimelike_str_as_index(fill_val, exp_dtype): + data = ["2022-01-02", "2022-01-03", "2022-01-04", fill_val.date()] + index = DatetimeIndex(data, tz=fill_val.tz, dtype=exp_dtype) + df = DataFrame([10, 11, 12, 14], columns=["a"], index=index) + # adding new row using an unexisting datetime-like str index + df.loc["2022-01-08", "a"] = 13 + + data.append("2022-01-08") + expected_index = DatetimeIndex(data, dtype=exp_dtype) + tm.assert_index_equal(df.index, expected_index, exact=True) + + +def test_loc_set_int_dtype(): + # GH#23326 + df = DataFrame([list("abc")]) + df.loc[:, "col1"] = 5 + + expected = DataFrame({0: ["a"], 1: ["b"], 2: ["c"], "col1": [5]}) + tm.assert_frame_equal(df, expected) + + +@pytest.mark.filterwarnings(r"ignore:Period with BDay freq is deprecated:FutureWarning") +@pytest.mark.filterwarnings(r"ignore:PeriodDtype\[B\] is deprecated:FutureWarning") +def test_loc_periodindex_3_levels(): + # GH#24091 + p_index = PeriodIndex( + ["20181101 1100", "20181101 1200", "20181102 1300", "20181102 1400"], + name="datetime", + freq="B", + ) + mi_series = DataFrame( + [["A", "B", 1.0], ["A", "C", 2.0], ["Z", "Q", 3.0], ["W", "F", 4.0]], + index=p_index, + columns=["ONE", "TWO", "VALUES"], + ) + mi_series = mi_series.set_index(["ONE", "TWO"], append=True)["VALUES"] + assert mi_series.loc[(p_index[0], "A", "B")] == 1.0 + + +def test_loc_setitem_pyarrow_strings(): + # GH#52319 + pytest.importorskip("pyarrow") + df = DataFrame( + { + "strings": Series(["A", "B", "C"], dtype="string[pyarrow]"), + "ids": Series([True, True, False]), + } + ) + new_value = Series(["X", "Y"]) + df.loc[df.ids, "strings"] = new_value + + expected_df = DataFrame( + { + "strings": Series(["X", "Y", "C"], dtype="string[pyarrow]"), + "ids": Series([True, True, False]), + } + ) + + tm.assert_frame_equal(df, expected_df) + + +class TestLocSeries: + @pytest.mark.parametrize("val,expected", [(2**63 - 1, 3), (2**63, 4)]) + def test_loc_uint64(self, val, expected): + # see GH#19399 + ser = Series({2**63 - 1: 3, 2**63: 4}) + assert ser.loc[val] == expected + + def test_loc_getitem(self, string_series, datetime_series): + inds = string_series.index[[3, 4, 7]] + tm.assert_series_equal(string_series.loc[inds], string_series.reindex(inds)) + tm.assert_series_equal(string_series.iloc[5::2], string_series[5::2]) + + # slice with indices + d1, d2 = datetime_series.index[[5, 15]] + result = datetime_series.loc[d1:d2] + expected = datetime_series.truncate(d1, d2) + tm.assert_series_equal(result, expected) + + # boolean + mask = string_series > string_series.median() + tm.assert_series_equal(string_series.loc[mask], string_series[mask]) + + # ask for index value + assert datetime_series.loc[d1] == datetime_series[d1] + assert datetime_series.loc[d2] == datetime_series[d2] + + def test_loc_getitem_not_monotonic(self, datetime_series): + d1, d2 = datetime_series.index[[5, 15]] + + ts2 = datetime_series[::2].iloc[[1, 2, 0]] + + msg = r"Timestamp\('2000-01-10 00:00:00'\)" + with pytest.raises(KeyError, match=msg): + ts2.loc[d1:d2] + with pytest.raises(KeyError, match=msg): + ts2.loc[d1:d2] = 0 + + def test_loc_getitem_setitem_integer_slice_keyerrors(self): + ser = Series( + np.random.default_rng(2).standard_normal(10), index=list(range(0, 20, 2)) + ) + + # this is OK + cp = ser.copy() + cp.iloc[4:10] = 0 + assert (cp.iloc[4:10] == 0).all() + + # so is this + cp = ser.copy() + cp.iloc[3:11] = 0 + assert (cp.iloc[3:11] == 0).values.all() + + result = ser.iloc[2:6] + result2 = ser.loc[3:11] + expected = ser.reindex([4, 6, 8, 10]) + + tm.assert_series_equal(result, expected) + tm.assert_series_equal(result2, expected) + + # non-monotonic, raise KeyError + s2 = ser.iloc[list(range(5)) + list(range(9, 4, -1))] + msg = "non-monotonic index with a missing label 3" + with pytest.raises(KeyError, match=msg): + s2.loc[3:11] + with pytest.raises(KeyError, match=msg): + s2.loc[3:11] = 0 + + def test_loc_getitem_iterator(self, string_series): + idx = iter(string_series.index[:10]) + result = string_series.loc[idx] + tm.assert_series_equal(result, string_series[:10]) + + def test_loc_setitem_boolean(self, string_series): + mask = string_series > string_series.median() + + result = string_series.copy() + result.loc[mask] = 0 + expected = string_series + expected[mask] = 0 + tm.assert_series_equal(result, expected) + + def test_loc_setitem_corner(self, string_series): + inds = list(string_series.index[[5, 8, 12]]) + string_series.loc[inds] = 5 + msg = r"\['foo'\] not in index" + with pytest.raises(KeyError, match=msg): + string_series.loc[[*inds, "foo"]] = 5 + + def test_basic_setitem_with_labels(self, datetime_series): + indices = datetime_series.index[[5, 10, 15]] + + cp = datetime_series.copy() + exp = datetime_series.copy() + cp[indices] = 0 + exp.loc[indices] = 0 + tm.assert_series_equal(cp, exp) + + cp = datetime_series.copy() + exp = datetime_series.copy() + cp[indices[0] : indices[2]] = 0 + exp.loc[indices[0] : indices[2]] = 0 + tm.assert_series_equal(cp, exp) + + def test_loc_setitem_listlike_of_ints(self): + # integer indexes, be careful + ser = Series( + np.random.default_rng(2).standard_normal(10), index=list(range(0, 20, 2)) + ) + inds = [0, 4, 6] + arr_inds = np.array([0, 4, 6]) + + cp = ser.copy() + exp = ser.copy() + ser[inds] = 0 + ser.loc[inds] = 0 + tm.assert_series_equal(cp, exp) + + cp = ser.copy() + exp = ser.copy() + ser[arr_inds] = 0 + ser.loc[arr_inds] = 0 + tm.assert_series_equal(cp, exp) + + inds_notfound = [0, 4, 5, 6] + arr_inds_notfound = np.array([0, 4, 5, 6]) + msg = r"\[5\] not in index" + with pytest.raises(KeyError, match=msg): + ser[inds_notfound] = 0 + with pytest.raises(Exception, match=msg): + ser[arr_inds_notfound] = 0 + + def test_loc_setitem_dt64tz_values(self): + # GH#12089 + ser = Series( + date_range("2011-01-01", periods=3, tz="US/Eastern"), + index=["a", "b", "c"], + ) + s2 = ser.copy() + expected = Timestamp("2011-01-03", tz="US/Eastern") + s2.loc["a"] = expected + result = s2.loc["a"] + assert result == expected + + s2 = ser.copy() + s2.iloc[0] = expected + result = s2.iloc[0] + assert result == expected + + s2 = ser.copy() + s2["a"] = expected + result = s2["a"] + assert result == expected + + @pytest.mark.parametrize("array_fn", [np.array, pd.array, list, tuple]) + @pytest.mark.parametrize("size", [0, 4, 5, 6]) + def test_loc_iloc_setitem_with_listlike(self, size, array_fn): + # GH37748 + # testing insertion, in a Series of size N (here 5), of a listlike object + # of size 0, N-1, N, N+1 + + arr = array_fn([0] * size) + expected = Series([arr, 0, 0, 0, 0], index=list("abcde"), dtype=object) + + ser = Series(0, index=list("abcde"), dtype=object) + ser.loc["a"] = arr + tm.assert_series_equal(ser, expected) + + ser = Series(0, index=list("abcde"), dtype=object) + ser.iloc[0] = arr + tm.assert_series_equal(ser, expected) + + @pytest.mark.parametrize("indexer", [IndexSlice["A", :], ("A", slice(None))]) + def test_loc_series_getitem_too_many_dimensions(self, indexer): + # GH#35349 + ser = Series( + index=MultiIndex.from_tuples([("A", "0"), ("A", "1"), ("B", "0")]), + data=[21, 22, 23], + ) + msg = "Too many indexers" + with pytest.raises(IndexingError, match=msg): + ser.loc[indexer, :] + + with pytest.raises(IndexingError, match=msg): + ser.loc[indexer, :] = 1 + + def test_loc_setitem(self, string_series): + inds = string_series.index[[3, 4, 7]] + + result = string_series.copy() + result.loc[inds] = 5 + + expected = string_series.copy() + expected.iloc[[3, 4, 7]] = 5 + tm.assert_series_equal(result, expected) + + result.iloc[5:10] = 10 + expected[5:10] = 10 + tm.assert_series_equal(result, expected) + + # set slice with indices + d1, d2 = string_series.index[[5, 15]] + result.loc[d1:d2] = 6 + expected[5:16] = 6 # because it's inclusive + tm.assert_series_equal(result, expected) + + # set index value + string_series.loc[d1] = 4 + string_series.loc[d2] = 6 + assert string_series[d1] == 4 + assert string_series[d2] == 6 + + @pytest.mark.parametrize("dtype", ["object", "string"]) + def test_loc_assign_dict_to_row(self, dtype): + # GH41044 + df = DataFrame({"A": ["abc", "def"], "B": ["ghi", "jkl"]}, dtype=dtype) + df.loc[0, :] = {"A": "newA", "B": "newB"} + + expected = DataFrame({"A": ["newA", "def"], "B": ["newB", "jkl"]}, dtype=dtype) + + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_dict_timedelta_multiple_set(self): + # GH 16309 + result = DataFrame(columns=["time", "value"]) + result.loc[1] = {"time": Timedelta(6, unit="s"), "value": "foo"} + result.loc[1] = {"time": Timedelta(6, unit="s"), "value": "foo"} + expected = DataFrame( + [[Timedelta(6, unit="s"), "foo"]], columns=["time", "value"], index=[1] + ) + tm.assert_frame_equal(result, expected) + + def test_loc_set_multiple_items_in_multiple_new_columns(self): + # GH 25594 + df = DataFrame(index=[1, 2], columns=["a"]) + df.loc[1, ["b", "c"]] = [6, 7] + + expected = DataFrame( + { + "a": Series([np.nan, np.nan], dtype="object"), + "b": [6, np.nan], + "c": [7, np.nan], + }, + index=[1, 2], + ) + + tm.assert_frame_equal(df, expected) + + def test_getitem_loc_str_periodindex(self): + # GH#33964 + msg = "Period with BDay freq is deprecated" + with tm.assert_produces_warning(FutureWarning, match=msg): + index = pd.period_range(start="2000", periods=20, freq="B") + series = Series(range(20), index=index) + assert series.loc["2000-01-14"] == 9 + + def test_loc_nonunique_masked_index(self): + # GH 57027 + ids = list(range(11)) + index = Index(ids * 1000, dtype="Int64") + df = DataFrame({"val": np.arange(len(index), dtype=np.intp)}, index=index) + result = df.loc[ids] + expected = DataFrame( + {"val": index.argsort(kind="stable").astype(np.intp)}, + index=Index(np.array(ids).repeat(1000), dtype="Int64"), + ) + tm.assert_frame_equal(result, expected) + + def test_loc_index_alignment_for_series(self): + # GH #56024 + df = DataFrame({"a": [1, 2], "b": [3, 4]}) + other = Series([200, 999], index=[1, 0]) + df.loc[:, "a"] = other + expected = DataFrame({"a": [999, 200], "b": [3, 4]}) + tm.assert_frame_equal(expected, df) + + @pytest.mark.parametrize("dtype", ["str", object]) + def test_loc_reindexing_of_empty_index(self, dtype): + # GH 57735 + df = DataFrame(index=[1, 1, 2, 2], data=["1", "1", "2", "2"], dtype=dtype) + df.loc[Series([False] * 4, index=df.index, name=0), 0] = df[0] + expected = DataFrame(index=[1, 1, 2, 2], data=["1", "1", "2", "2"], dtype=dtype) + tm.assert_frame_equal(df, expected) + + @pytest.mark.parametrize( + "df, row_index, col_index, expected_df", + [ + [ + DataFrame([[1, 2, 3], [4, 5, 6]], columns=list("ABC")), + slice(0, 3), + ["A", "B", "C"], + DataFrame([[10, 10, 10], [20, 20, 20]], columns=list("ABC")), + ], + [ + DataFrame([[1, 2, 3], [4, 5, 6]], columns=list("ABC")), + slice(None), + ["A", "B", "C"], + DataFrame([[10, 10, 10], [20, 20, 20]], columns=list("ABC")), + ], + [ + DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=list("ABC")), + [True, True, True], + ["A", "B", "C"], + DataFrame( + [[10, 10, 10], [20, 20, 20], [30, 30, 30]], columns=list("ABC") + ), + ], + [ + DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=list("ABC")), + slice(0, 4), + ["A", "B", "C"], + DataFrame( + [[10, 10, 10], [20, 20, 20], [30, 30, 30]], columns=list("ABC") + ), + ], + [ + DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=list("ABC")), + slice(None), + slice("A", "C"), + DataFrame( + [[10, 10, 10], [20, 20, 20], [30, 30, 30]], columns=list("ABC") + ), + ], + [ + DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=list("ABC")), + slice(None), + Series( + { + "A": True, + "C": False, + "B": True, + } + ), + DataFrame([[10, 10, 3], [20, 20, 6], [30, 30, 9]], columns=list("ABC")), + ], + ], + ) + def test_loc_set_series_to_multiple_columns( + self, df, row_index, col_index, expected_df + ): + # GH 59933 + df.loc[row_index, col_index] = Series([10, 20, 30]) + tm.assert_frame_equal(df, expected_df) + + def test_loc_setitem_matching_index(self): + # GH 25548 + s = Series(0.0, index=list("abcd")) + s1 = Series(1.0, index=list("ab")) + s2 = Series(2.0, index=list("xy")) + + # Test matching indices + s.loc[["a", "b"]] = s1 + + result = s[["a", "b"]] + expected = s1 + tm.assert_series_equal(result, expected) + + # Test unmatched indices + s.loc[["a", "b"]] = s2 + + result = s[["a", "b"]] + expected = Series([np.nan, np.nan], index=["a", "b"]) + tm.assert_series_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_na_indexing.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_na_indexing.py new file mode 100644 index 0000000000000000000000000000000000000000..d4ad350a64e4dcd3310ddcdbdf17892c5220af24 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_na_indexing.py @@ -0,0 +1,74 @@ +import pytest + +import pandas as pd +import pandas._testing as tm + + +@pytest.mark.parametrize( + "values, dtype", + [ + ([], "object"), + ([1, 2, 3], "int64"), + ([1.0, 2.0, 3.0], "float64"), + (["a", "b", "c"], "object"), + (["a", "b", "c"], "string"), + ([1, 2, 3], "datetime64[ns]"), + ([1, 2, 3], "datetime64[ns, CET]"), + ([1, 2, 3], "timedelta64[ns]"), + (["2000", "2001", "2002"], "Period[D]"), + ([1, 0, 3], "Sparse"), + ([pd.Interval(0, 1), pd.Interval(1, 2), pd.Interval(3, 4)], "interval"), + ], +) +@pytest.mark.parametrize( + "mask", [[True, False, False], [True, True, True], [False, False, False]] +) +@pytest.mark.parametrize("indexer_class", [list, pd.array, pd.Index, pd.Series]) +@pytest.mark.parametrize("frame", [True, False]) +def test_series_mask_boolean(values, dtype, mask, indexer_class, frame): + # In case len(values) < 3 + index = ["a", "b", "c"][: len(values)] + mask = mask[: len(values)] + + obj = pd.Series(values, dtype=dtype, index=index) + if frame: + if len(values) == 0: + # Otherwise obj is an empty DataFrame with shape (0, 1) + obj = pd.DataFrame(dtype=dtype, index=index) + else: + obj = obj.to_frame() + + if indexer_class is pd.array: + mask = pd.array(mask, dtype="boolean") + elif indexer_class is pd.Series: + mask = pd.Series(mask, index=obj.index, dtype="boolean") + else: + mask = indexer_class(mask) + + expected = obj[mask] + + result = obj[mask] + tm.assert_equal(result, expected) + + if indexer_class is pd.Series: + msg = "iLocation based boolean indexing cannot use an indexable as a mask" + with pytest.raises(ValueError, match=msg): + result = obj.iloc[mask] + else: + result = obj.iloc[mask] + tm.assert_equal(result, expected) + + result = obj.loc[mask] + tm.assert_equal(result, expected) + + +def test_na_treated_as_false(frame_or_series, indexer_sli): + # https://github.com/pandas-dev/pandas/issues/31503 + obj = frame_or_series([1, 2, 3]) + + mask = pd.array([True, False, None], dtype="boolean") + + result = indexer_sli(obj)[mask] + expected = indexer_sli(obj)[mask.fillna(False)] + + tm.assert_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_partial.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_partial.py new file mode 100644 index 0000000000000000000000000000000000000000..8b0666b3be5c125c0c7177d789a9fdfaa59a0a28 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_partial.py @@ -0,0 +1,694 @@ +""" +test setting *parts* of objects both positionally and label based + +TODO: these should be split among the indexer tests +""" + +import numpy as np +import pytest + +import pandas as pd +from pandas import ( + DataFrame, + Index, + Period, + Series, + Timestamp, + date_range, + period_range, +) +import pandas._testing as tm + + +class TestEmptyFrameSetitemExpansion: + def test_empty_frame_setitem_index_name_retained(self): + # GH#31368 empty frame has non-None index.name -> retained + df = DataFrame({}, index=pd.RangeIndex(0, name="df_index")) + series = Series(1.23, index=pd.RangeIndex(4, name="series_index")) + + df["series"] = series + expected = DataFrame( + {"series": [1.23] * 4}, + index=pd.RangeIndex(4, name="df_index"), + columns=Index(["series"]), + ) + + tm.assert_frame_equal(df, expected) + + def test_empty_frame_setitem_index_name_inherited(self): + # GH#36527 empty frame has None index.name -> not retained + df = DataFrame() + series = Series(1.23, index=pd.RangeIndex(4, name="series_index")) + df["series"] = series + expected = DataFrame( + {"series": [1.23] * 4}, + index=pd.RangeIndex(4, name="series_index"), + columns=Index(["series"]), + ) + tm.assert_frame_equal(df, expected) + + def test_loc_setitem_zerolen_series_columns_align(self): + # columns will align + df = DataFrame(columns=["A", "B"]) + df.loc[0] = Series(1, index=range(4)) + expected = DataFrame(columns=["A", "B"], index=[0], dtype=np.float64) + tm.assert_frame_equal(df, expected) + + # columns will align + df = DataFrame(columns=["A", "B"]) + df.loc[0] = Series(1, index=["B"]) + + exp = DataFrame([[np.nan, 1]], columns=["A", "B"], index=[0], dtype="float64") + tm.assert_frame_equal(df, exp) + + def test_loc_setitem_zerolen_list_length_must_match_columns(self): + # list-like must conform + df = DataFrame(columns=["A", "B"]) + + msg = "cannot set a row with mismatched columns" + with pytest.raises(ValueError, match=msg): + df.loc[0] = [1, 2, 3] + + df = DataFrame(columns=["A", "B"]) + df.loc[3] = [6, 7] # length matches len(df.columns) --> OK! + + exp = DataFrame([[6, 7]], index=[3], columns=["A", "B"], dtype=np.int64) + tm.assert_frame_equal(df, exp) + + def test_partial_set_empty_frame(self): + # partially set with an empty object + # frame + df = DataFrame() + + msg = "cannot set a frame with no defined columns" + + with pytest.raises(ValueError, match=msg): + df.loc[1] = 1 + + with pytest.raises(ValueError, match=msg): + df.loc[1] = Series([1], index=["foo"]) + + msg = "cannot set a frame with no defined index and a scalar" + with pytest.raises(ValueError, match=msg): + df.loc[:, 1] = 1 + + def test_partial_set_empty_frame2(self): + # these work as they don't really change + # anything but the index + # GH#5632 + expected = DataFrame(columns=Index(["foo"]), index=Index([], dtype="object")) + + df = DataFrame(index=Index([], dtype="object")) + df["foo"] = Series([], dtype="object") + + tm.assert_frame_equal(df, expected) + + df = DataFrame(index=Index([])) + df["foo"] = Series(df.index) + + tm.assert_frame_equal(df, expected) + + df = DataFrame(index=Index([])) + df["foo"] = df.index + + tm.assert_frame_equal(df, expected) + + def test_partial_set_empty_frame3(self): + expected = DataFrame(columns=Index(["foo"]), index=Index([], dtype="int64")) + expected["foo"] = expected["foo"].astype("float64") + + df = DataFrame(index=Index([], dtype="int64")) + df["foo"] = [] + + tm.assert_frame_equal(df, expected) + + df = DataFrame(index=Index([], dtype="int64")) + df["foo"] = Series(np.arange(len(df)), dtype="float64") + + tm.assert_frame_equal(df, expected) + + def test_partial_set_empty_frame4(self): + df = DataFrame(index=Index([], dtype="int64")) + df["foo"] = range(len(df)) + + expected = DataFrame(columns=Index(["foo"]), index=Index([], dtype="int64")) + # range is int-dtype-like, so we get int64 dtype + expected["foo"] = expected["foo"].astype("int64") + tm.assert_frame_equal(df, expected) + + def test_partial_set_empty_frame5(self): + df = DataFrame() + tm.assert_index_equal(df.columns, pd.RangeIndex(0)) + df2 = DataFrame() + df2[1] = Series([1], index=["foo"]) + df.loc[:, 1] = Series([1], index=["foo"]) + tm.assert_frame_equal(df, DataFrame([[1]], index=["foo"], columns=[1])) + tm.assert_frame_equal(df, df2) + + def test_partial_set_empty_frame_no_index(self): + # no index to start + expected = DataFrame({0: Series(1, index=range(4))}, columns=["A", "B", 0]) + + df = DataFrame(columns=["A", "B"]) + df[0] = Series(1, index=range(4)) + tm.assert_frame_equal(df, expected) + + df = DataFrame(columns=["A", "B"]) + df.loc[:, 0] = Series(1, index=range(4)) + tm.assert_frame_equal(df, expected) + + def test_partial_set_empty_frame_row(self): + # GH#5720, GH#5744 + # don't create rows when empty + expected = DataFrame(columns=["A", "B", "New"], index=Index([], dtype="int64")) + expected["A"] = expected["A"].astype("int64") + expected["B"] = expected["B"].astype("float64") + expected["New"] = expected["New"].astype("float64") + + df = DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]}) + y = df[df.A > 5] + y["New"] = np.nan + tm.assert_frame_equal(y, expected) + + expected = DataFrame(columns=["a", "b", "c c", "d"]) + expected["d"] = expected["d"].astype("int64") + df = DataFrame(columns=["a", "b", "c c"]) + df["d"] = 3 + tm.assert_frame_equal(df, expected) + tm.assert_series_equal(df["c c"], Series(name="c c", dtype=object)) + + # reindex columns is ok + df = DataFrame({"A": [1, 2, 3], "B": [1.2, 4.2, 5.2]}) + y = df[df.A > 5] + result = y.reindex(columns=["A", "B", "C"]) + expected = DataFrame(columns=["A", "B", "C"]) + expected["A"] = expected["A"].astype("int64") + expected["B"] = expected["B"].astype("float64") + expected["C"] = expected["C"].astype("float64") + tm.assert_frame_equal(result, expected) + + def test_partial_set_empty_frame_set_series(self): + # GH#5756 + # setting with empty Series + df = DataFrame(Series(dtype=object)) + expected = DataFrame({0: Series(dtype=object)}) + tm.assert_frame_equal(df, expected) + + df = DataFrame(Series(name="foo", dtype=object)) + expected = DataFrame({"foo": Series(dtype=object)}) + tm.assert_frame_equal(df, expected) + + def test_partial_set_empty_frame_empty_copy_assignment(self): + # GH#5932 + # copy on empty with assignment fails + df = DataFrame(index=[0]) + df = df.copy() + df["a"] = 0 + expected = DataFrame(0, index=[0], columns=Index(["a"])) + tm.assert_frame_equal(df, expected) + + def test_partial_set_empty_frame_empty_consistencies(self, using_infer_string): + # GH#6171 + # consistency on empty frames + df = DataFrame(columns=["x", "y"]) + df["x"] = [1, 2] + expected = DataFrame({"x": [1, 2], "y": [np.nan, np.nan]}) + tm.assert_frame_equal(df, expected, check_dtype=False) + + df = DataFrame(columns=["x", "y"]) + df["x"] = ["1", "2"] + expected = DataFrame( + { + "x": Series( + ["1", "2"], + dtype=object if not using_infer_string else "str", + ), + "y": Series([np.nan, np.nan], dtype=object), + } + ) + tm.assert_frame_equal(df, expected) + + df = DataFrame(columns=["x", "y"]) + df.loc[0, "x"] = 1 + expected = DataFrame({"x": [1], "y": [np.nan]}) + tm.assert_frame_equal(df, expected, check_dtype=False) + + +class TestPartialSetting: + def test_partial_setting(self): + # GH2578, allow ix and friends to partially set + + # series + s_orig = Series([1, 2, 3]) + + s = s_orig.copy() + s[5] = 5 + expected = Series([1, 2, 3, 5], index=[0, 1, 2, 5]) + tm.assert_series_equal(s, expected) + + s = s_orig.copy() + s.loc[5] = 5 + expected = Series([1, 2, 3, 5], index=[0, 1, 2, 5]) + tm.assert_series_equal(s, expected) + + s = s_orig.copy() + s[5] = 5.0 + expected = Series([1, 2, 3, 5.0], index=[0, 1, 2, 5]) + tm.assert_series_equal(s, expected) + + s = s_orig.copy() + s.loc[5] = 5.0 + expected = Series([1, 2, 3, 5.0], index=[0, 1, 2, 5]) + tm.assert_series_equal(s, expected) + + # iloc/iat raise + s = s_orig.copy() + + msg = "iloc cannot enlarge its target object" + with pytest.raises(IndexError, match=msg): + s.iloc[3] = 5.0 + + msg = "index 3 is out of bounds for axis 0 with size 3" + with pytest.raises(IndexError, match=msg): + s.iat[3] = 5.0 + + @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning") + def test_partial_setting_frame(self): + df_orig = DataFrame( + np.arange(6).reshape(3, 2), columns=["A", "B"], dtype="int64" + ) + + # iloc/iat raise + df = df_orig.copy() + + msg = "iloc cannot enlarge its target object" + with pytest.raises(IndexError, match=msg): + df.iloc[4, 2] = 5.0 + + msg = "index 2 is out of bounds for axis 0 with size 2" + with pytest.raises(IndexError, match=msg): + df.iat[4, 2] = 5.0 + + # row setting where it exists + expected = DataFrame({"A": [0, 4, 4], "B": [1, 5, 5]}) + df = df_orig.copy() + df.iloc[1] = df.iloc[2] + tm.assert_frame_equal(df, expected) + + expected = DataFrame({"A": [0, 4, 4], "B": [1, 5, 5]}) + df = df_orig.copy() + df.loc[1] = df.loc[2] + tm.assert_frame_equal(df, expected) + + # like 2578, partial setting with dtype preservation + expected = DataFrame({"A": [0, 2, 4, 4], "B": [1, 3, 5, 5]}) + df = df_orig.copy() + df.loc[3] = df.loc[2] + tm.assert_frame_equal(df, expected) + + # single dtype frame, overwrite + expected = DataFrame({"A": [0, 2, 4], "B": [0, 2, 4]}) + df = df_orig.copy() + df.loc[:, "B"] = df.loc[:, "A"] + tm.assert_frame_equal(df, expected) + + # mixed dtype frame, overwrite + expected = DataFrame({"A": [0, 2, 4], "B": Series([0.0, 2.0, 4.0])}) + df = df_orig.copy() + df["B"] = df["B"].astype(np.float64) + # as of 2.0, df.loc[:, "B"] = ... attempts (and here succeeds) at + # setting inplace + df.loc[:, "B"] = df.loc[:, "A"] + tm.assert_frame_equal(df, expected) + + # single dtype frame, partial setting + expected = df_orig.copy() + expected["C"] = df["A"] + df = df_orig.copy() + df.loc[:, "C"] = df.loc[:, "A"] + tm.assert_frame_equal(df, expected) + + # mixed frame, partial setting + expected = df_orig.copy() + expected["C"] = df["A"] + df = df_orig.copy() + df.loc[:, "C"] = df.loc[:, "A"] + tm.assert_frame_equal(df, expected) + + def test_partial_setting2(self): + # GH 8473 + dates = date_range("1/1/2000", periods=8) + df_orig = DataFrame( + np.random.default_rng(2).standard_normal((8, 4)), + index=dates, + columns=["A", "B", "C", "D"], + ) + + expected = pd.concat( + [df_orig, DataFrame({"A": 7}, index=dates[-1:] + dates.freq)], sort=True + ) + df = df_orig.copy() + df.loc[dates[-1] + dates.freq, "A"] = 7 + tm.assert_frame_equal(df, expected) + df = df_orig.copy() + df.at[dates[-1] + dates.freq, "A"] = 7 + tm.assert_frame_equal(df, expected) + + exp_other = DataFrame({0: 7}, index=dates[-1:] + dates.freq) + expected = pd.concat([df_orig, exp_other], axis=1) + + df = df_orig.copy() + df.loc[dates[-1] + dates.freq, 0] = 7 + tm.assert_frame_equal(df, expected) + df = df_orig.copy() + df.at[dates[-1] + dates.freq, 0] = 7 + tm.assert_frame_equal(df, expected) + + def test_partial_setting_mixed_dtype(self): + # in a mixed dtype environment, try to preserve dtypes + # by appending + df = DataFrame([[True, 1], [False, 2]], columns=["female", "fitness"]) + + s = df.loc[1].copy() + s.name = 2 + expected = pd.concat([df, DataFrame(s).T.infer_objects()]) + + df.loc[2] = df.loc[1] + tm.assert_frame_equal(df, expected) + + def test_series_partial_set(self): + # partial set with new index + # Regression from GH4825 + ser = Series([0.1, 0.2], index=[1, 2]) + + # loc equiv to .reindex + expected = Series([np.nan, 0.2, np.nan], index=[3, 2, 3]) + with pytest.raises(KeyError, match=r"not in index"): + ser.loc[[3, 2, 3]] + + result = ser.reindex([3, 2, 3]) + tm.assert_series_equal(result, expected, check_index_type=True) + + expected = Series([np.nan, 0.2, np.nan, np.nan], index=[3, 2, 3, "x"]) + with pytest.raises(KeyError, match="not in index"): + ser.loc[[3, 2, 3, "x"]] + + result = ser.reindex([3, 2, 3, "x"]) + tm.assert_series_equal(result, expected, check_index_type=True) + + expected = Series([0.2, 0.2, 0.1], index=[2, 2, 1]) + result = ser.loc[[2, 2, 1]] + tm.assert_series_equal(result, expected, check_index_type=True) + + expected = Series([0.2, 0.2, np.nan, 0.1], index=[2, 2, "x", 1]) + with pytest.raises(KeyError, match="not in index"): + ser.loc[[2, 2, "x", 1]] + + result = ser.reindex([2, 2, "x", 1]) + tm.assert_series_equal(result, expected, check_index_type=True) + + # raises as nothing is in the index + msg = ( + rf"\"None of \[Index\(\[3, 3, 3\], dtype='{np.dtype(int)}'\)\] " + r"are in the \[index\]\"" + ) + with pytest.raises(KeyError, match=msg): + ser.loc[[3, 3, 3]] + + expected = Series([0.2, 0.2, np.nan], index=[2, 2, 3]) + with pytest.raises(KeyError, match="not in index"): + ser.loc[[2, 2, 3]] + + result = ser.reindex([2, 2, 3]) + tm.assert_series_equal(result, expected, check_index_type=True) + + s = Series([0.1, 0.2, 0.3], index=[1, 2, 3]) + expected = Series([0.3, np.nan, np.nan], index=[3, 4, 4]) + with pytest.raises(KeyError, match="not in index"): + s.loc[[3, 4, 4]] + + result = s.reindex([3, 4, 4]) + tm.assert_series_equal(result, expected, check_index_type=True) + + s = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4]) + expected = Series([np.nan, 0.3, 0.3], index=[5, 3, 3]) + with pytest.raises(KeyError, match="not in index"): + s.loc[[5, 3, 3]] + + result = s.reindex([5, 3, 3]) + tm.assert_series_equal(result, expected, check_index_type=True) + + s = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4]) + expected = Series([np.nan, 0.4, 0.4], index=[5, 4, 4]) + with pytest.raises(KeyError, match="not in index"): + s.loc[[5, 4, 4]] + + result = s.reindex([5, 4, 4]) + tm.assert_series_equal(result, expected, check_index_type=True) + + s = Series([0.1, 0.2, 0.3, 0.4], index=[4, 5, 6, 7]) + expected = Series([0.4, np.nan, np.nan], index=[7, 2, 2]) + with pytest.raises(KeyError, match="not in index"): + s.loc[[7, 2, 2]] + + result = s.reindex([7, 2, 2]) + tm.assert_series_equal(result, expected, check_index_type=True) + + s = Series([0.1, 0.2, 0.3, 0.4], index=[1, 2, 3, 4]) + expected = Series([0.4, np.nan, np.nan], index=[4, 5, 5]) + with pytest.raises(KeyError, match="not in index"): + s.loc[[4, 5, 5]] + + result = s.reindex([4, 5, 5]) + tm.assert_series_equal(result, expected, check_index_type=True) + + # iloc + expected = Series([0.2, 0.2, 0.1, 0.1], index=[2, 2, 1, 1]) + result = ser.iloc[[1, 1, 0, 0]] + tm.assert_series_equal(result, expected, check_index_type=True) + + def test_series_partial_set_with_name(self): + # GH 11497 + + idx = Index([1, 2], dtype="int64", name="idx") + ser = Series([0.1, 0.2], index=idx, name="s") + + # loc + with pytest.raises(KeyError, match=r"\[3\] not in index"): + ser.loc[[3, 2, 3]] + + with pytest.raises(KeyError, match=r"not in index"): + ser.loc[[3, 2, 3, "x"]] + + exp_idx = Index([2, 2, 1], dtype="int64", name="idx") + expected = Series([0.2, 0.2, 0.1], index=exp_idx, name="s") + result = ser.loc[[2, 2, 1]] + tm.assert_series_equal(result, expected, check_index_type=True) + + with pytest.raises(KeyError, match=r"\['x'\] not in index"): + ser.loc[[2, 2, "x", 1]] + + # raises as nothing is in the index + msg = ( + rf"\"None of \[Index\(\[3, 3, 3\], dtype='{np.dtype(int)}', " + r"name='idx'\)\] are in the \[index\]\"" + ) + with pytest.raises(KeyError, match=msg): + ser.loc[[3, 3, 3]] + + with pytest.raises(KeyError, match="not in index"): + ser.loc[[2, 2, 3]] + + idx = Index([1, 2, 3], dtype="int64", name="idx") + with pytest.raises(KeyError, match="not in index"): + Series([0.1, 0.2, 0.3], index=idx, name="s").loc[[3, 4, 4]] + + idx = Index([1, 2, 3, 4], dtype="int64", name="idx") + with pytest.raises(KeyError, match="not in index"): + Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[5, 3, 3]] + + idx = Index([1, 2, 3, 4], dtype="int64", name="idx") + with pytest.raises(KeyError, match="not in index"): + Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[5, 4, 4]] + + idx = Index([4, 5, 6, 7], dtype="int64", name="idx") + with pytest.raises(KeyError, match="not in index"): + Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[7, 2, 2]] + + idx = Index([1, 2, 3, 4], dtype="int64", name="idx") + with pytest.raises(KeyError, match="not in index"): + Series([0.1, 0.2, 0.3, 0.4], index=idx, name="s").loc[[4, 5, 5]] + + # iloc + exp_idx = Index([2, 2, 1, 1], dtype="int64", name="idx") + expected = Series([0.2, 0.2, 0.1, 0.1], index=exp_idx, name="s") + result = ser.iloc[[1, 1, 0, 0]] + tm.assert_series_equal(result, expected, check_index_type=True) + + @pytest.mark.parametrize("key", [100, 100.0]) + def test_setitem_with_expansion_numeric_into_datetimeindex(self, key): + # GH#4940 inserting non-strings + orig = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + columns=Index(list("ABCD"), dtype=object), + index=date_range("2000-01-01", periods=10, freq="B"), + ) + df = orig.copy() + + df.loc[key, :] = df.iloc[0] + ex_index = Index([*list(orig.index), key], dtype=object, name=orig.index.name) + ex_data = np.concatenate([orig.values, df.iloc[[0]].values], axis=0) + expected = DataFrame(ex_data, index=ex_index, columns=orig.columns) + + tm.assert_frame_equal(df, expected) + + def test_partial_set_invalid(self): + # GH 4940 + # allow only setting of 'valid' values + + orig = DataFrame( + np.random.default_rng(2).standard_normal((10, 4)), + columns=Index(list("ABCD"), dtype=object), + index=date_range("2000-01-01", periods=10, freq="B"), + ) + + # allow object conversion here + df = orig.copy() + df.loc["a", :] = df.iloc[0] + ser = Series(df.iloc[0], name="a") + exp = pd.concat([orig, DataFrame(ser).T.infer_objects()]) + tm.assert_frame_equal(df, exp) + tm.assert_index_equal(df.index, Index([*orig.index.tolist(), "a"])) + assert df.index.dtype == "object" + + @pytest.mark.parametrize( + "idx,labels,expected_idx", + [ + ( + period_range(start="2000", periods=20, freq="D"), + ["2000-01-04", "2000-01-08", "2000-01-12"], + [ + Period("2000-01-04", freq="D"), + Period("2000-01-08", freq="D"), + Period("2000-01-12", freq="D"), + ], + ), + ( + date_range(start="2000", periods=20, freq="D", unit="s"), + ["2000-01-04", "2000-01-08", "2000-01-12"], + [ + Timestamp("2000-01-04").as_unit("s"), + Timestamp("2000-01-08").as_unit("s"), + Timestamp("2000-01-12").as_unit("s"), + ], + ), + ( + pd.timedelta_range(start="1 day", periods=20), + ["4D", "8D", "12D"], + [pd.Timedelta("4 day"), pd.Timedelta("8 day"), pd.Timedelta("12 day")], + ), + ], + ) + def test_loc_with_list_of_strings_representing_datetimes( + self, idx, labels, expected_idx, frame_or_series + ): + # GH 11278 + obj = frame_or_series(range(20), index=idx) + + expected_value = [3, 7, 11] + expected = frame_or_series(expected_value, expected_idx) + + tm.assert_equal(expected, obj.loc[labels]) + if frame_or_series is Series: + tm.assert_series_equal(expected, obj[labels]) + + @pytest.mark.parametrize( + "idx,labels", + [ + ( + period_range(start="2000", periods=20, freq="D"), + ["2000-01-04", "2000-01-30"], + ), + ( + date_range(start="2000", periods=20, freq="D"), + ["2000-01-04", "2000-01-30"], + ), + (pd.timedelta_range(start="1 day", periods=20), ["3 day", "30 day"]), + ], + ) + def test_loc_with_list_of_strings_representing_datetimes_missing_value( + self, idx, labels + ): + # GH 11278 + ser = Series(range(20), index=idx) + df = DataFrame(range(20), index=idx) + msg = r"not in index" + + with pytest.raises(KeyError, match=msg): + ser.loc[labels] + with pytest.raises(KeyError, match=msg): + ser[labels] + with pytest.raises(KeyError, match=msg): + df.loc[labels] + + @pytest.mark.parametrize( + "idx,labels,msg", + [ + ( + period_range(start="2000", periods=20, freq="D"), + Index(["4D", "8D"], dtype=object), + ( + r"None of \[Index\(\['4D', '8D'\], dtype='object'\)\] " + r"are in the \[index\]" + ), + ), + ( + date_range(start="2000", periods=20, freq="D"), + Index(["4D", "8D"], dtype=object), + ( + r"None of \[Index\(\['4D', '8D'\], dtype='object'\)\] " + r"are in the \[index\]" + ), + ), + ( + pd.timedelta_range(start="1 day", periods=20), + Index(["2000-01-04", "2000-01-08"], dtype=object), + ( + r"None of \[Index\(\['2000-01-04', '2000-01-08'\], " + r"dtype='object'\)\] are in the \[index\]" + ), + ), + ], + ) + def test_loc_with_list_of_strings_representing_datetimes_not_matched_type( + self, idx, labels, msg + ): + # GH 11278 + ser = Series(range(20), index=idx) + df = DataFrame(range(20), index=idx) + + with pytest.raises(KeyError, match=msg): + ser.loc[labels] + with pytest.raises(KeyError, match=msg): + ser[labels] + with pytest.raises(KeyError, match=msg): + df.loc[labels] + + +class TestStringSlicing: + def test_slice_irregular_datetime_index_with_nan(self): + # GH36953 + index = pd.to_datetime(["2012-01-01", "2012-01-02", "2012-01-03", None]) + df = DataFrame(range(len(index)), index=index) + expected = DataFrame(range(len(index[:3])), index=index[:3]) + with pytest.raises(KeyError, match="non-existing keys is not allowed"): + # Upper bound is not in index (which is unordered) + # GH53983 + # GH37819 + df["2012-01-01":"2012-01-04"] + # Need this precision for right bound since the right slice + # bound is "rounded" up to the largest timepoint smaller than + # the next "resolution"-step of the provided point. + # e.g. 2012-01-03 is rounded up to 2012-01-04 - 1ns + result = df["2012-01-01":"2012-01-03 00:00:00.000000000"] + tm.assert_frame_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/indexing/test_scalar.py b/lib/python3.11/site-packages/pandas/tests/indexing/test_scalar.py new file mode 100644 index 0000000000000000000000000000000000000000..730fe584d7f0772c4b3b505f788cf997606ca298 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/indexing/test_scalar.py @@ -0,0 +1,318 @@ +"""test scalar indexing, including at and iat""" + +from datetime import ( + datetime, + timedelta, +) +import itertools + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + Index, + Series, + Timedelta, + Timestamp, + date_range, +) +import pandas._testing as tm + + +def generate_indices(f, values=False): + """ + generate the indices + if values is True , use the axis values + is False, use the range + """ + axes = f.axes + if values: + axes = (list(range(len(ax))) for ax in axes) + + return itertools.product(*axes) + + +class TestScalar: + @pytest.mark.parametrize("dtype", [np.int64, np.uint64]) + def test_iat_set_ints(self, dtype, frame_or_series): + f = frame_or_series(range(3), index=Index([0, 1, 2], dtype=dtype)) + indices = generate_indices(f, True) + for i in indices: + f.iat[i] = 1 + expected = f.values[i] + tm.assert_almost_equal(expected, 1) + + @pytest.mark.parametrize( + "index", + [ + Index(list("abcd"), dtype=object), + date_range("20130101", periods=4), + Index(range(0, 8, 2), dtype=np.float64), + ], + ) + def test_iat_set_other(self, index, frame_or_series): + f = frame_or_series(range(len(index)), index=index) + msg = "iAt based indexing can only have integer indexers" + idx = next(generate_indices(f, False)) + with pytest.raises(ValueError, match=msg): + f.iat[idx] = 1 + + @pytest.mark.parametrize( + "index", + [ + Index(list("abcd"), dtype=object), + date_range("20130101", periods=4), + Index(range(0, 8, 2), dtype=np.float64), + Index(range(0, 8, 2), dtype=np.uint64), + Index(range(0, 8, 2), dtype=np.int64), + ], + ) + def test_at_set_ints_other(self, index, frame_or_series): + f = frame_or_series(range(len(index)), index=index) + indices = generate_indices(f, False) + for i in indices: + f.at[i] = 1 + expected = f.loc[i] + tm.assert_almost_equal(expected, 1) + + +class TestAtAndiAT: + # at and iat tests that don't need Base class + + def test_float_index_at_iat(self): + ser = Series([1, 2, 3], index=[0.1, 0.2, 0.3]) + for el, item in ser.items(): + assert ser.at[el] == item + for i in range(len(ser)): + assert ser.iat[i] == i + 1 + + def test_at_iat_coercion(self): + # as timestamp is not a tuple! + dates = date_range("1/1/2000", periods=8) + df = DataFrame( + np.random.default_rng(2).standard_normal((8, 4)), + index=dates, + columns=["A", "B", "C", "D"], + ) + s = df["A"] + + result = s.at[dates[5]] + xp = s.values[5] + assert result == xp + + @pytest.mark.parametrize( + "ser, expected", + [ + [ + Series(["2014-01-01", "2014-02-02"], dtype="datetime64[ns]"), + Timestamp("2014-02-02"), + ], + [ + Series(["1 days", "2 days"], dtype="timedelta64[ns]"), + Timedelta("2 days"), + ], + ], + ) + def test_iloc_iat_coercion_datelike(self, indexer_ial, ser, expected): + # GH 7729 + # make sure we are boxing the returns + result = indexer_ial(ser)[1] + assert result == expected + + def test_imethods_with_dups(self): + # GH6493 + # iat/iloc with dups + + s = Series(range(5), index=[1, 1, 2, 2, 3], dtype="int64") + result = s.iloc[2] + assert result == 2 + result = s.iat[2] + assert result == 2 + + msg = "index 10 is out of bounds for axis 0 with size 5" + with pytest.raises(IndexError, match=msg): + s.iat[10] + msg = "index -10 is out of bounds for axis 0 with size 5" + with pytest.raises(IndexError, match=msg): + s.iat[-10] + + result = s.iloc[[2, 3]] + expected = Series([2, 3], [2, 2], dtype="int64") + tm.assert_series_equal(result, expected) + + df = s.to_frame() + result = df.iloc[2] + expected = Series(2, index=[0], name=2) + tm.assert_series_equal(result, expected) + + result = df.iat[2, 0] + assert result == 2 + + def test_frame_at_with_duplicate_axes(self): + # GH#33041 + arr = np.random.default_rng(2).standard_normal(6).reshape(3, 2) + df = DataFrame(arr, columns=["A", "A"]) + + result = df.at[0, "A"] + expected = df.iloc[0].copy() + + tm.assert_series_equal(result, expected) + + result = df.T.at["A", 0] + tm.assert_series_equal(result, expected) + + # setter + df.at[1, "A"] = 2 + expected = Series([2.0, 2.0], index=["A", "A"], name=1) + tm.assert_series_equal(df.iloc[1], expected) + + def test_at_getitem_dt64tz_values(self): + # gh-15822 + df = DataFrame( + { + "name": ["John", "Anderson"], + "date": [ + Timestamp(2017, 3, 13, 13, 32, 56), + Timestamp(2017, 2, 16, 12, 10, 3), + ], + } + ) + df["date"] = df["date"].dt.tz_localize("Asia/Shanghai") + + expected = Timestamp("2017-03-13 13:32:56+0800", tz="Asia/Shanghai") + + result = df.loc[0, "date"] + assert result == expected + + result = df.at[0, "date"] + assert result == expected + + def test_mixed_index_at_iat_loc_iloc_series(self): + # GH 19860 + s = Series([1, 2, 3, 4, 5], index=["a", "b", "c", 1, 2]) + for el, item in s.items(): + assert s.at[el] == s.loc[el] == item + for i in range(len(s)): + assert s.iat[i] == s.iloc[i] == i + 1 + + with pytest.raises(KeyError, match="^4$"): + s.at[4] + with pytest.raises(KeyError, match="^4$"): + s.loc[4] + + def test_mixed_index_at_iat_loc_iloc_dataframe(self): + # GH 19860 + df = DataFrame( + [[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], columns=["a", "b", "c", 1, 2] + ) + for rowIdx, row in df.iterrows(): + for el, item in row.items(): + assert df.at[rowIdx, el] == df.loc[rowIdx, el] == item + + for row in range(2): + for i in range(5): + assert df.iat[row, i] == df.iloc[row, i] == row * 5 + i + + with pytest.raises(KeyError, match="^3$"): + df.at[0, 3] + with pytest.raises(KeyError, match="^3$"): + df.loc[0, 3] + + def test_iat_setter_incompatible_assignment(self): + # GH 23236 + result = DataFrame({"a": [0.0, 1.0], "b": [4, 5]}) + result.iat[0, 0] = None + expected = DataFrame({"a": [None, 1], "b": [4, 5]}) + tm.assert_frame_equal(result, expected) + + +def test_iat_dont_wrap_object_datetimelike(): + # GH#32809 .iat calls go through DataFrame._get_value, should not + # call maybe_box_datetimelike + dti = date_range("2016-01-01", periods=3) + tdi = dti - dti + ser = Series(dti.to_pydatetime(), dtype=object) + ser2 = Series(tdi.to_pytimedelta(), dtype=object) + df = DataFrame({"A": ser, "B": ser2}) + assert (df.dtypes == object).all() + + for result in [df.at[0, "A"], df.iat[0, 0], df.loc[0, "A"], df.iloc[0, 0]]: + assert result is ser[0] + assert isinstance(result, datetime) + assert not isinstance(result, Timestamp) + + for result in [df.at[1, "B"], df.iat[1, 1], df.loc[1, "B"], df.iloc[1, 1]]: + assert result is ser2[1] + assert isinstance(result, timedelta) + assert not isinstance(result, Timedelta) + + +def test_at_with_tuple_index_get(): + # GH 26989 + # DataFrame.at getter works with Index of tuples + df = DataFrame({"a": [1, 2]}, index=[(1, 2), (3, 4)]) + assert df.index.nlevels == 1 + assert df.at[(1, 2), "a"] == 1 + + # Series.at getter works with Index of tuples + series = df["a"] + assert series.index.nlevels == 1 + assert series.at[(1, 2)] == 1 + + +@pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning") +def test_at_with_tuple_index_set(): + # GH 26989 + # DataFrame.at setter works with Index of tuples + df = DataFrame({"a": [1, 2]}, index=[(1, 2), (3, 4)]) + assert df.index.nlevels == 1 + df.at[(1, 2), "a"] = 2 + assert df.at[(1, 2), "a"] == 2 + + # Series.at setter works with Index of tuples + series = df["a"] + assert series.index.nlevels == 1 + series.at[1, 2] = 3 + assert series.at[1, 2] == 3 + + +class TestMultiIndexScalar: + def test_multiindex_at_get(self): + # GH 26989 + # DataFrame.at and DataFrame.loc getter works with MultiIndex + df = DataFrame({"a": [1, 2]}, index=[[1, 2], [3, 4]]) + assert df.index.nlevels == 2 + assert df.at[(1, 3), "a"] == 1 + assert df.loc[(1, 3), "a"] == 1 + + # Series.at and Series.loc getter works with MultiIndex + series = df["a"] + assert series.index.nlevels == 2 + assert series.at[1, 3] == 1 + assert series.loc[1, 3] == 1 + + @pytest.mark.filterwarnings("ignore:Setting a value on a view:FutureWarning") + def test_multiindex_at_set(self): + # GH 26989 + # DataFrame.at and DataFrame.loc setter works with MultiIndex + df = DataFrame({"a": [1, 2]}, index=[[1, 2], [3, 4]]) + assert df.index.nlevels == 2 + df.at[(1, 3), "a"] = 3 + assert df.at[(1, 3), "a"] == 3 + df.loc[(1, 3), "a"] = 4 + assert df.loc[(1, 3), "a"] == 4 + + # Series.at and Series.loc setter works with MultiIndex + series = df["a"] + assert series.index.nlevels == 2 + series.at[1, 3] = 5 + assert series.at[1, 3] == 5 + series.loc[1, 3] = 6 + assert series.loc[1, 3] == 6 + + def test_multiindex_at_get_one_level(self): + # GH#38053 + s2 = Series((0, 1), index=[[False, True]]) + result = s2.at[False] + assert result == 0 diff --git a/lib/python3.11/site-packages/pandas/tests/interchange/__init__.py b/lib/python3.11/site-packages/pandas/tests/interchange/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..02e66e9a67d97695d3e5371b82d3a330898fb492 Binary files /dev/null and b/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/test_impl.cpython-311.pyc b/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/test_impl.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0ee04911666f4a4bcc4b2fc36c7abcb71b136dd5 Binary files /dev/null and b/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/test_impl.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/test_spec_conformance.cpython-311.pyc b/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/test_spec_conformance.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4a29f0bdf4edf0a0848336e42631e8afe4ee37e9 Binary files /dev/null and b/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/test_spec_conformance.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/test_utils.cpython-311.pyc b/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/test_utils.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..399b214b8c7ee0b263a29d328fec2fd62e55506e Binary files /dev/null and b/lib/python3.11/site-packages/pandas/tests/interchange/__pycache__/test_utils.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/pandas/tests/interchange/test_impl.py b/lib/python3.11/site-packages/pandas/tests/interchange/test_impl.py new file mode 100644 index 0000000000000000000000000000000000000000..4e6fa4e2c83e1e539a638044684c0f9f4060d2b5 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/interchange/test_impl.py @@ -0,0 +1,699 @@ +from datetime import ( + datetime, + timezone, +) + +import numpy as np +import pytest + +from pandas._libs.tslibs import iNaT +from pandas.compat import ( + is_ci_environment, + is_platform_windows, +) +from pandas.compat.pyarrow import pa_version_under22p0 + +import pandas as pd +import pandas._testing as tm +from pandas.core.interchange.column import PandasColumn +from pandas.core.interchange.dataframe_protocol import ( + ColumnNullType, + DtypeKind, +) +from pandas.core.interchange.from_dataframe import from_dataframe +from pandas.core.interchange.utils import ArrowCTypes + + +@pytest.mark.parametrize("data", [("ordered", True), ("unordered", False)]) +def test_categorical_dtype(data): + data_categorical = { + "ordered": pd.Categorical(list("testdata") * 30, ordered=True), + "unordered": pd.Categorical(list("testdata") * 30, ordered=False), + } + df = pd.DataFrame({"A": (data_categorical[data[0]])}) + + with tm.assert_produces_warning(match="Interchange"): + col = df.__dataframe__().get_column_by_name("A") + assert col.dtype[0] == DtypeKind.CATEGORICAL + assert col.null_count == 0 + assert col.describe_null == (ColumnNullType.USE_SENTINEL, -1) + assert col.num_chunks() == 1 + desc_cat = col.describe_categorical + assert desc_cat["is_ordered"] == data[1] + assert desc_cat["is_dictionary"] is True + assert isinstance(desc_cat["categories"], PandasColumn) + tm.assert_series_equal( + desc_cat["categories"]._col, pd.Series(["a", "d", "e", "s", "t"]) + ) + + with tm.assert_produces_warning(match="Interchange"): + tm.assert_frame_equal(df, from_dataframe(df.__dataframe__())) + + +def test_categorical_pyarrow(): + # GH 49889 + pa = pytest.importorskip("pyarrow", "11.0.0") + + arr = ["Mon", "Tue", "Mon", "Wed", "Mon", "Thu", "Fri", "Sat", "Sun"] + table = pa.table({"weekday": pa.array(arr).dictionary_encode()}) + exchange_df = table.__dataframe__() + with tm.assert_produces_warning(match="Interchange"): + result = from_dataframe(exchange_df) + weekday = pd.Categorical( + arr, categories=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"] + ) + expected = pd.DataFrame({"weekday": weekday}) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.filterwarnings( + "ignore:Constructing a Categorical with a dtype and values containing" +) +def test_empty_categorical_pyarrow(): + # https://github.com/pandas-dev/pandas/issues/53077 + pa = pytest.importorskip("pyarrow", "11.0.0") + + arr = [None] + table = pa.table({"arr": pa.array(arr, "float64").dictionary_encode()}) + exchange_df = table.__dataframe__() + with tm.assert_produces_warning(match="Interchange"): + result = pd.api.interchange.from_dataframe(exchange_df) + expected = pd.DataFrame({"arr": pd.Categorical([np.nan])}) + tm.assert_frame_equal(result, expected) + + +def test_large_string_pyarrow(): + # GH 52795 + pa = pytest.importorskip("pyarrow", "11.0.0") + + arr = ["Mon", "Tue"] + table = pa.table({"weekday": pa.array(arr, "large_string")}) + exchange_df = table.__dataframe__() + with tm.assert_produces_warning(match="Interchange"): + result = from_dataframe(exchange_df) + expected = pd.DataFrame({"weekday": ["Mon", "Tue"]}) + tm.assert_frame_equal(result, expected) + + # check round-trip + # Don't check stacklevel as PyArrow calls the deprecated `__dataframe__` method. + with tm.assert_produces_warning(match="Interchange", check_stacklevel=False): + assert pa.Table.equals(pa.interchange.from_dataframe(result), table) + + +@pytest.mark.parametrize( + ("offset", "length", "expected_values"), + [ + (0, None, [3.3, float("nan"), 2.1]), + (1, None, [float("nan"), 2.1]), + (2, None, [2.1]), + (0, 2, [3.3, float("nan")]), + (0, 1, [3.3]), + (1, 1, [float("nan")]), + ], +) +def test_bitmasks_pyarrow(offset, length, expected_values): + # GH 52795 + pa = pytest.importorskip("pyarrow", "11.0.0") + + arr = [3.3, None, 2.1] + table = pa.table({"arr": arr}).slice(offset, length) + exchange_df = table.__dataframe__() + with tm.assert_produces_warning(match="Interchange"): + result = from_dataframe(exchange_df) + expected = pd.DataFrame({"arr": expected_values}) + tm.assert_frame_equal(result, expected) + + # check round-trip + # Don't check stacklevel as PyArrow calls the deprecated `__dataframe__` method. + with tm.assert_produces_warning(match="Interchange", check_stacklevel=False): + assert pa.Table.equals(pa.interchange.from_dataframe(result), table) + + +@pytest.mark.parametrize( + "data", + [ + lambda: np.random.default_rng(2).integers(-100, 100), + lambda: np.random.default_rng(2).integers(1, 100), + lambda: np.random.default_rng(2).random(), + lambda: np.random.default_rng(2).choice([True, False]), + lambda: datetime( + year=np.random.default_rng(2).integers(1900, 2100), + month=np.random.default_rng(2).integers(1, 12), + day=np.random.default_rng(2).integers(1, 20), + ), + ], +) +def test_dataframe(data): + NCOLS, NROWS = 10, 20 + data = { + f"col{int((i - NCOLS / 2) % NCOLS + 1)}": [data() for _ in range(NROWS)] + for i in range(NCOLS) + } + df = pd.DataFrame(data) + + with tm.assert_produces_warning(match="Interchange"): + df2 = df.__dataframe__() + + assert df2.num_columns() == NCOLS + assert df2.num_rows() == NROWS + + assert list(df2.column_names()) == list(data.keys()) + + indices = (0, 2) + names = tuple(list(data.keys())[idx] for idx in indices) + + with tm.assert_produces_warning(match="Interchange"): + result = from_dataframe(df2.select_columns(indices)) + expected = from_dataframe(df2.select_columns_by_name(names)) + tm.assert_frame_equal(result, expected) + + assert isinstance(result.attrs["_INTERCHANGE_PROTOCOL_BUFFERS"], list) + assert isinstance(expected.attrs["_INTERCHANGE_PROTOCOL_BUFFERS"], list) + + +def test_missing_from_masked(): + df = pd.DataFrame( + { + "x": np.array([1.0, 2.0, 3.0, 4.0, 0.0]), + "y": np.array([1.5, 2.5, 3.5, 4.5, 0]), + "z": np.array([1.0, 0.0, 1.0, 1.0, 1.0]), + } + ) + + rng = np.random.default_rng(2) + dict_null = {col: rng.integers(low=0, high=len(df)) for col in df.columns} + for col, num_nulls in dict_null.items(): + null_idx = df.index[ + rng.choice(np.arange(len(df)), size=num_nulls, replace=False) + ] + df.loc[null_idx, col] = None + + with tm.assert_produces_warning(match="Interchange"): + df2 = df.__dataframe__() + + assert df2.get_column_by_name("x").null_count == dict_null["x"] + assert df2.get_column_by_name("y").null_count == dict_null["y"] + assert df2.get_column_by_name("z").null_count == dict_null["z"] + + +@pytest.mark.parametrize( + "data", + [ + {"x": [1.5, 2.5, 3.5], "y": [9.2, 10.5, 11.8]}, + {"x": [1, 2, 0], "y": [9.2, 10.5, 11.8]}, + { + "x": np.array([True, True, False]), + "y": np.array([1, 2, 0]), + "z": np.array([9.2, 10.5, 11.8]), + }, + ], +) +def test_mixed_data(data): + df = pd.DataFrame(data) + with tm.assert_produces_warning(match="Interchange"): + df2 = df.__dataframe__() + + for col_name in df.columns: + assert df2.get_column_by_name(col_name).null_count == 0 + + +def test_mixed_missing(): + df = pd.DataFrame( + { + "x": np.array([True, None, False, None, True]), + "y": np.array([None, 2, None, 1, 2]), + "z": np.array([9.2, 10.5, None, 11.8, None]), + } + ) + + with tm.assert_produces_warning(match="Interchange"): + df2 = df.__dataframe__() + + for col_name in df.columns: + assert df2.get_column_by_name(col_name).null_count == 2 + + +def test_string(): + string_data = { + "separator data": [ + "abC|DeF,Hik", + "234,3245.67", + "gSaf,qWer|Gre", + "asd3,4sad|", + np.nan, + ] + } + test_str_data = string_data["separator data"] + [""] + df = pd.DataFrame({"A": test_str_data}) + with tm.assert_produces_warning(match="Interchange"): + col = df.__dataframe__().get_column_by_name("A") + + assert col.size() == 6 + assert col.null_count == 1 + assert col.dtype[0] == DtypeKind.STRING + assert col.describe_null == (ColumnNullType.USE_BYTEMASK, 0) + + df_sliced = df[1:] + with tm.assert_produces_warning(match="Interchange"): + col = df_sliced.__dataframe__().get_column_by_name("A") + assert col.size() == 5 + assert col.null_count == 1 + assert col.dtype[0] == DtypeKind.STRING + assert col.describe_null == (ColumnNullType.USE_BYTEMASK, 0) + + +def test_nonstring_object(): + df = pd.DataFrame({"A": ["a", 10, 1.0, ()]}) + with tm.assert_produces_warning(match="Interchange"): + col = df.__dataframe__().get_column_by_name("A") + with pytest.raises(NotImplementedError, match="not supported yet"): + col.dtype + + +def test_datetime(): + df = pd.DataFrame({"A": [pd.Timestamp("2022-01-01"), pd.NaT]}) + with tm.assert_produces_warning(match="Interchange"): + col = df.__dataframe__().get_column_by_name("A") + + assert col.size() == 2 + assert col.null_count == 1 + assert col.dtype[0] == DtypeKind.DATETIME + assert col.describe_null == (ColumnNullType.USE_SENTINEL, iNaT) + + with tm.assert_produces_warning(match="Interchange"): + tm.assert_frame_equal(df, from_dataframe(df.__dataframe__())) + + +def test_categorical_to_numpy_dlpack(): + # https://github.com/pandas-dev/pandas/issues/48393 + df = pd.DataFrame({"A": pd.Categorical(["a", "b", "a"])}) + with tm.assert_produces_warning(match="Interchange"): + col = df.__dataframe__().get_column_by_name("A") + result = np.from_dlpack(col.get_buffers()["data"][0]) + expected = np.array([0, 1, 0], dtype="int8") + tm.assert_numpy_array_equal(result, expected) + + +@pytest.mark.parametrize("data", [{}, {"a": []}]) +def test_empty_pyarrow(data): + # GH 53155 + pytest.importorskip("pyarrow", "14.0.0") + from pyarrow.interchange import from_dataframe as pa_from_dataframe + + expected = pd.DataFrame(data) + # Don't check stacklevel as PyArrow calls the deprecated `__dataframe__` method. + with tm.assert_produces_warning(match="Interchange", check_stacklevel=False): + arrow_df = pa_from_dataframe(expected) + result = from_dataframe(arrow_df) + tm.assert_frame_equal(result, expected, check_column_type=False) + + +def test_multi_chunk_pyarrow() -> None: + pa = pytest.importorskip("pyarrow", "14.0.0") + n_legs = pa.chunked_array([[2, 2, 4], [4, 5, 100]]) + names = ["n_legs"] + table = pa.table([n_legs], names=names) + with pytest.raises( + RuntimeError, + match="Cannot do zero copy conversion into multi-column DataFrame block", + ): + pd.api.interchange.from_dataframe(table, allow_copy=False) + + +def test_multi_chunk_column() -> None: + pytest.importorskip("pyarrow", "11.0.0") + ser = pd.Series([1, 2, None], dtype="Int64[pyarrow]") + df = pd.concat([ser, ser], ignore_index=True).to_frame("a") + df_orig = df.copy() + + with tm.assert_produces_warning(match="Interchange"): + with pytest.raises( + RuntimeError, + match="Found multi-chunk pyarrow array, but `allow_copy` is False", + ): + pd.api.interchange.from_dataframe(df.__dataframe__(allow_copy=False)) + with tm.assert_produces_warning(match="Interchange"): + result = pd.api.interchange.from_dataframe(df.__dataframe__(allow_copy=True)) + # Interchange protocol defaults to creating numpy-backed columns, so currently this + # is 'float64'. + expected = pd.DataFrame({"a": [1.0, 2.0, None, 1.0, 2.0, None]}, dtype="float64") + tm.assert_frame_equal(result, expected) + + # Check that the rechunking we did didn't modify the original DataFrame. + tm.assert_frame_equal(df, df_orig) + assert len(df["a"].array._pa_array.chunks) == 2 + assert len(df_orig["a"].array._pa_array.chunks) == 2 + + +def test_timestamp_ns_pyarrow(): + # GH 56712 + pytest.importorskip("pyarrow", "11.0.0") + timestamp_args = { + "year": 2000, + "month": 1, + "day": 1, + "hour": 1, + "minute": 1, + "second": 1, + } + df = pd.Series( + [datetime(**timestamp_args)], + dtype="timestamp[ns][pyarrow]", + name="col0", + ).to_frame() + + with tm.assert_produces_warning(match="Interchange"): + dfi = df.__dataframe__() + result = pd.api.interchange.from_dataframe(dfi)["col0"].item() + + expected = pd.Timestamp(**timestamp_args) + assert result == expected + + +@pytest.mark.parametrize("tz", ["UTC", "US/Pacific"]) +def test_datetimetzdtype(tz, unit): + # GH 54239 + tz_data = ( + pd.date_range("2018-01-01", periods=5, freq="D").tz_localize(tz).as_unit(unit) + ) + df = pd.DataFrame({"ts_tz": tz_data}) + with tm.assert_produces_warning(match="Interchange"): + tm.assert_frame_equal(df, from_dataframe(df.__dataframe__())) + + +def test_interchange_from_non_pandas_tz_aware(request): + # GH 54239, 54287 + pa = pytest.importorskip("pyarrow", "11.0.0") + import pyarrow.compute as pc + + if is_platform_windows() and is_ci_environment() and pa_version_under22p0: + mark = pytest.mark.xfail( + raises=pa.ArrowInvalid, + reason=( + "TODO: Set ARROW_TIMEZONE_DATABASE environment variable " + "on CI to path to the tzdata for pyarrow." + ), + ) + request.applymarker(mark) + + arr = pa.array([datetime(2020, 1, 1), None, datetime(2020, 1, 2)]) + arr = pc.assume_timezone(arr, "Asia/Kathmandu") + table = pa.table({"arr": arr}) + exchange_df = table.__dataframe__() + with tm.assert_produces_warning(match="Interchange"): + result = from_dataframe(exchange_df) + + expected = pd.DataFrame( + ["2020-01-01 00:00:00+05:45", "NaT", "2020-01-02 00:00:00+05:45"], + columns=["arr"], + dtype="datetime64[us, Asia/Kathmandu]", + ) + tm.assert_frame_equal(expected, result) + + +def test_interchange_from_corrected_buffer_dtypes(monkeypatch) -> None: + # https://github.com/pandas-dev/pandas/issues/54781 + with tm.assert_produces_warning(match="Interchange"): + df = pd.DataFrame({"a": ["foo", "bar"]}).__dataframe__() + interchange = df.__dataframe__() + column = interchange.get_column_by_name("a") + buffers = column.get_buffers() + buffers_data = buffers["data"] + buffer_dtype = buffers_data[1] + buffer_dtype = ( + DtypeKind.UINT, + 8, + ArrowCTypes.UINT8, + buffer_dtype[3], + ) + buffers["data"] = (buffers_data[0], buffer_dtype) + column.get_buffers = lambda: buffers + interchange.get_column_by_name = lambda _: column + monkeypatch.setattr(df, "__dataframe__", lambda allow_copy: interchange) + with tm.assert_produces_warning(match="Interchange"): + pd.api.interchange.from_dataframe(df) + + +def test_empty_string_column(): + # https://github.com/pandas-dev/pandas/issues/56703 + df = pd.DataFrame({"a": []}, dtype=str) + with tm.assert_produces_warning(match="Interchange"): + df2 = df.__dataframe__() + result = pd.api.interchange.from_dataframe(df2) + tm.assert_frame_equal(df, result) + + +def test_large_string(): + # GH#56702 + pytest.importorskip("pyarrow") + df = pd.DataFrame({"a": ["x"]}, dtype="large_string[pyarrow]") + # Don't check stacklevel as PyArrow calls the deprecated `__dataframe__` method. + with tm.assert_produces_warning(match="Interchange", check_stacklevel=False): + result = pd.api.interchange.from_dataframe(df.__dataframe__()) + expected = pd.DataFrame({"a": ["x"]}, dtype="str") + tm.assert_frame_equal(result, expected) + + +def test_non_str_names(): + # https://github.com/pandas-dev/pandas/issues/56701 + df = pd.Series([1, 2, 3], name=0).to_frame() + with tm.assert_produces_warning(match="Interchange"): + names = df.__dataframe__().column_names() + assert names == ["0"] + + +def test_non_str_names_w_duplicates(): + # https://github.com/pandas-dev/pandas/issues/56701 + df = pd.DataFrame({"0": [1, 2, 3], 0: [4, 5, 6]}) + with tm.assert_produces_warning(match="Interchange"): + dfi = df.__dataframe__() + with tm.assert_produces_warning(match="Interchange"): + with pytest.raises( + TypeError, + match=( + "Expected a Series, got a DataFrame. This likely happened because you " + "called __dataframe__ on a DataFrame which, after converting column " + r"names to string, resulted in duplicated names: Index\(\['0', '0'\], " + r"dtype='(str|object)'\). Please rename these columns before using the " + "interchange protocol." + ), + ): + pd.api.interchange.from_dataframe(dfi, allow_copy=False) + + +@pytest.mark.parametrize( + ("data", "dtype", "expected_dtype"), + [ + ([1, 2, None], "Int64", "int64"), + ([1, 2, None], "Int64[pyarrow]", "int64"), + ([1, 2, None], "Int8", "int8"), + ([1, 2, None], "Int8[pyarrow]", "int8"), + ( + [1, 2, None], + "UInt64", + "uint64", + ), + ( + [1, 2, None], + "UInt64[pyarrow]", + "uint64", + ), + ([1.0, 2.25, None], "Float32", "float32"), + ([1.0, 2.25, None], "Float32[pyarrow]", "float32"), + ([True, False, None], "boolean", "bool"), + ([True, False, None], "boolean[pyarrow]", "bool"), + (["much ado", "about", None], pd.StringDtype(na_value=np.nan), "large_string"), + (["much ado", "about", None], "string[pyarrow]", "large_string"), + ( + [datetime(2020, 1, 1), datetime(2020, 1, 2), None], + "timestamp[ns][pyarrow]", + "timestamp[ns]", + ), + ( + [datetime(2020, 1, 1), datetime(2020, 1, 2), None], + "timestamp[us][pyarrow]", + "timestamp[us]", + ), + ( + [ + datetime(2020, 1, 1, tzinfo=timezone.utc), + datetime(2020, 1, 2, tzinfo=timezone.utc), + None, + ], + "timestamp[us, Asia/Kathmandu][pyarrow]", + "timestamp[us, tz=Asia/Kathmandu]", + ), + ], +) +def test_pandas_nullable_with_missing_values( + data: list, dtype: str, expected_dtype: str +) -> None: + # https://github.com/pandas-dev/pandas/issues/57643 + # https://github.com/pandas-dev/pandas/issues/57664 + pa = pytest.importorskip("pyarrow", "14.0.0") + import pyarrow.interchange as pai + + if expected_dtype == "timestamp[us, tz=Asia/Kathmandu]": + expected_dtype = pa.timestamp("us", "Asia/Kathmandu") + + df = pd.DataFrame({"a": data}, dtype=dtype) + with tm.assert_produces_warning(match="Interchange"): + result = pai.from_dataframe(df.__dataframe__())["a"] + assert result.type == expected_dtype + assert result[0].as_py() == data[0] + assert result[1].as_py() == data[1] + assert result[2].as_py() is None + + +@pytest.mark.parametrize( + ("data", "dtype", "expected_dtype"), + [ + ([1, 2, 3], "Int64", "int64"), + ([1, 2, 3], "Int64[pyarrow]", "int64"), + ([1, 2, 3], "Int8", "int8"), + ([1, 2, 3], "Int8[pyarrow]", "int8"), + ( + [1, 2, 3], + "UInt64", + "uint64", + ), + ( + [1, 2, 3], + "UInt64[pyarrow]", + "uint64", + ), + ([1.0, 2.25, 5.0], "Float32", "float32"), + ([1.0, 2.25, 5.0], "Float32[pyarrow]", "float32"), + ([True, False, False], "boolean", "bool"), + ([True, False, False], "boolean[pyarrow]", "bool"), + ( + ["much ado", "about", "nothing"], + pd.StringDtype(na_value=np.nan), + "large_string", + ), + (["much ado", "about", "nothing"], "string[pyarrow]", "large_string"), + ( + [datetime(2020, 1, 1), datetime(2020, 1, 2), datetime(2020, 1, 3)], + "timestamp[ns][pyarrow]", + "timestamp[ns]", + ), + ( + [datetime(2020, 1, 1), datetime(2020, 1, 2), datetime(2020, 1, 3)], + "timestamp[us][pyarrow]", + "timestamp[us]", + ), + ( + [ + datetime(2020, 1, 1, tzinfo=timezone.utc), + datetime(2020, 1, 2, tzinfo=timezone.utc), + datetime(2020, 1, 3, tzinfo=timezone.utc), + ], + "timestamp[us, Asia/Kathmandu][pyarrow]", + "timestamp[us, tz=Asia/Kathmandu]", + ), + ], +) +def test_pandas_nullable_without_missing_values( + data: list, dtype: str, expected_dtype: str +) -> None: + # https://github.com/pandas-dev/pandas/issues/57643 + pa = pytest.importorskip("pyarrow", "14.0.0") + import pyarrow.interchange as pai + + if expected_dtype == "timestamp[us, tz=Asia/Kathmandu]": + expected_dtype = pa.timestamp("us", "Asia/Kathmandu") + + df = pd.DataFrame({"a": data}, dtype=dtype) + with tm.assert_produces_warning(match="Interchange"): + result = pai.from_dataframe(df.__dataframe__())["a"] + assert result.type == expected_dtype + assert result[0].as_py() == data[0] + assert result[1].as_py() == data[1] + assert result[2].as_py() == data[2] + + +def test_string_validity_buffer() -> None: + # https://github.com/pandas-dev/pandas/issues/57761 + pytest.importorskip("pyarrow", "11.0.0") + df = pd.DataFrame({"a": ["x"]}, dtype="large_string[pyarrow]") + with tm.assert_produces_warning(match="Interchange"): + result = df.__dataframe__().get_column_by_name("a").get_buffers()["validity"] + assert result is None + + +def test_string_validity_buffer_no_missing() -> None: + # https://github.com/pandas-dev/pandas/issues/57762 + pytest.importorskip("pyarrow", "11.0.0") + df = pd.DataFrame({"a": ["x", None]}, dtype="large_string[pyarrow]") + with tm.assert_produces_warning(match="Interchange"): + validity = df.__dataframe__().get_column_by_name("a").get_buffers()["validity"] + assert validity is not None + result = validity[1] + expected = (DtypeKind.BOOL, 1, ArrowCTypes.BOOL, "=") + assert result == expected + + +def test_empty_dataframe(): + # https://github.com/pandas-dev/pandas/issues/56700 + df = pd.DataFrame({"a": []}, dtype="int8") + with tm.assert_produces_warning(match="Interchange"): + dfi = df.__dataframe__() + result = pd.api.interchange.from_dataframe(dfi, allow_copy=False) + expected = pd.DataFrame({"a": []}, dtype="int8") + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + ("data", "expected_dtype", "expected_buffer_dtype"), + [ + ( + pd.Series(["a", "b", "a"], dtype="category"), + (DtypeKind.CATEGORICAL, 8, "c", "="), + (DtypeKind.INT, 8, "c", "|"), + ), + ( + pd.Series( + [datetime(2022, 1, 1), datetime(2022, 1, 2), datetime(2022, 1, 3)], + dtype="M8[ns]", + ), + (DtypeKind.DATETIME, 64, "tsn:", "="), + (DtypeKind.INT, 64, ArrowCTypes.INT64, "="), + ), + ( + pd.Series(["a", "bc", None]), + (DtypeKind.STRING, 8, ArrowCTypes.STRING, "="), + (DtypeKind.UINT, 8, ArrowCTypes.UINT8, "="), + ), + ( + pd.Series([1, 2, 3]), + (DtypeKind.INT, 64, ArrowCTypes.INT64, "="), + (DtypeKind.INT, 64, ArrowCTypes.INT64, "="), + ), + ( + pd.Series([1.5, 2, 3]), + (DtypeKind.FLOAT, 64, ArrowCTypes.FLOAT64, "="), + (DtypeKind.FLOAT, 64, ArrowCTypes.FLOAT64, "="), + ), + ], +) +def test_buffer_dtype_categorical( + data: pd.Series, + expected_dtype: tuple[DtypeKind, int, str, str], + expected_buffer_dtype: tuple[DtypeKind, int, str, str], +) -> None: + # https://github.com/pandas-dev/pandas/issues/54781 + df = pd.DataFrame({"data": data}) + with tm.assert_produces_warning(match="Interchange"): + dfi = df.__dataframe__() + col = dfi.get_column_by_name("data") + assert col.dtype == expected_dtype + assert col.get_buffers()["data"][1] == expected_buffer_dtype + + +def test_from_dataframe_list_dtype(): + pa = pytest.importorskip("pyarrow", "14.0.0") + data = {"a": [[1, 2], [4, 5, 6]]} + tbl = pa.table(data) + result = from_dataframe(tbl) + expected = pd.DataFrame(data) + tm.assert_frame_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/interchange/test_spec_conformance.py b/lib/python3.11/site-packages/pandas/tests/interchange/test_spec_conformance.py new file mode 100644 index 0000000000000000000000000000000000000000..04e19b290f886a4cd37e8c41ef2f2e65d7e678e8 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/interchange/test_spec_conformance.py @@ -0,0 +1,187 @@ +""" +A verbatim copy (vendored) of the spec tests. +Taken from https://github.com/data-apis/dataframe-api +""" + +import ctypes +import math + +import pytest + +import pandas as pd +import pandas._testing as tm + + +@pytest.fixture +def df_from_dict(): + def maker(dct, is_categorical=False): + df = pd.DataFrame(dct) + return df.astype("category") if is_categorical else df + + return maker + + +@pytest.mark.parametrize( + "test_data", + [ + {"a": ["foo", "bar"], "b": ["baz", "qux"]}, + {"a": [1.5, 2.5, 3.5], "b": [9.2, 10.5, 11.8]}, + {"A": [1, 2, 3, 4], "B": [1, 2, 3, 4]}, + ], + ids=["str_data", "float_data", "int_data"], +) +def test_only_one_dtype(test_data, df_from_dict): + columns = list(test_data.keys()) + df = df_from_dict(test_data) + with tm.assert_produces_warning(match="Interchange"): + dfX = df.__dataframe__() + + column_size = len(test_data[columns[0]]) + for column in columns: + null_count = dfX.get_column_by_name(column).null_count + assert null_count == 0 + assert isinstance(null_count, int) + assert dfX.get_column_by_name(column).size() == column_size + assert dfX.get_column_by_name(column).offset == 0 + + +def test_mixed_dtypes(df_from_dict): + df = df_from_dict( + { + "a": [1, 2, 3], # dtype kind INT = 0 + "b": [3, 4, 5], # dtype kind INT = 0 + "c": [1.5, 2.5, 3.5], # dtype kind FLOAT = 2 + "d": [9, 10, 11], # dtype kind INT = 0 + "e": [True, False, True], # dtype kind BOOLEAN = 20 + "f": ["a", "", "c"], # dtype kind STRING = 21 + } + ) + with tm.assert_produces_warning(match="Interchange"): + dfX = df.__dataframe__() + # for meanings of dtype[0] see the spec; we cannot import the spec here as this + # file is expected to be vendored *anywhere*; + # values for dtype[0] are explained above + columns = {"a": 0, "b": 0, "c": 2, "d": 0, "e": 20, "f": 21} + + for column, kind in columns.items(): + colX = dfX.get_column_by_name(column) + assert colX.null_count == 0 + assert isinstance(colX.null_count, int) + assert colX.size() == 3 + assert colX.offset == 0 + + assert colX.dtype[0] == kind + + assert dfX.get_column_by_name("c").dtype[1] == 64 + + +def test_na_float(df_from_dict): + df = df_from_dict({"a": [1.0, math.nan, 2.0]}) + with tm.assert_produces_warning(match="Interchange"): + dfX = df.__dataframe__() + colX = dfX.get_column_by_name("a") + assert colX.null_count == 1 + assert isinstance(colX.null_count, int) + + +def test_noncategorical(df_from_dict): + df = df_from_dict({"a": [1, 2, 3]}) + with tm.assert_produces_warning(match="Interchange"): + dfX = df.__dataframe__() + colX = dfX.get_column_by_name("a") + with pytest.raises(TypeError, match=".*categorical.*"): + colX.describe_categorical + + +def test_categorical(df_from_dict): + df = df_from_dict( + {"weekday": ["Mon", "Tue", "Mon", "Wed", "Mon", "Thu", "Fri", "Sat", "Sun"]}, + is_categorical=True, + ) + + with tm.assert_produces_warning(match="Interchange"): + colX = df.__dataframe__().get_column_by_name("weekday") + categorical = colX.describe_categorical + assert isinstance(categorical["is_ordered"], bool) + assert isinstance(categorical["is_dictionary"], bool) + + +def test_dataframe(df_from_dict): + df = df_from_dict( + {"x": [True, True, False], "y": [1, 2, 0], "z": [9.2, 10.5, 11.8]} + ) + with tm.assert_produces_warning(match="Interchange"): + dfX = df.__dataframe__() + + assert dfX.num_columns() == 3 + assert dfX.num_rows() == 3 + assert dfX.num_chunks() == 1 + assert list(dfX.column_names()) == ["x", "y", "z"] + assert list(dfX.select_columns((0, 2)).column_names()) == list( + dfX.select_columns_by_name(("x", "z")).column_names() + ) + + +@pytest.mark.parametrize(["size", "n_chunks"], [(10, 3), (12, 3), (12, 5)]) +def test_df_get_chunks(size, n_chunks, df_from_dict): + df = df_from_dict({"x": list(range(size))}) + with tm.assert_produces_warning(match="Interchange"): + dfX = df.__dataframe__() + chunks = list(dfX.get_chunks(n_chunks)) + assert len(chunks) == n_chunks + assert sum(chunk.num_rows() for chunk in chunks) == size + + +@pytest.mark.parametrize(["size", "n_chunks"], [(10, 3), (12, 3), (12, 5)]) +def test_column_get_chunks(size, n_chunks, df_from_dict): + df = df_from_dict({"x": list(range(size))}) + with tm.assert_produces_warning(match="Interchange"): + dfX = df.__dataframe__() + chunks = list(dfX.get_column(0).get_chunks(n_chunks)) + assert len(chunks) == n_chunks + assert sum(chunk.size() for chunk in chunks) == size + + +def test_get_columns(df_from_dict): + df = df_from_dict({"a": [0, 1], "b": [2.5, 3.5]}) + with tm.assert_produces_warning(match="Interchange"): + dfX = df.__dataframe__() + for colX in dfX.get_columns(): + assert colX.size() == 2 + assert colX.num_chunks() == 1 + # for meanings of dtype[0] see the spec; we cannot import the spec here as this + # file is expected to be vendored *anywhere* + assert dfX.get_column(0).dtype[0] == 0 # INT + assert dfX.get_column(1).dtype[0] == 2 # FLOAT + + +def test_buffer(df_from_dict): + arr = [0, 1, -1] + df = df_from_dict({"a": arr}) + with tm.assert_produces_warning(match="Interchange"): + dfX = df.__dataframe__() + colX = dfX.get_column(0) + bufX = colX.get_buffers() + + dataBuf, dataDtype = bufX["data"] + + assert dataBuf.bufsize > 0 + assert dataBuf.ptr != 0 + device, _ = dataBuf.__dlpack_device__() + + # for meanings of dtype[0] see the spec; we cannot import the spec here as this + # file is expected to be vendored *anywhere* + assert dataDtype[0] == 0 # INT + + if device == 1: # CPU-only as we're going to directly read memory here + bitwidth = dataDtype[1] + ctype = { + 8: ctypes.c_int8, + 16: ctypes.c_int16, + 32: ctypes.c_int32, + 64: ctypes.c_int64, + }[bitwidth] + + for idx, truth in enumerate(arr): + val = ctype.from_address(dataBuf.ptr + idx * (bitwidth // 8)).value + assert val == truth, f"Buffer at index {idx} mismatch" diff --git a/lib/python3.11/site-packages/pandas/tests/interchange/test_utils.py b/lib/python3.11/site-packages/pandas/tests/interchange/test_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a47bc2752ff32f5eb7630a3960e7611242cb73e3 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/interchange/test_utils.py @@ -0,0 +1,89 @@ +import numpy as np +import pytest + +import pandas as pd +from pandas.core.interchange.utils import dtype_to_arrow_c_fmt + +# TODO: use ArrowSchema to get reference C-string. +# At the time, there is no way to access ArrowSchema holding a type format string +# from python. The only way to access it is to export the structure to a C-pointer, +# see DataType._export_to_c() method defined in +# https://github.com/apache/arrow/blob/master/python/pyarrow/types.pxi + + +@pytest.mark.parametrize( + "pandas_dtype, c_string", + [ + (np.dtype("bool"), "b"), + (np.dtype("int8"), "c"), + (np.dtype("uint8"), "C"), + (np.dtype("int16"), "s"), + (np.dtype("uint16"), "S"), + (np.dtype("int32"), "i"), + (np.dtype("uint32"), "I"), + (np.dtype("int64"), "l"), + (np.dtype("uint64"), "L"), + (np.dtype("float16"), "e"), + (np.dtype("float32"), "f"), + (np.dtype("float64"), "g"), + (pd.Series(["a"]).dtype, "u"), + ( + pd.Series([0]).astype("datetime64[ns]").dtype, + "tsn:", + ), + (pd.CategoricalDtype(["a"]), "l"), + (np.dtype("O"), "u"), + ], +) +def test_dtype_to_arrow_c_fmt(pandas_dtype, c_string): # PR01 + """Test ``dtype_to_arrow_c_fmt`` utility function.""" + assert dtype_to_arrow_c_fmt(pandas_dtype) == c_string + + +@pytest.mark.parametrize( + "pa_dtype, args_kwargs, c_string", + [ + ["null", {}, "n"], + ["bool_", {}, "b"], + ["uint8", {}, "C"], + ["uint16", {}, "S"], + ["uint32", {}, "I"], + ["uint64", {}, "L"], + ["int8", {}, "c"], + ["int16", {}, "S"], + ["int32", {}, "i"], + ["int64", {}, "l"], + ["float16", {}, "e"], + ["float32", {}, "f"], + ["float64", {}, "g"], + ["string", {}, "u"], + ["binary", {}, "z"], + ["time32", ("s",), "tts"], + ["time32", ("ms",), "ttm"], + ["time64", ("us",), "ttu"], + ["time64", ("ns",), "ttn"], + ["date32", {}, "tdD"], + ["date64", {}, "tdm"], + ["timestamp", {"unit": "s"}, "tss:"], + ["timestamp", {"unit": "ms"}, "tsm:"], + ["timestamp", {"unit": "us"}, "tsu:"], + ["timestamp", {"unit": "ns"}, "tsn:"], + ["timestamp", {"unit": "ns", "tz": "UTC"}, "tsn:UTC"], + ["duration", ("s",), "tDs"], + ["duration", ("ms",), "tDm"], + ["duration", ("us",), "tDu"], + ["duration", ("ns",), "tDn"], + ["decimal128", {"precision": 4, "scale": 2}, "d:4,2"], + ], +) +def test_dtype_to_arrow_c_fmt_arrowdtype(pa_dtype, args_kwargs, c_string): + # GH 52323 + pa = pytest.importorskip("pyarrow") + if not args_kwargs: + pa_type = getattr(pa, pa_dtype)() + elif isinstance(args_kwargs, tuple): + pa_type = getattr(pa, pa_dtype)(*args_kwargs) + else: + pa_type = getattr(pa, pa_dtype)(**args_kwargs) + arrow_type = pd.ArrowDtype(pa_type) + assert dtype_to_arrow_c_fmt(arrow_type) == c_string diff --git a/lib/python3.11/site-packages/pandas/tests/internals/__init__.py b/lib/python3.11/site-packages/pandas/tests/internals/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/lib/python3.11/site-packages/pandas/tests/internals/__pycache__/__init__.cpython-311.pyc b/lib/python3.11/site-packages/pandas/tests/internals/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cf8234556a552a2209fc19458d769192b75bdd8b Binary files /dev/null and b/lib/python3.11/site-packages/pandas/tests/internals/__pycache__/__init__.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/pandas/tests/internals/__pycache__/test_api.cpython-311.pyc b/lib/python3.11/site-packages/pandas/tests/internals/__pycache__/test_api.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ba8a1d2ead75d8041471b9321fb7fbb36a200448 Binary files /dev/null and b/lib/python3.11/site-packages/pandas/tests/internals/__pycache__/test_api.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/pandas/tests/internals/__pycache__/test_internals.cpython-311.pyc b/lib/python3.11/site-packages/pandas/tests/internals/__pycache__/test_internals.cpython-311.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fc9fcf65d483b9232c29d1a158b6edcf8775a7b9 Binary files /dev/null and b/lib/python3.11/site-packages/pandas/tests/internals/__pycache__/test_internals.cpython-311.pyc differ diff --git a/lib/python3.11/site-packages/pandas/tests/internals/test_api.py b/lib/python3.11/site-packages/pandas/tests/internals/test_api.py new file mode 100644 index 0000000000000000000000000000000000000000..9368105a1fa5b192e7b223529e527390b9d6d7bf --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/internals/test_api.py @@ -0,0 +1,178 @@ +""" +Tests for the pseudo-public API implemented in internals/api.py and exposed +in core.internals +""" + +import datetime + +import numpy as np +import pytest + +from pandas.errors import Pandas4Warning + +import pandas as pd +import pandas._testing as tm +from pandas.api.internals import create_dataframe_from_blocks +from pandas.core import internals +from pandas.core.internals import api + + +def test_internals_api(): + assert internals.make_block is api.make_block + + +def test_namespace(): + # SUBJECT TO CHANGE + + modules = [ + "blocks", + "concat", + "managers", + "construction", + "api", + "ops", + ] + expected = [ + "make_block", + "BlockManager", + "SingleBlockManager", + "concatenate_managers", + ] + + result = [x for x in dir(internals) if not x.startswith("__")] + assert set(result) == set(expected + modules), set(result) ^ set(expected + modules) + + +@pytest.mark.parametrize( + "name", + [ + "Block", + "ExtensionBlock", + ], +) +def test_deprecations(name): + # GH#55139 + msg = f"{name} is deprecated.* Use public APIs instead" + with tm.assert_produces_warning(Pandas4Warning, match=msg): + getattr(internals, name) + + +def test_make_block_2d_with_dti(): + # GH#41168 + dti = pd.date_range("2012", periods=3, tz="UTC") + + msg = "make_block is deprecated" + with tm.assert_produces_warning(Pandas4Warning, match=msg): + blk = api.make_block(dti, placement=[0]) + + assert blk.shape == (1, 3) + assert blk.values.shape == (1, 3) + + +def test_create_block_manager_from_blocks_deprecated(): + # GH#33892 + # If they must, downstream packages should get this from internals.api, + # not internals. + msg = ( + "create_block_manager_from_blocks is deprecated and will be " + "removed in a future version. Use public APIs instead" + ) + with tm.assert_produces_warning(Pandas4Warning, match=msg): + internals.create_block_manager_from_blocks + + +def test_maybe_infer_ndim_deprecated(): + # GH#40226 + msg = "maybe_infer_ndim is deprecated and will be removed in a future version." + arr = np.arange(5) + bp = pd._libs.internals.BlockPlacement([1]) + with tm.assert_produces_warning(DeprecationWarning, match=msg): + internals.api.maybe_infer_ndim(arr, bp, 1) + + +def test_create_dataframe_from_blocks(float_frame): + block = float_frame._mgr.blocks[0] + index = float_frame.index.copy() + columns = float_frame.columns.copy() + + result = create_dataframe_from_blocks( + [(block.values, block.mgr_locs.as_array)], index=index, columns=columns + ) + tm.assert_frame_equal(result, float_frame) + + +def test_create_dataframe_from_blocks_types(): + df = pd.DataFrame( + { + "int": list(range(1, 4)), + "uint": np.arange(3, 6).astype("uint8"), + "float": [2.0, np.nan, 3.0], + "bool": np.array([True, False, True]), + "boolean": pd.array([True, False, None], dtype="boolean"), + "string": list("abc"), + "datetime": pd.date_range("20130101", periods=3), + "datetimetz": pd.date_range("20130101", periods=3).tz_localize( + "Europe/Brussels" + ), + "timedelta": pd.timedelta_range("1 day", periods=3), + "period": pd.period_range("2012-01-01", periods=3, freq="D"), + "categorical": pd.Categorical(["a", "b", "a"]), + "interval": pd.IntervalIndex.from_tuples([(0, 1), (1, 2), (3, 4)]), + } + ) + + result = create_dataframe_from_blocks( + [(block.values, block.mgr_locs.as_array) for block in df._mgr.blocks], + index=df.index, + columns=df.columns, + ) + tm.assert_frame_equal(result, df) + + +def test_create_dataframe_from_blocks_datetimelike(): + # extension dtypes that have an exact matching numpy dtype can also be + # be passed as a numpy array + index, columns = pd.RangeIndex(3), pd.Index(["a", "b", "c", "d"]) + + block_array1 = np.arange( + datetime.datetime(2020, 1, 1), + datetime.datetime(2020, 1, 7), + step=datetime.timedelta(1), + ).reshape((2, 3)) + block_array2 = np.arange( + datetime.timedelta(1), datetime.timedelta(7), step=datetime.timedelta(1) + ).reshape((2, 3)) + result = create_dataframe_from_blocks( + [(block_array1, np.array([0, 2])), (block_array2, np.array([1, 3]))], + index=index, + columns=columns, + ) + expected = pd.DataFrame( + { + "a": pd.date_range("2020-01-01", periods=3, unit="us"), + "b": pd.timedelta_range("1 days", periods=3, unit="us"), + "c": pd.date_range("2020-01-04", periods=3, unit="us"), + "d": pd.timedelta_range("4 days", periods=3, unit="us"), + } + ) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "array", + [ + pd.date_range("2020-01-01", periods=3), + pd.date_range("2020-01-01", periods=3, tz="UTC"), + pd.period_range("2012-01-01", periods=3, freq="D"), + pd.timedelta_range("1 day", periods=3), + ], +) +def test_create_dataframe_from_blocks_1dEA(array): + # ExtensionArrays can be passed as 1D even if stored under the hood as 2D + df = pd.DataFrame({"a": array}) + + block = df._mgr.blocks[0] + result = create_dataframe_from_blocks( + [(block.values[0], block.mgr_locs.as_array)], index=df.index, columns=df.columns + ) + tm.assert_frame_equal(result, df) diff --git a/lib/python3.11/site-packages/pandas/tests/internals/test_internals.py b/lib/python3.11/site-packages/pandas/tests/internals/test_internals.py new file mode 100644 index 0000000000000000000000000000000000000000..8852ae81bddf2032f3cf03105917dde9fb6637bf --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/internals/test_internals.py @@ -0,0 +1,1421 @@ +from datetime import ( + date, + datetime, +) +import itertools +import re + +import numpy as np +import pytest + +from pandas._libs.internals import BlockPlacement +from pandas.compat import IS64 +from pandas.errors import Pandas4Warning + +from pandas.core.dtypes.common import is_scalar + +import pandas as pd +from pandas import ( + Categorical, + DataFrame, + DatetimeIndex, + Index, + IntervalIndex, + Series, + Timedelta, + Timestamp, + period_range, +) +import pandas._testing as tm +import pandas.core.algorithms as algos +from pandas.core.arrays import ( + DatetimeArray, + SparseArray, + TimedeltaArray, +) +from pandas.core.internals import ( + BlockManager, + SingleBlockManager, + make_block, +) +from pandas.core.internals.blocks import ( + ensure_block_shape, + maybe_coerce_values, + new_block, +) + + +@pytest.fixture(params=[new_block, make_block]) +def block_maker(request): + """ + Fixture to test both the internal new_block and pseudo-public make_block. + """ + return request.param + + +@pytest.fixture +def mgr(): + return create_mgr( + "a: f8; b: object; c: f8; d: object; e: f8;" + "f: bool; g: i8; h: complex; i: datetime-1; j: datetime-2;" + "k: M8[ns, US/Eastern]; l: M8[ns, CET];" + ) + + +def assert_block_equal(left, right): + tm.assert_numpy_array_equal(left.values, right.values) + assert left.dtype == right.dtype + assert isinstance(left.mgr_locs, BlockPlacement) + assert isinstance(right.mgr_locs, BlockPlacement) + tm.assert_numpy_array_equal(left.mgr_locs.as_array, right.mgr_locs.as_array) + + +def get_numeric_mat(shape): + arr = np.arange(shape[0]) + return np.lib.stride_tricks.as_strided( + x=arr, shape=shape, strides=(arr.itemsize,) + (0,) * (len(shape) - 1) + ).copy() + + +N = 10 + + +def create_block(typestr, placement, item_shape=None, num_offset=0, maker=new_block): + """ + Supported typestr: + + * float, f8, f4, f2 + * int, i8, i4, i2, i1 + * uint, u8, u4, u2, u1 + * complex, c16, c8 + * bool + * object, string, O + * datetime, dt, M8[ns], M8[ns, tz] + * timedelta, td, m8[ns] + * sparse (SparseArray with fill_value=0.0) + * sparse_na (SparseArray with fill_value=np.nan) + * category, category2 + + """ + placement = BlockPlacement(placement) + num_items = len(placement) + + if item_shape is None: + item_shape = (N,) + + shape = (num_items, *item_shape) + + mat = get_numeric_mat(shape) + + if typestr in ( + "float", + "f8", + "f4", + "f2", + "int", + "i8", + "i4", + "i2", + "i1", + "uint", + "u8", + "u4", + "u2", + "u1", + ): + values = mat.astype(typestr) + num_offset + elif typestr in ("complex", "c16", "c8"): + values = 1.0j * (mat.astype(typestr) + num_offset) + elif typestr in ("object", "string", "O"): + values = np.reshape([f"A{i:d}" for i in mat.ravel() + num_offset], shape) + elif typestr in ("b", "bool"): + values = np.ones(shape, dtype=np.bool_) + elif typestr in ("datetime", "dt", "M8[ns]"): + values = (mat * 1e9).astype("M8[ns]") + elif typestr.startswith("M8[ns"): + # datetime with tz + m = re.search(r"M8\[ns,\s*(\w+\/?\w*)\]", typestr) + assert m is not None, f"incompatible typestr -> {typestr}" + tz = m.groups()[0] + assert num_items == 1, "must have only 1 num items for a tz-aware" + values = DatetimeIndex(np.arange(N) * 10**9, tz=tz)._data + values = ensure_block_shape(values, ndim=len(shape)) + elif typestr in ("timedelta", "td", "m8[ns]"): + values = (mat * 1).astype("m8[ns]") + elif typestr in ("category",): + values = Categorical([1, 1, 2, 2, 3, 3, 3, 3, 4, 4]) + elif typestr in ("category2",): + values = Categorical(["a", "a", "a", "a", "b", "b", "c", "c", "c", "d"]) + elif typestr in ("sparse", "sparse_na"): + if shape[-1] != 10: + # We also are implicitly assuming this in the category cases above + raise NotImplementedError + + assert all(s == 1 for s in shape[:-1]) + if typestr.endswith("_na"): + fill_value = np.nan + else: + fill_value = 0.0 + values = SparseArray( + [fill_value, fill_value, 1, 2, 3, fill_value, 4, 5, fill_value, 6], + fill_value=fill_value, + ) + arr = values.sp_values.view() + arr += num_offset - 1 + else: + raise ValueError(f'Unsupported typestr: "{typestr}"') + + values = maybe_coerce_values(values) + return maker(values, placement=placement, ndim=len(shape)) + + +def create_single_mgr(typestr, num_rows=None): + if num_rows is None: + num_rows = N + + return SingleBlockManager( + create_block(typestr, placement=slice(0, num_rows), item_shape=()), + Index(np.arange(num_rows)), + ) + + +def create_mgr(descr, item_shape=None): + """ + Construct BlockManager from string description. + + String description syntax looks similar to np.matrix initializer. It looks + like this:: + + a,b,c: f8; d,e,f: i8 + + Rules are rather simple: + + * see list of supported datatypes in `create_block` method + * components are semicolon-separated + * each component is `NAME,NAME,NAME: DTYPE_ID` + * whitespace around colons & semicolons are removed + * components with same DTYPE_ID are combined into single block + * to force multiple blocks with same dtype, use '-SUFFIX':: + + "a:f8-1; b:f8-2; c:f8-foobar" + + """ + if item_shape is None: + item_shape = (N,) + + offset = 0 + mgr_items = [] + block_placements = {} + for d in descr.split(";"): + d = d.strip() + if not len(d): + continue + names, blockstr = d.partition(":")[::2] + blockstr = blockstr.strip() + names = names.strip().split(",") + + mgr_items.extend(names) + placement = list(np.arange(len(names)) + offset) + try: + block_placements[blockstr].extend(placement) + except KeyError: + block_placements[blockstr] = placement + offset += len(names) + + mgr_items = Index(mgr_items) + + blocks = [] + num_offset = 0 + for blockstr, placement in block_placements.items(): + typestr = blockstr.split("-")[0] + blocks.append( + create_block( + typestr, placement, item_shape=item_shape, num_offset=num_offset + ) + ) + num_offset += len(placement) + + sblocks = sorted(blocks, key=lambda b: b.mgr_locs[0]) + return BlockManager( + tuple(sblocks), + [mgr_items] + [Index(np.arange(n)) for n in item_shape], + ) + + +@pytest.fixture +def fblock(): + return create_block("float", [0, 2, 4]) + + +class TestBlock: + def test_constructor(self): + int32block = create_block("i4", [0]) + assert int32block.dtype == np.int32 + + @pytest.mark.parametrize( + "typ, data", + [ + ["float", [0, 2, 4]], + ["complex", [7]], + ["object", [1, 3]], + ["bool", [5]], + ], + ) + def test_pickle(self, typ, data, temp_file): + blk = create_block(typ, data) + assert_block_equal(tm.round_trip_pickle(blk, temp_file), blk) + + def test_mgr_locs(self, fblock): + assert isinstance(fblock.mgr_locs, BlockPlacement) + tm.assert_numpy_array_equal( + fblock.mgr_locs.as_array, np.array([0, 2, 4], dtype=np.intp) + ) + + def test_attrs(self, fblock): + assert fblock.shape == fblock.values.shape + assert fblock.dtype == fblock.values.dtype + assert len(fblock) == len(fblock.values) + + def test_copy(self, fblock): + cop = fblock.copy(deep=True) + assert cop is not fblock + assert_block_equal(fblock, cop) + + def test_delete(self, fblock): + newb = fblock.copy(deep=True) + locs = newb.mgr_locs + nb = newb.delete(0)[0] + assert newb.mgr_locs is locs + + assert nb is not newb + + tm.assert_numpy_array_equal( + nb.mgr_locs.as_array, np.array([2, 4], dtype=np.intp) + ) + assert not (newb.values[0] == 1).all() + assert (nb.values[0] == 1).all() + + newb = fblock.copy(deep=True) + locs = newb.mgr_locs + nb = newb.delete(1) + assert len(nb) == 2 + assert newb.mgr_locs is locs + + tm.assert_numpy_array_equal( + nb[0].mgr_locs.as_array, np.array([0], dtype=np.intp) + ) + tm.assert_numpy_array_equal( + nb[1].mgr_locs.as_array, np.array([4], dtype=np.intp) + ) + assert not (newb.values[1] == 2).all() + assert (nb[1].values[0] == 2).all() + + newb = fblock.copy(deep=True) + nb = newb.delete(2) + assert len(nb) == 1 + tm.assert_numpy_array_equal( + nb[0].mgr_locs.as_array, np.array([0, 2], dtype=np.intp) + ) + assert (nb[0].values[1] == 1).all() + + newb = fblock.copy(deep=True) + + with pytest.raises(IndexError, match=None): + newb.delete(3) + + def test_delete_datetimelike(self): + # dont use np.delete on values, as that will coerce from DTA/TDA to ndarray + arr = np.arange(20, dtype="i8").reshape(5, 4).view("m8[ns]") + df = DataFrame(arr) + blk = df._mgr.blocks[0] + assert isinstance(blk.values, TimedeltaArray) + + nb = blk.delete(1) + assert len(nb) == 2 + assert isinstance(nb[0].values, TimedeltaArray) + assert isinstance(nb[1].values, TimedeltaArray) + + df = DataFrame(arr.view("M8[ns]")) + blk = df._mgr.blocks[0] + assert isinstance(blk.values, DatetimeArray) + + nb = blk.delete([1, 3]) + assert len(nb) == 2 + assert isinstance(nb[0].values, DatetimeArray) + assert isinstance(nb[1].values, DatetimeArray) + + def test_split(self): + # GH#37799 + values = np.random.default_rng(2).standard_normal((3, 4)) + blk = new_block(values, placement=BlockPlacement([3, 1, 6]), ndim=2) + result = list(blk._split()) + + # check that we get views, not copies + values[:] = -9999 + assert (blk.values == -9999).all() + + assert len(result) == 3 + expected = [ + new_block(values[[0]], placement=BlockPlacement([3]), ndim=2), + new_block(values[[1]], placement=BlockPlacement([1]), ndim=2), + new_block(values[[2]], placement=BlockPlacement([6]), ndim=2), + ] + for res, exp in zip(result, expected): + assert_block_equal(res, exp) + + +class TestBlockManager: + def test_attrs(self): + mgr = create_mgr("a,b,c: f8-1; d,e,f: f8-2") + assert mgr.nblocks == 2 + assert len(mgr) == 6 + + def test_duplicate_ref_loc_failure(self): + tmp_mgr = create_mgr("a:bool; a: f8") + + axes, blocks = tmp_mgr.axes, tmp_mgr.blocks + + blocks[0].mgr_locs = BlockPlacement(np.array([0])) + blocks[1].mgr_locs = BlockPlacement(np.array([0])) + + # test trying to create block manager with overlapping ref locs + + msg = "Gaps in blk ref_locs" + + mgr = BlockManager(blocks, axes) + with pytest.raises(AssertionError, match=msg): + mgr._rebuild_blknos_and_blklocs() + + blocks[0].mgr_locs = BlockPlacement(np.array([0])) + blocks[1].mgr_locs = BlockPlacement(np.array([1])) + mgr = BlockManager(blocks, axes) + mgr.iget(1) + + def test_pickle(self, mgr, temp_file): + mgr2 = tm.round_trip_pickle(mgr, temp_file) + tm.assert_frame_equal( + DataFrame._from_mgr(mgr, axes=mgr.axes), + DataFrame._from_mgr(mgr2, axes=mgr2.axes), + ) + + # GH2431 + assert hasattr(mgr2, "_is_consolidated") + assert hasattr(mgr2, "_known_consolidated") + + # reset to False on load + assert not mgr2._is_consolidated + assert not mgr2._known_consolidated + + @pytest.mark.parametrize("mgr_string", ["a,a,a:f8", "a: f8; a: i8"]) + def test_non_unique_pickle(self, mgr_string, temp_file): + mgr = create_mgr(mgr_string) + mgr2 = tm.round_trip_pickle(mgr, temp_file) + tm.assert_frame_equal( + DataFrame._from_mgr(mgr, axes=mgr.axes), + DataFrame._from_mgr(mgr2, axes=mgr2.axes), + ) + + def test_categorical_block_pickle(self, temp_file): + mgr = create_mgr("a: category") + mgr2 = tm.round_trip_pickle(mgr, temp_file) + tm.assert_frame_equal( + DataFrame._from_mgr(mgr, axes=mgr.axes), + DataFrame._from_mgr(mgr2, axes=mgr2.axes), + ) + + smgr = create_single_mgr("category") + smgr2 = tm.round_trip_pickle(smgr, temp_file) + tm.assert_series_equal( + Series()._constructor_from_mgr(smgr, axes=smgr.axes), + Series()._constructor_from_mgr(smgr2, axes=smgr2.axes), + ) + + def test_iget(self): + cols = Index(list("abc")) + values = np.random.default_rng(2).random((3, 3)) + block = new_block( + values=values.copy(), + placement=BlockPlacement(np.arange(3, dtype=np.intp)), + ndim=values.ndim, + ) + mgr = BlockManager(blocks=(block,), axes=[cols, Index(np.arange(3))]) + + tm.assert_almost_equal(mgr.iget(0).internal_values(), values[0]) + tm.assert_almost_equal(mgr.iget(1).internal_values(), values[1]) + tm.assert_almost_equal(mgr.iget(2).internal_values(), values[2]) + + def test_set(self): + mgr = create_mgr("a,b,c: int", item_shape=(3,)) + + mgr.insert(len(mgr.items), "d", np.array(["foo"] * 3)) + mgr.iset(1, np.array(["bar"] * 3)) + tm.assert_numpy_array_equal(mgr.iget(0).internal_values(), np.array([0] * 3)) + tm.assert_numpy_array_equal( + mgr.iget(1).internal_values(), np.array(["bar"] * 3, dtype=np.object_) + ) + tm.assert_numpy_array_equal(mgr.iget(2).internal_values(), np.array([2] * 3)) + tm.assert_numpy_array_equal( + mgr.iget(3).internal_values(), np.array(["foo"] * 3, dtype=np.object_) + ) + + def test_set_change_dtype(self, mgr): + mgr.insert(len(mgr.items), "baz", np.zeros(N, dtype=bool)) + + mgr.iset(mgr.items.get_loc("baz"), np.repeat("foo", N)) + idx = mgr.items.get_loc("baz") + assert mgr.iget(idx).dtype == np.object_ + + mgr2 = mgr.consolidate() + mgr2.iset(mgr2.items.get_loc("baz"), np.repeat("foo", N)) + idx = mgr2.items.get_loc("baz") + assert mgr2.iget(idx).dtype == np.object_ + + mgr2.insert( + len(mgr2.items), + "quux", + np.random.default_rng(2).standard_normal(N).astype(int), + ) + idx = mgr2.items.get_loc("quux") + assert mgr2.iget(idx).dtype == np.dtype(int) + + mgr2.iset( + mgr2.items.get_loc("quux"), np.random.default_rng(2).standard_normal(N) + ) + assert mgr2.iget(idx).dtype == np.float64 + + def test_copy(self, mgr): + cp = mgr.copy(deep=False) + for blk, cp_blk in zip(mgr.blocks, cp.blocks): + # view assertion + tm.assert_equal(cp_blk.values, blk.values) + if isinstance(blk.values, np.ndarray): + assert cp_blk.values.base.base is blk.values.base + else: + # DatetimeTZBlock has DatetimeIndex values + assert cp_blk.values._ndarray.base is blk.values._ndarray.base + + # copy(deep=True) consolidates, so the block-wise assertions will + # fail is mgr is not consolidated + mgr._consolidate_inplace() + cp = mgr.copy(deep=True) + for blk, cp_blk in zip(mgr.blocks, cp.blocks): + bvals = blk.values + cpvals = cp_blk.values + + tm.assert_equal(cpvals, bvals) + + if isinstance(cpvals, np.ndarray): + lbase = cpvals.base + rbase = bvals.base + else: + lbase = cpvals._ndarray.base + rbase = bvals._ndarray.base + + # copy assertion we either have a None for a base or in case of + # some blocks it is an array (e.g. datetimetz), but was copied + if isinstance(cpvals, DatetimeArray): + assert (lbase is None and rbase is None) or (lbase is not rbase) + elif not isinstance(cpvals, np.ndarray): + assert lbase is not rbase + else: + assert lbase is None and rbase is None + + def test_sparse(self): + mgr = create_mgr("a: sparse-1; b: sparse-2") + assert mgr.as_array().dtype == np.float64 + + def test_sparse_mixed(self): + mgr = create_mgr("a: sparse-1; b: sparse-2; c: f8") + assert len(mgr.blocks) == 3 + assert isinstance(mgr, BlockManager) + + @pytest.mark.parametrize( + "mgr_string, dtype", + [("c: f4; d: f2", np.float32), ("c: f4; d: f2; e: f8", np.float64)], + ) + def test_as_array_float(self, mgr_string, dtype): + mgr = create_mgr(mgr_string) + assert mgr.as_array().dtype == dtype + + @pytest.mark.parametrize( + "mgr_string, dtype", + [ + ("a: bool-1; b: bool-2", np.bool_), + ("a: i8-1; b: i8-2; c: i4; d: i2; e: u1", np.int64), + ("c: i4; d: i2; e: u1", np.int32), + ], + ) + def test_as_array_int_bool(self, mgr_string, dtype): + mgr = create_mgr(mgr_string) + assert mgr.as_array().dtype == dtype + + def test_as_array_datetime(self): + mgr = create_mgr("h: datetime-1; g: datetime-2") + assert mgr.as_array().dtype == "M8[ns]" + + def test_as_array_datetime_tz(self): + mgr = create_mgr("h: M8[ns, US/Eastern]; g: M8[ns, CET]") + assert mgr.iget(0).dtype == "datetime64[ns, US/Eastern]" + assert mgr.iget(1).dtype == "datetime64[ns, CET]" + assert mgr.as_array().dtype == "object" + + @pytest.mark.parametrize("t", ["float16", "float32", "float64", "int32", "int64"]) + def test_astype(self, t): + # coerce all + mgr = create_mgr("c: f4; d: f2; e: f8") + + t = np.dtype(t) + tmgr = mgr.astype(t) + assert tmgr.iget(0).dtype.type == t + assert tmgr.iget(1).dtype.type == t + assert tmgr.iget(2).dtype.type == t + + # mixed + mgr = create_mgr("a,b: object; c: bool; d: datetime; e: f4; f: f2; g: f8") + + t = np.dtype(t) + tmgr = mgr.astype(t, errors="ignore") + assert tmgr.iget(2).dtype.type == t + assert tmgr.iget(4).dtype.type == t + assert tmgr.iget(5).dtype.type == t + assert tmgr.iget(6).dtype.type == t + + assert tmgr.iget(0).dtype.type == np.object_ + assert tmgr.iget(1).dtype.type == np.object_ + if t != np.int64: + assert tmgr.iget(3).dtype.type == np.datetime64 + else: + assert tmgr.iget(3).dtype.type == t + + def test_convert(self, using_infer_string): + def _compare(old_mgr, new_mgr): + """compare the blocks, numeric compare ==, object don't""" + old_blocks = set(old_mgr.blocks) + new_blocks = set(new_mgr.blocks) + assert len(old_blocks) == len(new_blocks) + + # compare non-numeric + for b in old_blocks: + found = False + for nb in new_blocks: + if (b.values == nb.values).all(): + found = True + break + assert found + + for b in new_blocks: + found = False + for ob in old_blocks: + if (b.values == ob.values).all(): + found = True + break + assert found + + # noops + mgr = create_mgr("f: i8; g: f8") + new_mgr = mgr.convert() + _compare(mgr, new_mgr) + + # convert + mgr = create_mgr("a,b,foo: object; f: i8; g: f8") + mgr.iset(0, np.array(["1"] * N, dtype=np.object_)) + mgr.iset(1, np.array(["2."] * N, dtype=np.object_)) + mgr.iset(2, np.array(["foo."] * N, dtype=np.object_)) + new_mgr = mgr.convert() + dtype = "str" if using_infer_string else np.object_ + assert new_mgr.iget(0).dtype == dtype + assert new_mgr.iget(1).dtype == dtype + assert new_mgr.iget(2).dtype == dtype + assert new_mgr.iget(3).dtype == np.int64 + assert new_mgr.iget(4).dtype == np.float64 + + mgr = create_mgr( + "a,b,foo: object; f: i4; bool: bool; dt: datetime; i: i8; g: f8; h: f2" + ) + mgr.iset(0, np.array(["1"] * N, dtype=np.object_)) + mgr.iset(1, np.array(["2."] * N, dtype=np.object_)) + mgr.iset(2, np.array(["foo."] * N, dtype=np.object_)) + new_mgr = mgr.convert() + assert new_mgr.iget(0).dtype == dtype + assert new_mgr.iget(1).dtype == dtype + assert new_mgr.iget(2).dtype == dtype + assert new_mgr.iget(3).dtype == np.int32 + assert new_mgr.iget(4).dtype == np.bool_ + assert new_mgr.iget(5).dtype.type, np.datetime64 + assert new_mgr.iget(6).dtype == np.int64 + assert new_mgr.iget(7).dtype == np.float64 + assert new_mgr.iget(8).dtype == np.float16 + + def test_interleave(self): + # self + for dtype in ["f8", "i8", "object", "bool", "complex", "M8[ns]", "m8[ns]"]: + mgr = create_mgr(f"a: {dtype}") + assert mgr.as_array().dtype == dtype + mgr = create_mgr(f"a: {dtype}; b: {dtype}") + assert mgr.as_array().dtype == dtype + + @pytest.mark.parametrize( + "mgr_string, dtype", + [ + ("a: category", "i8"), + ("a: category; b: category", "i8"), + ("a: category; b: category2", "object"), + ("a: category2", "object"), + ("a: category2; b: category2", "object"), + ("a: f8", "f8"), + ("a: f8; b: i8", "f8"), + ("a: f4; b: i8", "f8"), + ("a: f4; b: i8; d: object", "object"), + ("a: bool; b: i8", "object"), + ("a: complex", "complex"), + ("a: f8; b: category", "object"), + ("a: M8[ns]; b: category", "object"), + ("a: M8[ns]; b: bool", "object"), + ("a: M8[ns]; b: i8", "object"), + ("a: m8[ns]; b: bool", "object"), + ("a: m8[ns]; b: i8", "object"), + ("a: M8[ns]; b: m8[ns]", "object"), + ], + ) + def test_interleave_dtype(self, mgr_string, dtype): + # will be converted according the actual dtype of the underlying + mgr = create_mgr("a: category") + assert mgr.as_array().dtype == "i8" + mgr = create_mgr("a: category; b: category2") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: category2") + assert mgr.as_array().dtype == "object" + + # combinations + mgr = create_mgr("a: f8") + assert mgr.as_array().dtype == "f8" + mgr = create_mgr("a: f8; b: i8") + assert mgr.as_array().dtype == "f8" + mgr = create_mgr("a: f4; b: i8") + assert mgr.as_array().dtype == "f8" + mgr = create_mgr("a: f4; b: i8; d: object") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: bool; b: i8") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: complex") + assert mgr.as_array().dtype == "complex" + mgr = create_mgr("a: f8; b: category") + assert mgr.as_array().dtype == "f8" + mgr = create_mgr("a: M8[ns]; b: category") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: M8[ns]; b: bool") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: M8[ns]; b: i8") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: m8[ns]; b: bool") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: m8[ns]; b: i8") + assert mgr.as_array().dtype == "object" + mgr = create_mgr("a: M8[ns]; b: m8[ns]") + assert mgr.as_array().dtype == "object" + + def test_consolidate_ordering_issues(self, mgr): + mgr.iset(mgr.items.get_loc("f"), np.random.default_rng(2).standard_normal(N)) + mgr.iset(mgr.items.get_loc("d"), np.random.default_rng(2).standard_normal(N)) + mgr.iset(mgr.items.get_loc("b"), np.random.default_rng(2).standard_normal(N)) + mgr.iset(mgr.items.get_loc("g"), np.random.default_rng(2).standard_normal(N)) + mgr.iset(mgr.items.get_loc("h"), np.random.default_rng(2).standard_normal(N)) + + # we have datetime/tz blocks in mgr + cons = mgr.consolidate() + assert cons.nblocks == 4 + cons = mgr.consolidate().get_numeric_data() + assert cons.nblocks == 1 + assert isinstance(cons.blocks[0].mgr_locs, BlockPlacement) + tm.assert_numpy_array_equal( + cons.blocks[0].mgr_locs.as_array, np.arange(len(cons.items), dtype=np.intp) + ) + + def test_reindex_items(self): + # mgr is not consolidated, f8 & f8-2 blocks + mgr = create_mgr("a: f8; b: i8; c: f8; d: i8; e: f8; f: bool; g: f8-2") + + reindexed = mgr.reindex_axis(["g", "c", "a", "d"], axis=0) + assert not reindexed.is_consolidated() + + tm.assert_index_equal(reindexed.items, Index(["g", "c", "a", "d"])) + tm.assert_almost_equal( + mgr.iget(6).internal_values(), reindexed.iget(0).internal_values() + ) + tm.assert_almost_equal( + mgr.iget(2).internal_values(), reindexed.iget(1).internal_values() + ) + tm.assert_almost_equal( + mgr.iget(0).internal_values(), reindexed.iget(2).internal_values() + ) + tm.assert_almost_equal( + mgr.iget(3).internal_values(), reindexed.iget(3).internal_values() + ) + + def test_get_numeric_data(self): + mgr = create_mgr( + "int: int; float: float; complex: complex;" + "str: object; bool: bool; obj: object; dt: datetime", + item_shape=(3,), + ) + mgr.iset(5, np.array([1, 2, 3], dtype=np.object_)) + + numeric = mgr.get_numeric_data() + tm.assert_index_equal(numeric.items, Index(["int", "float", "complex", "bool"])) + tm.assert_almost_equal( + mgr.iget(mgr.items.get_loc("float")).internal_values(), + numeric.iget(numeric.items.get_loc("float")).internal_values(), + ) + + # Check sharing + numeric.iset( + numeric.items.get_loc("float"), + np.array([100.0, 200.0, 300.0]), + inplace=True, + ) + tm.assert_almost_equal( + mgr.iget(mgr.items.get_loc("float")).internal_values(), + np.array([1.0, 1.0, 1.0]), + ) + + def test_get_bool_data(self): + mgr = create_mgr( + "int: int; float: float; complex: complex;" + "str: object; bool: bool; obj: object; dt: datetime", + item_shape=(3,), + ) + mgr.iset(6, np.array([True, False, True], dtype=np.object_)) + + bools = mgr.get_bool_data() + tm.assert_index_equal(bools.items, Index(["bool"])) + tm.assert_almost_equal( + mgr.iget(mgr.items.get_loc("bool")).internal_values(), + bools.iget(bools.items.get_loc("bool")).internal_values(), + ) + + bools.iset(0, np.array([True, False, True]), inplace=True) + tm.assert_numpy_array_equal( + mgr.iget(mgr.items.get_loc("bool")).internal_values(), + np.array([True, True, True]), + ) + + def test_unicode_repr_doesnt_raise(self): + repr(create_mgr("b,\u05d0: object")) + + @pytest.mark.parametrize( + "mgr_string", ["a,b,c: i8-1; d,e,f: i8-2", "a,a,a: i8-1; b,b,b: i8-2"] + ) + def test_equals(self, mgr_string): + # unique items + bm1 = create_mgr(mgr_string) + bm2 = BlockManager(bm1.blocks[::-1], bm1.axes) + assert bm1.equals(bm2) + + @pytest.mark.parametrize( + "mgr_string", + [ + "a:i8;b:f8", # basic case + "a:i8;b:f8;c:c8;d:b", # many types + "a:i8;e:dt;f:td;g:string", # more types + "a:i8;b:category;c:category2", # categories + "c:sparse;d:sparse_na;b:f8", # sparse + ], + ) + def test_equals_block_order_different_dtypes(self, mgr_string): + # GH 9330 + bm = create_mgr(mgr_string) + block_perms = itertools.permutations(bm.blocks) + for bm_perm in block_perms: + bm_this = BlockManager(tuple(bm_perm), bm.axes) + assert bm.equals(bm_this) + assert bm_this.equals(bm) + + def test_single_mgr_ctor(self): + mgr = create_single_mgr("f8", num_rows=5) + assert mgr.external_values().tolist() == [0.0, 1.0, 2.0, 3.0, 4.0] + + @pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0]) + def test_validate_bool_args(self, value): + bm1 = create_mgr("a,b,c: i8-1; d,e,f: i8-2") + + msg = ( + 'For argument "inplace" expected type bool, ' + f"received type {type(value).__name__}." + ) + with pytest.raises(ValueError, match=msg): + bm1.replace_list([1], [2], inplace=value) + + def test_iset_split_block(self): + bm = create_mgr("a,b,c: i8; d: f8") + bm._iset_split_block(0, np.array([0])) + tm.assert_numpy_array_equal( + bm.blklocs, np.array([0, 0, 1, 0], dtype="int64" if IS64 else "int32") + ) + # First indexer currently does not have a block associated with it in case + tm.assert_numpy_array_equal( + bm.blknos, np.array([0, 0, 0, 1], dtype="int64" if IS64 else "int32") + ) + assert len(bm.blocks) == 2 + + def test_iset_split_block_values(self): + bm = create_mgr("a,b,c: i8; d: f8") + bm._iset_split_block(0, np.array([0]), np.array([list(range(10))])) + tm.assert_numpy_array_equal( + bm.blklocs, np.array([0, 0, 1, 0], dtype="int64" if IS64 else "int32") + ) + # First indexer currently does not have a block associated with it in case + tm.assert_numpy_array_equal( + bm.blknos, np.array([0, 2, 2, 1], dtype="int64" if IS64 else "int32") + ) + assert len(bm.blocks) == 3 + + +def _as_array(mgr): + if mgr.ndim == 1: + return mgr.external_values() + return mgr.as_array().T + + +class TestIndexing: + # Nosetests-style data-driven tests. + # + # This test applies different indexing routines to block managers and + # compares the outcome to the result of same operations on np.ndarray. + # + # NOTE: sparse (SparseBlock with fill_value != np.nan) fail a lot of tests + # and are disabled. + + MANAGERS = [ + create_single_mgr("f8", N), + create_single_mgr("i8", N), + # 2-dim + create_mgr("a,b,c,d,e,f: f8", item_shape=(N,)), + create_mgr("a,b,c,d,e,f: i8", item_shape=(N,)), + create_mgr("a,b: f8; c,d: i8; e,f: string", item_shape=(N,)), + create_mgr("a,b: f8; c,d: i8; e,f: f8", item_shape=(N,)), + ] + + @pytest.mark.parametrize("mgr", MANAGERS) + def test_get_slice(self, mgr): + def assert_slice_ok(mgr, axis, slobj): + mat = _as_array(mgr) + + # we maybe using an ndarray to test slicing and + # might not be the full length of the axis + if isinstance(slobj, np.ndarray): + ax = mgr.axes[axis] + if len(ax) and len(slobj) and len(slobj) != len(ax): + slobj = np.concatenate( + [slobj, np.zeros(len(ax) - len(slobj), dtype=bool)] + ) + + if isinstance(slobj, slice): + sliced = mgr.get_slice(slobj, axis=axis) + elif ( + mgr.ndim == 1 + and axis == 0 + and isinstance(slobj, np.ndarray) + and slobj.dtype == bool + ): + sliced = mgr.get_rows_with_mask(slobj) + else: + # BlockManager doesn't support non-slice, SingleBlockManager + # doesn't support axis > 0 + raise TypeError(slobj) + + mat_slobj = (slice(None),) * axis + (slobj,) + tm.assert_numpy_array_equal( + mat[mat_slobj], _as_array(sliced), check_dtype=False + ) + tm.assert_index_equal(mgr.axes[axis][slobj], sliced.axes[axis]) + + assert mgr.ndim <= 2, mgr.ndim + for ax in range(mgr.ndim): + # slice + assert_slice_ok(mgr, ax, slice(None)) + assert_slice_ok(mgr, ax, slice(3)) + assert_slice_ok(mgr, ax, slice(100)) + assert_slice_ok(mgr, ax, slice(1, 4)) + assert_slice_ok(mgr, ax, slice(3, 0, -2)) + + if mgr.ndim < 2: + # 2D only support slice objects + + # boolean mask + assert_slice_ok(mgr, ax, np.ones(mgr.shape[ax], dtype=np.bool_)) + assert_slice_ok(mgr, ax, np.zeros(mgr.shape[ax], dtype=np.bool_)) + + if mgr.shape[ax] >= 3: + assert_slice_ok(mgr, ax, np.arange(mgr.shape[ax]) % 3 == 0) + assert_slice_ok( + mgr, ax, np.array([True, True, False], dtype=np.bool_) + ) + + @pytest.mark.parametrize("mgr", MANAGERS) + def test_take(self, mgr): + def assert_take_ok(mgr, axis, indexer): + mat = _as_array(mgr) + taken = mgr.take(indexer, axis) + tm.assert_numpy_array_equal( + np.take(mat, indexer, axis), _as_array(taken), check_dtype=False + ) + tm.assert_index_equal(mgr.axes[axis].take(indexer), taken.axes[axis]) + + for ax in range(mgr.ndim): + # take/fancy indexer + assert_take_ok(mgr, ax, indexer=np.array([], dtype=np.intp)) + assert_take_ok(mgr, ax, indexer=np.array([0, 0, 0], dtype=np.intp)) + assert_take_ok( + mgr, ax, indexer=np.array(list(range(mgr.shape[ax])), dtype=np.intp) + ) + + if mgr.shape[ax] >= 3: + assert_take_ok(mgr, ax, indexer=np.array([0, 1, 2], dtype=np.intp)) + assert_take_ok(mgr, ax, indexer=np.array([-1, -2, -3], dtype=np.intp)) + + @pytest.mark.parametrize("mgr", MANAGERS) + @pytest.mark.parametrize("fill_value", [None, np.nan, 100.0]) + def test_reindex_axis(self, fill_value, mgr): + def assert_reindex_axis_is_ok(mgr, axis, new_labels, fill_value): + mat = _as_array(mgr) + indexer = mgr.axes[axis].get_indexer_for(new_labels) + + reindexed = mgr.reindex_axis(new_labels, axis, fill_value=fill_value) + tm.assert_numpy_array_equal( + algos.take_nd(mat, indexer, axis, fill_value=fill_value), + _as_array(reindexed), + check_dtype=False, + ) + tm.assert_index_equal(reindexed.axes[axis], new_labels) + + for ax in range(mgr.ndim): + assert_reindex_axis_is_ok(mgr, ax, Index([]), fill_value) + assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax], fill_value) + assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][[0, 0, 0]], fill_value) + assert_reindex_axis_is_ok(mgr, ax, Index(["foo", "bar", "baz"]), fill_value) + assert_reindex_axis_is_ok( + mgr, ax, Index(["foo", mgr.axes[ax][0], "baz"]), fill_value + ) + + if mgr.shape[ax] >= 3: + assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][:-3], fill_value) + assert_reindex_axis_is_ok(mgr, ax, mgr.axes[ax][-3::-1], fill_value) + assert_reindex_axis_is_ok( + mgr, ax, mgr.axes[ax][[0, 1, 2, 0, 1, 2]], fill_value + ) + + @pytest.mark.parametrize("mgr", MANAGERS) + @pytest.mark.parametrize("fill_value", [None, np.nan, 100.0]) + def test_reindex_indexer(self, fill_value, mgr): + def assert_reindex_indexer_is_ok(mgr, axis, new_labels, indexer, fill_value): + mat = _as_array(mgr) + reindexed_mat = algos.take_nd(mat, indexer, axis, fill_value=fill_value) + reindexed = mgr.reindex_indexer( + new_labels, indexer, axis, fill_value=fill_value + ) + tm.assert_numpy_array_equal( + reindexed_mat, _as_array(reindexed), check_dtype=False + ) + tm.assert_index_equal(reindexed.axes[axis], new_labels) + + for ax in range(mgr.ndim): + assert_reindex_indexer_is_ok( + mgr, ax, Index([]), np.array([], dtype=np.intp), fill_value + ) + assert_reindex_indexer_is_ok( + mgr, ax, mgr.axes[ax], np.arange(mgr.shape[ax]), fill_value + ) + assert_reindex_indexer_is_ok( + mgr, + ax, + Index(["foo"] * mgr.shape[ax]), + np.arange(mgr.shape[ax]), + fill_value, + ) + assert_reindex_indexer_is_ok( + mgr, ax, mgr.axes[ax][::-1], np.arange(mgr.shape[ax]), fill_value + ) + assert_reindex_indexer_is_ok( + mgr, ax, mgr.axes[ax], np.arange(mgr.shape[ax])[::-1], fill_value + ) + assert_reindex_indexer_is_ok( + mgr, ax, Index(["foo", "bar", "baz"]), np.array([0, 0, 0]), fill_value + ) + assert_reindex_indexer_is_ok( + mgr, ax, Index(["foo", "bar", "baz"]), np.array([-1, 0, -1]), fill_value + ) + assert_reindex_indexer_is_ok( + mgr, + ax, + Index(["foo", mgr.axes[ax][0], "baz"]), + np.array([-1, -1, -1]), + fill_value, + ) + + if mgr.shape[ax] >= 3: + assert_reindex_indexer_is_ok( + mgr, + ax, + Index(["foo", "bar", "baz"]), + np.array([0, 1, 2]), + fill_value, + ) + + +class TestBlockPlacement: + @pytest.mark.parametrize( + "slc, expected", + [ + (slice(0, 4), 4), + (slice(0, 4, 2), 2), + (slice(0, 3, 2), 2), + (slice(0, 1, 2), 1), + (slice(1, 0, -1), 1), + ], + ) + def test_slice_len(self, slc, expected): + assert len(BlockPlacement(slc)) == expected + + @pytest.mark.parametrize("slc", [slice(1, 1, 0), slice(1, 2, 0)]) + def test_zero_step_raises(self, slc): + msg = "slice step cannot be zero" + with pytest.raises(ValueError, match=msg): + BlockPlacement(slc) + + def test_slice_canonize_negative_stop(self): + # GH#37524 negative stop is OK with negative step and positive start + slc = slice(3, -1, -2) + + bp = BlockPlacement(slc) + assert bp.indexer == slice(3, None, -2) + + @pytest.mark.parametrize( + "slc", + [ + slice(None, None), + slice(10, None), + slice(None, None, -1), + slice(None, 10, -1), + # These are "unbounded" because negative index will + # change depending on container shape. + slice(-1, None), + slice(None, -1), + slice(-1, -1), + slice(-1, None, -1), + slice(None, -1, -1), + slice(-1, -1, -1), + ], + ) + def test_unbounded_slice_raises(self, slc): + msg = "unbounded slice" + with pytest.raises(ValueError, match=msg): + BlockPlacement(slc) + + @pytest.mark.parametrize( + "slc", + [ + slice(0, 0), + slice(100, 0), + slice(100, 100), + slice(100, 100, -1), + slice(0, 100, -1), + ], + ) + def test_not_slice_like_slices(self, slc): + assert not BlockPlacement(slc).is_slice_like + + @pytest.mark.parametrize( + "arr, slc", + [ + ([0], slice(0, 1, 1)), + ([100], slice(100, 101, 1)), + ([0, 1, 2], slice(0, 3, 1)), + ([0, 5, 10], slice(0, 15, 5)), + ([0, 100], slice(0, 200, 100)), + ([2, 1], slice(2, 0, -1)), + ], + ) + def test_array_to_slice_conversion(self, arr, slc): + assert BlockPlacement(arr).as_slice == slc + + @pytest.mark.parametrize( + "arr", + [ + [], + [-1], + [-1, -2, -3], + [-10], + [-1, 0, 1, 2], + [-2, 0, 2, 4], + [1, 0, -1], + [1, 1, 1], + ], + ) + def test_not_slice_like_arrays(self, arr): + assert not BlockPlacement(arr).is_slice_like + + @pytest.mark.parametrize( + "slc, expected", + [(slice(0, 3), [0, 1, 2]), (slice(0, 0), []), (slice(3, 0), [])], + ) + def test_slice_iter(self, slc, expected): + assert list(BlockPlacement(slc)) == expected + + @pytest.mark.parametrize( + "slc, arr", + [ + (slice(0, 3), [0, 1, 2]), + (slice(0, 0), []), + (slice(3, 0), []), + (slice(3, 0, -1), [3, 2, 1]), + ], + ) + def test_slice_to_array_conversion(self, slc, arr): + tm.assert_numpy_array_equal( + BlockPlacement(slc).as_array, np.asarray(arr, dtype=np.intp) + ) + + def test_blockplacement_add(self): + bpl = BlockPlacement(slice(0, 5)) + assert bpl.add(1).as_slice == slice(1, 6, 1) + assert bpl.add(np.arange(5)).as_slice == slice(0, 10, 2) + assert list(bpl.add(np.arange(5, 0, -1))) == [5, 5, 5, 5, 5] + + @pytest.mark.parametrize( + "val, inc, expected", + [ + (slice(0, 0), 0, []), + (slice(1, 4), 0, [1, 2, 3]), + (slice(3, 0, -1), 0, [3, 2, 1]), + ([1, 2, 4], 0, [1, 2, 4]), + (slice(0, 0), 10, []), + (slice(1, 4), 10, [11, 12, 13]), + (slice(3, 0, -1), 10, [13, 12, 11]), + ([1, 2, 4], 10, [11, 12, 14]), + (slice(0, 0), -1, []), + (slice(1, 4), -1, [0, 1, 2]), + ([1, 2, 4], -1, [0, 1, 3]), + ], + ) + def test_blockplacement_add_int(self, val, inc, expected): + assert list(BlockPlacement(val).add(inc)) == expected + + @pytest.mark.parametrize("val", [slice(1, 4), [1, 2, 4]]) + def test_blockplacement_add_int_raises(self, val): + msg = "iadd causes length change" + with pytest.raises(ValueError, match=msg): + BlockPlacement(val).add(-10) + + +class TestCanHoldElement: + @pytest.fixture( + params=[ + lambda x: x, + lambda x: x.to_series(), + lambda x: x._data, + lambda x: list(x), + lambda x: x.astype(object), + lambda x: np.asarray(x), + lambda x: x[0], + lambda x: x[:0], + ] + ) + def element(self, request): + """ + Functions that take an Index and return an element that should have + blk._can_hold_element(element) for a Block with this index's dtype. + """ + return request.param + + def test_datetime_block_can_hold_element(self): + block = create_block("datetime", [0]) + + assert block._can_hold_element([]) + + # We will check that block._can_hold_element iff arr.__setitem__ works + arr = pd.array(block.values.ravel()) + + # coerce None + assert block._can_hold_element(None) + arr[0] = None + assert arr[0] is pd.NaT + + # coerce different types of datetime objects + vals = [np.datetime64("2010-10-10"), datetime(2010, 10, 10)] + for val in vals: + assert block._can_hold_element(val) + arr[0] = val + + val = date(2010, 10, 10) + assert not block._can_hold_element(val) + + msg = ( + "value should be a 'Timestamp', 'NaT', " + "or array of those. Got 'date' instead." + ) + with pytest.raises(TypeError, match=msg): + arr[0] = val + + @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) + def test_interval_can_hold_element_emptylist(self, dtype, element): + arr = np.array([1, 3, 4], dtype=dtype) + ii = IntervalIndex.from_breaks(arr) + blk = new_block(ii._data, BlockPlacement([1]), ndim=2) + + assert blk._can_hold_element([]) + # TODO: check this holds for all blocks + + @pytest.mark.parametrize("dtype", [np.int64, np.uint64, np.float64]) + def test_interval_can_hold_element(self, dtype, element): + arr = np.array([1, 3, 4, 9], dtype=dtype) + ii = IntervalIndex.from_breaks(arr) + blk = new_block(ii._data, BlockPlacement([1]), ndim=2) + + elem = element(ii) + self.check_series_setitem(elem, ii, True) + assert blk._can_hold_element(elem) + + # Careful: to get the expected Series-inplace behavior we need + # `elem` to not have the same length as `arr` + ii2 = IntervalIndex.from_breaks(arr[:-1], closed="neither") + elem = element(ii2) + with pytest.raises(TypeError, match="Invalid value"): + self.check_series_setitem(elem, ii, False) + assert not blk._can_hold_element(elem) + + ii3 = IntervalIndex.from_breaks([Timestamp(1), Timestamp(3), Timestamp(4)]) + elem = element(ii3) + with pytest.raises(TypeError, match="Invalid value"): + self.check_series_setitem(elem, ii, False) + assert not blk._can_hold_element(elem) + + ii4 = IntervalIndex.from_breaks([Timedelta(1), Timedelta(3), Timedelta(4)]) + elem = element(ii4) + with pytest.raises(TypeError, match="Invalid value"): + self.check_series_setitem(elem, ii, False) + assert not blk._can_hold_element(elem) + + def test_period_can_hold_element_emptylist(self): + pi = period_range("2016", periods=3, freq="Y") + blk = new_block(pi._data.reshape(1, 3), BlockPlacement([1]), ndim=2) + + assert blk._can_hold_element([]) + + def test_period_can_hold_element(self, element): + pi = period_range("2016", periods=3, freq="Y") + + elem = element(pi) + self.check_series_setitem(elem, pi, True) + + # Careful: to get the expected Series-inplace behavior we need + # `elem` to not have the same length as `arr` + pi2 = pi.asfreq("D")[:-1] + elem = element(pi2) + with pytest.raises(TypeError, match="Invalid value"): + self.check_series_setitem(elem, pi, False) + + dti = pi.to_timestamp("s")[:-1] + elem = element(dti) + with pytest.raises(TypeError, match="Invalid value"): + self.check_series_setitem(elem, pi, False) + + def test_period_reindex_axis(self): + # GH#60273 Test reindexing of block with PeriodDtype + pi = period_range("2020", periods=5, freq="Y") + blk = new_block(pi._data.reshape(5, 1), BlockPlacement(slice(5)), ndim=2) + mgr = BlockManager(blocks=(blk,), axes=[Index(np.arange(5)), Index(["a"])]) + reindexed = mgr.reindex_axis(Index([0, 2, 4]), axis=0) + result = DataFrame._from_mgr(reindexed, axes=reindexed.axes) + expected = DataFrame([[pi[0], pi[2], pi[4]]], columns=[0, 2, 4], index=["a"]) + tm.assert_frame_equal(result, expected) + + def check_can_hold_element(self, obj, elem, inplace: bool): + blk = obj._mgr.blocks[0] + if inplace: + assert blk._can_hold_element(elem) + else: + assert not blk._can_hold_element(elem) + + def check_series_setitem(self, elem, index: Index, inplace: bool): + arr = index._data.copy() + ser = Series(arr, copy=False) + + self.check_can_hold_element(ser, elem, inplace) + + if is_scalar(elem): + ser[0] = elem + else: + ser[: len(elem)] = elem + + if inplace: + assert ser._values is arr # i.e. setting was done inplace + else: + assert ser.dtype == object + + +class TestShouldStore: + def test_should_store_categorical(self): + cat = Categorical(["A", "B", "C"]) + df = DataFrame(cat) + blk = df._mgr.blocks[0] + + # matching dtype + assert blk.should_store(cat) + assert blk.should_store(cat[:-1]) + + # different dtype + assert not blk.should_store(cat.as_ordered()) + + # ndarray instead of Categorical + assert not blk.should_store(np.asarray(cat)) + + +def test_validate_ndim(): + values = np.array([1.0, 2.0]) + placement = BlockPlacement(slice(2)) + msg = r"Wrong number of dimensions. values.ndim != ndim \[1 != 2\]" + + depr_msg = "make_block is deprecated" + with pytest.raises(ValueError, match=msg): + with tm.assert_produces_warning(Pandas4Warning, match=depr_msg): + make_block(values, placement, ndim=2) + + +def test_block_shape(): + idx = Index([0, 1, 2, 3, 4]) + a = Series([1, 2, 3]).reindex(idx) + b = Series(Categorical([1, 2, 3])).reindex(idx) + + assert a._mgr.blocks[0].mgr_locs.indexer == b._mgr.blocks[0].mgr_locs.indexer + + +def test_make_block_no_pandas_array(block_maker): + # https://github.com/pandas-dev/pandas/pull/24866 + arr = pd.arrays.NumpyExtensionArray(np.array([1, 2])) + + depr_msg = "make_block is deprecated" + warn = DeprecationWarning if block_maker is make_block else None + + # NumpyExtensionArray, no dtype + with tm.assert_produces_warning(warn, match=depr_msg): + result = block_maker(arr, BlockPlacement(slice(len(arr))), ndim=arr.ndim) + assert result.dtype.kind in ["i", "u"] + + if block_maker is make_block: + # new_block requires caller to unwrap NumpyExtensionArray + assert result.is_extension is False + + # NumpyExtensionArray, NumpyEADtype + with tm.assert_produces_warning(warn, match=depr_msg): + result = block_maker(arr, slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim) + assert result.dtype.kind in ["i", "u"] + assert result.is_extension is False + + # new_block no longer accepts dtype keyword + # ndarray, NumpyEADtype + with tm.assert_produces_warning(warn, match=depr_msg): + result = block_maker( + arr.to_numpy(), slice(len(arr)), dtype=arr.dtype, ndim=arr.ndim + ) + assert result.dtype.kind in ["i", "u"] + assert result.is_extension is False diff --git a/lib/python3.11/site-packages/pandas/tests/io/__init__.py b/lib/python3.11/site-packages/pandas/tests/io/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git 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b/lib/python3.11/site-packages/pandas/tests/io/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..5ce44e87570a77aa31b2cc534ced2c2797e66c74 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/conftest.py @@ -0,0 +1,197 @@ +import uuid + +import pytest + +from pandas.compat import ( + is_ci_environment, + is_platform_arm, + is_platform_mac, + is_platform_windows, +) +import pandas.util._test_decorators as td + +import pandas.io.common as icom +from pandas.io.parsers import read_csv + + +@pytest.fixture +def compression_to_extension(): + return {value: key for key, value in icom.extension_to_compression.items()} + + +@pytest.fixture +def tips_file(datapath): + """Path to the tips dataset""" + return datapath("io", "data", "csv", "tips.csv") + + +@pytest.fixture +def jsonl_file(datapath): + """Path to a JSONL dataset""" + return datapath("io", "parser", "data", "items.jsonl") + + +@pytest.fixture +def salaries_table(datapath): + """DataFrame with the salaries dataset""" + return read_csv(datapath("io", "parser", "data", "salaries.csv"), sep="\t") + + +@pytest.fixture +def feather_file(datapath): + return datapath("io", "data", "feather", "feather-0_3_1.feather") + + +@pytest.fixture +def xml_file(datapath): + return datapath("io", "data", "xml", "books.xml") + + +@pytest.fixture(scope="session") +def aws_credentials(monkeysession): + """Mocked AWS Credentials for moto.""" + monkeysession.setenv("AWS_ACCESS_KEY_ID", "testing") + monkeysession.setenv("AWS_SECRET_ACCESS_KEY", "testing") + monkeysession.setenv("AWS_SECURITY_TOKEN", "testing") + monkeysession.setenv("AWS_SESSION_AWS_SESSION_TOKEN", "testing") + monkeysession.setenv("AWS_DEFAULT_REGION", "us-east-1") + + +@pytest.fixture(scope="session") +def moto_server(aws_credentials): + # use service container for Linux on GitHub Actions + if is_ci_environment() and not ( + is_platform_mac() or is_platform_arm() or is_platform_windows() + ): + yield "http://localhost:5000" + else: + moto_server = pytest.importorskip("moto.server") + server = moto_server.ThreadedMotoServer(port=0) + server.start() + host, port = server.get_host_and_port() + yield f"http://{host}:{port}" + server.stop() + + +@pytest.fixture +def moto_s3_resource(moto_server): + boto3 = pytest.importorskip("boto3") + s3 = boto3.resource("s3", endpoint_url=moto_server) + return s3 + + +@pytest.fixture(scope="session") +def s3so(moto_server): + return { + "client_kwargs": { + "endpoint_url": moto_server, + } + } + + +@pytest.fixture +def s3_bucket_public(moto_s3_resource): + """ + Create a public S3 bucket using moto. + """ + bucket_name = f"pandas-test-{uuid.uuid4()}" + bucket = moto_s3_resource.Bucket(bucket_name) + bucket.create(ACL="public-read") + yield bucket + bucket.objects.delete() + bucket.delete() + + +@pytest.fixture +def s3_bucket_private(moto_s3_resource): + """ + Create a private S3 bucket using moto. + """ + bucket_name = f"cant_get_it-{uuid.uuid4()}" + bucket = moto_s3_resource.Bucket(bucket_name) + bucket.create(ACL="private") + yield bucket + bucket.objects.delete() + bucket.delete() + + +@pytest.fixture +def s3_bucket_public_with_data( + s3_bucket_public, tips_file, jsonl_file, feather_file, xml_file +): + """ + The following datasets + are loaded. + + - tips.csv + - tips.csv.gz + - tips.csv.bz2 + - items.jsonl + """ + test_s3_files = [ + ("tips#1.csv", tips_file), + ("tips.csv", tips_file), + ("tips.csv.gz", tips_file + ".gz"), + ("tips.csv.bz2", tips_file + ".bz2"), + ("items.jsonl", jsonl_file), + ("simple_dataset.feather", feather_file), + ("books.xml", xml_file), + ] + for s3_key, file_name in test_s3_files: + with open(file_name, "rb") as f: + s3_bucket_public.put_object(Key=s3_key, Body=f) + return s3_bucket_public + + +@pytest.fixture +def s3_bucket_private_with_data( + s3_bucket_private, tips_file, jsonl_file, feather_file, xml_file +): + """ + The following datasets + are loaded. + + - tips.csv + - tips.csv.gz + - tips.csv.bz2 + - items.jsonl + """ + test_s3_files = [ + ("tips#1.csv", tips_file), + ("tips.csv", tips_file), + ("tips.csv.gz", tips_file + ".gz"), + ("tips.csv.bz2", tips_file + ".bz2"), + ("items.jsonl", jsonl_file), + ("simple_dataset.feather", feather_file), + ("books.xml", xml_file), + ] + for s3_key, file_name in test_s3_files: + with open(file_name, "rb") as f: + s3_bucket_private.put_object(Key=s3_key, Body=f) + return s3_bucket_private + + +_compression_formats_params = [ + (".no_compress", None), + ("", None), + (".gz", "gzip"), + (".GZ", "gzip"), + (".bz2", "bz2"), + (".BZ2", "bz2"), + (".zip", "zip"), + (".ZIP", "zip"), + (".xz", "xz"), + (".XZ", "xz"), + pytest.param((".zst", "zstd"), marks=td.skip_if_no("zstandard")), + pytest.param((".ZST", "zstd"), marks=td.skip_if_no("zstandard")), +] + + +@pytest.fixture(params=_compression_formats_params[1:]) +def compression_format(request): + return request.param + + +@pytest.fixture(params=_compression_formats_params) +def 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b/lib/python3.11/site-packages/pandas/tests/io/excel/test_odf.py new file mode 100644 index 0000000000000000000000000000000000000000..7ce720c9e1345722f222d46de4e8b103c4a7ede0 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/excel/test_odf.py @@ -0,0 +1,72 @@ +import functools + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + +pytest.importorskip("odf") + + +@pytest.fixture(autouse=True) +def cd_and_set_engine(monkeypatch, datapath): + func = functools.partial(pd.read_excel, engine="odf") + monkeypatch.setattr(pd, "read_excel", func) + monkeypatch.chdir(datapath("io", "data", "excel")) + + +def test_read_invalid_types_raises(): + # the invalid_value_type.ods required manually editing + # of the included content.xml file + with pytest.raises(ValueError, match="Unrecognized type awesome_new_type"): + pd.read_excel("invalid_value_type.ods") + + +def test_read_writer_table(): + # Also test reading tables from a text OpenDocument file + # (.odt) + index = pd.Index(["Row 1", "Row 2", "Row 3"], name="Header") + expected = pd.DataFrame( + [[1, np.nan, 7], [2, np.nan, 8], [3, np.nan, 9]], + index=index, + columns=["Column 1", "Unnamed: 2", "Column 3"], + ) + + result = pd.read_excel("writertable.odt", sheet_name="Table1", index_col=0) + + tm.assert_frame_equal(result, expected) + + +def test_read_newlines_between_xml_elements_table(): + # GH#45598 + expected = pd.DataFrame( + [[1.0, 4.0, 7], [np.nan, np.nan, 8], [3.0, 6.0, 9]], + columns=["Column 1", "Column 2", "Column 3"], + ) + + result = pd.read_excel("test_newlines.ods") + + tm.assert_frame_equal(result, expected) + + +def test_read_unempty_cells(): + expected = pd.DataFrame( + [1, np.nan, 3, np.nan, 5], + columns=["Column 1"], + ) + + result = pd.read_excel("test_unempty_cells.ods") + + tm.assert_frame_equal(result, expected) + + +def test_read_cell_annotation(): + expected = pd.DataFrame( + ["test", np.nan, "test 3"], + columns=["Column 1"], + ) + + result = pd.read_excel("test_cell_annotation.ods") + + tm.assert_frame_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/io/excel/test_odswriter.py b/lib/python3.11/site-packages/pandas/tests/io/excel/test_odswriter.py new file mode 100644 index 0000000000000000000000000000000000000000..7843bb59f97cf6e95903c2869d33460843aa9169 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/excel/test_odswriter.py @@ -0,0 +1,106 @@ +from datetime import ( + date, + datetime, +) +import re +import uuid + +import pytest + +import pandas as pd + +from pandas.io.excel import ExcelWriter + +odf = pytest.importorskip("odf") + + +@pytest.fixture +def ext(): + return ".ods" + + +@pytest.fixture +def tmp_excel(ext, tmp_path): + tmp = tmp_path / f"{uuid.uuid4()}{ext}" + tmp.touch() + return str(tmp) + + +def test_write_append_mode_raises(tmp_excel): + msg = "Append mode is not supported with odf!" + + with pytest.raises(ValueError, match=msg): + ExcelWriter(tmp_excel, engine="odf", mode="a") + + +@pytest.mark.parametrize("engine_kwargs", [None, {"kwarg": 1}]) +def test_engine_kwargs(tmp_excel, engine_kwargs): + # GH 42286 + # GH 43445 + # test for error: OpenDocumentSpreadsheet does not accept any arguments + if engine_kwargs is not None: + error = re.escape( + "OpenDocumentSpreadsheet() got an unexpected keyword argument 'kwarg'" + ) + with pytest.raises( + TypeError, + match=error, + ): + ExcelWriter(tmp_excel, engine="odf", engine_kwargs=engine_kwargs) + else: + with ExcelWriter(tmp_excel, engine="odf", engine_kwargs=engine_kwargs) as _: + pass + + +def test_book_and_sheets_consistent(tmp_excel): + # GH#45687 - Ensure sheets is updated if user modifies book + with ExcelWriter(tmp_excel) as writer: + assert writer.sheets == {} + table = odf.table.Table(name="test_name") + writer.book.spreadsheet.addElement(table) + assert writer.sheets == {"test_name": table} + + +@pytest.mark.parametrize( + ["value", "cell_value_type", "cell_value_attribute", "cell_value"], + argvalues=[ + (True, "boolean", "boolean-value", "true"), + ("test string", "string", "string-value", "test string"), + (1, "float", "value", "1"), + (1.5, "float", "value", "1.5"), + ( + datetime(2010, 10, 10, 10, 10, 10), + "date", + "date-value", + "2010-10-10T10:10:10", + ), + (date(2010, 10, 10), "date", "date-value", "2010-10-10"), + ], +) +def test_cell_value_type( + tmp_excel, value, cell_value_type, cell_value_attribute, cell_value +): + # GH#54994 ODS: cell attributes should follow specification + # http://docs.oasis-open.org/office/v1.2/os/OpenDocument-v1.2-os-part1.html#refTable13 + from odf.namespaces import OFFICENS + from odf.table import ( + TableCell, + TableRow, + ) + + table_cell_name = TableCell().qname + + pd.DataFrame([[value]]).to_excel(tmp_excel, header=False, index=False) + + with pd.ExcelFile(tmp_excel) as wb: + sheet = wb._reader.get_sheet_by_index(0) + sheet_rows = sheet.getElementsByType(TableRow) + sheet_cells = [ + x + for x in sheet_rows[0].childNodes + if hasattr(x, "qname") and x.qname == table_cell_name + ] + + cell = sheet_cells[0] + assert cell.attributes.get((OFFICENS, "value-type")) == cell_value_type + assert cell.attributes.get((OFFICENS, cell_value_attribute)) == cell_value diff --git a/lib/python3.11/site-packages/pandas/tests/io/excel/test_openpyxl.py b/lib/python3.11/site-packages/pandas/tests/io/excel/test_openpyxl.py new file mode 100644 index 0000000000000000000000000000000000000000..5b4bbb9e686d3308b5fa698ab7e2607c29872efe --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/excel/test_openpyxl.py @@ -0,0 +1,431 @@ +import contextlib +from pathlib import Path +import re +import uuid + +import numpy as np +import pytest + +import pandas as pd +from pandas import DataFrame +import pandas._testing as tm + +from pandas.io.excel import ( + ExcelWriter, + _OpenpyxlWriter, +) +from pandas.io.excel._openpyxl import OpenpyxlReader + +openpyxl = pytest.importorskip("openpyxl") + + +@pytest.fixture +def ext(): + return ".xlsx" + + +@pytest.fixture +def tmp_excel(ext, tmp_path): + tmp = tmp_path / f"{uuid.uuid4()}{ext}" + tmp.touch() + return str(tmp) + + +def test_to_excel_styleconverter(): + from openpyxl import styles + + hstyle = { + "font": {"color": "00FF0000", "bold": True}, + "borders": {"top": "thin", "right": "thin", "bottom": "thin", "left": "thin"}, + "alignment": {"horizontal": "center", "vertical": "top"}, + "fill": {"patternType": "solid", "fgColor": {"rgb": "006666FF", "tint": 0.3}}, + "number_format": {"format_code": "0.00"}, + "protection": {"locked": True, "hidden": False}, + } + + font_color = styles.Color("00FF0000") + font = styles.Font(bold=True, color=font_color) + side = styles.Side(style=styles.borders.BORDER_THIN) + border = styles.Border(top=side, right=side, bottom=side, left=side) + alignment = styles.Alignment(horizontal="center", vertical="top") + fill_color = styles.Color(rgb="006666FF", tint=0.3) + fill = styles.PatternFill(patternType="solid", fgColor=fill_color) + + number_format = "0.00" + + protection = styles.Protection(locked=True, hidden=False) + + kw = _OpenpyxlWriter._convert_to_style_kwargs(hstyle) + assert kw["font"] == font + assert kw["border"] == border + assert kw["alignment"] == alignment + assert kw["fill"] == fill + assert kw["number_format"] == number_format + assert kw["protection"] == protection + + +def test_write_cells_merge_styled(tmp_excel): + from pandas.io.formats.excel import ExcelCell + + sheet_name = "merge_styled" + + sty_b1 = {"font": {"color": "00FF0000"}} + sty_a2 = {"font": {"color": "0000FF00"}} + + initial_cells = [ + ExcelCell(col=1, row=0, val=42, style=sty_b1), + ExcelCell(col=0, row=1, val=99, style=sty_a2), + ] + + sty_merged = {"font": {"color": "000000FF", "bold": True}} + sty_kwargs = _OpenpyxlWriter._convert_to_style_kwargs(sty_merged) + openpyxl_sty_merged = sty_kwargs["font"] + merge_cells = [ + ExcelCell( + col=0, row=0, val="pandas", mergestart=1, mergeend=1, style=sty_merged + ) + ] + + with _OpenpyxlWriter(tmp_excel) as writer: + writer._write_cells(initial_cells, sheet_name=sheet_name) + writer._write_cells(merge_cells, sheet_name=sheet_name) + + wks = writer.sheets[sheet_name] + xcell_b1 = wks["B1"] + xcell_a2 = wks["A2"] + assert xcell_b1.font == openpyxl_sty_merged + assert xcell_a2.font == openpyxl_sty_merged + + +@pytest.mark.parametrize("iso_dates", [True, False]) +def test_engine_kwargs_write(tmp_excel, iso_dates): + # GH 42286 GH 43445 + engine_kwargs = {"iso_dates": iso_dates} + with ExcelWriter( + tmp_excel, engine="openpyxl", engine_kwargs=engine_kwargs + ) as writer: + assert writer.book.iso_dates == iso_dates + # ExcelWriter won't allow us to close without writing something + DataFrame().to_excel(writer) + + +def test_engine_kwargs_append_invalid(tmp_excel): + # GH 43445 + # test whether an invalid engine kwargs actually raises + DataFrame(["hello", "world"]).to_excel(tmp_excel) + with pytest.raises( + TypeError, + match=re.escape( + "load_workbook() got an unexpected keyword argument 'apple_banana'" + ), + ): + with ExcelWriter( + tmp_excel, + engine="openpyxl", + mode="a", + engine_kwargs={"apple_banana": "fruit"}, + ) as writer: + # ExcelWriter needs us to write something to close properly + DataFrame(["good"]).to_excel(writer, sheet_name="Sheet2") + + +@pytest.mark.parametrize("data_only, expected", [(True, 0), (False, "=1+1")]) +def test_engine_kwargs_append_data_only(tmp_excel, data_only, expected): + # GH 43445 + # tests whether the data_only engine_kwarg actually works well for + # openpyxl's load_workbook + DataFrame(["=1+1"]).to_excel(tmp_excel) + with ExcelWriter( + tmp_excel, engine="openpyxl", mode="a", engine_kwargs={"data_only": data_only} + ) as writer: + assert writer.sheets["Sheet1"]["B2"].value == expected + # ExcelWriter needs us to writer something to close properly? + DataFrame().to_excel(writer, sheet_name="Sheet2") + + # ensure that data_only also works for reading + # and that formulas/values roundtrip + assert ( + pd.read_excel( + tmp_excel, + sheet_name="Sheet1", + engine="openpyxl", + engine_kwargs={"data_only": data_only}, + ).iloc[0, 1] + == expected + ) + + +@pytest.mark.parametrize("kwarg_name", ["read_only", "data_only"]) +@pytest.mark.parametrize("kwarg_value", [True, False]) +def test_engine_kwargs_append_reader(datapath, ext, kwarg_name, kwarg_value): + # GH 55027 + # test that `read_only` and `data_only` can be passed to + # `openpyxl.reader.excel.load_workbook` via `engine_kwargs` + filename = datapath("io", "data", "excel", "test1" + ext) + with contextlib.closing( + OpenpyxlReader(filename, engine_kwargs={kwarg_name: kwarg_value}) + ) as reader: + assert getattr(reader.book, kwarg_name) == kwarg_value + + +@pytest.mark.parametrize( + "mode,expected", [("w", ["baz"]), ("a", ["foo", "bar", "baz"])] +) +def test_write_append_mode(tmp_excel, mode, expected): + df = DataFrame([1], columns=["baz"]) + + wb = openpyxl.Workbook() + wb.worksheets[0].title = "foo" + wb.worksheets[0]["A1"].value = "foo" + wb.create_sheet("bar") + wb.worksheets[1]["A1"].value = "bar" + wb.save(tmp_excel) + + with ExcelWriter(tmp_excel, engine="openpyxl", mode=mode) as writer: + df.to_excel(writer, sheet_name="baz", index=False) + + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb2: + result = [sheet.title for sheet in wb2.worksheets] + assert result == expected + + for index, cell_value in enumerate(expected): + assert wb2.worksheets[index]["A1"].value == cell_value + + +@pytest.mark.parametrize( + "if_sheet_exists,num_sheets,expected", + [ + ("new", 2, ["apple", "banana"]), + ("replace", 1, ["pear"]), + ("overlay", 1, ["pear", "banana"]), + ], +) +def test_if_sheet_exists_append_modes(tmp_excel, if_sheet_exists, num_sheets, expected): + # GH 40230 + df1 = DataFrame({"fruit": ["apple", "banana"]}) + df2 = DataFrame({"fruit": ["pear"]}) + + df1.to_excel(tmp_excel, engine="openpyxl", sheet_name="foo", index=False) + with ExcelWriter( + tmp_excel, engine="openpyxl", mode="a", if_sheet_exists=if_sheet_exists + ) as writer: + df2.to_excel(writer, sheet_name="foo", index=False) + + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + assert len(wb.sheetnames) == num_sheets + assert wb.sheetnames[0] == "foo" + result = pd.read_excel(wb, "foo", engine="openpyxl") + assert list(result["fruit"]) == expected + if len(wb.sheetnames) == 2: + result = pd.read_excel(wb, wb.sheetnames[1], engine="openpyxl") + tm.assert_frame_equal(result, df2) + + +@pytest.mark.parametrize( + "startrow, startcol, greeting, goodbye", + [ + (0, 0, ["poop", "world"], ["goodbye", "people"]), + (0, 1, ["hello", "world"], ["poop", "people"]), + (1, 0, ["hello", "poop"], ["goodbye", "people"]), + (1, 1, ["hello", "world"], ["goodbye", "poop"]), + ], +) +def test_append_overlay_startrow_startcol( + tmp_excel, startrow, startcol, greeting, goodbye +): + df1 = DataFrame({"greeting": ["hello", "world"], "goodbye": ["goodbye", "people"]}) + df2 = DataFrame(["poop"]) + + df1.to_excel(tmp_excel, engine="openpyxl", sheet_name="poo", index=False) + with ExcelWriter( + tmp_excel, engine="openpyxl", mode="a", if_sheet_exists="overlay" + ) as writer: + # use startrow+1 because we don't have a header + df2.to_excel( + writer, + index=False, + header=False, + startrow=startrow + 1, + startcol=startcol, + sheet_name="poo", + ) + + result = pd.read_excel(tmp_excel, sheet_name="poo", engine="openpyxl") + expected = DataFrame({"greeting": greeting, "goodbye": goodbye}) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "if_sheet_exists,msg", + [ + ( + "invalid", + "'invalid' is not valid for if_sheet_exists. Valid options " + "are 'error', 'new', 'replace' and 'overlay'.", + ), + ( + "error", + "Sheet 'foo' already exists and if_sheet_exists is set to 'error'.", + ), + ( + None, + "Sheet 'foo' already exists and if_sheet_exists is set to 'error'.", + ), + ], +) +def test_if_sheet_exists_raises(tmp_excel, if_sheet_exists, msg): + # GH 40230 + df = DataFrame({"fruit": ["pear"]}) + df.to_excel(tmp_excel, sheet_name="foo", engine="openpyxl") + with pytest.raises(ValueError, match=re.escape(msg)): + with ExcelWriter( + tmp_excel, engine="openpyxl", mode="a", if_sheet_exists=if_sheet_exists + ) as writer: + df.to_excel(writer, sheet_name="foo") + + +def test_to_excel_with_openpyxl_engine(tmp_excel): + # GH 29854 + df1 = DataFrame({"A": np.linspace(1, 10, 10)}) + df2 = DataFrame({"B": np.linspace(1, 20, 10)}) + df = pd.concat([df1, df2], axis=1) + styled = df.style.map( + lambda val: f"color: {'red' if val < 0 else 'black'}" + ).highlight_max() + + styled.to_excel(tmp_excel, engine="openpyxl") + + +@pytest.mark.parametrize("read_only", [True, False]) +def test_read_workbook(datapath, ext, read_only): + # GH 39528 + filename = datapath("io", "data", "excel", "test1" + ext) + with contextlib.closing( + openpyxl.load_workbook(filename, read_only=read_only) + ) as wb: + result = pd.read_excel(wb, engine="openpyxl") + expected = pd.read_excel(filename) + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "header, expected_data", + [ + ( + 0, + { + "Title": [np.nan, "A", 1, 2, 3], + "Unnamed: 1": [np.nan, "B", 4, 5, 6], + "Unnamed: 2": [np.nan, "C", 7, 8, 9], + }, + ), + (2, {"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}), + ], +) +@pytest.mark.parametrize( + "filename", ["dimension_missing", "dimension_small", "dimension_large"] +) +# When read_only is None, use read_excel instead of a workbook +@pytest.mark.parametrize("read_only", [True, False, None]) +def test_read_with_bad_dimension( + datapath, ext, header, expected_data, filename, read_only +): + # GH 38956, 39001 - no/incorrect dimension information + path = datapath("io", "data", "excel", f"{filename}{ext}") + if read_only is None: + result = pd.read_excel(path, header=header) + else: + with contextlib.closing( + openpyxl.load_workbook(path, read_only=read_only) + ) as wb: + result = pd.read_excel(wb, engine="openpyxl", header=header) + expected = DataFrame(expected_data) + tm.assert_frame_equal(result, expected) + + +def test_append_mode_file(tmp_excel): + # GH 39576 + df = DataFrame() + + df.to_excel(tmp_excel, engine="openpyxl") + + with ExcelWriter( + tmp_excel, mode="a", engine="openpyxl", if_sheet_exists="new" + ) as writer: + df.to_excel(writer) + + # make sure that zip files are not concatenated by making sure that + # "docProps/app.xml" only occurs twice in the file + data = Path(tmp_excel).read_bytes() + first = data.find(b"docProps/app.xml") + second = data.find(b"docProps/app.xml", first + 1) + third = data.find(b"docProps/app.xml", second + 1) + assert second != -1 and third == -1 + + +# When read_only is None, use read_excel instead of a workbook +@pytest.mark.parametrize("read_only", [True, False, None]) +def test_read_with_empty_trailing_rows(datapath, ext, read_only): + # GH 39181 + path = datapath("io", "data", "excel", f"empty_trailing_rows{ext}") + if read_only is None: + result = pd.read_excel(path) + else: + with contextlib.closing( + openpyxl.load_workbook(path, read_only=read_only) + ) as wb: + result = pd.read_excel(wb, engine="openpyxl") + expected = DataFrame( + { + "Title": [np.nan, "A", 1, 2, 3], + "Unnamed: 1": [np.nan, "B", 4, 5, 6], + "Unnamed: 2": [np.nan, "C", 7, 8, 9], + } + ) + tm.assert_frame_equal(result, expected) + + +# When read_only is None, use read_excel instead of a workbook +@pytest.mark.parametrize("read_only", [True, False, None]) +def test_read_empty_with_blank_row(datapath, ext, read_only): + # GH 39547 - empty excel file with a row that has no data + path = datapath("io", "data", "excel", f"empty_with_blank_row{ext}") + if read_only is None: + result = pd.read_excel(path) + else: + with contextlib.closing( + openpyxl.load_workbook(path, read_only=read_only) + ) as wb: + result = pd.read_excel(wb, engine="openpyxl") + expected = DataFrame() + tm.assert_frame_equal(result, expected) + + +def test_book_and_sheets_consistent(tmp_excel): + # GH#45687 - Ensure sheets is updated if user modifies book + with ExcelWriter(tmp_excel, engine="openpyxl") as writer: + assert writer.sheets == {} + sheet = writer.book.create_sheet("test_name", 0) + assert writer.sheets == {"test_name": sheet} + + +def test_ints_spelled_with_decimals(datapath, ext): + # GH 46988 - openpyxl returns this sheet with floats + path = datapath("io", "data", "excel", f"ints_spelled_with_decimals{ext}") + result = pd.read_excel(path) + expected = DataFrame(range(2, 12), columns=[1]) + tm.assert_frame_equal(result, expected) + + +def test_read_multiindex_header_no_index_names(datapath, ext): + # GH#47487 + path = datapath("io", "data", "excel", f"multiindex_no_index_names{ext}") + result = pd.read_excel(path, index_col=[0, 1, 2], header=[0, 1, 2]) + expected = DataFrame( + [[np.nan, "x", "x", "x"], ["x", np.nan, np.nan, np.nan]], + columns=pd.MultiIndex.from_tuples( + [("X", "Y", "A1"), ("X", "Y", "A2"), ("XX", "YY", "B1"), ("XX", "YY", "B2")] + ), + index=pd.MultiIndex.from_tuples([("A", "AA", "AAA"), ("A", "BB", "BBB")]), + ) + tm.assert_frame_equal(result, expected) diff --git a/lib/python3.11/site-packages/pandas/tests/io/excel/test_readers.py b/lib/python3.11/site-packages/pandas/tests/io/excel/test_readers.py new file mode 100644 index 0000000000000000000000000000000000000000..830c2fb40ffbea96c4f00d09b39282cd10e9da01 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/excel/test_readers.py @@ -0,0 +1,1743 @@ +from __future__ import annotations + +from datetime import ( + datetime, + time, +) +from functools import partial +from io import BytesIO +import os +from pathlib import Path +import platform +import re +from urllib.error import URLError +import uuid +from zipfile import BadZipFile + +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + Series, + read_csv, +) +import pandas._testing as tm + +read_ext_params = [".xls", ".xlsx", ".xlsm", ".xlsb", ".ods"] +engine_params = [ + # Add any engines to test here + # When defusedxml is installed it triggers deprecation warnings for + # xlrd and openpyxl, so catch those here + pytest.param( + "xlrd", + marks=[ + td.skip_if_no("xlrd"), + ], + ), + pytest.param( + "openpyxl", + marks=[ + td.skip_if_no("openpyxl"), + ], + ), + pytest.param( + None, + marks=[ + td.skip_if_no("xlrd"), + ], + ), + pytest.param("pyxlsb", marks=td.skip_if_no("pyxlsb")), + pytest.param("odf", marks=td.skip_if_no("odf")), + pytest.param("calamine", marks=td.skip_if_no("python_calamine")), +] + + +def _is_valid_engine_ext_pair(engine, read_ext: str) -> bool: + """ + Filter out invalid (engine, ext) pairs instead of skipping, as that + produces 500+ pytest.skips. + """ + engine = engine.values[0] + if engine == "openpyxl" and read_ext == ".xls": + return False + if engine == "odf" and read_ext != ".ods": + return False + if read_ext == ".ods" and engine not in {"odf", "calamine"}: + return False + if engine == "pyxlsb" and read_ext != ".xlsb": + return False + if read_ext == ".xlsb" and engine not in {"pyxlsb", "calamine"}: + return False + if engine == "xlrd" and read_ext != ".xls": + return False + return True + + +def _transfer_marks(engine, read_ext): + """ + engine gives us a pytest.param object with some marks, read_ext is just + a string. We need to generate a new pytest.param inheriting the marks. + """ + values = (*engine.values, read_ext) + new_param = pytest.param(values, marks=engine.marks) + return new_param + + +@pytest.fixture( + params=[ + _transfer_marks(eng, ext) + for eng in engine_params + for ext in read_ext_params + if _is_valid_engine_ext_pair(eng, ext) + ], + ids=str, +) +def engine_and_read_ext(request): + """ + Fixture for Excel reader engine and read_ext, only including valid pairs. + """ + return request.param + + +@pytest.fixture +def engine(engine_and_read_ext): + engine, read_ext = engine_and_read_ext + return engine + + +@pytest.fixture +def read_ext(engine_and_read_ext): + engine, read_ext = engine_and_read_ext + return read_ext + + +@pytest.fixture +def tmp_excel(read_ext, tmp_path): + tmp = tmp_path / f"{uuid.uuid4()}{read_ext}" + tmp.touch() + return str(tmp) + + +@pytest.fixture +def df_ref(datapath): + """ + Obtain the reference data from read_csv with the Python engine. + """ + filepath = datapath("io", "data", "csv", "test1.csv") + df_ref = read_csv(filepath, index_col=0, parse_dates=True, engine="python") + return df_ref + + +def get_exp_unit(read_ext: str, engine: str | None) -> str: + return "us" + + +def adjust_expected(expected: DataFrame, read_ext: str, engine: str | None) -> None: + expected.index.name = None + unit = get_exp_unit(read_ext, engine) + # error: "Index" has no attribute "as_unit" + expected.index = expected.index.as_unit(unit) # type: ignore[attr-defined] + + +def xfail_datetimes_with_pyxlsb(engine, request): + if engine == "pyxlsb": + request.applymarker( + pytest.mark.xfail( + reason="Sheets containing datetimes not supported by pyxlsb" + ) + ) + + +class TestReaders: + @pytest.mark.parametrize("col", [[True, None, False], [True], [True, False]]) + def test_read_excel_type_check(self, col, tmp_excel, read_ext): + # GH 58159 + if read_ext in (".xlsb", ".xls"): + pytest.skip(f"No engine for filetype: '{read_ext}'") + df = DataFrame({"bool_column": col}, dtype="boolean") + df.to_excel(tmp_excel, index=False) + df2 = pd.read_excel(tmp_excel, dtype={"bool_column": "boolean"}) + tm.assert_frame_equal(df, df2) + + def test_pass_none_type(self, datapath): + # GH 58159 + f_path = datapath("io", "data", "excel", "test_none_type.xlsx") + + with pd.ExcelFile(f_path) as excel: + parsed = pd.read_excel( + excel, + sheet_name="Sheet1", + keep_default_na=True, + na_values=["nan", "None", "abcd"], + dtype="boolean", + engine="openpyxl", + ) + expected = DataFrame( + {"Test": [True, None, False, None, False, None, True]}, + dtype="boolean", + ) + + tm.assert_frame_equal(parsed, expected) + + @pytest.fixture(autouse=True) + def cd_and_set_engine(self, engine, datapath, monkeypatch): + """ + Change directory and set engine for read_excel calls. + """ + func = partial(pd.read_excel, engine=engine) + monkeypatch.chdir(datapath("io", "data", "excel")) + monkeypatch.setattr(pd, "read_excel", func) + + def test_engine_used(self, read_ext, engine, monkeypatch): + # GH 38884 + def parser(self, *args, **kwargs): + return self.engine + + monkeypatch.setattr(pd.ExcelFile, "parse", parser) + + expected_defaults = { + "xlsx": "openpyxl", + "xlsm": "openpyxl", + "xlsb": "pyxlsb", + "xls": "xlrd", + "ods": "odf", + } + + with open("test1" + read_ext, "rb") as f: + result = pd.read_excel(f) + + if engine is not None: + expected = engine + else: + expected = expected_defaults[read_ext[1:]] + assert result == expected + + def test_engine_kwargs(self, read_ext, engine): + # GH#52214 + expected_defaults = { + "xlsx": {"foo": "abcd"}, + "xlsm": {"foo": 123}, + "xlsb": {"foo": "True"}, + "xls": {"foo": True}, + "ods": {"foo": "abcd"}, + } + + if engine in {"xlrd", "pyxlsb"}: + msg = re.escape(r"open_workbook() got an unexpected keyword argument 'foo'") + elif engine == "odf": + msg = re.escape(r"load() got an unexpected keyword argument 'foo'") + else: + msg = re.escape(r"load_workbook() got an unexpected keyword argument 'foo'") + + if engine is not None: + with pytest.raises(TypeError, match=msg): + pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet1", + index_col=0, + engine_kwargs=expected_defaults[read_ext[1:]], + ) + + def test_usecols_int(self, read_ext): + # usecols as int + msg = "Passing an integer for `usecols`" + with pytest.raises(ValueError, match=msg): + pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=3 + ) + + # usecols as int + with pytest.raises(ValueError, match=msg): + pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet2", + skiprows=[1], + index_col=0, + usecols=3, + ) + + def test_usecols_list(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref[["B", "C"]] + adjust_expected(expected, read_ext, engine) + + df1 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=[0, 2, 3] + ) + df2 = pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet2", + skiprows=[1], + index_col=0, + usecols=[0, 2, 3], + ) + + # TODO add index to xls file) + tm.assert_frame_equal(df1, expected) + tm.assert_frame_equal(df2, expected) + + def test_usecols_str(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref[["A", "B", "C"]] + adjust_expected(expected, read_ext, engine) + + df2 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A:D" + ) + df3 = pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet2", + skiprows=[1], + index_col=0, + usecols="A:D", + ) + + # TODO add index to xls, read xls ignores index name ? + tm.assert_frame_equal(df2, expected) + tm.assert_frame_equal(df3, expected) + + expected = df_ref[["B", "C"]] + adjust_expected(expected, read_ext, engine) + + df2 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,C,D" + ) + df3 = pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet2", + skiprows=[1], + index_col=0, + usecols="A,C,D", + ) + # TODO add index to xls file + tm.assert_frame_equal(df2, expected) + tm.assert_frame_equal(df3, expected) + + df2 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,C:D" + ) + df3 = pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet2", + skiprows=[1], + index_col=0, + usecols="A,C:D", + ) + tm.assert_frame_equal(df2, expected) + tm.assert_frame_equal(df3, expected) + + @pytest.mark.parametrize( + "usecols", [[0, 1, 3], [0, 3, 1], [1, 0, 3], [1, 3, 0], [3, 0, 1], [3, 1, 0]] + ) + def test_usecols_diff_positional_int_columns_order( + self, request, engine, read_ext, usecols, df_ref + ): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref[["A", "C"]] + adjust_expected(expected, read_ext, engine) + + result = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols=usecols + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("usecols", [["B", "D"], ["D", "B"]]) + def test_usecols_diff_positional_str_columns_order(self, read_ext, usecols, df_ref): + expected = df_ref[["B", "D"]] + expected.index = range(len(expected)) + + result = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", usecols=usecols) + tm.assert_frame_equal(result, expected) + + def test_read_excel_without_slicing(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref + adjust_expected(expected, read_ext, engine) + + result = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", index_col=0) + tm.assert_frame_equal(result, expected) + + def test_usecols_excel_range_str(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref[["C", "D"]] + adjust_expected(expected, read_ext, engine) + + result = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, usecols="A,D:E" + ) + tm.assert_frame_equal(result, expected) + + def test_usecols_excel_range_str_invalid(self, read_ext): + msg = "Invalid column name: E1" + + with pytest.raises(ValueError, match=msg): + pd.read_excel("test1" + read_ext, sheet_name="Sheet1", usecols="D:E1") + + def test_index_col_label_error(self, read_ext): + msg = "list indices must be integers.*, not str" + + with pytest.raises(TypeError, match=msg): + pd.read_excel( + "test1" + read_ext, + sheet_name="Sheet1", + index_col=["A"], + usecols=["A", "C"], + ) + + def test_index_col_str(self, read_ext): + # see gh-52716 + result = pd.read_excel("test1" + read_ext, sheet_name="Sheet3", index_col="A") + expected = DataFrame( + columns=["B", "C", "D", "E", "F"], index=Index([], name="A") + ) + tm.assert_frame_equal(result, expected) + + def test_index_col_empty(self, read_ext): + # see gh-9208 + result = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet3", index_col=["A", "B", "C"] + ) + expected = DataFrame( + columns=["D", "E", "F"], + index=MultiIndex(levels=[[]] * 3, codes=[[]] * 3, names=["A", "B", "C"]), + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("index_col", [None, 2]) + def test_index_col_with_unnamed(self, read_ext, index_col): + # see gh-18792 + result = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet4", index_col=index_col + ) + expected = DataFrame( + [["i1", "a", "x"], ["i2", "b", "y"]], columns=["Unnamed: 0", "col1", "col2"] + ) + if index_col: + expected = expected.set_index(expected.columns[index_col]) + + tm.assert_frame_equal(result, expected) + + def test_usecols_pass_non_existent_column(self, read_ext): + msg = ( + "Usecols do not match columns, " + "columns expected but not found: " + r"\['E'\]" + ) + + with pytest.raises(ValueError, match=msg): + pd.read_excel("test1" + read_ext, usecols=["E"]) + + def test_usecols_wrong_type(self, read_ext): + msg = ( + "'usecols' must either be list-like of " + "all strings, all unicode, all integers or a callable." + ) + + with pytest.raises(ValueError, match=msg): + pd.read_excel("test1" + read_ext, usecols=["E1", 0]) + + def test_excel_stop_iterator(self, read_ext): + parsed = pd.read_excel("test2" + read_ext, sheet_name="Sheet1") + expected = DataFrame([["aaaa", "bbbbb"]], columns=["Test", "Test1"]) + tm.assert_frame_equal(parsed, expected) + + def test_excel_cell_error_na(self, request, engine, read_ext): + xfail_datetimes_with_pyxlsb(engine, request) + + # https://github.com/tafia/calamine/issues/355 + if engine == "calamine" and read_ext == ".ods": + request.applymarker( + pytest.mark.xfail(reason="Calamine can't extract error from ods files") + ) + + parsed = pd.read_excel("test3" + read_ext, sheet_name="Sheet1") + expected = DataFrame([[np.nan]], columns=["Test"]) + tm.assert_frame_equal(parsed, expected) + + def test_excel_table(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref + adjust_expected(expected, read_ext, engine) + + df1 = pd.read_excel("test1" + read_ext, sheet_name="Sheet1", index_col=0) + df2 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet2", skiprows=[1], index_col=0 + ) + # TODO add index to file + tm.assert_frame_equal(df1, expected) + tm.assert_frame_equal(df2, expected) + + df3 = pd.read_excel( + "test1" + read_ext, sheet_name="Sheet1", index_col=0, skipfooter=1 + ) + tm.assert_frame_equal(df3, df1.iloc[:-1]) + + def test_reader_special_dtypes(self, request, engine, read_ext): + xfail_datetimes_with_pyxlsb(engine, request) + + unit = get_exp_unit(read_ext, engine) + expected = DataFrame.from_dict( + { + "IntCol": [1, 2, -3, 4, 0], + "FloatCol": [1.25, 2.25, 1.83, 1.92, 0.0000000005], + "BoolCol": [True, False, True, True, False], + "StrCol": [1, 2, 3, 4, 5], + "Str2Col": ["a", 3, "c", "d", "e"], + "DateCol": Index( + [ + datetime(2013, 10, 30), + datetime(2013, 10, 31), + datetime(1905, 1, 1), + datetime(2013, 12, 14), + datetime(2015, 3, 14), + ], + dtype=f"M8[{unit}]", + ), + }, + ) + basename = "test_types" + + # should read in correctly and infer types + actual = pd.read_excel(basename + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, expected) + + # if not coercing number, then int comes in as float + float_expected = expected.copy() + float_expected.loc[float_expected.index[1], "Str2Col"] = 3.0 + actual = pd.read_excel(basename + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, float_expected) + + # check setting Index (assuming xls and xlsx are the same here) + for icol, name in enumerate(expected.columns): + actual = pd.read_excel( + basename + read_ext, sheet_name="Sheet1", index_col=icol + ) + exp = expected.set_index(name) + tm.assert_frame_equal(actual, exp) + + expected["StrCol"] = expected["StrCol"].apply(str) + actual = pd.read_excel( + basename + read_ext, sheet_name="Sheet1", converters={"StrCol": str} + ) + tm.assert_frame_equal(actual, expected) + + # GH8212 - support for converters and missing values + def test_reader_converters(self, read_ext): + basename = "test_converters" + + expected = DataFrame.from_dict( + { + "IntCol": [1, 2, -3, -1000, 0], + "FloatCol": [12.5, np.nan, 18.3, 19.2, 0.000000005], + "BoolCol": ["Found", "Found", "Found", "Not found", "Found"], + "StrCol": ["1", np.nan, "3", "4", "5"], + } + ) + + converters = { + "IntCol": lambda x: int(x) if x != "" else -1000, + "FloatCol": lambda x: 10 * x if x else np.nan, + 2: lambda x: "Found" if x != "" else "Not found", + 3: lambda x: str(x) if x else "", + } + + # should read in correctly and set types of single cells (not array + # dtypes) + actual = pd.read_excel( + basename + read_ext, sheet_name="Sheet1", converters=converters + ) + tm.assert_frame_equal(actual, expected) + + def test_reader_dtype(self, read_ext): + # GH 8212 + basename = "testdtype" + actual = pd.read_excel(basename + read_ext) + + expected = DataFrame( + { + "a": [1, 2, 3, 4], + "b": [2.5, 3.5, 4.5, 5.5], + "c": [1, 2, 3, 4], + "d": [1.0, 2.0, np.nan, 4.0], + } + ) + + tm.assert_frame_equal(actual, expected) + + actual = pd.read_excel( + basename + read_ext, dtype={"a": "float64", "b": "float32", "c": str} + ) + + expected["a"] = expected["a"].astype("float64") + expected["b"] = expected["b"].astype("float32") + expected["c"] = Series(["001", "002", "003", "004"], dtype="str") + tm.assert_frame_equal(actual, expected) + + msg = "Unable to convert column d to type int64" + with pytest.raises(ValueError, match=msg): + pd.read_excel(basename + read_ext, dtype={"d": "int64"}) + + @pytest.mark.parametrize( + "dtype,expected", + [ + ( + None, + { + "a": [1, 2, 3, 4], + "b": [2.5, 3.5, 4.5, 5.5], + "c": [1, 2, 3, 4], + "d": [1.0, 2.0, np.nan, 4.0], + }, + ), + ( + {"a": "float64", "b": "float32", "c": str, "d": str}, + { + "a": Series([1, 2, 3, 4], dtype="float64"), + "b": Series([2.5, 3.5, 4.5, 5.5], dtype="float32"), + "c": Series(["001", "002", "003", "004"], dtype="str"), + "d": Series(["1", "2", np.nan, "4"], dtype="str"), + }, + ), + ], + ) + def test_reader_dtype_str(self, read_ext, dtype, expected): + # see gh-20377 + basename = "testdtype" + + actual = pd.read_excel(basename + read_ext, dtype=dtype) + expected = DataFrame(expected) + tm.assert_frame_equal(actual, expected) + + def test_dtype_backend(self, read_ext, dtype_backend, engine, tmp_excel): + # GH#36712 + if read_ext in (".xlsb", ".xls"): + pytest.skip(f"No engine for filetype: '{read_ext}'") + + df = DataFrame( + { + "a": Series([1, 3], dtype="Int64"), + "b": Series([2.5, 4.5], dtype="Float64"), + "c": Series([True, False], dtype="boolean"), + "d": Series(["a", "b"], dtype="string"), + "e": Series([pd.NA, 6], dtype="Int64"), + "f": Series([pd.NA, 7.5], dtype="Float64"), + "g": Series([pd.NA, True], dtype="boolean"), + "h": Series([pd.NA, "a"], dtype="string"), + "i": Series([pd.Timestamp("2019-12-31")] * 2), + "j": Series([pd.NA, pd.NA], dtype="Int64"), + } + ) + df.to_excel(tmp_excel, sheet_name="test", index=False) + result = pd.read_excel( + tmp_excel, sheet_name="test", dtype_backend=dtype_backend + ) + if dtype_backend == "pyarrow": + import pyarrow as pa + + from pandas.arrays import ArrowExtensionArray + + expected = DataFrame( + { + col: ArrowExtensionArray(pa.array(df[col], from_pandas=True)) + for col in df.columns + } + ) + + # pandas uses large_string by default, but pyarrow infers string + expected["d"] = expected["d"].astype(pd.ArrowDtype(pa.string())) + expected["h"] = expected["h"].astype(pd.ArrowDtype(pa.string())) + # pyarrow by default infers timestamp resolution as us, not ns + expected["i"] = ArrowExtensionArray( + expected["i"].array._pa_array.cast(pa.timestamp(unit="us")) + ) + # pyarrow supports a null type, so don't have to default to Int64 + expected["j"] = ArrowExtensionArray(pa.array([None, None])) + else: + expected = df + unit = get_exp_unit(read_ext, engine) + expected["i"] = expected["i"].astype(f"M8[{unit}]") + + tm.assert_frame_equal(result, expected) + + def test_dtype_backend_and_dtype(self, read_ext, tmp_excel): + # GH#36712 + if read_ext in (".xlsb", ".xls"): + pytest.skip(f"No engine for filetype: '{read_ext}'") + + df = DataFrame({"a": [np.nan, 1.0], "b": [2.5, np.nan]}) + df.to_excel(tmp_excel, sheet_name="test", index=False) + result = pd.read_excel( + tmp_excel, + sheet_name="test", + dtype_backend="numpy_nullable", + dtype="float64", + ) + tm.assert_frame_equal(result, df) + + def test_dtype_backend_string(self, read_ext, string_storage, tmp_excel): + # GH#36712 + if read_ext in (".xlsb", ".xls"): + pytest.skip(f"No engine for filetype: '{read_ext}'") + + df = DataFrame( + { + "a": np.array(["a", "b"], dtype=np.object_), + "b": np.array(["x", pd.NA], dtype=np.object_), + } + ) + df.to_excel(tmp_excel, sheet_name="test", index=False) + + with pd.option_context("mode.string_storage", string_storage): + result = pd.read_excel( + tmp_excel, sheet_name="test", dtype_backend="numpy_nullable" + ) + + expected = DataFrame( + { + "a": Series(["a", "b"], dtype=pd.StringDtype(string_storage)), + "b": Series(["x", None], dtype=pd.StringDtype(string_storage)), + } + ) + # the storage of the str columns' Index is also affected by the + # string_storage setting -> ignore that for checking the result + tm.assert_frame_equal(result, expected, check_column_type=False) + + @pytest.mark.parametrize("dtypes, exp_value", [({}, 1), ({"a.1": "int64"}, 1)]) + def test_dtype_mangle_dup_cols(self, read_ext, dtypes, exp_value): + # GH#35211 + basename = "df_mangle_dup_col_dtypes" + dtype_dict = {"a": object, **dtypes} + dtype_dict_copy = dtype_dict.copy() + # GH#42462 + result = pd.read_excel(basename + read_ext, dtype=dtype_dict) + expected = DataFrame( + { + "a": Series([1], dtype=object), + "a.1": Series([exp_value], dtype=object if not dtypes else None), + } + ) + assert dtype_dict == dtype_dict_copy, "dtype dict changed" + tm.assert_frame_equal(result, expected) + + def test_reader_spaces(self, read_ext): + # see gh-32207 + basename = "test_spaces" + + actual = pd.read_excel(basename + read_ext) + expected = DataFrame( + { + "testcol": [ + "this is great", + "4 spaces", + "1 trailing ", + " 1 leading", + "2 spaces multiple times", + ] + } + ) + tm.assert_frame_equal(actual, expected) + + # gh-36122, gh-35802 + @pytest.mark.parametrize( + "basename,expected", + [ + ("gh-35802", DataFrame({"COLUMN": ["Test (1)"]})), + ("gh-36122", DataFrame(columns=["got 2nd sa"])), + ], + ) + def test_read_excel_ods_nested_xml(self, engine, read_ext, basename, expected): + # see gh-35802 + if engine != "odf": + pytest.skip(f"Skipped for engine: {engine}") + + actual = pd.read_excel(basename + read_ext) + tm.assert_frame_equal(actual, expected) + + def test_reading_all_sheets(self, read_ext): + # Test reading all sheet names by setting sheet_name to None, + # Ensure a dict is returned. + # See PR #9450 + basename = "test_multisheet" + dfs = pd.read_excel(basename + read_ext, sheet_name=None) + # ensure this is not alphabetical to test order preservation + expected_keys = ["Charlie", "Alpha", "Beta"] + tm.assert_contains_all(expected_keys, dfs.keys()) + # Issue 9930 + # Ensure sheet order is preserved + assert expected_keys == list(dfs.keys()) + + def test_reading_multiple_specific_sheets(self, read_ext): + # Test reading specific sheet names by specifying a mixed list + # of integers and strings, and confirm that duplicated sheet + # references (positions/names) are removed properly. + # Ensure a dict is returned + # See PR #9450 + basename = "test_multisheet" + # Explicitly request duplicates. Only the set should be returned. + expected_keys = [2, "Charlie", "Charlie"] + dfs = pd.read_excel(basename + read_ext, sheet_name=expected_keys) + expected_keys = list(set(expected_keys)) + tm.assert_contains_all(expected_keys, dfs.keys()) + assert len(expected_keys) == len(dfs.keys()) + + def test_reading_all_sheets_with_blank(self, read_ext): + # Test reading all sheet names by setting sheet_name to None, + # In the case where some sheets are blank. + # Issue #11711 + basename = "blank_with_header" + dfs = pd.read_excel(basename + read_ext, sheet_name=None) + expected_keys = ["Sheet1", "Sheet2", "Sheet3"] + tm.assert_contains_all(expected_keys, dfs.keys()) + + # GH6403 + def test_read_excel_blank(self, read_ext): + actual = pd.read_excel("blank" + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, DataFrame()) + + def test_read_excel_blank_with_header(self, read_ext): + expected = DataFrame(columns=["col_1", "col_2"]) + actual = pd.read_excel("blank_with_header" + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, expected) + + def test_exception_message_includes_sheet_name(self, read_ext): + # GH 48706 + with pytest.raises(ValueError, match=r" \(sheet: Sheet1\)$"): + pd.read_excel("blank_with_header" + read_ext, header=[1], sheet_name=None) + with pytest.raises(ZeroDivisionError, match=r" \(sheet: Sheet1\)$"): + pd.read_excel("test1" + read_ext, usecols=lambda x: 1 / 0, sheet_name=None) + + @pytest.mark.filterwarnings("ignore:Cell A4 is marked:UserWarning:openpyxl") + def test_date_conversion_overflow(self, request, engine, read_ext): + # GH 10001 : pandas.ExcelFile ignore parse_dates=False + xfail_datetimes_with_pyxlsb(engine, request) + + expected = DataFrame( + [ + [pd.Timestamp("2016-03-12"), "Marc Johnson"], + [pd.Timestamp("2016-03-16"), "Jack Black"], + [1e20, "Timothy Brown"], + ], + columns=["DateColWithBigInt", "StringCol"], + ) + + if engine == "openpyxl": + request.applymarker( + pytest.mark.xfail(reason="Maybe not supported by openpyxl") + ) + + if engine is None and read_ext in (".xlsx", ".xlsm"): + # GH 35029 + request.applymarker( + pytest.mark.xfail(reason="Defaults to openpyxl, maybe not supported") + ) + + result = pd.read_excel("testdateoverflow" + read_ext) + tm.assert_frame_equal(result, expected) + + def test_sheet_name(self, request, read_ext, engine, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + filename = "test1" + sheet_name = "Sheet1" + + expected = df_ref + adjust_expected(expected, read_ext, engine) + + df1 = pd.read_excel( + filename + read_ext, sheet_name=sheet_name, index_col=0 + ) # doc + df2 = pd.read_excel(filename + read_ext, index_col=0, sheet_name=sheet_name) + + tm.assert_frame_equal(df1, expected) + tm.assert_frame_equal(df2, expected) + + def test_excel_read_buffer(self, read_ext): + pth = "test1" + read_ext + expected = pd.read_excel(pth, sheet_name="Sheet1", index_col=0) + with open(pth, "rb") as f: + actual = pd.read_excel(f, sheet_name="Sheet1", index_col=0) + tm.assert_frame_equal(expected, actual) + + def test_bad_engine_raises(self): + bad_engine = "foo" + with pytest.raises(ValueError, match="Unknown engine: foo"): + pd.read_excel("", engine=bad_engine) + + @pytest.mark.parametrize( + "sheet_name", + [3, [0, 3], [3, 0], "Sheet4", ["Sheet1", "Sheet4"], ["Sheet4", "Sheet1"]], + ) + def test_bad_sheetname_raises(self, read_ext, sheet_name): + # GH 39250 + msg = "Worksheet index 3 is invalid|Worksheet named 'Sheet4' not found" + with pytest.raises(ValueError, match=msg): + pd.read_excel("blank" + read_ext, sheet_name=sheet_name) + + def test_missing_file_raises(self, read_ext): + bad_file = f"foo{read_ext}" + # CI tests with other languages, translates to "No such file or directory" + match = "|".join( + [ + "(No such file or directory", + "没有那个文件或目录", + "File o directory non esistente)", + ] + ) + with pytest.raises(FileNotFoundError, match=match): + pd.read_excel(bad_file) + + def test_corrupt_bytes_raises(self, engine): + bad_stream = b"foo" + if engine is None: + error = ValueError + msg = ( + "Excel file format cannot be determined, you must " + "specify an engine manually." + ) + elif engine == "xlrd": + from xlrd import XLRDError + + error = XLRDError + msg = ( + "Unsupported format, or corrupt file: Expected BOF record; found b'foo'" + ) + elif engine == "calamine": + from python_calamine import CalamineError + + error = CalamineError + msg = "Cannot detect file format" + else: + error = BadZipFile + msg = "File is not a zip file" + with pytest.raises(error, match=msg): + pd.read_excel(BytesIO(bad_stream)) + + @pytest.mark.network + @pytest.mark.single_cpu + def test_read_from_http_url(self, httpserver, read_ext): + with open("test1" + read_ext, "rb") as f: + httpserver.serve_content(content=f.read()) + url_table = pd.read_excel(httpserver.url) + local_table = pd.read_excel("test1" + read_ext) + tm.assert_frame_equal(url_table, local_table) + + @td.skip_if_not_us_locale + @pytest.mark.single_cpu + def test_read_from_s3_url(self, read_ext, s3_bucket_public, s3so): + with open("test1" + read_ext, "rb") as f: + s3_bucket_public.put_object(Key="test1" + read_ext, Body=f) + + url = f"s3://{s3_bucket_public.name}/test1" + read_ext + + url_table = pd.read_excel(url, storage_options=s3so) + local_table = pd.read_excel("test1" + read_ext) + tm.assert_frame_equal(url_table, local_table) + + @pytest.mark.single_cpu + def test_read_from_s3_object(self, read_ext, s3_bucket_public, s3so): + # GH 38788 + with open("test1" + read_ext, "rb") as f: + s3_bucket_public.put_object(Key="test1" + read_ext, Body=f) + + import s3fs + + s3 = s3fs.S3FileSystem(**s3so) + + with s3.open(f"s3://{s3_bucket_public.name}/test1" + read_ext) as f: + url_table = pd.read_excel(f) + + local_table = pd.read_excel("test1" + read_ext) + tm.assert_frame_equal(url_table, local_table) + + @pytest.mark.slow + def test_read_from_file_url(self, read_ext, datapath): + # FILE + localtable = os.path.join(datapath("io", "data", "excel"), "test1" + read_ext) + local_table = pd.read_excel(localtable) + + try: + url_table = pd.read_excel("file://localhost/" + localtable) + except URLError: + # fails on some systems + platform_info = " ".join(platform.uname()).strip() + pytest.skip(f"failing on {platform_info}") + + tm.assert_frame_equal(url_table, local_table) + + def test_read_from_pathlib_path(self, read_ext): + # GH12655 + str_path = "test1" + read_ext + expected = pd.read_excel(str_path, sheet_name="Sheet1", index_col=0) + + path_obj = Path("test1" + read_ext) + actual = pd.read_excel(path_obj, sheet_name="Sheet1", index_col=0) + + tm.assert_frame_equal(expected, actual) + + def test_close_from_py_localpath(self, read_ext): + # GH31467 + str_path = os.path.join("test1" + read_ext) + with open(str_path, "rb") as f: + x = pd.read_excel(f, sheet_name="Sheet1", index_col=0) + del x + # should not throw an exception because the passed file was closed + f.read() + + def test_reader_seconds(self, request, engine, read_ext): + xfail_datetimes_with_pyxlsb(engine, request) + + # GH 55045 + if engine == "calamine" and read_ext == ".ods": + request.applymarker( + pytest.mark.xfail( + reason="ODS file contains bad datetime (seconds as text)" + ) + ) + + # Test reading times with and without milliseconds. GH5945. + expected = DataFrame.from_dict( + { + "Time": [ + time(1, 2, 3), + time(2, 45, 56, 100000), + time(4, 29, 49, 200000), + time(6, 13, 42, 300000), + time(7, 57, 35, 400000), + time(9, 41, 28, 500000), + time(11, 25, 21, 600000), + time(13, 9, 14, 700000), + time(14, 53, 7, 800000), + time(16, 37, 0, 900000), + time(18, 20, 54), + ] + } + ) + + actual = pd.read_excel("times_1900" + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, expected) + + actual = pd.read_excel("times_1904" + read_ext, sheet_name="Sheet1") + tm.assert_frame_equal(actual, expected) + + def test_read_excel_multiindex(self, request, engine, read_ext): + # see gh-4679 + xfail_datetimes_with_pyxlsb(engine, request) + + unit = get_exp_unit(read_ext, engine) + + mi = MultiIndex.from_product([["foo", "bar"], ["a", "b"]]) + mi_file = "testmultiindex" + read_ext + + # "mi_column" sheet + expected = DataFrame( + [ + [1, 2.5, pd.Timestamp("2015-01-01"), True], + [2, 3.5, pd.Timestamp("2015-01-02"), False], + [3, 4.5, pd.Timestamp("2015-01-03"), False], + [4, 5.5, pd.Timestamp("2015-01-04"), True], + ], + columns=mi, + ) + expected[mi[2]] = expected[mi[2]].astype(f"M8[{unit}]") + + actual = pd.read_excel( + mi_file, sheet_name="mi_column", header=[0, 1], index_col=0 + ) + tm.assert_frame_equal(actual, expected) + + # "mi_index" sheet + expected.index = mi + expected.columns = ["a", "b", "c", "d"] + + actual = pd.read_excel(mi_file, sheet_name="mi_index", index_col=[0, 1]) + tm.assert_frame_equal(actual, expected) + + # "both" sheet + expected.columns = mi + + actual = pd.read_excel( + mi_file, sheet_name="both", index_col=[0, 1], header=[0, 1] + ) + tm.assert_frame_equal(actual, expected) + + # "mi_index_name" sheet + expected.columns = ["a", "b", "c", "d"] + expected.index = mi.set_names(["ilvl1", "ilvl2"]) + + actual = pd.read_excel(mi_file, sheet_name="mi_index_name", index_col=[0, 1]) + tm.assert_frame_equal(actual, expected) + + # "mi_column_name" sheet + expected.index = range(4) + expected.columns = mi.set_names(["c1", "c2"]) + actual = pd.read_excel( + mi_file, sheet_name="mi_column_name", header=[0, 1], index_col=0 + ) + tm.assert_frame_equal(actual, expected) + + # see gh-11317 + # "name_with_int" sheet + expected.columns = mi.set_levels([1, 2], level=1).set_names(["c1", "c2"]) + + actual = pd.read_excel( + mi_file, sheet_name="name_with_int", index_col=0, header=[0, 1] + ) + tm.assert_frame_equal(actual, expected) + + # "both_name" sheet + expected.columns = mi.set_names(["c1", "c2"]) + expected.index = mi.set_names(["ilvl1", "ilvl2"]) + + actual = pd.read_excel( + mi_file, sheet_name="both_name", index_col=[0, 1], header=[0, 1] + ) + tm.assert_frame_equal(actual, expected) + + # "both_skiprows" sheet + actual = pd.read_excel( + mi_file, + sheet_name="both_name_skiprows", + index_col=[0, 1], + header=[0, 1], + skiprows=2, + ) + tm.assert_frame_equal(actual, expected) + + @pytest.mark.parametrize( + "sheet_name,idx_lvl2", + [ + ("both_name_blank_after_mi_name", [np.nan, "b", "a", "b"]), + ("both_name_multiple_blanks", [np.nan] * 4), + ], + ) + def test_read_excel_multiindex_blank_after_name( + self, request, engine, read_ext, sheet_name, idx_lvl2 + ): + # GH34673 + xfail_datetimes_with_pyxlsb(engine, request) + + mi_file = "testmultiindex" + read_ext + mi = MultiIndex.from_product([["foo", "bar"], ["a", "b"]], names=["c1", "c2"]) + + unit = get_exp_unit(read_ext, engine) + expected = DataFrame( + [ + [1, 2.5, pd.Timestamp("2015-01-01"), True], + [2, 3.5, pd.Timestamp("2015-01-02"), False], + [3, 4.5, pd.Timestamp("2015-01-03"), False], + [4, 5.5, pd.Timestamp("2015-01-04"), True], + ], + columns=mi, + index=MultiIndex.from_arrays( + (["foo", "foo", "bar", "bar"], idx_lvl2), + names=["ilvl1", "ilvl2"], + ), + ) + expected[mi[2]] = expected[mi[2]].astype(f"M8[{unit}]") + result = pd.read_excel( + mi_file, + sheet_name=sheet_name, + index_col=[0, 1], + header=[0, 1], + ) + tm.assert_frame_equal(result, expected) + + def test_read_excel_multiindex_header_only(self, read_ext): + # see gh-11733. + # + # Don't try to parse a header name if there isn't one. + mi_file = "testmultiindex" + read_ext + result = pd.read_excel(mi_file, sheet_name="index_col_none", header=[0, 1]) + + exp_columns = MultiIndex.from_product([("A", "B"), ("key", "val")]) + expected = DataFrame([[1, 2, 3, 4]] * 2, columns=exp_columns) + tm.assert_frame_equal(result, expected) + + def test_excel_old_index_format(self, read_ext): + # see gh-4679 + filename = "test_index_name_pre17" + read_ext + + # We detect headers to determine if index names exist, so + # that "index" name in the "names" version of the data will + # now be interpreted as rows that include null data. + data = np.array( + [ + [np.nan, np.nan, np.nan, np.nan, np.nan], + ["R0C0", "R0C1", "R0C2", "R0C3", "R0C4"], + ["R1C0", "R1C1", "R1C2", "R1C3", "R1C4"], + ["R2C0", "R2C1", "R2C2", "R2C3", "R2C4"], + ["R3C0", "R3C1", "R3C2", "R3C3", "R3C4"], + ["R4C0", "R4C1", "R4C2", "R4C3", "R4C4"], + ], + dtype=object, + ) + columns = ["C_l0_g0", "C_l0_g1", "C_l0_g2", "C_l0_g3", "C_l0_g4"] + mi = MultiIndex( + levels=[ + ["R0", "R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], + ["R1", "R_l1_g0", "R_l1_g1", "R_l1_g2", "R_l1_g3", "R_l1_g4"], + ], + codes=[[0, 1, 2, 3, 4, 5], [0, 1, 2, 3, 4, 5]], + names=[None, None], + ) + si = Index( + ["R0", "R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], name=None + ) + + expected = DataFrame(data, index=si, columns=columns) + + actual = pd.read_excel(filename, sheet_name="single_names", index_col=0) + tm.assert_frame_equal(actual, expected) + + expected.index = mi + + actual = pd.read_excel(filename, sheet_name="multi_names", index_col=[0, 1]) + tm.assert_frame_equal(actual, expected) + + # The analogous versions of the "names" version data + # where there are explicitly no names for the indices. + data = np.array( + [ + ["R0C0", "R0C1", "R0C2", "R0C3", "R0C4"], + ["R1C0", "R1C1", "R1C2", "R1C3", "R1C4"], + ["R2C0", "R2C1", "R2C2", "R2C3", "R2C4"], + ["R3C0", "R3C1", "R3C2", "R3C3", "R3C4"], + ["R4C0", "R4C1", "R4C2", "R4C3", "R4C4"], + ] + ) + columns = ["C_l0_g0", "C_l0_g1", "C_l0_g2", "C_l0_g3", "C_l0_g4"] + mi = MultiIndex( + levels=[ + ["R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], + ["R_l1_g0", "R_l1_g1", "R_l1_g2", "R_l1_g3", "R_l1_g4"], + ], + codes=[[0, 1, 2, 3, 4], [0, 1, 2, 3, 4]], + names=[None, None], + ) + si = Index(["R_l0_g0", "R_l0_g1", "R_l0_g2", "R_l0_g3", "R_l0_g4"], name=None) + + expected = DataFrame(data, index=si, columns=columns) + + actual = pd.read_excel(filename, sheet_name="single_no_names", index_col=0) + tm.assert_frame_equal(actual, expected) + + expected.index = mi + + actual = pd.read_excel(filename, sheet_name="multi_no_names", index_col=[0, 1]) + tm.assert_frame_equal(actual, expected) + + def test_read_excel_bool_header_arg(self, read_ext): + # GH 6114 + msg = "Passing a bool to header is invalid" + for arg in [True, False]: + with pytest.raises(TypeError, match=msg): + pd.read_excel("test1" + read_ext, header=arg) + + def test_read_excel_skiprows(self, request, engine, read_ext): + # GH 4903 + xfail_datetimes_with_pyxlsb(engine, request) + + unit = get_exp_unit(read_ext, engine) + + actual = pd.read_excel( + "testskiprows" + read_ext, sheet_name="skiprows_list", skiprows=[0, 2] + ) + expected = DataFrame( + [ + [1, 2.5, pd.Timestamp("2015-01-01"), True], + [2, 3.5, pd.Timestamp("2015-01-02"), False], + [3, 4.5, pd.Timestamp("2015-01-03"), False], + [4, 5.5, pd.Timestamp("2015-01-04"), True], + ], + columns=["a", "b", "c", "d"], + ) + expected["c"] = expected["c"].astype(f"M8[{unit}]") + tm.assert_frame_equal(actual, expected) + + actual = pd.read_excel( + "testskiprows" + read_ext, + sheet_name="skiprows_list", + skiprows=np.array([0, 2]), + ) + tm.assert_frame_equal(actual, expected) + + # GH36435 + actual = pd.read_excel( + "testskiprows" + read_ext, + sheet_name="skiprows_list", + skiprows=lambda x: x in [0, 2], + ) + tm.assert_frame_equal(actual, expected) + + actual = pd.read_excel( + "testskiprows" + read_ext, + sheet_name="skiprows_list", + skiprows=3, + names=["a", "b", "c", "d"], + ) + expected = DataFrame( + [ + # [1, 2.5, pd.Timestamp("2015-01-01"), True], + [2, 3.5, pd.Timestamp("2015-01-02"), False], + [3, 4.5, pd.Timestamp("2015-01-03"), False], + [4, 5.5, pd.Timestamp("2015-01-04"), True], + ], + columns=["a", "b", "c", "d"], + ) + expected["c"] = expected["c"].astype(f"M8[{unit}]") + tm.assert_frame_equal(actual, expected) + + def test_read_excel_skiprows_callable_not_in(self, request, engine, read_ext): + # GH 4903 + xfail_datetimes_with_pyxlsb(engine, request) + unit = get_exp_unit(read_ext, engine) + + actual = pd.read_excel( + "testskiprows" + read_ext, + sheet_name="skiprows_list", + skiprows=lambda x: x not in [1, 3, 5], + ) + expected = DataFrame( + [ + [1, 2.5, pd.Timestamp("2015-01-01"), True], + # [2, 3.5, pd.Timestamp("2015-01-02"), False], + [3, 4.5, pd.Timestamp("2015-01-03"), False], + # [4, 5.5, pd.Timestamp("2015-01-04"), True], + ], + columns=["a", "b", "c", "d"], + ) + expected["c"] = expected["c"].astype(f"M8[{unit}]") + tm.assert_frame_equal(actual, expected) + + def test_read_excel_nrows(self, read_ext): + # GH 16645 + num_rows_to_pull = 5 + actual = pd.read_excel("test1" + read_ext, nrows=num_rows_to_pull) + expected = pd.read_excel("test1" + read_ext) + expected = expected[:num_rows_to_pull] + tm.assert_frame_equal(actual, expected) + + def test_read_excel_nrows_greater_than_nrows_in_file(self, read_ext): + # GH 16645 + expected = pd.read_excel("test1" + read_ext) + num_records_in_file = len(expected) + num_rows_to_pull = num_records_in_file + 10 + actual = pd.read_excel("test1" + read_ext, nrows=num_rows_to_pull) + tm.assert_frame_equal(actual, expected) + + def test_read_excel_nrows_non_integer_parameter(self, read_ext): + # GH 16645 + msg = "'nrows' must be an integer >=0" + with pytest.raises(ValueError, match=msg): + pd.read_excel("test1" + read_ext, nrows="5") + + @pytest.mark.parametrize( + "filename,sheet_name,header,index_col,skiprows", + [ + ("testmultiindex", "mi_column", [0, 1], 0, None), + ("testmultiindex", "mi_index", None, [0, 1], None), + ("testmultiindex", "both", [0, 1], [0, 1], None), + ("testmultiindex", "mi_column_name", [0, 1], 0, None), + ("testskiprows", "skiprows_list", None, None, [0, 2]), + ("testskiprows", "skiprows_list", None, None, lambda x: x in (0, 2)), + ], + ) + def test_read_excel_nrows_params( + self, read_ext, filename, sheet_name, header, index_col, skiprows + ): + """ + For various parameters, we should get the same result whether we + limit the rows during load (nrows=3) or after (df.iloc[:3]). + """ + # GH 46894 + expected = pd.read_excel( + filename + read_ext, + sheet_name=sheet_name, + header=header, + index_col=index_col, + skiprows=skiprows, + ).iloc[:3] + actual = pd.read_excel( + filename + read_ext, + sheet_name=sheet_name, + header=header, + index_col=index_col, + skiprows=skiprows, + nrows=3, + ) + tm.assert_frame_equal(actual, expected) + + def test_deprecated_kwargs(self, read_ext): + with pytest.raises(TypeError, match="but 3 positional arguments"): + pd.read_excel("test1" + read_ext, "Sheet1", 0) + + def test_no_header_with_list_index_col(self, read_ext): + # GH 31783 + file_name = "testmultiindex" + read_ext + data = [("B", "B"), ("key", "val"), (3, 4), (3, 4)] + idx = MultiIndex.from_tuples( + [("A", "A"), ("key", "val"), (1, 2), (1, 2)], names=(0, 1) + ) + expected = DataFrame(data, index=idx, columns=(2, 3)) + result = pd.read_excel( + file_name, sheet_name="index_col_none", index_col=[0, 1], header=None + ) + tm.assert_frame_equal(expected, result) + + def test_one_col_noskip_blank_line(self, read_ext): + # GH 39808 + file_name = "one_col_blank_line" + read_ext + data = [0.5, np.nan, 1, 2] + expected = DataFrame(data, columns=["numbers"]) + result = pd.read_excel(file_name) + tm.assert_frame_equal(result, expected) + + def test_multiheader_two_blank_lines(self, read_ext): + # GH 40442 + file_name = "testmultiindex" + read_ext + columns = MultiIndex.from_tuples([("a", "A"), ("b", "B")]) + data = [[np.nan, np.nan], [np.nan, np.nan], [1, 3], [2, 4]] + expected = DataFrame(data, columns=columns) + result = pd.read_excel( + file_name, sheet_name="mi_column_empty_rows", header=[0, 1] + ) + tm.assert_frame_equal(result, expected) + + def test_trailing_blanks(self, read_ext): + """ + Sheets can contain blank cells with no data. Some of our readers + were including those cells, creating many empty rows and columns + """ + file_name = "trailing_blanks" + read_ext + result = pd.read_excel(file_name) + assert result.shape == (3, 3) + + def test_ignore_chartsheets_by_str(self, request, engine, read_ext): + # GH 41448 + if read_ext == ".ods": + pytest.skip("chartsheets do not exist in the ODF format") + if engine == "pyxlsb": + request.applymarker( + pytest.mark.xfail( + reason="pyxlsb can't distinguish chartsheets from worksheets" + ) + ) + with pytest.raises(ValueError, match="Worksheet named 'Chart1' not found"): + pd.read_excel("chartsheet" + read_ext, sheet_name="Chart1") + + def test_ignore_chartsheets_by_int(self, request, engine, read_ext): + # GH 41448 + if read_ext == ".ods": + pytest.skip("chartsheets do not exist in the ODF format") + if engine == "pyxlsb": + request.applymarker( + pytest.mark.xfail( + reason="pyxlsb can't distinguish chartsheets from worksheets" + ) + ) + with pytest.raises( + ValueError, match="Worksheet index 1 is invalid, 1 worksheets found" + ): + pd.read_excel("chartsheet" + read_ext, sheet_name=1) + + def test_euro_decimal_format(self, read_ext): + # copied from read_csv + result = pd.read_excel("test_decimal" + read_ext, decimal=",", skiprows=1) + expected = DataFrame( + [ + [1, 1521.1541, 187101.9543, "ABC", "poi", 4.738797819], + [2, 121.12, 14897.76, "DEF", "uyt", 0.377320872], + [3, 878.158, 108013.434, "GHI", "rez", 2.735694704], + ], + columns=["Id", "Number1", "Number2", "Text1", "Text2", "Number3"], + ) + tm.assert_frame_equal(result, expected) + + +class TestExcelFileRead: + def test_raises_bytes_input(self, engine, read_ext): + # GH 53830 + msg = "Expected file path name or file-like object" + with pytest.raises(TypeError, match=msg): + with open("test1" + read_ext, "rb") as f: + pd.read_excel(f.read(), engine=engine) + + @pytest.fixture(autouse=True) + def cd_and_set_engine(self, engine, datapath, monkeypatch): + """ + Change directory and set engine for ExcelFile objects. + """ + func = partial(pd.ExcelFile, engine=engine) + monkeypatch.chdir(datapath("io", "data", "excel")) + monkeypatch.setattr(pd, "ExcelFile", func) + + def test_engine_used(self, read_ext, engine): + expected_defaults = { + "xlsx": "openpyxl", + "xlsm": "openpyxl", + "xlsb": "pyxlsb", + "xls": "xlrd", + "ods": "odf", + } + + with pd.ExcelFile("test1" + read_ext) as excel: + result = excel.engine + + if engine is not None: + expected = engine + else: + expected = expected_defaults[read_ext[1:]] + assert result == expected + + def test_excel_passes_na(self, read_ext): + with pd.ExcelFile("test4" + read_ext) as excel: + parsed = pd.read_excel( + excel, sheet_name="Sheet1", keep_default_na=False, na_values=["apple"] + ) + expected = DataFrame( + [["NA"], [1], ["NA"], [np.nan], ["rabbit"]], columns=["Test"] + ) + tm.assert_frame_equal(parsed, expected) + + with pd.ExcelFile("test4" + read_ext) as excel: + parsed = pd.read_excel( + excel, sheet_name="Sheet1", keep_default_na=True, na_values=["apple"] + ) + expected = DataFrame( + [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]], columns=["Test"] + ) + tm.assert_frame_equal(parsed, expected) + + # 13967 + with pd.ExcelFile("test5" + read_ext) as excel: + parsed = pd.read_excel( + excel, sheet_name="Sheet1", keep_default_na=False, na_values=["apple"] + ) + expected = DataFrame( + [["1.#QNAN"], [1], ["nan"], [np.nan], ["rabbit"]], columns=["Test"] + ) + tm.assert_frame_equal(parsed, expected) + + with pd.ExcelFile("test5" + read_ext) as excel: + parsed = pd.read_excel( + excel, sheet_name="Sheet1", keep_default_na=True, na_values=["apple"] + ) + expected = DataFrame( + [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]], columns=["Test"] + ) + tm.assert_frame_equal(parsed, expected) + + @pytest.mark.parametrize("na_filter", [None, True, False]) + def test_excel_passes_na_filter(self, read_ext, na_filter): + # gh-25453 + kwargs = {} + + if na_filter is not None: + kwargs["na_filter"] = na_filter + + with pd.ExcelFile("test5" + read_ext) as excel: + parsed = pd.read_excel( + excel, + sheet_name="Sheet1", + keep_default_na=True, + na_values=["apple"], + **kwargs, + ) + + if na_filter is False: + expected = [["1.#QNAN"], [1], ["nan"], ["apple"], ["rabbit"]] + else: + expected = [[np.nan], [1], [np.nan], [np.nan], ["rabbit"]] + + expected = DataFrame(expected, columns=["Test"]) + tm.assert_frame_equal(parsed, expected) + + def test_excel_table_sheet_by_index(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref + adjust_expected(expected, read_ext, engine) + + with pd.ExcelFile("test1" + read_ext) as excel: + df1 = pd.read_excel(excel, sheet_name=0, index_col=0) + df2 = pd.read_excel(excel, sheet_name=1, skiprows=[1], index_col=0) + tm.assert_frame_equal(df1, expected) + tm.assert_frame_equal(df2, expected) + + with pd.ExcelFile("test1" + read_ext) as excel: + df1 = excel.parse(0, index_col=0) + df2 = excel.parse(1, skiprows=[1], index_col=0) + tm.assert_frame_equal(df1, expected) + tm.assert_frame_equal(df2, expected) + + with pd.ExcelFile("test1" + read_ext) as excel: + df3 = pd.read_excel(excel, sheet_name=0, index_col=0, skipfooter=1) + tm.assert_frame_equal(df3, df1.iloc[:-1]) + + with pd.ExcelFile("test1" + read_ext) as excel: + df3 = excel.parse(0, index_col=0, skipfooter=1) + + tm.assert_frame_equal(df3, df1.iloc[:-1]) + + def test_sheet_name(self, request, engine, read_ext, df_ref): + xfail_datetimes_with_pyxlsb(engine, request) + + expected = df_ref + adjust_expected(expected, read_ext, engine) + + filename = "test1" + sheet_name = "Sheet1" + + with pd.ExcelFile(filename + read_ext) as excel: + df1_parse = excel.parse(sheet_name=sheet_name, index_col=0) # doc + + with pd.ExcelFile(filename + read_ext) as excel: + df2_parse = excel.parse(index_col=0, sheet_name=sheet_name) + + tm.assert_frame_equal(df1_parse, expected) + tm.assert_frame_equal(df2_parse, expected) + + @pytest.mark.parametrize( + "sheet_name", + [3, [0, 3], [3, 0], "Sheet4", ["Sheet1", "Sheet4"], ["Sheet4", "Sheet1"]], + ) + def test_bad_sheetname_raises(self, read_ext, sheet_name): + # GH 39250 + msg = "Worksheet index 3 is invalid|Worksheet named 'Sheet4' not found" + with pytest.raises(ValueError, match=msg): + with pd.ExcelFile("blank" + read_ext) as excel: + excel.parse(sheet_name=sheet_name) + + def test_excel_read_buffer(self, engine, read_ext): + pth = "test1" + read_ext + expected = pd.read_excel(pth, sheet_name="Sheet1", index_col=0, engine=engine) + + with open(pth, "rb") as f: + with pd.ExcelFile(f) as xls: + actual = pd.read_excel(xls, sheet_name="Sheet1", index_col=0) + + tm.assert_frame_equal(expected, actual) + + def test_reader_closes_file(self, engine, read_ext): + with open("test1" + read_ext, "rb") as f: + with pd.ExcelFile(f) as xlsx: + # parses okay + pd.read_excel(xlsx, sheet_name="Sheet1", index_col=0, engine=engine) + + assert f.closed + + def test_conflicting_excel_engines(self, read_ext): + # GH 26566 + msg = "Engine should not be specified when passing an ExcelFile" + + with pd.ExcelFile("test1" + read_ext) as xl: + with pytest.raises(ValueError, match=msg): + pd.read_excel(xl, engine="foo") + + def test_excel_read_binary(self, engine, read_ext): + # GH 15914 + expected = pd.read_excel("test1" + read_ext, engine=engine) + + with open("test1" + read_ext, "rb") as f: + data = f.read() + + actual = pd.read_excel(BytesIO(data), engine=engine) + tm.assert_frame_equal(expected, actual) + + def test_excel_read_binary_via_read_excel(self, read_ext, engine): + # GH 38424 + with open("test1" + read_ext, "rb") as f: + result = pd.read_excel(f, engine=engine) + expected = pd.read_excel("test1" + read_ext, engine=engine) + tm.assert_frame_equal(result, expected) + + def test_read_excel_header_index_out_of_range(self, engine): + # GH#43143 + with open("df_header_oob.xlsx", "rb") as f: + with pytest.raises(ValueError, match="exceeds maximum"): + pd.read_excel(f, header=[0, 1]) + + @pytest.mark.parametrize("filename", ["df_empty.xlsx", "df_equals.xlsx"]) + def test_header_with_index_col(self, filename): + # GH 33476 + idx = Index(["Z"], name="I2") + cols = MultiIndex.from_tuples([("A", "B"), ("A", "B.1")], names=["I11", "I12"]) + expected = DataFrame([[1, 3]], index=idx, columns=cols, dtype="int64") + result = pd.read_excel( + filename, sheet_name="Sheet1", index_col=0, header=[0, 1] + ) + tm.assert_frame_equal(expected, result) + + def test_read_datetime_multiindex(self, request, engine, read_ext): + # GH 34748 + xfail_datetimes_with_pyxlsb(engine, request) + + f = "test_datetime_mi" + read_ext + with pd.ExcelFile(f) as excel: + actual = pd.read_excel(excel, header=[0, 1], index_col=0, engine=engine) + + unit = get_exp_unit(read_ext, engine) + + dti = pd.DatetimeIndex(["2020-02-29", "2020-03-01"], dtype=f"M8[{unit}]") + expected_column_index = MultiIndex.from_arrays( + [dti[:1], dti[1:]], + names=[ + dti[0].to_pydatetime(), + dti[1].to_pydatetime(), + ], + ) + expected = DataFrame([], index=[], columns=expected_column_index) + + tm.assert_frame_equal(expected, actual) + + def test_engine_invalid_option(self, read_ext): + # read_ext includes the '.' hence the weird formatting + with pytest.raises(ValueError, match="Value must be one of *"): + with pd.option_context(f"io.excel{read_ext}.reader", "abc"): + pass + + def test_ignore_chartsheets(self, request, engine, read_ext): + # GH 41448 + if read_ext == ".ods": + pytest.skip("chartsheets do not exist in the ODF format") + if engine == "pyxlsb": + request.applymarker( + pytest.mark.xfail( + reason="pyxlsb can't distinguish chartsheets from worksheets" + ) + ) + with pd.ExcelFile("chartsheet" + read_ext) as excel: + assert excel.sheet_names == ["Sheet1"] + + def test_corrupt_files_closed(self, engine, tmp_excel): + # GH41778 + errors = (BadZipFile,) + if engine is None: + pytest.skip(f"Invalid test for engine={engine}") + elif engine == "xlrd": + import xlrd + + errors = (BadZipFile, xlrd.biffh.XLRDError) + elif engine == "calamine": + from python_calamine import CalamineError + + errors = (CalamineError,) + + Path(tmp_excel).write_text("corrupt", encoding="utf-8") + with tm.assert_produces_warning(False): + try: + pd.ExcelFile(tmp_excel, engine=engine) + except errors: + pass diff --git a/lib/python3.11/site-packages/pandas/tests/io/excel/test_style.py b/lib/python3.11/site-packages/pandas/tests/io/excel/test_style.py new file mode 100644 index 0000000000000000000000000000000000000000..5afa2cae6a56cb87879d218a616f62788b50c103 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/excel/test_style.py @@ -0,0 +1,356 @@ +import contextlib +import uuid + +import numpy as np +import pytest + +import pandas.util._test_decorators as td + +from pandas import ( + DataFrame, + MultiIndex, + Timestamp, + period_range, + read_excel, +) +import pandas._testing as tm + +from pandas.io.excel import ExcelWriter +from pandas.io.formats.excel import ExcelFormatter + +pytest.importorskip("jinja2") +# jinja2 is currently required for Styler.__init__(). Technically Styler.to_excel +# could compute styles and render to excel without jinja2, since there is no +# 'template' file, but this needs the import error to delayed until render time. + + +@pytest.fixture +def tmp_excel(tmp_path): + tmp = tmp_path / f"{uuid.uuid4()}.xlsx" + tmp.touch() + return str(tmp) + + +def assert_equal_cell_styles(cell1, cell2): + # TODO: should find a better way to check equality + assert cell1.alignment.__dict__ == cell2.alignment.__dict__ + assert cell1.border.__dict__ == cell2.border.__dict__ + assert cell1.fill.__dict__ == cell2.fill.__dict__ + assert cell1.font.__dict__ == cell2.font.__dict__ + assert cell1.number_format == cell2.number_format + assert cell1.protection.__dict__ == cell2.protection.__dict__ + + +def test_styler_default_values(tmp_excel): + # GH 54154 + openpyxl = pytest.importorskip("openpyxl") + df = DataFrame([{"A": 1, "B": 2, "C": 3}, {"A": 1, "B": 2, "C": 3}]) + + with ExcelWriter(tmp_excel, engine="openpyxl") as writer: + df.to_excel(writer, sheet_name="custom") + + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + # Check font, spacing, indentation + assert wb["custom"].cell(1, 1).font.bold is False + assert wb["custom"].cell(1, 1).alignment.horizontal is None + assert wb["custom"].cell(1, 1).alignment.vertical is None + + # Check border + assert wb["custom"].cell(1, 1).border.bottom.color is None + assert wb["custom"].cell(1, 1).border.top.color is None + assert wb["custom"].cell(1, 1).border.left.color is None + assert wb["custom"].cell(1, 1).border.right.color is None + + +@pytest.mark.parametrize("engine", ["xlsxwriter", "openpyxl"]) +def test_styler_to_excel_unstyled(engine, tmp_excel): + # compare DataFrame.to_excel and Styler.to_excel when no styles applied + pytest.importorskip(engine) + df = DataFrame(np.random.default_rng(2).standard_normal((2, 2))) + with ExcelWriter(tmp_excel, engine=engine) as writer: + df.to_excel(writer, sheet_name="dataframe") + df.style.to_excel(writer, sheet_name="unstyled") + + openpyxl = pytest.importorskip("openpyxl") # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + for col1, col2 in zip( + wb["dataframe"].columns, + wb["unstyled"].columns, + strict=True, + ): + assert len(col1) == len(col2) + for cell1, cell2 in zip(col1, col2, strict=True): + assert cell1.value == cell2.value + assert_equal_cell_styles(cell1, cell2) + + +shared_style_params = [ + ( + "background-color: #111222", + ["fill", "fgColor", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ( + "color: #111222", + ["font", "color", "value"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ("font-family: Arial;", ["font", "name"], "arial"), + ("font-weight: bold;", ["font", "b"], True), + ("font-style: italic;", ["font", "i"], True), + ("text-decoration: underline;", ["font", "u"], "single"), + ("number-format: $??,???.00;", ["number_format"], "$??,???.00"), + ("text-align: left;", ["alignment", "horizontal"], "left"), + ( + "vertical-align: bottom;", + ["alignment", "vertical"], + {"xlsxwriter": None, "openpyxl": "bottom"}, # xlsxwriter Fails + ), + ("vertical-align: middle;", ["alignment", "vertical"], "center"), + # Border widths + ("border-left: 2pt solid red", ["border", "left", "style"], "medium"), + ("border-left: 1pt dotted red", ["border", "left", "style"], "dotted"), + ("border-left: 2pt dotted red", ["border", "left", "style"], "mediumDashDotDot"), + ("border-left: 1pt dashed red", ["border", "left", "style"], "dashed"), + ("border-left: 2pt dashed red", ["border", "left", "style"], "mediumDashed"), + ("border-left: 1pt solid red", ["border", "left", "style"], "thin"), + ("border-left: 3pt solid red", ["border", "left", "style"], "thick"), + # Border expansion + ( + "border-left: 2pt solid #111222", + ["border", "left", "color", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ("border: 1pt solid red", ["border", "top", "style"], "thin"), + ( + "border: 1pt solid #111222", + ["border", "top", "color", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ("border: 1pt solid red", ["border", "right", "style"], "thin"), + ( + "border: 1pt solid #111222", + ["border", "right", "color", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ("border: 1pt solid red", ["border", "bottom", "style"], "thin"), + ( + "border: 1pt solid #111222", + ["border", "bottom", "color", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + ("border: 1pt solid red", ["border", "left", "style"], "thin"), + ( + "border: 1pt solid #111222", + ["border", "left", "color", "rgb"], + {"xlsxwriter": "FF111222", "openpyxl": "00111222"}, + ), + # Border styles + ( + "border-left-style: hair; border-left-color: black", + ["border", "left", "style"], + "hair", + ), +] + + +def test_styler_custom_style(tmp_excel): + # GH 54154 + css_style = "background-color: #111222" + openpyxl = pytest.importorskip("openpyxl") + df = DataFrame([{"A": 1, "B": 2}, {"A": 1, "B": 2}]) + + with ExcelWriter(tmp_excel, engine="openpyxl") as writer: + styler = df.style.map(lambda x: css_style) + styler.to_excel(writer, sheet_name="custom", index=False) + + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + # Check font, spacing, indentation + assert wb["custom"].cell(1, 1).font.bold is False + assert wb["custom"].cell(1, 1).alignment.horizontal is None + assert wb["custom"].cell(1, 1).alignment.vertical is None + + # Check border + assert wb["custom"].cell(1, 1).border.bottom.color is None + assert wb["custom"].cell(1, 1).border.top.color is None + assert wb["custom"].cell(1, 1).border.left.color is None + assert wb["custom"].cell(1, 1).border.right.color is None + + # Check background color + assert wb["custom"].cell(2, 1).fill.fgColor.index == "00111222" + assert wb["custom"].cell(3, 1).fill.fgColor.index == "00111222" + assert wb["custom"].cell(2, 2).fill.fgColor.index == "00111222" + assert wb["custom"].cell(3, 2).fill.fgColor.index == "00111222" + + +@pytest.mark.parametrize("engine", ["xlsxwriter", "openpyxl"]) +@pytest.mark.parametrize("css, attrs, expected", shared_style_params) +def test_styler_to_excel_basic(engine, css, attrs, expected, tmp_excel): + pytest.importorskip(engine) + df = DataFrame(np.random.default_rng(2).standard_normal((1, 1))) + styler = df.style.map(lambda x: css) + + with ExcelWriter(tmp_excel, engine=engine) as writer: + df.to_excel(writer, sheet_name="dataframe") + styler.to_excel(writer, sheet_name="styled") + + openpyxl = pytest.importorskip("openpyxl") # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + # test unstyled data cell does not have expected styles + # test styled cell has expected styles + u_cell, s_cell = wb["dataframe"].cell(2, 2), wb["styled"].cell(2, 2) + for attr in attrs: + u_cell, s_cell = getattr(u_cell, attr, None), getattr(s_cell, attr) + + if isinstance(expected, dict): + assert u_cell is None or u_cell != expected[engine] + assert s_cell == expected[engine] + else: + assert u_cell is None or u_cell != expected + assert s_cell == expected + + +@pytest.mark.parametrize("engine", ["xlsxwriter", "openpyxl"]) +@pytest.mark.parametrize("css, attrs, expected", shared_style_params) +def test_styler_to_excel_basic_indexes(engine, css, attrs, expected, tmp_excel): + pytest.importorskip(engine) + df = DataFrame(np.random.default_rng(2).standard_normal((1, 1))) + + styler = df.style + styler.map_index(lambda x: css, axis=0) + styler.map_index(lambda x: css, axis=1) + + null_styler = df.style + null_styler.map(lambda x: "null: css;") + null_styler.map_index(lambda x: "null: css;", axis=0) + null_styler.map_index(lambda x: "null: css;", axis=1) + + with ExcelWriter(tmp_excel, engine=engine) as writer: + null_styler.to_excel(writer, sheet_name="null_styled") + styler.to_excel(writer, sheet_name="styled") + + openpyxl = pytest.importorskip("openpyxl") # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + # test null styled index cells does not have expected styles + # test styled cell has expected styles + ui_cell, si_cell = wb["null_styled"].cell(2, 1), wb["styled"].cell(2, 1) + uc_cell, sc_cell = wb["null_styled"].cell(1, 2), wb["styled"].cell(1, 2) + for attr in attrs: + ui_cell, si_cell = getattr(ui_cell, attr, None), getattr(si_cell, attr) + uc_cell, sc_cell = getattr(uc_cell, attr, None), getattr(sc_cell, attr) + + if isinstance(expected, dict): + assert ui_cell is None or ui_cell != expected[engine] + assert si_cell == expected[engine] + assert uc_cell is None or uc_cell != expected[engine] + assert sc_cell == expected[engine] + else: + assert ui_cell is None or ui_cell != expected + assert si_cell == expected + assert uc_cell is None or uc_cell != expected + assert sc_cell == expected + + +# From https://openpyxl.readthedocs.io/en/stable/api/openpyxl.styles.borders.html +# Note: Leaving behavior of "width"-type styles undefined; user should use border-width +# instead +excel_border_styles = [ + # "thin", + "dashed", + "mediumDashDot", + "dashDotDot", + "hair", + "dotted", + "mediumDashDotDot", + # "medium", + "double", + "dashDot", + "slantDashDot", + # "thick", + "mediumDashed", +] + + +@pytest.mark.parametrize("engine", ["xlsxwriter", "openpyxl"]) +@pytest.mark.parametrize("border_style", excel_border_styles) +def test_styler_to_excel_border_style(engine, border_style, tmp_excel): + css = f"border-left: {border_style} black thin" + attrs = ["border", "left", "style"] + expected = border_style + + pytest.importorskip(engine) + df = DataFrame(np.random.default_rng(2).standard_normal((1, 1))) + styler = df.style.map(lambda x: css) + + with ExcelWriter(tmp_excel, engine=engine) as writer: + df.to_excel(writer, sheet_name="dataframe") + styler.to_excel(writer, sheet_name="styled") + + openpyxl = pytest.importorskip("openpyxl") # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + # test unstyled data cell does not have expected styles + # test styled cell has expected styles + u_cell, s_cell = wb["dataframe"].cell(2, 2), wb["styled"].cell(2, 2) + for attr in attrs: + u_cell, s_cell = getattr(u_cell, attr, None), getattr(s_cell, attr) + + if isinstance(expected, dict): + assert u_cell is None or u_cell != expected[engine] + assert s_cell == expected[engine] + else: + assert u_cell is None or u_cell != expected + assert s_cell == expected + + +def test_styler_custom_converter(tmp_excel): + openpyxl = pytest.importorskip("openpyxl") + + def custom_converter(css): + return {"font": {"color": {"rgb": "111222"}}} + + df = DataFrame(np.random.default_rng(2).standard_normal((1, 1))) + styler = df.style.map(lambda x: "color: #888999") + with ExcelWriter(tmp_excel, engine="openpyxl") as writer: + ExcelFormatter(styler, style_converter=custom_converter).write( + writer, sheet_name="custom" + ) + + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + assert wb["custom"].cell(2, 2).font.color.value == "00111222" + + +@pytest.mark.single_cpu +@td.skip_if_not_us_locale +def test_styler_to_s3(s3_bucket_public, s3so): + # GH#46381 + mock_bucket_name = s3_bucket_public.name + target_file = f"{uuid.uuid4()}.xlsx" + df = DataFrame({"x": [1, 2, 3], "y": [2, 4, 6]}) + styler = df.style.set_sticky(axis="index") + uri = f"s3://{mock_bucket_name}/{target_file}" + styler.to_excel(uri, storage_options=s3so) + result = read_excel(uri, index_col=0, storage_options=s3so) + tm.assert_frame_equal(result, df) + + +@pytest.mark.parametrize("merge_cells", [True, False, "columns"]) +def test_format_hierarchical_rows_periodindex(merge_cells): + # GH#60099 + df = DataFrame( + {"A": [1, 2]}, + index=MultiIndex.from_arrays( + [ + period_range(start="2006-10-06", end="2006-10-07", freq="D"), + ["X", "Y"], + ], + names=["date", "category"], + ), + ) + formatter = ExcelFormatter(df, merge_cells=merge_cells) + formatted_cells = formatter._format_hierarchical_rows() + + for cell in formatted_cells: + if cell.row != 0 and cell.col == 0: + assert isinstance(cell.val, Timestamp), ( + "Period should be converted to Timestamp" + ) diff --git a/lib/python3.11/site-packages/pandas/tests/io/excel/test_writers.py b/lib/python3.11/site-packages/pandas/tests/io/excel/test_writers.py new file mode 100644 index 0000000000000000000000000000000000000000..a6a26f0ca027c673c16d7263103d57736880f94c --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/excel/test_writers.py @@ -0,0 +1,1765 @@ +import contextlib +from datetime import ( + date, + datetime, + timedelta, +) +from decimal import Decimal +from functools import partial +from io import BytesIO +import os +import pathlib +import re +import uuid + +import numpy as np +import pytest + +from pandas.compat._optional import import_optional_dependency +import pandas.util._test_decorators as td + +import pandas as pd +from pandas import ( + DataFrame, + Index, + MultiIndex, + date_range, + option_context, + period_range, +) +import pandas._testing as tm + +from pandas.io.excel import ( + ExcelFile, + ExcelWriter, + _OpenpyxlWriter, + _XlsxWriter, + register_writer, +) +from pandas.io.excel._util import _writers + + +def get_exp_unit(path: str) -> str: + return "us" + + +@pytest.fixture +def frame(float_frame): + """ + Returns the first ten items in fixture "float_frame". + """ + return float_frame[:10] + + +@pytest.fixture(params=[True, False, "columns"]) +def merge_cells(request): + return request.param + + +@pytest.fixture +def tmp_excel(ext, tmp_path): + """ + Fixture to open file for use in each test case. + """ + tmp = tmp_path / f"{uuid.uuid4()}{ext}" + tmp.touch() + return str(tmp) + + +@pytest.fixture +def set_engine(engine, ext): + """ + Fixture to set engine for use in each test case. + + Rather than requiring `engine=...` to be provided explicitly as an + argument in each test, this fixture sets a global option to dictate + which engine should be used to write Excel files. After executing + the test it rolls back said change to the global option. + """ + option_name = f"io.excel.{ext.strip('.')}.writer" + with option_context(option_name, engine): + yield + + +@pytest.mark.parametrize( + "ext", + [ + pytest.param(".xlsx", marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")]), + pytest.param(".xlsm", marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")]), + pytest.param( + ".xlsx", marks=[td.skip_if_no("xlsxwriter"), td.skip_if_no("xlrd")] + ), + pytest.param(".ods", marks=td.skip_if_no("odf")), + ], +) +class TestRoundTrip: + @pytest.mark.parametrize( + "header,expected", + [(None, [np.nan] * 4), (0, {"Unnamed: 0": [np.nan] * 3})], + ) + def test_read_one_empty_col_no_header(self, tmp_excel, header, expected): + # xref gh-12292 + filename = "no_header" + df = DataFrame([["", 1, 100], ["", 2, 200], ["", 3, 300], ["", 4, 400]]) + + df.to_excel(tmp_excel, sheet_name=filename, index=False, header=False) + result = pd.read_excel( + tmp_excel, sheet_name=filename, usecols=[0], header=header + ) + expected = DataFrame(expected) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "header,expected_extra", + [(None, [0]), (0, [])], + ) + def test_read_one_empty_col_with_header(self, tmp_excel, header, expected_extra): + filename = "with_header" + df = DataFrame([["", 1, 100], ["", 2, 200], ["", 3, 300], ["", 4, 400]]) + + df.to_excel(tmp_excel, sheet_name="with_header", index=False, header=True) + result = pd.read_excel( + tmp_excel, sheet_name=filename, usecols=[0], header=header + ) + expected = DataFrame(expected_extra + [np.nan] * 4) + tm.assert_frame_equal(result, expected) + + def test_set_column_names_in_parameter(self, tmp_excel): + # GH 12870 : pass down column names associated with + # keyword argument names + refdf = DataFrame([[1, "foo"], [2, "bar"], [3, "baz"]], columns=["a", "b"]) + + with ExcelWriter(tmp_excel) as writer: + refdf.to_excel(writer, sheet_name="Data_no_head", header=False, index=False) + refdf.to_excel(writer, sheet_name="Data_with_head", index=False) + + refdf.columns = ["A", "B"] + + with ExcelFile(tmp_excel) as reader: + xlsdf_no_head = pd.read_excel( + reader, sheet_name="Data_no_head", header=None, names=["A", "B"] + ) + xlsdf_with_head = pd.read_excel( + reader, + sheet_name="Data_with_head", + index_col=None, + names=["A", "B"], + ) + + tm.assert_frame_equal(xlsdf_no_head, refdf) + tm.assert_frame_equal(xlsdf_with_head, refdf) + + def test_creating_and_reading_multiple_sheets(self, tmp_excel): + # see gh-9450 + # + # Test reading multiple sheets, from a runtime + # created Excel file with multiple sheets. + def tdf(col_sheet_name): + d, i = [11, 22, 33], [1, 2, 3] + return DataFrame(d, i, columns=[col_sheet_name]) + + sheets = ["AAA", "BBB", "CCC"] + + dfs = [tdf(s) for s in sheets] + dfs = dict(zip(sheets, dfs)) + + with ExcelWriter(tmp_excel) as ew: + for sheetname, df in dfs.items(): + df.to_excel(ew, sheet_name=sheetname) + + dfs_returned = pd.read_excel(tmp_excel, sheet_name=sheets, index_col=0) + + for s in sheets: + tm.assert_frame_equal(dfs[s], dfs_returned[s]) + + def test_read_excel_multiindex_empty_level(self, tmp_excel): + # see gh-12453 + df = DataFrame( + { + ("One", "x"): {0: 1}, + ("Two", "X"): {0: 3}, + ("Two", "Y"): {0: 7}, + ("Zero", ""): {0: 0}, + } + ) + + expected = DataFrame( + { + ("One", "x"): {0: 1}, + ("Two", "X"): {0: 3}, + ("Two", "Y"): {0: 7}, + ("Zero", "Unnamed: 4_level_1"): {0: 0}, + } + ) + + df.to_excel(tmp_excel) + actual = pd.read_excel(tmp_excel, header=[0, 1], index_col=0) + tm.assert_frame_equal(actual, expected) + + df = DataFrame( + { + ("Beg", ""): {0: 0}, + ("Middle", "x"): {0: 1}, + ("Tail", "X"): {0: 3}, + ("Tail", "Y"): {0: 7}, + } + ) + + expected = DataFrame( + { + ("Beg", "Unnamed: 1_level_1"): {0: 0}, + ("Middle", "x"): {0: 1}, + ("Tail", "X"): {0: 3}, + ("Tail", "Y"): {0: 7}, + } + ) + + df.to_excel(tmp_excel) + actual = pd.read_excel(tmp_excel, header=[0, 1], index_col=0) + tm.assert_frame_equal(actual, expected) + + @pytest.mark.parametrize("c_idx_names", ["a", None]) + @pytest.mark.parametrize("r_idx_names", ["b", None]) + @pytest.mark.parametrize("c_idx_levels", [1, 3]) + @pytest.mark.parametrize("r_idx_levels", [1, 3]) + def test_excel_multindex_roundtrip( + self, + tmp_excel, + c_idx_names, + r_idx_names, + c_idx_levels, + r_idx_levels, + ): + # see gh-4679 + # Empty name case current read in as + # unnamed levels, not Nones. + check_names = bool(r_idx_names) or r_idx_levels <= 1 + + if c_idx_levels == 1: + columns = Index(list("abcde")) + else: + columns = MultiIndex.from_arrays( + [range(5) for _ in range(c_idx_levels)], + names=[f"{c_idx_names}-{i}" for i in range(c_idx_levels)], + ) + if r_idx_levels == 1: + index = Index(list("ghijk")) + else: + index = MultiIndex.from_arrays( + [range(5) for _ in range(r_idx_levels)], + names=[f"{r_idx_names}-{i}" for i in range(r_idx_levels)], + ) + df = DataFrame( + 1.1 * np.ones((5, 5)), + columns=columns, + index=index, + ) + df.to_excel(tmp_excel) + + act = pd.read_excel( + tmp_excel, + index_col=list(range(r_idx_levels)), + header=list(range(c_idx_levels)), + ) + tm.assert_frame_equal(df, act, check_names=check_names) + + df.iloc[0, :] = np.nan + df.to_excel(tmp_excel) + + act = pd.read_excel( + tmp_excel, + index_col=list(range(r_idx_levels)), + header=list(range(c_idx_levels)), + ) + tm.assert_frame_equal(df, act, check_names=check_names) + + df.iloc[-1, :] = np.nan + df.to_excel(tmp_excel) + act = pd.read_excel( + tmp_excel, + index_col=list(range(r_idx_levels)), + header=list(range(c_idx_levels)), + ) + tm.assert_frame_equal(df, act, check_names=check_names) + + def test_read_excel_parse_dates(self, tmp_excel): + # see gh-11544, gh-12051 + df = DataFrame( + {"col": [1, 2, 3], "date_strings": date_range("2012-01-01", periods=3)} + ) + df2 = df.copy() + df2["date_strings"] = df2["date_strings"].dt.strftime("%m/%d/%Y") + + df2.to_excel(tmp_excel) + + res = pd.read_excel(tmp_excel, index_col=0) + tm.assert_frame_equal(df2, res) + + res = pd.read_excel(tmp_excel, parse_dates=["date_strings"], index_col=0) + expected = df[:] + expected["date_strings"] = expected["date_strings"].astype("M8[us]") + tm.assert_frame_equal(res, expected) + + res = pd.read_excel( + tmp_excel, parse_dates=["date_strings"], date_format="%m/%d/%Y", index_col=0 + ) + expected["date_strings"] = expected["date_strings"].astype("M8[us]") + tm.assert_frame_equal(expected, res) + + def test_multiindex_interval_datetimes(self, tmp_excel): + # GH 30986 + midx = MultiIndex.from_arrays( + [ + range(4), + pd.interval_range( + start=pd.Timestamp("2020-01-01"), periods=4, freq="6ME" + ), + ] + ) + df = DataFrame(range(4), index=midx) + df.to_excel(tmp_excel) + result = pd.read_excel(tmp_excel, index_col=[0, 1]) + expected = DataFrame( + range(4), + MultiIndex.from_arrays( + [ + range(4), + [ + "(2020-01-31 00:00:00, 2020-07-31 00:00:00]", + "(2020-07-31 00:00:00, 2021-01-31 00:00:00]", + "(2021-01-31 00:00:00, 2021-07-31 00:00:00]", + "(2021-07-31 00:00:00, 2022-01-31 00:00:00]", + ], + ] + ), + columns=Index([0]), + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("merge_cells", [True, False, "columns"]) + def test_excel_round_trip_with_periodindex(self, tmp_excel, merge_cells): + # GH#60099 + df = DataFrame( + {"A": [1, 2]}, + index=MultiIndex.from_arrays( + [ + period_range(start="2006-10-06", end="2006-10-07", freq="D"), + ["X", "Y"], + ], + names=["date", "category"], + ), + ) + df.to_excel(tmp_excel, merge_cells=merge_cells) + result = pd.read_excel(tmp_excel, index_col=[0, 1]) + expected = DataFrame( + {"A": [1, 2]}, + MultiIndex.from_arrays( + [ + [ + pd.to_datetime("2006-10-06 00:00:00").as_unit("s"), + pd.to_datetime("2006-10-07 00:00:00").as_unit("s"), + ], + ["X", "Y"], + ], + names=["date", "category"], + ), + ) + time_format = "datetime64[us]" + expected.index = expected.index.set_levels( + expected.index.levels[0].astype(time_format), level=0 + ) + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "engine,ext", + [ + pytest.param( + "openpyxl", + ".xlsx", + marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")], + ), + pytest.param( + "openpyxl", + ".xlsm", + marks=[td.skip_if_no("openpyxl"), td.skip_if_no("xlrd")], + ), + pytest.param( + "xlsxwriter", + ".xlsx", + marks=[td.skip_if_no("xlsxwriter"), td.skip_if_no("xlrd")], + ), + pytest.param("odf", ".ods", marks=td.skip_if_no("odf")), + ], +) +@pytest.mark.usefixtures("set_engine") +class TestExcelWriter: + def test_excel_sheet_size(self, tmp_excel): + # GH 26080 + breaking_row_count = 2**20 + 1 + breaking_col_count = 2**14 + 1 + # purposely using two arrays to prevent memory issues while testing + row_arr = np.zeros(shape=(breaking_row_count, 1)) + col_arr = np.zeros(shape=(1, breaking_col_count)) + row_df = DataFrame(row_arr) + col_df = DataFrame(col_arr) + + msg = "sheet is too large" + with pytest.raises(ValueError, match=msg): + row_df.to_excel(tmp_excel) + + with pytest.raises(ValueError, match=msg): + col_df.to_excel(tmp_excel) + + def test_excel_sheet_by_name_raise(self, tmp_excel): + gt = DataFrame( + np.random.default_rng(2).standard_normal((10, 2)), + index=Index(list(range(10))), + ) + gt.to_excel(tmp_excel) + + with ExcelFile(tmp_excel) as xl: + df = pd.read_excel(xl, sheet_name=0, index_col=0) + + tm.assert_frame_equal(gt, df) + + msg = "Worksheet named '0' not found" + with pytest.raises(ValueError, match=msg): + pd.read_excel(xl, "0") + + def test_excel_writer_context_manager(self, frame, tmp_excel): + with ExcelWriter(tmp_excel) as writer: + frame.to_excel(writer, sheet_name="Data1") + frame2 = frame.copy() + frame2.columns = frame.columns[::-1] + frame2.to_excel(writer, sheet_name="Data2") + + with ExcelFile(tmp_excel) as reader: + found_df = pd.read_excel(reader, sheet_name="Data1", index_col=0) + found_df2 = pd.read_excel(reader, sheet_name="Data2", index_col=0) + + tm.assert_frame_equal(found_df, frame) + tm.assert_frame_equal(found_df2, frame2) + + def test_roundtrip(self, frame, tmp_excel): + frame = frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + + frame.to_excel(tmp_excel, sheet_name="test1") + frame.to_excel(tmp_excel, sheet_name="test1", columns=["A", "B"]) + frame.to_excel(tmp_excel, sheet_name="test1", header=False) + frame.to_excel(tmp_excel, sheet_name="test1", index=False) + + # test roundtrip + frame.to_excel(tmp_excel, sheet_name="test1") + recons = pd.read_excel(tmp_excel, sheet_name="test1", index_col=0) + tm.assert_frame_equal(frame, recons) + + frame.to_excel(tmp_excel, sheet_name="test1", index=False) + recons = pd.read_excel(tmp_excel, sheet_name="test1", index_col=None) + recons.index = frame.index + tm.assert_frame_equal(frame, recons) + + frame.to_excel(tmp_excel, sheet_name="test1", na_rep="NA") + recons = pd.read_excel( + tmp_excel, sheet_name="test1", index_col=0, na_values=["NA"] + ) + tm.assert_frame_equal(frame, recons) + + # GH 3611 + frame.to_excel(tmp_excel, sheet_name="test1", na_rep="88") + recons = pd.read_excel( + tmp_excel, sheet_name="test1", index_col=0, na_values=["88"] + ) + tm.assert_frame_equal(frame, recons) + + frame.to_excel(tmp_excel, sheet_name="test1", na_rep="88") + recons = pd.read_excel( + tmp_excel, sheet_name="test1", index_col=0, na_values=[88, 88.0] + ) + tm.assert_frame_equal(frame, recons) + + # GH 6573 + frame.to_excel(tmp_excel, sheet_name="Sheet1") + recons = pd.read_excel(tmp_excel, index_col=0) + tm.assert_frame_equal(frame, recons) + + frame.to_excel(tmp_excel, sheet_name="0") + recons = pd.read_excel(tmp_excel, index_col=0) + tm.assert_frame_equal(frame, recons) + + # GH 8825 Pandas Series should provide to_excel method + s = frame["A"] + s.to_excel(tmp_excel) + recons = pd.read_excel(tmp_excel, index_col=0) + tm.assert_frame_equal(s.to_frame(), recons) + + def test_mixed(self, frame, tmp_excel): + mixed_frame = frame.copy() + mixed_frame["foo"] = "bar" + + mixed_frame.to_excel(tmp_excel, sheet_name="test1") + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(mixed_frame, recons) + + def test_ts_frame(self, tmp_excel): + unit = get_exp_unit(tmp_excel) + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=5, freq="B"), + ) + + # freq doesn't round-trip + index = pd.DatetimeIndex(np.asarray(df.index), freq=None) + df.index = index + + expected = df[:] + expected.index = expected.index.as_unit(unit) + + df.to_excel(tmp_excel, sheet_name="test1") + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(expected, recons) + + def test_basics_with_nan(self, frame, tmp_excel): + frame = frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + frame.to_excel(tmp_excel, sheet_name="test1") + frame.to_excel(tmp_excel, sheet_name="test1", columns=["A", "B"]) + frame.to_excel(tmp_excel, sheet_name="test1", header=False) + frame.to_excel(tmp_excel, sheet_name="test1", index=False) + + @pytest.mark.parametrize("np_type", [np.int8, np.int16, np.int32, np.int64]) + def test_int_types(self, np_type, tmp_excel): + # Test np.int values read come back as int + # (rather than float which is Excel's format). + df = DataFrame( + np.random.default_rng(2).integers(-10, 10, size=(10, 2)), + dtype=np_type, + index=Index(list(range(10))), + ) + df.to_excel(tmp_excel, sheet_name="test1") + + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + + int_frame = df.astype(np.int64) + tm.assert_frame_equal(int_frame, recons) + + recons2 = pd.read_excel(tmp_excel, sheet_name="test1", index_col=0) + tm.assert_frame_equal(int_frame, recons2) + + @pytest.mark.parametrize("np_type", [np.float16, np.float32, np.float64]) + def test_float_types(self, np_type, tmp_excel): + # Test np.float values read come back as float. + df = DataFrame( + np.random.default_rng(2).random(10), + dtype=np_type, + index=Index(list(range(10))), + ) + df.to_excel(tmp_excel, sheet_name="test1") + + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype( + np_type + ) + + tm.assert_frame_equal(df, recons) + + def test_bool_types(self, tmp_excel): + # Test np.bool_ values read come back as float. + df = DataFrame([1, 0, True, False], dtype=np.bool_, index=Index(list(range(4)))) + df.to_excel(tmp_excel, sheet_name="test1") + + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype( + np.bool_ + ) + + tm.assert_frame_equal(df, recons) + + def test_inf_roundtrip(self, tmp_excel): + df = DataFrame([(1, np.inf), (2, 3), (5, -np.inf)], index=Index(list(range(3)))) + df.to_excel(tmp_excel, sheet_name="test1") + + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + + tm.assert_frame_equal(df, recons) + + def test_sheets(self, frame, tmp_excel): + # freq doesn't round-trip + unit = get_exp_unit(tmp_excel) + tsframe = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=5, freq="B"), + ) + + index = pd.DatetimeIndex(np.asarray(tsframe.index), freq=None) + tsframe.index = index + + expected = tsframe[:] + expected.index = expected.index.as_unit(unit) + + frame = frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + + frame.to_excel(tmp_excel, sheet_name="test1") + frame.to_excel(tmp_excel, sheet_name="test1", columns=["A", "B"]) + frame.to_excel(tmp_excel, sheet_name="test1", header=False) + frame.to_excel(tmp_excel, sheet_name="test1", index=False) + + # Test writing to separate sheets + with ExcelWriter(tmp_excel) as writer: + frame.to_excel(writer, sheet_name="test1") + tsframe.to_excel(writer, sheet_name="test2") + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(frame, recons) + recons = pd.read_excel(reader, sheet_name="test2", index_col=0) + tm.assert_frame_equal(expected, recons) + assert 2 == len(reader.sheet_names) + assert "test1" == reader.sheet_names[0] + assert "test2" == reader.sheet_names[1] + + def test_colaliases(self, frame, tmp_excel): + frame = frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + + frame.to_excel(tmp_excel, sheet_name="test1") + frame.to_excel(tmp_excel, sheet_name="test1", columns=["A", "B"]) + frame.to_excel(tmp_excel, sheet_name="test1", header=False) + frame.to_excel(tmp_excel, sheet_name="test1", index=False) + + # column aliases + col_aliases = Index(["AA", "X", "Y", "Z"]) + frame.to_excel(tmp_excel, sheet_name="test1", header=col_aliases) + with ExcelFile(tmp_excel) as reader: + rs = pd.read_excel(reader, sheet_name="test1", index_col=0) + xp = frame.copy() + xp.columns = col_aliases + tm.assert_frame_equal(xp, rs) + + def test_roundtrip_indexlabels(self, merge_cells, frame, tmp_excel): + frame = frame.copy() + frame.iloc[:5, frame.columns.get_loc("A")] = np.nan + + frame.to_excel(tmp_excel, sheet_name="test1") + frame.to_excel(tmp_excel, sheet_name="test1", columns=["A", "B"]) + frame.to_excel(tmp_excel, sheet_name="test1", header=False) + frame.to_excel(tmp_excel, sheet_name="test1", index=False) + + # test index_label + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0 + df.to_excel( + tmp_excel, sheet_name="test1", index_label=["test"], merge_cells=merge_cells + ) + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype( + np.int64 + ) + df.index.names = ["test"] + assert df.index.names == recons.index.names + + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) >= 0 + df.to_excel( + tmp_excel, + sheet_name="test1", + index_label=["test", "dummy", "dummy2"], + merge_cells=merge_cells, + ) + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype( + np.int64 + ) + df.index.names = ["test"] + assert df.index.names == recons.index.names + + df = ( + DataFrame( + np.random.default_rng(2).standard_normal((10, 2)), + index=Index(list(range(10))), + ) + >= 0 + ) + df.to_excel( + tmp_excel, sheet_name="test1", index_label="test", merge_cells=merge_cells + ) + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0).astype( + np.int64 + ) + df.index.names = ["test"] + tm.assert_frame_equal(df, recons.astype(bool)) + + frame.to_excel( + tmp_excel, + sheet_name="test1", + columns=["A", "B", "C", "D"], + index=False, + merge_cells=merge_cells, + ) + # take 'A' and 'B' as indexes (same row as cols 'C', 'D') + df = frame.copy() + df = df.set_index(["A", "B"]) + + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1]) + tm.assert_frame_equal(df, recons) + + def test_excel_roundtrip_indexname(self, merge_cells, tmp_excel): + df = DataFrame(np.random.default_rng(2).standard_normal((10, 4))) + df.index.name = "foo" + + df.to_excel(tmp_excel, merge_cells=merge_cells) + + with ExcelFile(tmp_excel) as xf: + result = pd.read_excel(xf, sheet_name=xf.sheet_names[0], index_col=0) + + tm.assert_frame_equal(result, df) + assert result.index.name == "foo" + + def test_excel_roundtrip_datetime(self, merge_cells, tmp_excel): + # datetime.date, not sure what to test here exactly + unit = get_exp_unit(tmp_excel) + + # freq does not round-trip + tsframe = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=5, freq="B"), + ) + index = pd.DatetimeIndex(np.asarray(tsframe.index), freq=None) + tsframe.index = index + + tsf = tsframe.copy() + + tsf.index = [x.date() for x in tsframe.index] + tsf.to_excel(tmp_excel, sheet_name="test1", merge_cells=merge_cells) + + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + + expected = tsframe[:] + expected.index = expected.index.as_unit(unit) + tm.assert_frame_equal(expected, recons) + + def test_excel_date_datetime_format(self, ext, tmp_excel, tmp_path): + # see gh-4133 + # + # Excel output format strings + unit = get_exp_unit(tmp_excel) + df = DataFrame( + [ + [date(2014, 1, 31), date(1999, 9, 24)], + [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)], + ], + index=["DATE", "DATETIME"], + columns=["X", "Y"], + ) + df_expected = DataFrame( + [ + [datetime(2014, 1, 31), datetime(1999, 9, 24)], + [datetime(1998, 5, 26, 23, 33, 4), datetime(2014, 2, 28, 13, 5, 13)], + ], + index=["DATE", "DATETIME"], + columns=["X", "Y"], + ) + df_expected = df_expected.astype(f"M8[{unit}]") + + filename2 = tmp_path / f"tmp2{ext}" + filename2.touch() + with ExcelWriter(tmp_excel) as writer1: + df.to_excel(writer1, sheet_name="test1") + + with ExcelWriter( + filename2, + date_format="DD.MM.YYYY", + datetime_format="DD.MM.YYYY HH-MM-SS", + ) as writer2: + df.to_excel(writer2, sheet_name="test1") + + with ExcelFile(tmp_excel) as reader1: + rs1 = pd.read_excel(reader1, sheet_name="test1", index_col=0) + + with ExcelFile(filename2) as reader2: + rs2 = pd.read_excel(reader2, sheet_name="test1", index_col=0) + + # TODO: why do we get different units? + rs2 = rs2.astype(f"M8[{unit}]") + + tm.assert_frame_equal(rs1, rs2) + + # Since the reader returns a datetime object for dates, + # we need to use df_expected to check the result. + tm.assert_frame_equal(rs2, df_expected) + + @pytest.mark.filterwarnings( + "ignore:invalid value encountered in cast:RuntimeWarning" + ) + def test_to_excel_interval_no_labels(self, tmp_excel, using_infer_string): + # see gh-19242 + # + # Test writing Interval without labels. + df = DataFrame( + np.random.default_rng(2).integers(-10, 10, size=(20, 1)), dtype=np.int64 + ) + expected = df.copy() + + df["new"] = pd.cut(df[0], 10) + expected["new"] = pd.cut(expected[0], 10).astype( + str if not using_infer_string else "str" + ) + + df.to_excel(tmp_excel, sheet_name="test1") + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(expected, recons) + + def test_to_excel_interval_labels(self, tmp_excel): + # see gh-19242 + # + # Test writing Interval with labels. + df = DataFrame( + np.random.default_rng(2).integers(-10, 10, size=(20, 1)), dtype=np.int64 + ) + expected = df.copy() + intervals = pd.cut( + df[0], 10, labels=["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"] + ) + df["new"] = intervals + expected["new"] = pd.Series(list(intervals)) + + df.to_excel(tmp_excel, sheet_name="test1") + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(expected, recons) + + def test_to_excel_timedelta(self, tmp_excel): + # see gh-19242, gh-9155 + # + # Test writing timedelta to xls. + df = DataFrame( + np.random.default_rng(2).integers(-10, 10, size=(20, 1)), + columns=["A"], + dtype=np.int64, + ) + expected = df.copy() + + df["new"] = df["A"].apply(lambda x: timedelta(seconds=x)) + expected["new"] = expected["A"].apply( + lambda x: timedelta(seconds=x).total_seconds() / 86400 + ) + + df.to_excel(tmp_excel, sheet_name="test1") + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=0) + tm.assert_frame_equal(expected, recons) + + def test_to_excel_periodindex(self, tmp_excel): + # xp has a PeriodIndex + df = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=5, freq="B"), + ) + xp = df.resample("ME").mean().to_period("M") + + xp.to_excel(tmp_excel, sheet_name="sht1") + + with ExcelFile(tmp_excel) as reader: + rs = pd.read_excel(reader, sheet_name="sht1", index_col=0) + tm.assert_frame_equal(xp, rs.to_period("M")) + + def test_to_excel_multiindex(self, merge_cells, frame, tmp_excel): + arrays = np.arange(len(frame.index) * 2, dtype=np.int64).reshape(2, -1) + new_index = MultiIndex.from_arrays(arrays, names=["first", "second"]) + frame.index = new_index + + frame.to_excel(tmp_excel, sheet_name="test1", header=False) + frame.to_excel(tmp_excel, sheet_name="test1", columns=["A", "B"]) + + # round trip + frame.to_excel(tmp_excel, sheet_name="test1", merge_cells=merge_cells) + with ExcelFile(tmp_excel) as reader: + df = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1]) + tm.assert_frame_equal(frame, df) + + # GH13511 + def test_to_excel_multiindex_nan_label(self, merge_cells, tmp_excel): + df = DataFrame( + { + "A": [None, 2, 3], + "B": [10, 20, 30], + "C": np.random.default_rng(2).random(3), + } + ) + df = df.set_index(["A", "B"]) + + df.to_excel(tmp_excel, merge_cells=merge_cells) + df1 = pd.read_excel(tmp_excel, index_col=[0, 1]) + tm.assert_frame_equal(df, df1) + + # Test for Issue 11328. If column indices are integers, make + # sure they are handled correctly for either setting of + # merge_cells + def test_to_excel_multiindex_cols(self, merge_cells, tmp_excel): + # GH#11328 + frame = DataFrame( + { + "A": [1, 2, 3], + "B": [4, 5, 6], + "C": [7, 8, 9], + } + ) + arrays = np.arange(len(frame.index) * 2, dtype=np.int64).reshape(2, -1) + new_index = MultiIndex.from_arrays(arrays, names=["first", "second"]) + frame.index = new_index + + new_cols_index = MultiIndex.from_tuples([(40, 1), (40, 2), (50, 1)]) + frame.columns = new_cols_index + frame.to_excel(tmp_excel, sheet_name="test1", merge_cells=merge_cells) + + # Check round trip + with ExcelFile(tmp_excel) as reader: + result = pd.read_excel( + reader, sheet_name="test1", header=[0, 1], index_col=[0, 1] + ) + tm.assert_frame_equal(result, frame) + + # GH#60274 + # Check with header/index_col None to determine which cells were merged + with ExcelFile(tmp_excel) as reader: + result = pd.read_excel( + reader, sheet_name="test1", header=None, index_col=None + ) + expected = DataFrame( + { + 0: [np.nan, np.nan, "first", 0, 1, 2], + 1: [np.nan, np.nan, "second", 3, 4, 5], + 2: [40.0, 1.0, np.nan, 1.0, 2.0, 3.0], + 3: [np.nan, 2.0, np.nan, 4.0, 5.0, 6.0], + 4: [50.0, 1.0, np.nan, 7.0, 8.0, 9.0], + } + ) + if not merge_cells: + # MultiIndex column value is repeated + expected.loc[0, 3] = 40.0 + tm.assert_frame_equal(result, expected) + + def test_to_excel_multiindex_dates(self, merge_cells, tmp_excel): + # try multiindex with dates + unit = get_exp_unit(tmp_excel) + tsframe = DataFrame( + np.random.default_rng(2).standard_normal((5, 4)), + columns=Index(list("ABCD")), + index=date_range("2000-01-01", periods=5, freq="B"), + ) + tsframe.index = MultiIndex.from_arrays( + [ + tsframe.index.as_unit(unit), + np.arange(len(tsframe.index), dtype=np.int64), + ], + names=["time", "foo"], + ) + + tsframe.to_excel(tmp_excel, sheet_name="test1", merge_cells=merge_cells) + with ExcelFile(tmp_excel) as reader: + recons = pd.read_excel(reader, sheet_name="test1", index_col=[0, 1]) + + tm.assert_frame_equal(tsframe, recons) + assert recons.index.names == ("time", "foo") + + def test_to_excel_multiindex_no_write_index(self, tmp_excel): + # Test writing and re-reading a MI without the index. GH 5616. + + # Initial non-MI frame. + frame1 = DataFrame({"a": [10, 20], "b": [30, 40], "c": [50, 60]}) + + # Add a MI. + frame2 = frame1.copy() + multi_index = MultiIndex.from_tuples([(70, 80), (90, 100)]) + frame2.index = multi_index + + # Write out to Excel without the index. + frame2.to_excel(tmp_excel, sheet_name="test1", index=False) + + # Read it back in. + with ExcelFile(tmp_excel) as reader: + frame3 = pd.read_excel(reader, sheet_name="test1") + + # Test that it is the same as the initial frame. + tm.assert_frame_equal(frame1, frame3) + + def test_to_excel_empty_multiindex(self, tmp_excel): + # GH 19543. + expected = DataFrame([], columns=[0, 1, 2]) + + df = DataFrame([], index=MultiIndex.from_tuples([], names=[0, 1]), columns=[2]) + df.to_excel(tmp_excel, sheet_name="test1") + + with ExcelFile(tmp_excel) as reader: + result = pd.read_excel(reader, sheet_name="test1") + tm.assert_frame_equal( + result, expected, check_index_type=False, check_dtype=False + ) + + def test_to_excel_empty_multiindex_both_axes(self, tmp_excel): + # GH 57696 + df = DataFrame( + [], + index=MultiIndex.from_tuples([], names=[0, 1]), + columns=MultiIndex.from_tuples([("A", "B")]), + ) + df.to_excel(tmp_excel) + result = pd.read_excel(tmp_excel, header=[0, 1], index_col=[0, 1]) + tm.assert_frame_equal(result, df) + + def test_to_excel_float_format(self, tmp_excel): + df = DataFrame( + [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + df.to_excel(tmp_excel, sheet_name="test1", float_format="%.2f") + + with ExcelFile(tmp_excel) as reader: + result = pd.read_excel(reader, sheet_name="test1", index_col=0) + + expected = DataFrame( + [[0.12, 0.23, 0.57], [12.32, 123123.20, 321321.20]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + tm.assert_frame_equal(result, expected) + + def test_to_excel_datatypes_preserved(self, tmp_excel): + # Test that when writing and reading Excel with dtype=object, + # datatypes are preserved, except Decimals which should be + # stored as floats + + # see gh-49598 + df = DataFrame( + [ + [1.23, "1.23", Decimal("1.23")], + [4.56, "4.56", Decimal("4.56")], + ], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + df.to_excel(tmp_excel) + + with ExcelFile(tmp_excel) as reader: + result = pd.read_excel(reader, index_col=0, dtype=object) + + expected = DataFrame( + [ + [1.23, "1.23", 1.23], + [4.56, "4.56", 4.56], + ], + index=["A", "B"], + columns=["X", "Y", "Z"], + dtype=object, + ) + tm.assert_frame_equal(result, expected) + + def test_to_excel_output_encoding(self, tmp_excel): + # Avoid mixed inferred_type. + df = DataFrame( + [["\u0192", "\u0193", "\u0194"], ["\u0195", "\u0196", "\u0197"]], + index=["A\u0192", "B"], + columns=["X\u0193", "Y", "Z"], + ) + + df.to_excel(tmp_excel, sheet_name="TestSheet") + result = pd.read_excel(tmp_excel, sheet_name="TestSheet", index_col=0) + tm.assert_frame_equal(result, df) + + def test_to_excel_unicode_filename(self, ext, tmp_path): + filename = tmp_path / f"\u0192u.{ext}" + filename.touch() + df = DataFrame( + [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + df.to_excel(filename, sheet_name="test1", float_format="%.2f") + + with ExcelFile(filename) as reader: + result = pd.read_excel(reader, sheet_name="test1", index_col=0) + + expected = DataFrame( + [[0.12, 0.23, 0.57], [12.32, 123123.20, 321321.20]], + index=["A", "B"], + columns=["X", "Y", "Z"], + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("use_headers", [True, False]) + @pytest.mark.parametrize("r_idx_nlevels", [1, 2, 3]) + @pytest.mark.parametrize("c_idx_nlevels", [1, 2, 3]) + def test_excel_010_hemstring( + self, merge_cells, c_idx_nlevels, r_idx_nlevels, use_headers, tmp_excel + ): + def roundtrip(data, header=True, parser_hdr=0, index=True): + data.to_excel( + tmp_excel, header=header, merge_cells=merge_cells, index=index + ) + + with ExcelFile(tmp_excel) as xf: + return pd.read_excel( + xf, sheet_name=xf.sheet_names[0], header=parser_hdr + ) + + # Basic test. + parser_header = 0 if use_headers else None + res = roundtrip(DataFrame([0]), use_headers, parser_header) + + assert res.shape == (1, 2) + assert res.iloc[0, 0] is not np.nan + + # More complex tests with multi-index. + nrows = 5 + ncols = 3 + + # ensure limited functionality in 0.10 + # override of gh-2370 until sorted out in 0.11 + + if c_idx_nlevels == 1: + columns = Index([f"a-{i}" for i in range(ncols)], dtype=object) + else: + columns = MultiIndex.from_arrays( + [range(ncols) for _ in range(c_idx_nlevels)], + names=[f"i-{i}" for i in range(c_idx_nlevels)], + ) + if r_idx_nlevels == 1: + index = Index([f"b-{i}" for i in range(nrows)], dtype=object) + else: + index = MultiIndex.from_arrays( + [range(nrows) for _ in range(r_idx_nlevels)], + names=[f"j-{i}" for i in range(r_idx_nlevels)], + ) + + df = DataFrame( + np.ones((nrows, ncols)), + columns=columns, + index=index, + ) + + # This if will be removed once multi-column Excel writing + # is implemented. For now fixing gh-9794. + if c_idx_nlevels > 1: + msg = ( + "Writing to Excel with MultiIndex columns and no index " + "\\('index'=False\\) is not yet implemented." + ) + with pytest.raises(NotImplementedError, match=msg): + roundtrip(df, use_headers, index=False) + else: + res = roundtrip(df, use_headers) + + if use_headers: + assert res.shape == (nrows, ncols + r_idx_nlevels) + else: + # First row taken as columns. + assert res.shape == (nrows - 1, ncols + r_idx_nlevels) + + # No NaNs. + for r in range(len(res.index)): + for c in range(len(res.columns)): + assert res.iloc[r, c] is not np.nan + + def test_duplicated_columns(self, tmp_excel): + # see gh-5235 + df = DataFrame([[1, 2, 3], [1, 2, 3], [1, 2, 3]], columns=["A", "B", "B"]) + df.to_excel(tmp_excel, sheet_name="test1") + expected = DataFrame( + [[1, 2, 3], [1, 2, 3], [1, 2, 3]], columns=["A", "B", "B.1"] + ) + + # By default, we mangle. + result = pd.read_excel(tmp_excel, sheet_name="test1", index_col=0) + tm.assert_frame_equal(result, expected) + + # see gh-11007, gh-10970 + df = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "A", "B"]) + df.to_excel(tmp_excel, sheet_name="test1") + + result = pd.read_excel(tmp_excel, sheet_name="test1", index_col=0) + expected = DataFrame( + [[1, 2, 3, 4], [5, 6, 7, 8]], columns=["A", "B", "A.1", "B.1"] + ) + tm.assert_frame_equal(result, expected) + + # see gh-10982 + df.to_excel(tmp_excel, sheet_name="test1", index=False, header=False) + result = pd.read_excel(tmp_excel, sheet_name="test1", header=None) + + expected = DataFrame([[1, 2, 3, 4], [5, 6, 7, 8]]) + tm.assert_frame_equal(result, expected) + + def test_swapped_columns(self, tmp_excel): + # Test for issue #5427. + write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2]}) + write_frame.to_excel(tmp_excel, sheet_name="test1", columns=["B", "A"]) + + read_frame = pd.read_excel(tmp_excel, sheet_name="test1", header=0) + + tm.assert_series_equal(write_frame["A"], read_frame["A"]) + tm.assert_series_equal(write_frame["B"], read_frame["B"]) + + def test_invalid_columns(self, tmp_excel): + # see gh-10982 + write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2]}) + + with pytest.raises(KeyError, match="Not all names specified"): + write_frame.to_excel(tmp_excel, sheet_name="test1", columns=["B", "C"]) + + with pytest.raises( + KeyError, match="'passes columns are not ALL present dataframe'" + ): + write_frame.to_excel(tmp_excel, sheet_name="test1", columns=["C", "D"]) + + @pytest.mark.parametrize( + "to_excel_index,read_excel_index_col", + [ + (True, 0), # Include index in write to file + (False, None), # Dont include index in write to file + ], + ) + def test_write_subset_columns( + self, tmp_excel, to_excel_index, read_excel_index_col + ): + # GH 31677 + write_frame = DataFrame({"A": [1, 1, 1], "B": [2, 2, 2], "C": [3, 3, 3]}) + write_frame.to_excel( + tmp_excel, + sheet_name="col_subset_bug", + columns=["A", "B"], + index=to_excel_index, + ) + + expected = write_frame[["A", "B"]] + read_frame = pd.read_excel( + tmp_excel, sheet_name="col_subset_bug", index_col=read_excel_index_col + ) + + tm.assert_frame_equal(expected, read_frame) + + def test_comment_arg(self, tmp_excel): + # see gh-18735 + # + # Test the comment argument functionality to pd.read_excel. + + # Create file to read in. + df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]}) + df.to_excel(tmp_excel, sheet_name="test_c") + + # Read file without comment arg. + result1 = pd.read_excel(tmp_excel, sheet_name="test_c", index_col=0) + + result1.iloc[1, 0] = None + result1.iloc[1, 1] = None + result1.iloc[2, 1] = None + + result2 = pd.read_excel( + tmp_excel, sheet_name="test_c", comment="#", index_col=0 + ) + tm.assert_frame_equal(result1, result2) + + def test_comment_default(self, tmp_excel): + # Re issue #18735 + # Test the comment argument default to pd.read_excel + + # Create file to read in + df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]}) + df.to_excel(tmp_excel, sheet_name="test_c") + + # Read file with default and explicit comment=None + result1 = pd.read_excel(tmp_excel, sheet_name="test_c") + result2 = pd.read_excel(tmp_excel, sheet_name="test_c", comment=None) + tm.assert_frame_equal(result1, result2) + + def test_comment_used(self, tmp_excel): + # see gh-18735 + # + # Test the comment argument is working as expected when used. + + # Create file to read in. + df = DataFrame({"A": ["one", "#one", "one"], "B": ["two", "two", "#two"]}) + df.to_excel(tmp_excel, sheet_name="test_c") + + # Test read_frame_comment against manually produced expected output. + expected = DataFrame({"A": ["one", None, "one"], "B": ["two", None, None]}) + result = pd.read_excel(tmp_excel, sheet_name="test_c", comment="#", index_col=0) + tm.assert_frame_equal(result, expected) + + def test_comment_empty_line(self, tmp_excel): + # Re issue #18735 + # Test that pd.read_excel ignores commented lines at the end of file + + df = DataFrame({"a": ["1", "#2"], "b": ["2", "3"]}) + df.to_excel(tmp_excel, index=False) + + # Test that all-comment lines at EoF are ignored + expected = DataFrame({"a": [1], "b": [2]}) + result = pd.read_excel(tmp_excel, comment="#") + tm.assert_frame_equal(result, expected) + + def test_datetimes(self, tmp_excel): + # Test writing and reading datetimes. For issue #9139. (xref #9185) + unit = get_exp_unit(tmp_excel) + datetimes = [ + datetime(2013, 1, 13, 1, 2, 3), + datetime(2013, 1, 13, 2, 45, 56), + datetime(2013, 1, 13, 4, 29, 49), + datetime(2013, 1, 13, 6, 13, 42), + datetime(2013, 1, 13, 7, 57, 35), + datetime(2013, 1, 13, 9, 41, 28), + datetime(2013, 1, 13, 11, 25, 21), + datetime(2013, 1, 13, 13, 9, 14), + datetime(2013, 1, 13, 14, 53, 7), + datetime(2013, 1, 13, 16, 37, 0), + datetime(2013, 1, 13, 18, 20, 52), + ] + + write_frame = DataFrame({"A": datetimes}) + write_frame.to_excel(tmp_excel, sheet_name="Sheet1") + read_frame = pd.read_excel(tmp_excel, sheet_name="Sheet1", header=0) + + expected = write_frame.astype(f"M8[{unit}]") + tm.assert_series_equal(expected["A"], read_frame["A"]) + + def test_bytes_io(self, engine): + # see gh-7074 + with BytesIO() as bio: + df = DataFrame(np.random.default_rng(2).standard_normal((10, 2))) + + # Pass engine explicitly, as there is no file path to infer from. + with ExcelWriter(bio, engine=engine) as writer: + df.to_excel(writer) + + bio.seek(0) + reread_df = pd.read_excel(bio, index_col=0) + tm.assert_frame_equal(df, reread_df) + + def test_engine_kwargs(self, engine, tmp_excel): + # GH#52368 + df = DataFrame([{"A": 1, "B": 2}, {"A": 3, "B": 4}]) + + msgs = { + "odf": r"OpenDocumentSpreadsheet() got an unexpected keyword " + r"argument 'foo'", + "openpyxl": r"__init__() got an unexpected keyword argument 'foo'", + "xlsxwriter": r"__init__() got an unexpected keyword argument 'foo'", + } + + msgs["openpyxl"] = ( + "Workbook.__init__() got an unexpected keyword argument 'foo'" + ) + msgs["xlsxwriter"] = ( + "Workbook.__init__() got an unexpected keyword argument 'foo'" + ) + + # Handle change in error message for openpyxl (write and append mode) + if engine == "openpyxl" and not os.path.exists(tmp_excel): + msgs["openpyxl"] = ( + r"load_workbook() got an unexpected keyword argument 'foo'" + ) + + with pytest.raises(TypeError, match=re.escape(msgs[engine])): + df.to_excel( + tmp_excel, + engine=engine, + engine_kwargs={"foo": "bar"}, + ) + + def test_write_lists_dict(self, tmp_excel): + # see gh-8188. + df = DataFrame( + { + "mixed": ["a", ["b", "c"], {"d": "e", "f": 2}], + "numeric": [1, 2, 3.0], + "str": ["apple", "banana", "cherry"], + } + ) + df.to_excel(tmp_excel, sheet_name="Sheet1") + read = pd.read_excel(tmp_excel, sheet_name="Sheet1", header=0, index_col=0) + + expected = df.copy() + expected.mixed = expected.mixed.apply(str) + expected.numeric = expected.numeric.astype("int64") + + tm.assert_frame_equal(read, expected) + + def test_render_as_column_name(self, tmp_excel): + # see gh-34331 + df = DataFrame({"render": [1, 2], "data": [3, 4]}) + df.to_excel(tmp_excel, sheet_name="Sheet1") + read = pd.read_excel(tmp_excel, "Sheet1", index_col=0) + expected = df + tm.assert_frame_equal(read, expected) + + def test_true_and_false_value_options(self, tmp_excel): + # see gh-13347 + df = DataFrame([["foo", "bar"]], columns=["col1", "col2"], dtype=object) + expected = df.replace({"foo": True, "bar": False}).astype("bool") + + df.to_excel(tmp_excel) + read_frame = pd.read_excel( + tmp_excel, true_values=["foo"], false_values=["bar"], index_col=0 + ) + tm.assert_frame_equal(read_frame, expected) + + def test_freeze_panes(self, tmp_excel): + # see gh-15160 + expected = DataFrame([[1, 2], [3, 4]], columns=["col1", "col2"]) + expected.to_excel(tmp_excel, sheet_name="Sheet1", freeze_panes=(1, 1)) + + result = pd.read_excel(tmp_excel, index_col=0) + tm.assert_frame_equal(result, expected) + + def test_path_path_lib(self, engine, tmp_excel): + df = DataFrame( + 1.1 * np.arange(120).reshape((30, 4)), + columns=Index(list("ABCD")), + index=Index([f"i-{i}" for i in range(30)]), + ) + writer = partial(df.to_excel, engine=engine) + + reader = partial(pd.read_excel, index_col=0) + result = tm.round_trip_pathlib(writer, reader, pathlib.Path(tmp_excel)) + tm.assert_frame_equal(result, df) + + def test_merged_cell_custom_objects(self, tmp_excel): + # see GH-27006 + mi = MultiIndex.from_tuples( + [ + (pd.Period("2018"), pd.Period("2018Q1")), + (pd.Period("2018"), pd.Period("2018Q2")), + ] + ) + expected = DataFrame(np.ones((2, 2), dtype="int64"), columns=mi) + expected.to_excel(tmp_excel) + result = pd.read_excel(tmp_excel, header=[0, 1], index_col=0) + # need to convert PeriodIndexes to standard Indexes for assert equal + expected.columns = expected.columns.set_levels( + [[str(i) for i in mi.levels[0]], [str(i) for i in mi.levels[1]]], + level=[0, 1], + ) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize("dtype", [None, object]) + def test_raise_when_saving_timezones(self, dtype, tz_aware_fixture, tmp_excel): + # GH 27008, GH 7056 + tz = tz_aware_fixture + data = pd.Timestamp("2019", tz=tz) + df = DataFrame([data], dtype=dtype) + with pytest.raises(ValueError, match="Excel does not support"): + df.to_excel(tmp_excel) + + data = data.to_pydatetime() + df = DataFrame([data], dtype=dtype) + with pytest.raises(ValueError, match="Excel does not support"): + df.to_excel(tmp_excel) + + def test_excel_duplicate_columns_with_names(self, tmp_excel): + # GH#39695 + df = DataFrame({"A": [0, 1], "B": [10, 11]}) + df.to_excel(tmp_excel, columns=["A", "B", "A"], index=False) + + result = pd.read_excel(tmp_excel) + expected = DataFrame([[0, 10, 0], [1, 11, 1]], columns=["A", "B", "A.1"]) + tm.assert_frame_equal(result, expected) + + def test_if_sheet_exists_raises(self, tmp_excel): + # GH 40230 + msg = "if_sheet_exists is only valid in append mode (mode='a')" + + with pytest.raises(ValueError, match=re.escape(msg)): + ExcelWriter(tmp_excel, if_sheet_exists="replace") + + def test_excel_writer_empty_frame(self, engine, tmp_excel): + # GH#45793 + with ExcelWriter(tmp_excel, engine=engine) as writer: + DataFrame().to_excel(writer) + result = pd.read_excel(tmp_excel) + expected = DataFrame() + tm.assert_frame_equal(result, expected) + + def test_to_excel_empty_frame(self, engine, tmp_excel): + # GH#45793 + DataFrame().to_excel(tmp_excel, engine=engine) + result = pd.read_excel(tmp_excel) + expected = DataFrame() + tm.assert_frame_equal(result, expected) + + def test_to_excel_raising_warning_when_cell_character_exceed_limit(self): + # GH#56954 + df = DataFrame({"A": ["a" * 32768]}) + msg = r"Cell contents too long \(32768\), truncated to 32767 characters" + with tm.assert_produces_warning( + UserWarning, match=msg, raise_on_extra_warnings=False + ): + buf = BytesIO() + df.to_excel(buf) + + @pytest.mark.parametrize("with_index", [True, False]) + def test_autofilter(self, engine, with_index, tmp_excel): + # GH 61194 + df = DataFrame.from_dict([{"A": 1, "B": 2, "C": 3}, {"A": 4, "B": 5, "C": 6}]) + + if engine in ["odf"]: + with pytest.raises( + ValueError, match="Autofilter is not supported with odf!" + ): + df.to_excel(tmp_excel, engine=engine, autofilter=True, index=False) + else: + df.to_excel(tmp_excel, engine=engine, autofilter=True, index=with_index) + + openpyxl = pytest.importorskip( + "openpyxl" + ) # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + ws = wb.active + + assert ws.auto_filter.ref is not None + assert ws.auto_filter.ref == "A1:D3" if with_index else "A1:C3" + + def test_autofilter_with_startrow_startcol(self, engine, tmp_excel): + # GH 61194 + df = DataFrame.from_dict([{"A": 1, "B": 2, "C": 3}, {"A": 4, "B": 5, "C": 6}]) + + if engine in ["odf"]: + # odf does not support autofilter + with pytest.raises( + ValueError, match="Autofilter is not supported with odf!" + ): + df.to_excel(tmp_excel, engine=engine, autofilter=True, index=False) + else: + df.to_excel( + tmp_excel, engine=engine, autofilter=True, startrow=10, startcol=10 + ) + + openpyxl = pytest.importorskip( + "openpyxl" + ) # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + ws = wb.active + assert ws.auto_filter.ref is not None + # Autofiler range moved by 10x10 cells + assert ws.auto_filter.ref == "K11:N13" + + @pytest.mark.parametrize("merge_cells", [True, False]) + def test_autofilter_with_multiindex_index(self, engine, tmp_excel, merge_cells): + # GH 61194 + df = DataFrame( + { + "animal": ("horse", "horse", "dog", "dog"), + "color of fur": ("black", "white", "grey", "black"), + "name": ("Blacky", "Wendy", "Rufus", "Catchy"), + } + ) + # setup hierarchical index + mi_df = df.set_index(["animal", "color of fur"]) + if engine in ["odf"]: + # odf does not support autofilter + with pytest.raises( + ValueError, match="Autofilter is not supported with odf!" + ): + mi_df.to_excel( + tmp_excel, + engine=engine, + autofilter=True, + index=False, + merge_cells=merge_cells, + ) + elif merge_cells: + # multiindex and merge cells cannot be used simultaneously + with pytest.raises( + ValueError, + match="Excel filters merged cells by showing only the first row. " + "'autofilter' and 'merge_cells' cannot be used simultaneously.", + ): + mi_df.to_excel( + tmp_excel, + engine=engine, + autofilter=True, + index=True, + merge_cells=merge_cells, + ) + else: + mi_df.to_excel( + tmp_excel, + engine=engine, + autofilter=True, + index=True, + merge_cells=merge_cells, + ) + + # validate autofilter range + openpyxl = pytest.importorskip( + "openpyxl" + ) # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + ws = wb.active + + assert ws.auto_filter.ref is not None + assert ws.auto_filter.ref == "A1:C5" + + @pytest.mark.parametrize("merge_cells", [True, False]) + def test_autofilter_with_multiindex_columns(self, engine, tmp_excel, merge_cells): + # GH 61194 + columns = MultiIndex( + levels=[["x", "y"], ["w", "t"]], + codes=[[0, 0, 1], [0, 1, 0]], + ) + df = DataFrame([[1, 2, 3], [4, 5, 6]], columns=columns) + + if engine in ["odf"]: + # odf does not support autofilter + with pytest.raises( + ValueError, match="Autofilter is not supported with odf!" + ): + df.to_excel( + tmp_excel, + engine=engine, + autofilter=True, + index=False, + merge_cells=merge_cells, + ) + elif merge_cells: + # multiindex and merge cells cannot be used simultaneously + with pytest.raises( + ValueError, + match="Excel filters merged cells by showing only the first row. " + "'autofilter' and 'merge_cells' cannot be used simultaneously.", + ): + df.to_excel( + tmp_excel, + engine=engine, + autofilter=True, + index=True, + merge_cells=merge_cells, + ) + else: + df.to_excel( + tmp_excel, + engine=engine, + autofilter=True, + index=True, + merge_cells=merge_cells, + ) + + # validate autofilter range + openpyxl = pytest.importorskip( + "openpyxl" + ) # test loading only with openpyxl + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as wb: + ws = wb.active + + assert ws.auto_filter.ref is not None + assert ws.auto_filter.ref == "A2:D5" + + +class TestExcelWriterEngineTests: + @pytest.mark.parametrize( + "klass,ext", + [ + pytest.param(_XlsxWriter, ".xlsx", marks=td.skip_if_no("xlsxwriter")), + pytest.param(_OpenpyxlWriter, ".xlsx", marks=td.skip_if_no("openpyxl")), + ], + ) + def test_ExcelWriter_dispatch(self, klass, ext, tmp_excel): + with ExcelWriter(tmp_excel) as writer: + if ext == ".xlsx" and bool( + import_optional_dependency("xlsxwriter", errors="ignore") + ): + # xlsxwriter has preference over openpyxl if both installed + assert isinstance(writer, _XlsxWriter) + else: + assert isinstance(writer, klass) + + def test_ExcelWriter_dispatch_raises(self): + with pytest.raises(ValueError, match="No engine"): + ExcelWriter("nothing") + + def test_register_writer(self, tmp_path): + class DummyClass(ExcelWriter): + called_save = False + called_write_cells = False + called_sheets = False + _supported_extensions = ("xlsx", "xls") + _engine = "dummy" + + def book(self): + pass + + def _save(self): + type(self).called_save = True + + def _write_cells(self, *args, **kwargs): + type(self).called_write_cells = True + + @property + def sheets(self): + type(self).called_sheets = True + + @classmethod + def assert_called_and_reset(cls): + assert cls.called_save + assert cls.called_write_cells + assert not cls.called_sheets + cls.called_save = False + cls.called_write_cells = False + + register_writer(DummyClass) + + with option_context("io.excel.xlsx.writer", "dummy"): + filepath = tmp_path / "something.xlsx" + filepath.touch() + with ExcelWriter(filepath) as writer: + assert isinstance(writer, DummyClass) + df = DataFrame( + ["a"], + columns=Index(["b"], name="foo"), + index=Index(["c"], name="bar"), + ) + df.to_excel(filepath) + DummyClass.assert_called_and_reset() + + filepath2 = tmp_path / "something2.xlsx" + filepath2.touch() + df.to_excel(filepath2, engine="dummy") + DummyClass.assert_called_and_reset() + + +@td.skip_if_no("xlrd") +@td.skip_if_no("openpyxl") +class TestFSPath: + def test_excelfile_fspath(self, tmp_path): + path = tmp_path / "foo.xlsx" + path.touch() + df = DataFrame({"A": [1, 2]}) + df.to_excel(path) + with ExcelFile(path) as xl: + result = os.fspath(xl) + assert result == str(path) + + def test_excelwriter_fspath(self, tmp_path): + path = tmp_path / "foo.xlsx" + path.touch() + with ExcelWriter(path) as writer: + assert os.fspath(writer) == str(path) + + +@pytest.mark.parametrize("klass", _writers.values()) +def test_subclass_attr(klass): + # testing that subclasses of ExcelWriter don't have public attributes (issue 49602) + attrs_base = {name for name in dir(ExcelWriter) if not name.startswith("_")} + attrs_klass = {name for name in dir(klass) if not name.startswith("_")} + assert not attrs_base.symmetric_difference(attrs_klass) diff --git a/lib/python3.11/site-packages/pandas/tests/io/excel/test_xlrd.py b/lib/python3.11/site-packages/pandas/tests/io/excel/test_xlrd.py new file mode 100644 index 0000000000000000000000000000000000000000..c49cbaf7fb26c8824329d9f9907df8cce8ddbd29 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/excel/test_xlrd.py @@ -0,0 +1,71 @@ +import io + +import numpy as np +import pytest + +import pandas as pd +import pandas._testing as tm + +from pandas.io.excel import ExcelFile +from pandas.io.excel._base import inspect_excel_format + +xlrd = pytest.importorskip("xlrd") + + +@pytest.fixture +def read_ext_xlrd(): + """ + Valid extensions for reading Excel files with xlrd. + + Similar to read_ext, but excludes .ods, .xlsb, and for xlrd>2 .xlsx, .xlsm + """ + return ".xls" + + +def test_read_xlrd_book(read_ext_xlrd, datapath): + engine = "xlrd" + sheet_name = "Sheet1" + pth = datapath("io", "data", "excel", "test1.xls") + with xlrd.open_workbook(pth) as book: + with ExcelFile(book, engine=engine) as xl: + result = pd.read_excel(xl, sheet_name=sheet_name, index_col=0) + + expected = pd.read_excel( + book, sheet_name=sheet_name, engine=engine, index_col=0 + ) + tm.assert_frame_equal(result, expected) + + +def test_read_xlsx_fails(datapath): + # GH 29375 + from xlrd.biffh import XLRDError + + path = datapath("io", "data", "excel", "test1.xlsx") + with pytest.raises(XLRDError, match="Excel xlsx file; not supported"): + pd.read_excel(path, engine="xlrd") + + +def test_nan_in_xls(datapath): + # GH 54564 + path = datapath("io", "data", "excel", "test6.xls") + + expected = pd.DataFrame({0: np.r_[0, 2].astype("int64"), 1: np.r_[1, np.nan]}) + + result = pd.read_excel(path, header=None) + + tm.assert_frame_equal(result, expected) + + +@pytest.mark.parametrize( + "file_header", + [ + b"\x09\x00\x04\x00\x07\x00\x10\x00", + b"\x09\x02\x06\x00\x00\x00\x10\x00", + b"\x09\x04\x06\x00\x00\x00\x10\x00", + b"\xd0\xcf\x11\xe0\xa1\xb1\x1a\xe1", + ], +) +def test_read_old_xls_files(file_header): + # GH 41226 + f = io.BytesIO(file_header) + assert inspect_excel_format(f) == "xls" diff --git a/lib/python3.11/site-packages/pandas/tests/io/excel/test_xlsxwriter.py b/lib/python3.11/site-packages/pandas/tests/io/excel/test_xlsxwriter.py new file mode 100644 index 0000000000000000000000000000000000000000..b2e6c845e50199ec7ca7d336de3f1cd3478a8bbf --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/excel/test_xlsxwriter.py @@ -0,0 +1,86 @@ +import contextlib +import uuid + +import pytest + +from pandas import DataFrame + +from pandas.io.excel import ExcelWriter + +xlsxwriter = pytest.importorskip("xlsxwriter") + + +@pytest.fixture +def ext(): + return ".xlsx" + + +@pytest.fixture +def tmp_excel(ext, tmp_path): + tmp = tmp_path / f"{uuid.uuid4()}{ext}" + tmp.touch() + return str(tmp) + + +def test_column_format(tmp_excel): + # Test that column formats are applied to cells. Test for issue #9167. + # Applicable to xlsxwriter only. + openpyxl = pytest.importorskip("openpyxl") + + frame = DataFrame({"A": [123456, 123456], "B": [123456, 123456]}) + + with ExcelWriter(tmp_excel) as writer: + frame.to_excel(writer) + + # Add a number format to col B and ensure it is applied to cells. + num_format = "#,##0" + write_workbook = writer.book + write_worksheet = write_workbook.worksheets()[0] + col_format = write_workbook.add_format({"num_format": num_format}) + write_worksheet.set_column("B:B", None, col_format) + + with contextlib.closing(openpyxl.load_workbook(tmp_excel)) as read_workbook: + try: + read_worksheet = read_workbook["Sheet1"] + except TypeError: + # compat + read_worksheet = read_workbook.get_sheet_by_name(name="Sheet1") + + # Get the number format from the cell. + try: + cell = read_worksheet["B2"] + except TypeError: + # compat + cell = read_worksheet.cell("B2") + + try: + read_num_format = cell.number_format + except AttributeError: + read_num_format = cell.style.number_format._format_code + + assert read_num_format == num_format + + +def test_write_append_mode_raises(tmp_excel): + msg = "Append mode is not supported with xlsxwriter!" + + with pytest.raises(ValueError, match=msg): + ExcelWriter(tmp_excel, engine="xlsxwriter", mode="a") + + +@pytest.mark.parametrize("nan_inf_to_errors", [True, False]) +def test_engine_kwargs(tmp_excel, nan_inf_to_errors): + # GH 42286 + engine_kwargs = {"options": {"nan_inf_to_errors": nan_inf_to_errors}} + with ExcelWriter( + tmp_excel, engine="xlsxwriter", engine_kwargs=engine_kwargs + ) as writer: + assert writer.book.nan_inf_to_errors == nan_inf_to_errors + + +def test_book_and_sheets_consistent(tmp_excel): + # GH#45687 - Ensure sheets is updated if user modifies book + with ExcelWriter(tmp_excel, engine="xlsxwriter") as writer: + assert writer.sheets == {} + sheet = writer.book.add_worksheet("test_name") + assert writer.sheets == {"test_name": sheet} diff --git 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b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_bar.py new file mode 100644 index 0000000000000000000000000000000000000000..4de83fd1436e0d8f1aae150b92d4cc11ed62466d --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_bar.py @@ -0,0 +1,360 @@ +import io + +import numpy as np +import pytest + +from pandas import ( + NA, + DataFrame, + read_csv, +) + +pytest.importorskip("jinja2") + + +def bar_grad(a=None, b=None, c=None, d=None): + """Used in multiple tests to simplify formatting of expected result""" + ret = [("width", "10em")] + if all(x is None for x in [a, b, c, d]): + return ret + return [ + *ret, + ( + "background", + f"linear-gradient(90deg,{','.join([x for x in [a, b, c, d] if x])})", + ), + ] + + +def no_bar(): + return bar_grad() + + +def bar_to(x, color="#d65f5f"): + return bar_grad(f" {color} {x:.1f}%", f" transparent {x:.1f}%") + + +def bar_from_to(x, y, color="#d65f5f"): + return bar_grad( + f" transparent {x:.1f}%", + f" {color} {x:.1f}%", + f" {color} {y:.1f}%", + f" transparent {y:.1f}%", + ) + + +@pytest.fixture +def df_pos(): + return DataFrame([[1], [2], [3]]) + + +@pytest.fixture +def df_neg(): + return DataFrame([[-1], [-2], [-3]]) + + +@pytest.fixture +def df_mix(): + return DataFrame([[-3], [1], [2]]) + + +@pytest.mark.parametrize( + "align, exp", + [ + ("left", [no_bar(), bar_to(50), bar_to(100)]), + ("right", [bar_to(100), bar_from_to(50, 100), no_bar()]), + ("mid", [bar_to(33.33), bar_to(66.66), bar_to(100)]), + ("zero", [bar_from_to(50, 66.7), bar_from_to(50, 83.3), bar_from_to(50, 100)]), + ("mean", [bar_to(50), no_bar(), bar_from_to(50, 100)]), + (2.0, [bar_to(50), no_bar(), bar_from_to(50, 100)]), + (np.median, [bar_to(50), no_bar(), bar_from_to(50, 100)]), + ], +) +def test_align_positive_cases(df_pos, align, exp): + # test different align cases for all positive values + result = df_pos.style.bar(align=align)._compute().ctx + expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]} + assert result == expected + + +@pytest.mark.parametrize( + "align, exp", + [ + ("left", [bar_to(100), bar_to(50), no_bar()]), + ("right", [no_bar(), bar_from_to(50, 100), bar_to(100)]), + ("mid", [bar_from_to(66.66, 100), bar_from_to(33.33, 100), bar_to(100)]), + ("zero", [bar_from_to(33.33, 50), bar_from_to(16.66, 50), bar_to(50)]), + ("mean", [bar_from_to(50, 100), no_bar(), bar_to(50)]), + (-2.0, [bar_from_to(50, 100), no_bar(), bar_to(50)]), + (np.median, [bar_from_to(50, 100), no_bar(), bar_to(50)]), + ], +) +def test_align_negative_cases(df_neg, align, exp): + # test different align cases for all negative values + result = df_neg.style.bar(align=align)._compute().ctx + expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]} + assert result == expected + + +@pytest.mark.parametrize( + "align, exp", + [ + ("left", [no_bar(), bar_to(80), bar_to(100)]), + ("right", [bar_to(100), bar_from_to(80, 100), no_bar()]), + ("mid", [bar_to(60), bar_from_to(60, 80), bar_from_to(60, 100)]), + ("zero", [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]), + ("mean", [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]), + (-0.0, [bar_to(50), bar_from_to(50, 66.66), bar_from_to(50, 83.33)]), + (np.nanmedian, [bar_to(50), no_bar(), bar_from_to(50, 62.5)]), + ], +) +@pytest.mark.parametrize("nans", [True, False]) +def test_align_mixed_cases(df_mix, align, exp, nans): + # test different align cases for mixed positive and negative values + # also test no impact of NaNs and no_bar + expected = {(0, 0): exp[0], (1, 0): exp[1], (2, 0): exp[2]} + if nans: + df_mix.loc[3, :] = np.nan + expected.update({(3, 0): no_bar()}) + result = df_mix.style.bar(align=align)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "align, exp", + [ + ( + "left", + { + "index": [[no_bar(), no_bar()], [bar_to(100), bar_to(100)]], + "columns": [[no_bar(), bar_to(100)], [no_bar(), bar_to(100)]], + "none": [[no_bar(), bar_to(33.33)], [bar_to(66.66), bar_to(100)]], + }, + ), + ( + "mid", + { + "index": [[bar_to(33.33), bar_to(50)], [bar_to(100), bar_to(100)]], + "columns": [[bar_to(50), bar_to(100)], [bar_to(75), bar_to(100)]], + "none": [[bar_to(25), bar_to(50)], [bar_to(75), bar_to(100)]], + }, + ), + ( + "zero", + { + "index": [ + [bar_from_to(50, 66.66), bar_from_to(50, 75)], + [bar_from_to(50, 100), bar_from_to(50, 100)], + ], + "columns": [ + [bar_from_to(50, 75), bar_from_to(50, 100)], + [bar_from_to(50, 87.5), bar_from_to(50, 100)], + ], + "none": [ + [bar_from_to(50, 62.5), bar_from_to(50, 75)], + [bar_from_to(50, 87.5), bar_from_to(50, 100)], + ], + }, + ), + ( + 2, + { + "index": [ + [bar_to(50), no_bar()], + [bar_from_to(50, 100), bar_from_to(50, 100)], + ], + "columns": [ + [bar_to(50), no_bar()], + [bar_from_to(50, 75), bar_from_to(50, 100)], + ], + "none": [ + [bar_from_to(25, 50), no_bar()], + [bar_from_to(50, 75), bar_from_to(50, 100)], + ], + }, + ), + ], +) +@pytest.mark.parametrize("axis", ["index", "columns", "none"]) +def test_align_axis(align, exp, axis): + # test all axis combinations with positive values and different aligns + data = DataFrame([[1, 2], [3, 4]]) + result = ( + data.style.bar(align=align, axis=None if axis == "none" else axis) + ._compute() + .ctx + ) + expected = { + (0, 0): exp[axis][0][0], + (0, 1): exp[axis][0][1], + (1, 0): exp[axis][1][0], + (1, 1): exp[axis][1][1], + } + assert result == expected + + +@pytest.mark.parametrize( + "values, vmin, vmax", + [ + ("positive", 1.5, 2.5), + ("negative", -2.5, -1.5), + ("mixed", -2.5, 1.5), + ], +) +@pytest.mark.parametrize("nullify", [None, "vmin", "vmax"]) # test min/max separately +@pytest.mark.parametrize("align", ["left", "right", "zero", "mid"]) +def test_vmin_vmax_clipping(df_pos, df_neg, df_mix, values, vmin, vmax, nullify, align): + # test that clipping occurs if any vmin > data_values or vmax < data_values + if align == "mid": # mid acts as left or right in each case + if values == "positive": + align = "left" + elif values == "negative": + align = "right" + df = {"positive": df_pos, "negative": df_neg, "mixed": df_mix}[values] + vmin = None if nullify == "vmin" else vmin + vmax = None if nullify == "vmax" else vmax + + clip_df = df.where(df <= (vmax if vmax else 999), other=vmax) + clip_df = clip_df.where(clip_df >= (vmin if vmin else -999), other=vmin) + + result = ( + df.style.bar(align=align, vmin=vmin, vmax=vmax, color=["red", "green"]) + ._compute() + .ctx + ) + expected = clip_df.style.bar(align=align, color=["red", "green"])._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "values, vmin, vmax", + [ + ("positive", 0.5, 4.5), + ("negative", -4.5, -0.5), + ("mixed", -4.5, 4.5), + ], +) +@pytest.mark.parametrize("nullify", [None, "vmin", "vmax"]) # test min/max separately +@pytest.mark.parametrize("align", ["left", "right", "zero", "mid"]) +def test_vmin_vmax_widening(df_pos, df_neg, df_mix, values, vmin, vmax, nullify, align): + # test that widening occurs if any vmax > data_values or vmin < data_values + if align == "mid": # mid acts as left or right in each case + if values == "positive": + align = "left" + elif values == "negative": + align = "right" + df = {"positive": df_pos, "negative": df_neg, "mixed": df_mix}[values] + vmin = None if nullify == "vmin" else vmin + vmax = None if nullify == "vmax" else vmax + + expand_df = df.copy() + expand_df.loc[3, :], expand_df.loc[4, :] = vmin, vmax + + result = ( + df.style.bar(align=align, vmin=vmin, vmax=vmax, color=["red", "green"]) + ._compute() + .ctx + ) + expected = expand_df.style.bar(align=align, color=["red", "green"])._compute().ctx + assert result.items() <= expected.items() + + +def test_numerics(): + # test data is pre-selected for numeric values + data = DataFrame([[1, "a"], [2, "b"]]) + result = data.style.bar()._compute().ctx + assert (0, 1) not in result + assert (1, 1) not in result + + +@pytest.mark.parametrize( + "align, exp", + [ + ("left", [no_bar(), bar_to(100, "green")]), + ("right", [bar_to(100, "red"), no_bar()]), + ("mid", [bar_to(25, "red"), bar_from_to(25, 100, "green")]), + ("zero", [bar_from_to(33.33, 50, "red"), bar_from_to(50, 100, "green")]), + ], +) +def test_colors_mixed(align, exp): + data = DataFrame([[-1], [3]]) + result = data.style.bar(align=align, color=["red", "green"])._compute().ctx + assert result == {(0, 0): exp[0], (1, 0): exp[1]} + + +def test_bar_align_height(): + # test when keyword height is used 'no-repeat center' and 'background-size' present + data = DataFrame([[1], [2]]) + result = data.style.bar(align="left", height=50)._compute().ctx + bg_s = "linear-gradient(90deg, #d65f5f 100.0%, transparent 100.0%) no-repeat center" + expected = { + (0, 0): [("width", "10em")], + (1, 0): [ + ("width", "10em"), + ("background", bg_s), + ("background-size", "100% 50.0%"), + ], + } + assert result == expected + + +def test_bar_value_error_raises(): + df = DataFrame({"A": [-100, -60, -30, -20]}) + + msg = "`align` should be in {'left', 'right', 'mid', 'mean', 'zero'} or" + with pytest.raises(ValueError, match=msg): + df.style.bar(align="poorly", color=["#d65f5f", "#5fba7d"]).to_html() + + msg = r"`width` must be a value in \[0, 100\]" + with pytest.raises(ValueError, match=msg): + df.style.bar(width=200).to_html() + + msg = r"`height` must be a value in \[0, 100\]" + with pytest.raises(ValueError, match=msg): + df.style.bar(height=200).to_html() + + +def test_bar_color_and_cmap_error_raises(): + df = DataFrame({"A": [1, 2, 3, 4]}) + msg = "`color` and `cmap` cannot both be given" + # Test that providing both color and cmap raises a ValueError + with pytest.raises(ValueError, match=msg): + df.style.bar(color="#d65f5f", cmap="viridis").to_html() + + +def test_bar_invalid_color_type_error_raises(): + df = DataFrame({"A": [1, 2, 3, 4]}) + msg = ( + r"`color` must be string or list or tuple of 2 strings," + r"\(eg: color=\['#d65f5f', '#5fba7d'\]\)" + ) + # Test that providing an invalid color type raises a ValueError + with pytest.raises(ValueError, match=msg): + df.style.bar(color=123).to_html() + + # Test that providing a color list with more than two elements raises a ValueError + with pytest.raises(ValueError, match=msg): + df.style.bar(color=["#d65f5f", "#5fba7d", "#abcdef"]).to_html() + + +def test_styler_bar_with_NA_values(): + df1 = DataFrame({"A": [1, 2, NA, 4]}) + df2 = DataFrame([[NA, NA], [NA, NA]]) + expected_substring = "style type=" + html_output1 = df1.style.bar(subset="A").to_html() + html_output2 = df2.style.bar(align="left", axis=None).to_html() + assert expected_substring in html_output1 + assert expected_substring in html_output2 + + +def test_style_bar_with_pyarrow_NA_values(): + pytest.importorskip("pyarrow") + data = """name,age,test1,test2,teacher + Adam,15,95.0,80,Ashby + Bob,16,81.0,82,Ashby + Dave,16,89.0,84,Jones + Fred,15,,88,Jones""" + df = read_csv(io.StringIO(data), dtype_backend="pyarrow") + expected_substring = "style type=" + html_output = df.style.bar(subset="test1").to_html() + assert expected_substring in html_output diff --git a/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_exceptions.py b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_exceptions.py new file mode 100644 index 0000000000000000000000000000000000000000..d52e3a37e7693dadce34f73fc03a0790c7a0b4d3 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_exceptions.py @@ -0,0 +1,44 @@ +import pytest + +jinja2 = pytest.importorskip("jinja2") + +from pandas import ( + DataFrame, + MultiIndex, +) + +from pandas.io.formats.style import Styler + + +@pytest.fixture +def df(): + return DataFrame( + data=[[0, -0.609], [1, -1.228]], + columns=["A", "B"], + index=["x", "y"], + ) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0) + + +def test_concat_bad_columns(styler): + msg = "`other.data` must have same columns as `Styler.data" + with pytest.raises(ValueError, match=msg): + styler.concat(DataFrame([[1, 2]]).style) + + +def test_concat_bad_type(styler): + msg = "`other` must be of type `Styler`" + with pytest.raises(TypeError, match=msg): + styler.concat(DataFrame([[1, 2]])) + + +def test_concat_bad_index_levels(styler, df): + df = df.copy() + df.index = MultiIndex.from_tuples([(0, 0), (1, 1)]) + msg = "number of index levels must be same in `other`" + with pytest.raises(ValueError, match=msg): + styler.concat(df.style) diff --git a/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_format.py b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_format.py new file mode 100644 index 0000000000000000000000000000000000000000..ae68fcf9ef1fc99fa1e114265b0f8e58650e0170 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_format.py @@ -0,0 +1,661 @@ +import numpy as np +import pytest + +from pandas import ( + NA, + DataFrame, + IndexSlice, + MultiIndex, + NaT, + Timestamp, + option_context, +) + +pytest.importorskip("jinja2") +from pandas.io.formats.style import Styler +from pandas.io.formats.style_render import _str_escape + + +@pytest.fixture +def df(): + return DataFrame( + data=[[0, -0.609], [1, -1.228]], + columns=["A", "B"], + index=["x", "y"], + ) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0) + + +@pytest.fixture +def df_multi(): + return ( + DataFrame( + data=np.arange(16).reshape(4, 4), + columns=MultiIndex.from_product([["A", "B"], ["a", "b"]]), + index=MultiIndex.from_product([["X", "Y"], ["x", "y"]]), + ) + .rename_axis(["0_0", "0_1"], axis=0) + .rename_axis(["1_0", "1_1"], axis=1) + ) + + +@pytest.fixture +def styler_multi(df_multi): + return Styler(df_multi, uuid_len=0) + + +def test_display_format(styler): + ctx = styler.format("{:0.1f}")._translate(True, True) + assert all(["display_value" in c for c in row] for row in ctx["body"]) + assert all([len(c["display_value"]) <= 3 for c in row[1:]] for row in ctx["body"]) + assert len(ctx["body"][0][1]["display_value"].lstrip("-")) <= 3 + + +@pytest.mark.parametrize("index", [True, False]) +@pytest.mark.parametrize("columns", [True, False]) +def test_display_format_index(styler, index, columns): + exp_index = ["x", "y"] + if index: + styler.format_index(lambda v: v.upper(), axis=0) # test callable + exp_index = ["X", "Y"] + + exp_columns = ["A", "B"] + if columns: + styler.format_index("*{}*", axis=1) # test string + exp_columns = ["*A*", "*B*"] + + ctx = styler._translate(True, True) + + for r, row in enumerate(ctx["body"]): + assert row[0]["display_value"] == exp_index[r] + + for c, col in enumerate(ctx["head"][1:]): + assert col["display_value"] == exp_columns[c] + + +def test_format_dict(styler): + ctx = styler.format({"A": "{:0.1f}", "B": "{0:.2%}"})._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "0.0" + assert ctx["body"][0][2]["display_value"] == "-60.90%" + + +def test_format_index_dict(styler): + ctx = styler.format_index({0: lambda v: v.upper()})._translate(True, True) + for i, val in enumerate(["X", "Y"]): + assert ctx["body"][i][0]["display_value"] == val + + +def test_format_string(styler): + ctx = styler.format("{:.2f}")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "0.00" + assert ctx["body"][0][2]["display_value"] == "-0.61" + assert ctx["body"][1][1]["display_value"] == "1.00" + assert ctx["body"][1][2]["display_value"] == "-1.23" + + +def test_format_callable(styler): + ctx = styler.format(lambda v: "neg" if v < 0 else "pos")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "pos" + assert ctx["body"][0][2]["display_value"] == "neg" + assert ctx["body"][1][1]["display_value"] == "pos" + assert ctx["body"][1][2]["display_value"] == "neg" + + +def test_format_with_na_rep(): + # GH 21527 28358 + df = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"]) + + ctx = df.style.format(None, na_rep="-")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "-" + assert ctx["body"][0][2]["display_value"] == "-" + + ctx = df.style.format("{:.2%}", na_rep="-")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "-" + assert ctx["body"][0][2]["display_value"] == "-" + assert ctx["body"][1][1]["display_value"] == "110.00%" + assert ctx["body"][1][2]["display_value"] == "120.00%" + + ctx = df.style.format("{:.2%}", na_rep="-", subset=["B"])._translate(True, True) + assert ctx["body"][0][2]["display_value"] == "-" + assert ctx["body"][1][2]["display_value"] == "120.00%" + + +def test_format_index_with_na_rep(): + df = DataFrame([[1, 2, 3, 4, 5]], columns=["A", None, np.nan, NaT, NA]) + ctx = df.style.format_index(None, na_rep="--", axis=1)._translate(True, True) + assert ctx["head"][0][1]["display_value"] == "A" + for i in [2, 3, 4, 5]: + assert ctx["head"][0][i]["display_value"] == "--" + + +def test_format_non_numeric_na(): + # GH 21527 28358 + df = DataFrame( + { + "object": [None, np.nan, "foo"], + "datetime": [None, NaT, Timestamp("20120101")], + } + ) + ctx = df.style.format(None, na_rep="-")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "-" + assert ctx["body"][0][2]["display_value"] == "-" + assert ctx["body"][1][1]["display_value"] == "-" + assert ctx["body"][1][2]["display_value"] == "-" + + +@pytest.mark.parametrize( + "func, attr, kwargs", + [ + ("format", "_display_funcs", {}), + ("format_index", "_display_funcs_index", {"axis": 0}), + ("format_index", "_display_funcs_columns", {"axis": 1}), + ], +) +def test_format_clear(styler, func, attr, kwargs): + assert (0, 0) not in getattr(styler, attr) # using default + getattr(styler, func)("{:.2f}", **kwargs) + assert (0, 0) in getattr(styler, attr) # formatter is specified + getattr(styler, func)(**kwargs) + assert (0, 0) not in getattr(styler, attr) # formatter cleared to default + + +@pytest.mark.parametrize( + "escape, exp", + [ + ("html", "<>&"%$#_{}~^\\~ ^ \\ "), + ( + "latex", + '<>\\&"\\%\\$\\#\\_\\{\\}\\textasciitilde \\textasciicircum ' + "\\textbackslash \\textasciitilde \\space \\textasciicircum \\space " + "\\textbackslash \\space ", + ), + ], +) +def test_format_escape_html(escape, exp): + chars = '<>&"%$#_{}~^\\~ ^ \\ ' + df = DataFrame([[chars]]) + + s = Styler(df, uuid_len=0).format("&{0}&", escape=None) + expected = f'&{chars}&' + assert expected in s.to_html() + + # only the value should be escaped before passing to the formatter + s = Styler(df, uuid_len=0).format("&{0}&", escape=escape) + expected = f'&{exp}&' + assert expected in s.to_html() + + # also test format_index() + styler = Styler(DataFrame(columns=[chars]), uuid_len=0) + styler.format_index("&{0}&", escape=None, axis=1) + assert styler._translate(True, True)["head"][0][1]["display_value"] == f"&{chars}&" + styler.format_index("&{0}&", escape=escape, axis=1) + assert styler._translate(True, True)["head"][0][1]["display_value"] == f"&{exp}&" + + +@pytest.mark.parametrize( + "chars, expected", + [ + ( + r"$ \$&%#_{}~^\ $ &%#_{}~^\ $", + "".join( + [ + r"$ \$&%#_{}~^\ $ ", + r"\&\%\#\_\{\}\textasciitilde \textasciicircum ", + r"\textbackslash \space \$", + ] + ), + ), + ( + r"\( &%#_{}~^\ \) &%#_{}~^\ \(", + "".join( + [ + r"\( &%#_{}~^\ \) ", + r"\&\%\#\_\{\}\textasciitilde \textasciicircum ", + r"\textbackslash \space \textbackslash (", + ] + ), + ), + ( + r"$\&%#_{}^\$", + r"\$\textbackslash \&\%\#\_\{\}\textasciicircum \textbackslash \$", + ), + ( + r"$ \frac{1}{2} $ \( \frac{1}{2} \)", + "".join( + [ + r"$ \frac{1}{2} $", + r" \textbackslash ( \textbackslash frac\{1\}\{2\} \textbackslash )", + ] + ), + ), + ], +) +def test_format_escape_latex_math(chars, expected): + # GH 51903 + # latex-math escape works for each DataFrame cell separately. If we have + # a combination of dollar signs and brackets, the dollar sign would apply. + df = DataFrame([[chars]]) + s = df.style.format("{0}", escape="latex-math") + assert s._translate(True, True)["body"][0][1]["display_value"] == expected + + +def test_format_escape_na_rep(): + # tests the na_rep is not escaped + df = DataFrame([['<>&"', None]]) + s = Styler(df, uuid_len=0).format("X&{0}>X", escape="html", na_rep="&") + ex = 'X&<>&">X' + expected2 = '&' + assert ex in s.to_html() + assert expected2 in s.to_html() + + # also test for format_index() + df = DataFrame(columns=['<>&"', None]) + styler = Styler(df, uuid_len=0) + styler.format_index("X&{0}>X", escape="html", na_rep="&", axis=1) + ctx = styler._translate(True, True) + assert ctx["head"][0][1]["display_value"] == "X&<>&">X" + assert ctx["head"][0][2]["display_value"] == "&" + + +def test_format_escape_floats(styler): + # test given formatter for number format is not impacted by escape + s = styler.format("{:.1f}", escape="html") + for expected in [">0.0<", ">1.0<", ">-1.2<", ">-0.6<"]: + assert expected in s.to_html() + # tests precision of floats is not impacted by escape + s = styler.format(precision=1, escape="html") + for expected in [">0<", ">1<", ">-1.2<", ">-0.6<"]: + assert expected in s.to_html() + + +@pytest.mark.parametrize("formatter", [5, True, [2.0]]) +@pytest.mark.parametrize("func", ["format", "format_index"]) +def test_format_raises(styler, formatter, func): + with pytest.raises(TypeError, match="expected str or callable"): + getattr(styler, func)(formatter) + + +@pytest.mark.parametrize( + "precision, expected", + [ + (1, ["1.0", "2.0", "3.2", "4.6"]), + (2, ["1.00", "2.01", "3.21", "4.57"]), + (3, ["1.000", "2.009", "3.212", "4.566"]), + ], +) +def test_format_with_precision(precision, expected): + # Issue #13257 + df = DataFrame([[1.0, 2.0090, 3.2121, 4.566]], columns=[1.0, 2.0090, 3.2121, 4.566]) + styler = Styler(df) + styler.format(precision=precision) + styler.format_index(precision=precision, axis=1) + + ctx = styler._translate(True, True) + for col, exp in enumerate(expected): + assert ctx["body"][0][col + 1]["display_value"] == exp # format test + assert ctx["head"][0][col + 1]["display_value"] == exp # format_index test + + +@pytest.mark.parametrize("axis", [0, 1]) +@pytest.mark.parametrize( + "level, expected", + [ + (0, ["X", "X", "_", "_"]), # level int + ("zero", ["X", "X", "_", "_"]), # level name + (1, ["_", "_", "X", "X"]), # other level int + ("one", ["_", "_", "X", "X"]), # other level name + ([0, 1], ["X", "X", "X", "X"]), # both levels + ([0, "zero"], ["X", "X", "_", "_"]), # level int and name simultaneous + ([0, "one"], ["X", "X", "X", "X"]), # both levels as int and name + (["one", "zero"], ["X", "X", "X", "X"]), # both level names, reversed + ], +) +def test_format_index_level(axis, level, expected): + midx = MultiIndex.from_arrays([["_", "_"], ["_", "_"]], names=["zero", "one"]) + df = DataFrame([[1, 2], [3, 4]]) + if axis == 0: + df.index = midx + else: + df.columns = midx + + styler = df.style.format_index(lambda v: "X", level=level, axis=axis) + ctx = styler._translate(True, True) + + if axis == 0: # compare index + result = [ctx["body"][s][0]["display_value"] for s in range(2)] + result += [ctx["body"][s][1]["display_value"] for s in range(2)] + else: # compare columns + result = [ctx["head"][0][s + 1]["display_value"] for s in range(2)] + result += [ctx["head"][1][s + 1]["display_value"] for s in range(2)] + + assert expected == result + + +def test_format_subset(): + df = DataFrame([[0.1234, 0.1234], [1.1234, 1.1234]], columns=["a", "b"]) + ctx = df.style.format( + {"a": "{:0.1f}", "b": "{0:.2%}"}, subset=IndexSlice[0, :] + )._translate(True, True) + expected = "0.1" + raw_11 = "1.123400" + assert ctx["body"][0][1]["display_value"] == expected + assert ctx["body"][1][1]["display_value"] == raw_11 + assert ctx["body"][0][2]["display_value"] == "12.34%" + + ctx = df.style.format("{:0.1f}", subset=IndexSlice[0, :])._translate(True, True) + assert ctx["body"][0][1]["display_value"] == expected + assert ctx["body"][1][1]["display_value"] == raw_11 + + ctx = df.style.format("{:0.1f}", subset=IndexSlice["a"])._translate(True, True) + assert ctx["body"][0][1]["display_value"] == expected + assert ctx["body"][0][2]["display_value"] == "0.123400" + + ctx = df.style.format("{:0.1f}", subset=IndexSlice[0, "a"])._translate(True, True) + assert ctx["body"][0][1]["display_value"] == expected + assert ctx["body"][1][1]["display_value"] == raw_11 + + ctx = df.style.format("{:0.1f}", subset=IndexSlice[[0, 1], ["a"]])._translate( + True, True + ) + assert ctx["body"][0][1]["display_value"] == expected + assert ctx["body"][1][1]["display_value"] == "1.1" + assert ctx["body"][0][2]["display_value"] == "0.123400" + assert ctx["body"][1][2]["display_value"] == raw_11 + + +@pytest.mark.parametrize("formatter", [None, "{:,.1f}"]) +@pytest.mark.parametrize("decimal", [".", "*"]) +@pytest.mark.parametrize("precision", [None, 2]) +@pytest.mark.parametrize("func, col", [("format", 1), ("format_index", 0)]) +def test_format_thousands(formatter, decimal, precision, func, col): + styler = DataFrame([[1000000.123456789]], index=[1000000.123456789]).style + result = getattr(styler, func)( # testing float + thousands="_", formatter=formatter, decimal=decimal, precision=precision + )._translate(True, True) + assert "1_000_000" in result["body"][0][col]["display_value"] + + styler = DataFrame([[1000000]], index=[1000000]).style + result = getattr(styler, func)( # testing int + thousands="_", formatter=formatter, decimal=decimal, precision=precision + )._translate(True, True) + assert "1_000_000" in result["body"][0][col]["display_value"] + + styler = DataFrame([[1 + 1000000.123456789j]], index=[1 + 1000000.123456789j]).style + result = getattr(styler, func)( # testing complex + thousands="_", formatter=formatter, decimal=decimal, precision=precision + )._translate(True, True) + assert "1_000_000" in result["body"][0][col]["display_value"] + + +@pytest.mark.parametrize("formatter", [None, "{:,.4f}"]) +@pytest.mark.parametrize("thousands", [None, ",", "*"]) +@pytest.mark.parametrize("precision", [None, 4]) +@pytest.mark.parametrize("func, col", [("format", 1), ("format_index", 0)]) +def test_format_decimal(formatter, thousands, precision, func, col): + styler = DataFrame([[1000000.123456789]], index=[1000000.123456789]).style + result = getattr(styler, func)( # testing float + decimal="_", formatter=formatter, thousands=thousands, precision=precision + )._translate(True, True) + assert "000_123" in result["body"][0][col]["display_value"] + + styler = DataFrame([[1 + 1000000.123456789j]], index=[1 + 1000000.123456789j]).style + result = getattr(styler, func)( # testing complex + decimal="_", formatter=formatter, thousands=thousands, precision=precision + )._translate(True, True) + assert "000_123" in result["body"][0][col]["display_value"] + + +def test_str_escape_error(): + msg = "`escape` only permitted in {'html', 'latex', 'latex-math'}, got " + with pytest.raises(ValueError, match=msg): + _str_escape("text", "bad_escape") + + with pytest.raises(ValueError, match=msg): + _str_escape("text", []) + + _str_escape(2.00, "bad_escape") # OK since dtype is float + + +def test_long_int_formatting(): + df = DataFrame(data=[[1234567890123456789]], columns=["test"]) + styler = df.style + ctx = styler._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "1234567890123456789" + + styler = df.style.format(thousands="_") + ctx = styler._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "1_234_567_890_123_456_789" + + +def test_format_options(): + df = DataFrame({"int": [2000, 1], "float": [1.009, None], "str": ["&<", "&~"]}) + ctx = df.style._translate(True, True) + + # test option: na_rep + assert ctx["body"][1][2]["display_value"] == "nan" + with option_context("styler.format.na_rep", "MISSING"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][1][2]["display_value"] == "MISSING" + + # test option: decimal and precision + assert ctx["body"][0][2]["display_value"] == "1.009000" + with option_context("styler.format.decimal", "_"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][0][2]["display_value"] == "1_009000" + with option_context("styler.format.precision", 2): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][0][2]["display_value"] == "1.01" + + # test option: thousands + assert ctx["body"][0][1]["display_value"] == "2000" + with option_context("styler.format.thousands", "_"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][0][1]["display_value"] == "2_000" + + # test option: escape + assert ctx["body"][0][3]["display_value"] == "&<" + assert ctx["body"][1][3]["display_value"] == "&~" + with option_context("styler.format.escape", "html"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][0][3]["display_value"] == "&<" + with option_context("styler.format.escape", "latex"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][1][3]["display_value"] == "\\&\\textasciitilde " + with option_context("styler.format.escape", "latex-math"): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][1][3]["display_value"] == "\\&\\textasciitilde " + + # test option: formatter + with option_context("styler.format.formatter", {"int": "{:,.2f}"}): + ctx_with_op = df.style._translate(True, True) + assert ctx_with_op["body"][0][1]["display_value"] == "2,000.00" + + +def test_precision_zero(df): + styler = Styler(df, precision=0) + ctx = styler._translate(True, True) + assert ctx["body"][0][2]["display_value"] == "-1" + assert ctx["body"][1][2]["display_value"] == "-1" + + +@pytest.mark.parametrize( + "formatter, exp", + [ + (lambda x: f"{x:.3f}", "9.000"), + ("{:.2f}", "9.00"), + ({0: "{:.1f}"}, "9.0"), + (None, "9"), + ], +) +def test_formatter_options_validator(formatter, exp): + df = DataFrame([[9]]) + with option_context("styler.format.formatter", formatter): + assert f" {exp} " in df.style.to_latex() + + +def test_formatter_options_raises(): + msg = "Value must be an instance of" + with pytest.raises(ValueError, match=msg): + with option_context("styler.format.formatter", ["bad", "type"]): + DataFrame().style.to_latex() + + +def test_1level_multiindex(): + # GH 43383 + midx = MultiIndex.from_product([[1, 2]], names=[""]) + df = DataFrame(-1, index=midx, columns=[0, 1]) + ctx = df.style._translate(True, True) + assert ctx["body"][0][0]["display_value"] == "1" + assert ctx["body"][0][0]["is_visible"] is True + assert ctx["body"][1][0]["display_value"] == "2" + assert ctx["body"][1][0]["is_visible"] is True + + +def test_boolean_format(): + # gh 46384: booleans do not collapse to integer representation on display + df = DataFrame([[True, False]]) + ctx = df.style._translate(True, True) + assert ctx["body"][0][1]["display_value"] is True + assert ctx["body"][0][2]["display_value"] is False + + +@pytest.mark.parametrize( + "hide, labels", + [ + (False, [1, 2]), + (True, [1, 2, 3, 4]), + ], +) +def test_relabel_raise_length(styler_multi, hide, labels): + if hide: + styler_multi.hide(axis=0, subset=[("X", "x"), ("Y", "y")]) + with pytest.raises(ValueError, match="``labels`` must be of length equal"): + styler_multi.relabel_index(labels=labels) + + +def test_relabel_index(styler_multi): + labels = [(1, 2), (3, 4)] + styler_multi.hide(axis=0, subset=[("X", "x"), ("Y", "y")]) + styler_multi.relabel_index(labels=labels) + ctx = styler_multi._translate(True, True) + assert {"value": "X", "display_value": 1}.items() <= ctx["body"][0][0].items() + assert {"value": "y", "display_value": 2}.items() <= ctx["body"][0][1].items() + assert {"value": "Y", "display_value": 3}.items() <= ctx["body"][1][0].items() + assert {"value": "x", "display_value": 4}.items() <= ctx["body"][1][1].items() + + +def test_relabel_columns(styler_multi): + labels = [(1, 2), (3, 4)] + styler_multi.hide(axis=1, subset=[("A", "a"), ("B", "b")]) + styler_multi.relabel_index(axis=1, labels=labels) + ctx = styler_multi._translate(True, True) + assert {"value": "A", "display_value": 1}.items() <= ctx["head"][0][3].items() + assert {"value": "B", "display_value": 3}.items() <= ctx["head"][0][4].items() + assert {"value": "b", "display_value": 2}.items() <= ctx["head"][1][3].items() + assert {"value": "a", "display_value": 4}.items() <= ctx["head"][1][4].items() + + +def test_relabel_roundtrip(styler): + styler.relabel_index(["{}", "{}"]) + ctx = styler._translate(True, True) + assert {"value": "x", "display_value": "x"}.items() <= ctx["body"][0][0].items() + assert {"value": "y", "display_value": "y"}.items() <= ctx["body"][1][0].items() + + +@pytest.mark.parametrize("axis", [0, 1]) +@pytest.mark.parametrize( + "level, expected", + [ + (0, ["X", "one"]), # level int + ("zero", ["X", "one"]), # level name + (1, ["zero", "X"]), # other level int + ("one", ["zero", "X"]), # other level name + ([0, 1], ["X", "X"]), # both levels + ([0, "zero"], ["X", "one"]), # level int and name simultaneous + ([0, "one"], ["X", "X"]), # both levels as int and name + (["one", "zero"], ["X", "X"]), # both level names, reversed + ], +) +def test_format_index_names_level(axis, level, expected): + midx = MultiIndex.from_arrays([["_", "_"], ["_", "_"]], names=["zero", "one"]) + df = DataFrame([[1, 2], [3, 4]]) + if axis == 0: + df.index = midx + else: + df.columns = midx + + styler = df.style.format_index_names(lambda v: "X", level=level, axis=axis) + ctx = styler._translate(True, True) + + if axis == 0: # compare index + result = [ctx["head"][1][s]["display_value"] for s in range(2)] + else: # compare columns + result = [ctx["head"][s][0]["display_value"] for s in range(2)] + assert expected == result + + +@pytest.mark.parametrize( + "attr, kwargs", + [ + ("_display_funcs_index_names", {"axis": 0}), + ("_display_funcs_column_names", {"axis": 1}), + ], +) +def test_format_index_names_clear(styler, attr, kwargs): + assert 0 not in getattr(styler, attr) # using default + styler.format_index_names("{:.2f}", **kwargs) + assert 0 in getattr(styler, attr) # formatter is specified + styler.format_index_names(**kwargs) + assert 0 not in getattr(styler, attr) # formatter cleared to default + + +@pytest.mark.parametrize("axis", [0, 1]) +def test_format_index_names_callable(styler_multi, axis): + ctx = styler_multi.format_index_names( + lambda v: v.replace("_", "A"), axis=axis + )._translate(True, True) + result = [ + ctx["head"][2][0]["display_value"], + ctx["head"][2][1]["display_value"], + ctx["head"][0][1]["display_value"], + ctx["head"][1][1]["display_value"], + ] + if axis == 0: + expected = ["0A0", "0A1", "1_0", "1_1"] + else: + expected = ["0_0", "0_1", "1A0", "1A1"] + assert result == expected + + +def test_format_index_names_dict(styler_multi): + ctx = ( + styler_multi.format_index_names({"0_0": "{:<<5}"}) + .format_index_names({"1_1": "{:>>4}"}, axis=1) + ._translate(True, True) + ) + assert ctx["head"][2][0]["display_value"] == "0_0<<" + assert ctx["head"][1][1]["display_value"] == ">1_1" + + +def test_format_index_names_with_hidden_levels(styler_multi): + ctx = styler_multi._translate(True, True) + full_head_height = len(ctx["head"]) + full_head_width = len(ctx["head"][0]) + assert full_head_height == 3 + assert full_head_width == 6 + + ctx = ( + styler_multi.hide(axis=0, level=1) + .hide(axis=1, level=1) + .format_index_names("{:>>4}", axis=1) + .format_index_names("{:!<5}") + ._translate(True, True) + ) + assert len(ctx["head"]) == full_head_height - 1 + assert len(ctx["head"][0]) == full_head_width - 1 + assert ctx["head"][0][0]["display_value"] == ">1_0" + assert ctx["head"][1][0]["display_value"] == "0_0!!" diff --git a/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_highlight.py b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_highlight.py new file mode 100644 index 0000000000000000000000000000000000000000..98c1f70f08e897bfc7b37d9efe0130ef1708bf2c --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_highlight.py @@ -0,0 +1,221 @@ +import numpy as np +import pytest + +from pandas import ( + NA, + DataFrame, + IndexSlice, +) + +pytest.importorskip("jinja2") + +from pandas.io.formats.style import Styler + + +@pytest.fixture(params=[(None, "float64"), (NA, "Int64")]) +def df(request): + # GH 45804 + dtype = request.param[1] + item = np.nan if dtype == "float64" else NA + return DataFrame( + {"A": [0, item, 10], "B": [1, request.param[0], 2]}, dtype=request.param[1] + ) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0) + + +def test_highlight_null(styler): + result = styler.highlight_null()._compute().ctx + expected = { + (1, 0): [("background-color", "red")], + (1, 1): [("background-color", "red")], + } + assert result == expected + + +def test_highlight_null_subset(styler): + # GH 31345 + result = ( + styler.highlight_null(color="red", subset=["A"]) + .highlight_null(color="green", subset=["B"]) + ._compute() + .ctx + ) + expected = { + (1, 0): [("background-color", "red")], + (1, 1): [("background-color", "green")], + } + assert result == expected + + +@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"]) +def test_highlight_minmax_basic(df, f): + expected = { + (0, 1): [("background-color", "red")], + # ignores NaN row, + (2, 0): [("background-color", "red")], + } + if f == "highlight_min": + df = -df + result = getattr(df.style, f)(axis=1, color="red")._compute().ctx + assert result == expected + + +@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"]) +@pytest.mark.parametrize( + "kwargs", + [ + {"axis": None, "color": "red"}, # test axis + {"axis": 0, "subset": ["A"], "color": "red"}, # test subset and ignores NaN + {"axis": None, "props": "background-color: red"}, # test props + ], +) +def test_highlight_minmax_ext(df, f, kwargs): + expected = {(2, 0): [("background-color", "red")]} + if f == "highlight_min": + df = -df + result = getattr(df.style, f)(**kwargs)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize("f", ["highlight_min", "highlight_max"]) +@pytest.mark.parametrize("axis", [None, 0, 1]) +def test_highlight_minmax_nulls(f, axis): + # GH 42750 + expected = { + (1, 0): [("background-color", "yellow")], + (1, 1): [("background-color", "yellow")], + } + if axis == 1: + expected.update({(2, 1): [("background-color", "yellow")]}) + + if f == "highlight_max": + df = DataFrame({"a": [NA, 1, None], "b": [np.nan, 1, -1]}) + else: + df = DataFrame({"a": [NA, -1, None], "b": [np.nan, -1, 1]}) + + result = getattr(df.style, f)(axis=axis)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "kwargs", + [ + {"left": 0, "right": 1}, # test basic range + {"left": 0, "right": 1, "props": "background-color: yellow"}, # test props + {"left": -100, "right": 100, "subset": IndexSlice[[0, 1], :]}, # test subset + {"left": 0, "subset": IndexSlice[[0, 1], :]}, # test no right + {"right": 1}, # test no left + {"left": [0, 0, 11], "axis": 0}, # test left as sequence + {"left": DataFrame({"A": [0, 0, 11], "B": [1, 1, 11]}), "axis": None}, # axis + {"left": 0, "right": [0, 1], "axis": 1}, # test sequence right + ], +) +def test_highlight_between(styler, kwargs): + expected = { + (0, 0): [("background-color", "yellow")], + (0, 1): [("background-color", "yellow")], + } + result = styler.highlight_between(**kwargs)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "arg, map, axis", + [ + ("left", [1, 2], 0), # 0 axis has 3 elements not 2 + ("left", [1, 2, 3], 1), # 1 axis has 2 elements not 3 + ("left", np.array([[1, 2], [1, 2]]), None), # df is (2,3) not (2,2) + ("right", [1, 2], 0), # same tests as above for 'right' not 'left' + ("right", [1, 2, 3], 1), # .. + ("right", np.array([[1, 2], [1, 2]]), None), # .. + ], +) +def test_highlight_between_raises(arg, styler, map, axis): + msg = f"supplied '{arg}' is not correct shape" + with pytest.raises(ValueError, match=msg): + styler.highlight_between(**{arg: map, "axis": axis})._compute() + + +def test_highlight_between_raises2(styler): + msg = "values can be 'both', 'left', 'right', or 'neither'" + with pytest.raises(ValueError, match=msg): + styler.highlight_between(inclusive="badstring")._compute() + + with pytest.raises(ValueError, match=msg): + styler.highlight_between(inclusive=1)._compute() + + +@pytest.mark.parametrize( + "inclusive, expected", + [ + ( + "both", + { + (0, 0): [("background-color", "yellow")], + (0, 1): [("background-color", "yellow")], + }, + ), + ("neither", {}), + ("left", {(0, 0): [("background-color", "yellow")]}), + ("right", {(0, 1): [("background-color", "yellow")]}), + ], +) +def test_highlight_between_inclusive(styler, inclusive, expected): + kwargs = {"left": 0, "right": 1, "subset": IndexSlice[[0, 1], :]} + result = styler.highlight_between(**kwargs, inclusive=inclusive)._compute() + assert result.ctx == expected + + +@pytest.mark.parametrize( + "kwargs", + [ + {"q_left": 0.5, "q_right": 1, "axis": 0}, # base case + {"q_left": 0.5, "q_right": 1, "axis": None}, # test axis + {"q_left": 0, "q_right": 1, "subset": IndexSlice[2, :]}, # test subset + {"q_left": 0.5, "axis": 0}, # test no high + {"q_right": 1, "subset": IndexSlice[2, :], "axis": 1}, # test no low + {"q_left": 0.5, "axis": 0, "props": "background-color: yellow"}, # tst prop + ], +) +def test_highlight_quantile(styler, kwargs): + expected = { + (2, 0): [("background-color", "yellow")], + (2, 1): [("background-color", "yellow")], + } + result = styler.highlight_quantile(**kwargs)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "f,kwargs", + [ + ("highlight_min", {"axis": 1, "subset": IndexSlice[1, :]}), + ("highlight_max", {"axis": 0, "subset": [0]}), + ("highlight_quantile", {"axis": None, "q_left": 0.6, "q_right": 0.8}), + ("highlight_between", {"subset": [0]}), + ], +) +@pytest.mark.parametrize( + "dtype", + [ + int, + float, + "datetime64[ns]", + str, + "timedelta64[ns]", + ], +) +def test_all_highlight_dtypes(f, kwargs, dtype): + df = DataFrame([[0, 10], [20, 30]], dtype=dtype) + if f == "highlight_quantile" and isinstance(df.iloc[0, 0], (str)): + return None # quantile incompatible with str + if f == "highlight_between": + kwargs["left"] = df.iloc[1, 0] # set the range low for testing + + expected = {(1, 0): [("background-color", "yellow")]} + result = getattr(df.style, f)(**kwargs)._compute().ctx + assert result == expected diff --git a/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_html.py b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_html.py new file mode 100644 index 0000000000000000000000000000000000000000..0b225126b0a70d9eeca414dc0c11a7e9ca80befc --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_html.py @@ -0,0 +1,1038 @@ +import pathlib +from textwrap import ( + dedent, + indent, +) + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + MultiIndex, + option_context, +) + +jinja2 = pytest.importorskip("jinja2") +from pandas.io.formats.style import Styler + + +@pytest.fixture +def env(): + project_dir = pathlib.Path(__file__).parent.parent.parent.parent.parent.resolve() + template_dir = project_dir / "io" / "formats" / "templates" + loader = jinja2.FileSystemLoader(template_dir) + env = jinja2.Environment(loader=loader, trim_blocks=True) + return env + + +@pytest.fixture +def styler(): + return Styler(DataFrame([[2.61], [2.69]], index=["a", "b"], columns=["A"])) + + +@pytest.fixture +def styler_mi(): + midx = MultiIndex.from_product([["a", "b"], ["c", "d"]]) + return Styler(DataFrame(np.arange(16).reshape(4, 4), index=midx, columns=midx)) + + +@pytest.fixture +def styler_multi(): + df = DataFrame( + data=np.arange(16).reshape(4, 4), + columns=MultiIndex.from_product([["A", "B"], ["a", "b"]], names=["A&", "b&"]), + index=MultiIndex.from_product([["X", "Y"], ["x", "y"]], names=["X>", "y_"]), + ) + return Styler(df) + + +@pytest.fixture +def tpl_style(env): + return env.get_template("html_style.tpl") + + +@pytest.fixture +def tpl_table(env): + return env.get_template("html_table.tpl") + + +def test_html_template_extends_options(datapath): + # make sure if templates are edited tests are updated as are setup fixtures + # to understand the dependency + path = datapath("..", "io", "formats", "templates", "html.tpl") + with open(path, encoding="utf-8") as file: + result = file.read() + assert "{% include html_style_tpl %}" in result + assert "{% include html_table_tpl %}" in result + + +def test_exclude_styles(styler): + result = styler.to_html(exclude_styles=True, doctype_html=True) + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + + + + + +
 A
a2.610000
b2.690000
+ + + """ + ) + assert result == expected + + +def test_w3_html_format(styler): + styler.set_uuid("").set_table_styles([{"selector": "th", "props": "att2:v2;"}]).map( + lambda x: "att1:v1;" + ).set_table_attributes('class="my-cls1" style="attr3:v3;"').set_td_classes( + DataFrame(["my-cls2"], index=["a"], columns=["A"]) + ).format("{:.1f}").set_caption("A comprehensive test") + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + +
A comprehensive test
 A
a2.6
b2.7
+ """ + ) + assert expected == styler.to_html() + + +def test_colspan_w3(): + # GH 36223 + df = DataFrame(data=[[1, 2]], columns=[["l0", "l0"], ["l1a", "l1b"]]) + styler = Styler(df, uuid="_", cell_ids=False) + assert 'l0' in styler.to_html() + + +def test_rowspan_w3(): + # GH 38533 + df = DataFrame(data=[[1, 2]], index=[["l0", "l0"], ["l1a", "l1b"]]) + styler = Styler(df, uuid="_", cell_ids=False) + assert 'l0' in styler.to_html() + + +def test_styles(styler): + styler.set_uuid("abc") + styler.set_table_styles([{"selector": "td", "props": "color: red;"}]) + result = styler.to_html(doctype_html=True) + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + + + + + + +
 A
a2.610000
b2.690000
+ + + """ + ) + assert result == expected + + +def test_doctype(styler): + result = styler.to_html(doctype_html=False) + assert "" not in result + assert "" not in result + assert "" not in result + assert "" not in result + + +def test_doctype_encoding(styler): + with option_context("styler.render.encoding", "ASCII"): + result = styler.to_html(doctype_html=True) + assert '' in result + result = styler.to_html(doctype_html=True, encoding="ANSI") + assert '' in result + + +def test_bold_headers_arg(styler): + result = styler.to_html(bold_headers=True) + assert "th {\n font-weight: bold;\n}" in result + result = styler.to_html() + assert "th {\n font-weight: bold;\n}" not in result + + +def test_caption_arg(styler): + result = styler.to_html(caption="foo bar") + assert "foo bar" in result + result = styler.to_html() + assert "foo bar" not in result + + +def test_block_names(tpl_style, tpl_table): + # catch accidental removal of a block + expected_style = { + "before_style", + "style", + "table_styles", + "before_cellstyle", + "cellstyle", + } + expected_table = { + "before_table", + "table", + "caption", + "thead", + "tbody", + "after_table", + "before_head_rows", + "head_tr", + "after_head_rows", + "before_rows", + "tr", + "after_rows", + } + result1 = set(tpl_style.blocks) + assert result1 == expected_style + + result2 = set(tpl_table.blocks) + assert result2 == expected_table + + +def test_from_custom_template_table(tmpdir): + p = tmpdir.mkdir("tpl").join("myhtml_table.tpl") + p.write( + dedent( + """\ + {% extends "html_table.tpl" %} + {% block table %} +

{{custom_title}}

+ {{ super() }} + {% endblock table %}""" + ) + ) + result = Styler.from_custom_template(str(tmpdir.join("tpl")), "myhtml_table.tpl") + assert issubclass(result, Styler) + assert result.env is not Styler.env + assert result.template_html_table is not Styler.template_html_table + styler = result(DataFrame({"A": [1, 2]})) + assert "

My Title

\n\n\n + {{ super() }} + {% endblock style %}""" + ) + ) + result = Styler.from_custom_template( + str(tmpdir.join("tpl")), html_style="myhtml_style.tpl" + ) + assert issubclass(result, Styler) + assert result.env is not Styler.env + assert result.template_html_style is not Styler.template_html_style + styler = result(DataFrame({"A": [1, 2]})) + assert '\n\nfull cap" in styler.to_html() + + +@pytest.mark.parametrize("index", [False, True]) +@pytest.mark.parametrize("columns", [False, True]) +@pytest.mark.parametrize("index_name", [True, False]) +def test_sticky_basic(styler, index, columns, index_name): + if index_name: + styler.index.name = "some text" + if index: + styler.set_sticky(axis=0) + if columns: + styler.set_sticky(axis=1) + + left_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " left: 0px;\n z-index: {1};\n}}" + ) + top_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " top: {1}px;\n z-index: {2};\n{3}}}" + ) + + res = styler.set_uuid("").to_html() + + # test index stickys over thead and tbody + assert (left_css.format("thead tr th:nth-child(1)", "3 !important") in res) is index + assert (left_css.format("tbody tr th:nth-child(1)", "1") in res) is index + + # test column stickys including if name row + assert ( + top_css.format("thead tr:nth-child(1) th", "0", "2", " height: 25px;\n") in res + ) is (columns and index_name) + assert ( + top_css.format("thead tr:nth-child(2) th", "25", "2", " height: 25px;\n") + in res + ) is (columns and index_name) + assert (top_css.format("thead tr:nth-child(1) th", "0", "2", "") in res) is ( + columns and not index_name + ) + + +@pytest.mark.parametrize("index", [False, True]) +@pytest.mark.parametrize("columns", [False, True]) +def test_sticky_mi(styler_mi, index, columns): + if index: + styler_mi.set_sticky(axis=0) + if columns: + styler_mi.set_sticky(axis=1) + + left_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " left: {1}px;\n min-width: 75px;\n max-width: 75px;\n z-index: {2};\n}}" + ) + top_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " top: {1}px;\n height: 25px;\n z-index: {2};\n}}" + ) + + res = styler_mi.set_uuid("").to_html() + + # test the index stickys for thead and tbody over both levels + assert ( + left_css.format("thead tr th:nth-child(1)", "0", "3 !important") in res + ) is index + assert (left_css.format("tbody tr th.level0", "0", "1") in res) is index + assert ( + left_css.format("thead tr th:nth-child(2)", "75", "3 !important") in res + ) is index + assert (left_css.format("tbody tr th.level1", "75", "1") in res) is index + + # test the column stickys for each level row + assert (top_css.format("thead tr:nth-child(1) th", "0", "2") in res) is columns + assert (top_css.format("thead tr:nth-child(2) th", "25", "2") in res) is columns + + +@pytest.mark.parametrize("index", [False, True]) +@pytest.mark.parametrize("columns", [False, True]) +@pytest.mark.parametrize("levels", [[1], ["one"], "one"]) +def test_sticky_levels(styler_mi, index, columns, levels): + styler_mi.index.names, styler_mi.columns.names = ["zero", "one"], ["zero", "one"] + if index: + styler_mi.set_sticky(axis=0, levels=levels) + if columns: + styler_mi.set_sticky(axis=1, levels=levels) + + left_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " left: {1}px;\n min-width: 75px;\n max-width: 75px;\n z-index: {2};\n}}" + ) + top_css = ( + "#T_ {0} {{\n position: sticky;\n background-color: inherit;\n" + " top: {1}px;\n height: 25px;\n z-index: {2};\n}}" + ) + + res = styler_mi.set_uuid("").to_html() + + # test no sticking of level0 + assert "#T_ thead tr th:nth-child(1)" not in res + assert "#T_ tbody tr th.level0" not in res + assert "#T_ thead tr:nth-child(1) th" not in res + + # test sticking level1 + assert ( + left_css.format("thead tr th:nth-child(2)", "0", "3 !important") in res + ) is index + assert (left_css.format("tbody tr th.level1", "0", "1") in res) is index + assert (top_css.format("thead tr:nth-child(2) th", "0", "2") in res) is columns + + +def test_sticky_raises(styler): + with pytest.raises(ValueError, match="No axis named bad for object type DataFrame"): + styler.set_sticky(axis="bad") + + +@pytest.mark.parametrize( + "sparse_index, sparse_columns", + [(True, True), (True, False), (False, True), (False, False)], +) +def test_sparse_options(sparse_index, sparse_columns): + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df = DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], index=ridx, columns=cidx) + styler = df.style + + default_html = styler.to_html() # defaults under pd.options to (True , True) + + with option_context( + "styler.sparse.index", sparse_index, "styler.sparse.columns", sparse_columns + ): + html1 = styler.to_html() + assert (html1 == default_html) is (sparse_index and sparse_columns) + html2 = styler.to_html(sparse_index=sparse_index, sparse_columns=sparse_columns) + assert html1 == html2 + + +@pytest.mark.parametrize("index", [True, False]) +@pytest.mark.parametrize("columns", [True, False]) +def test_map_header_cell_ids(styler, index, columns): + # GH 41893 + func = lambda v: "attr: val;" + styler.uuid, styler.cell_ids = "", False + if index: + styler.map_index(func, axis="index") + if columns: + styler.map_index(func, axis="columns") + + result = styler.to_html() + + # test no data cell ids + assert '2.610000' in result + assert '2.690000' in result + + # test index header ids where needed and css styles + assert ( + 'a' in result + ) is index + assert ( + 'b' in result + ) is index + assert ("#T__level0_row0, #T__level0_row1 {\n attr: val;\n}" in result) is index + + # test column header ids where needed and css styles + assert ( + 'A' in result + ) is columns + assert ("#T__level0_col0 {\n attr: val;\n}" in result) is columns + + +@pytest.mark.parametrize("rows", [True, False]) +@pytest.mark.parametrize("cols", [True, False]) +def test_maximums(styler_mi, rows, cols): + result = styler_mi.to_html( + max_rows=2 if rows else None, + max_columns=2 if cols else None, + ) + + assert ">5" in result # [[0,1], [4,5]] always visible + assert (">8" in result) is not rows # first trimmed vertical element + assert (">2" in result) is not cols # first trimmed horizontal element + + +def test_replaced_css_class_names(): + css = { + "row_heading": "ROWHEAD", + # "col_heading": "COLHEAD", + "index_name": "IDXNAME", + # "col": "COL", + "row": "ROW", + # "col_trim": "COLTRIM", + "row_trim": "ROWTRIM", + "level": "LEVEL", + "data": "DATA", + "blank": "BLANK", + } + midx = MultiIndex.from_product([["a", "b"], ["c", "d"]]) + styler_mi = Styler( + DataFrame(np.arange(16).reshape(4, 4), index=midx, columns=midx), + uuid_len=0, + ).set_table_styles(css_class_names=css) + styler_mi.index.names = ["n1", "n2"] + styler_mi.hide(styler_mi.index[1:], axis=0) + styler_mi.hide(styler_mi.columns[1:], axis=1) + styler_mi.map_index(lambda v: "color: red;", axis=0) + styler_mi.map_index(lambda v: "color: green;", axis=1) + styler_mi.map(lambda v: "color: blue;") + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + + + + + + + + +
 n1a
 n2c
n1n2 
ac0
+ """ + ) + result = styler_mi.to_html() + assert result == expected + + +def test_include_css_style_rules_only_for_visible_cells(styler_mi): + # GH 43619 + result = ( + styler_mi.set_uuid("") + .map(lambda v: "color: blue;") + .hide(styler_mi.data.columns[1:], axis="columns") + .hide(styler_mi.data.index[1:], axis="index") + .to_html() + ) + expected_styles = dedent( + """\ + + """ + ) + assert expected_styles in result + + +def test_include_css_style_rules_only_for_visible_index_labels(styler_mi): + # GH 43619 + result = ( + styler_mi.set_uuid("") + .map_index(lambda v: "color: blue;", axis="index") + .hide(styler_mi.data.columns, axis="columns") + .hide(styler_mi.data.index[1:], axis="index") + .to_html() + ) + expected_styles = dedent( + """\ + + """ + ) + assert expected_styles in result + + +def test_include_css_style_rules_only_for_visible_column_labels(styler_mi): + # GH 43619 + result = ( + styler_mi.set_uuid("") + .map_index(lambda v: "color: blue;", axis="columns") + .hide(styler_mi.data.columns[1:], axis="columns") + .hide(styler_mi.data.index, axis="index") + .to_html() + ) + expected_styles = dedent( + """\ + + """ + ) + assert expected_styles in result + + +def test_hiding_index_columns_multiindex_alignment(): + # gh 43644 + midx = MultiIndex.from_product( + [["i0", "j0"], ["i1"], ["i2", "j2"]], names=["i-0", "i-1", "i-2"] + ) + cidx = MultiIndex.from_product( + [["c0"], ["c1", "d1"], ["c2", "d2"]], names=["c-0", "c-1", "c-2"] + ) + df = DataFrame(np.arange(16).reshape(4, 4), index=midx, columns=cidx) + styler = Styler(df, uuid_len=0) + styler.hide(level=1, axis=0).hide(level=0, axis=1) + styler.hide([("j0", "i1", "j2")], axis=0) + styler.hide([("c0", "d1", "d2")], axis=1) + result = styler.to_html() + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 c-1c1d1
 c-2c2d2c2
i-0i-2   
i0i2012
j2456
j0i28910
+ """ + ) + assert result == expected + + +def test_hiding_index_columns_multiindex_trimming(): + # gh 44272 + df = DataFrame(np.arange(64).reshape(8, 8)) + df.columns = MultiIndex.from_product([[0, 1, 2, 3], [0, 1]]) + df.index = MultiIndex.from_product([[0, 1, 2, 3], [0, 1]]) + df.index.names, df.columns.names = ["a", "b"], ["c", "d"] + styler = Styler(df, cell_ids=False, uuid_len=0) + styler.hide([(0, 0), (0, 1), (1, 0)], axis=1).hide([(0, 0), (0, 1), (1, 0)], axis=0) + with option_context("styler.render.max_rows", 4, "styler.render.max_columns", 4): + result = styler.to_html() + + expected = dedent( + """\ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 c123
 d1010...
ab     
1127282930...
2035363738...
143444546...
3051525354...
.....................
+ """ + ) + + assert result == expected + + +@pytest.mark.parametrize("type", ["data", "index"]) +@pytest.mark.parametrize( + "text, exp, found", + [ + ("no link, just text", False, ""), + ("subdomain not www: sub.web.com", False, ""), + ("www subdomain: www.web.com other", True, "www.web.com"), + ("scheme full structure: http://www.web.com", True, "http://www.web.com"), + ("scheme no top-level: http://www.web", True, "http://www.web"), + ("no scheme, no top-level: www.web", False, "www.web"), + ("https scheme: https://www.web.com", True, "https://www.web.com"), + ("ftp scheme: ftp://www.web", True, "ftp://www.web"), + ("ftps scheme: ftps://www.web", True, "ftps://www.web"), + ("subdirectories: www.web.com/directory", True, "www.web.com/directory"), + ("Multiple domains: www.1.2.3.4", True, "www.1.2.3.4"), + ("with port: http://web.com:80", True, "http://web.com:80"), + ( + "full net_loc scheme: http://user:pass@web.com", + True, + "http://user:pass@web.com", + ), + ( + "with valid special chars: http://web.com/,.':;~!@#$*()[]", + True, + "http://web.com/,.':;~!@#$*()[]", + ), + ], +) +def test_rendered_links(type, text, exp, found): + if type == "data": + df = DataFrame([text]) + styler = df.style.format(hyperlinks="html") + else: + df = DataFrame([0], index=[text]) + styler = df.style.format_index(hyperlinks="html") + + rendered = f'{found}' + result = styler.to_html() + assert (rendered in result) is exp + assert (text in result) is not exp # test conversion done when expected and not + + +def test_multiple_rendered_links(): + links = ("www.a.b", "http://a.c", "https://a.d", "ftp://a.e") + df = DataFrame(["text {} {} text {} {}".format(*links)]) + result = df.style.format(hyperlinks="html").to_html() + href = '{0}' + for link in links: + assert href.format(link) in result + assert href.format("text") not in result + + +def test_concat(styler): + other = styler.data.agg(["mean"]).style + styler.concat(other).set_uuid("X") + result = styler.to_html() + fp = "foot0_" + expected = dedent( + f"""\ + + b + 2.690000 + + + mean + 2.650000 + + + + """ + ) + assert expected in result + + +def test_concat_recursion(styler): + df = styler.data + styler1 = styler + styler2 = Styler(df.agg(["mean"]), precision=3) + styler3 = Styler(df.agg(["mean"]), precision=4) + styler1.concat(styler2.concat(styler3)).set_uuid("X") + result = styler.to_html() + # notice that the second concat (last of the output html), + # there are two `foot_` in the id and class + fp1 = "foot0_" + fp2 = "foot0_foot0_" + expected = dedent( + f"""\ + + b + 2.690000 + + + mean + 2.650 + + + mean + 2.6500 + + + + """ + ) + assert expected in result + + +def test_concat_chain(styler): + df = styler.data + styler1 = styler + styler2 = Styler(df.agg(["mean"]), precision=3) + styler3 = Styler(df.agg(["mean"]), precision=4) + styler1.concat(styler2).concat(styler3).set_uuid("X") + result = styler.to_html() + fp1 = "foot0_" + fp2 = "foot1_" + expected = dedent( + f"""\ + + b + 2.690000 + + + mean + 2.650 + + + mean + 2.6500 + + + + """ + ) + assert expected in result + + +def test_concat_combined(): + def html_lines(foot_prefix: str): + assert foot_prefix.endswith("_") or foot_prefix == "" + fp = foot_prefix + return indent( + dedent( + f"""\ + + a + 2.610000 + + + b + 2.690000 + + """ + ), + prefix=" " * 4, + ) + + df = DataFrame([[2.61], [2.69]], index=["a", "b"], columns=["A"]) + s1 = df.style.highlight_max(color="red") + s2 = df.style.highlight_max(color="green") + s3 = df.style.highlight_max(color="blue") + s4 = df.style.highlight_max(color="yellow") + + result = s1.concat(s2).concat(s3.concat(s4)).set_uuid("X").to_html() + expected_css = dedent( + """\ + + """ + ) + expected_table = ( + dedent( + """\ + + + + + + + + + """ + ) + + html_lines("") + + html_lines("foot0_") + + html_lines("foot1_") + + html_lines("foot1_foot0_") + + dedent( + """\ + +
 A
+ """ + ) + ) + assert expected_css + expected_table == result + + +def test_to_html_na_rep_non_scalar_data(datapath): + # GH47103 + df = DataFrame([{"a": 1, "b": [1, 2, 3], "c": np.nan}]) + result = df.style.format(na_rep="-").to_html(table_uuid="test") + expected = """\ + + + + + + + + + + + + + + + + + + +
 abc
01[1, 2, 3]-
+""" + assert result == expected + + +@pytest.mark.parametrize("escape_axis_0", [True, False]) +@pytest.mark.parametrize("escape_axis_1", [True, False]) +def test_format_index_names(styler_multi, escape_axis_0, escape_axis_1): + if escape_axis_0: + styler_multi.format_index_names(axis=0, escape="html") + expected_index = ["X>", "y_"] + else: + expected_index = ["X>", "y_"] + + if escape_axis_1: + styler_multi.format_index_names(axis=1, escape="html") + expected_columns = ["A&", "b&"] + else: + expected_columns = ["A&", "b&"] + + result = styler_multi.to_html(table_uuid="test") + for expected_str in expected_index + expected_columns: + assert f"{expected_str}" in result diff --git a/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_matplotlib.py b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_matplotlib.py new file mode 100644 index 0000000000000000000000000000000000000000..490bd45bfb2eea246f3c997a8a9e8298b2a02bf4 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_matplotlib.py @@ -0,0 +1,305 @@ +import numpy as np +import pytest + +from pandas import ( + DataFrame, + IndexSlice, + Series, +) + +mpl = pytest.importorskip("matplotlib") +pytest.importorskip("jinja2") + +from pandas.io.formats.style import Styler + +pytestmark = pytest.mark.usefixtures("mpl_cleanup") + + +@pytest.fixture +def df(): + return DataFrame([[1, 2], [2, 4]], columns=["A", "B"]) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0) + + +@pytest.fixture +def df_blank(): + return DataFrame([[0, 0], [0, 0]], columns=["A", "B"], index=["X", "Y"]) + + +@pytest.fixture +def styler_blank(df_blank): + return Styler(df_blank, uuid_len=0) + + +@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"]) +def test_function_gradient(styler, f): + for c_map in [None, "YlOrRd"]: + result = getattr(styler, f)(cmap=c_map)._compute().ctx + assert all("#" in x[0][1] for x in result.values()) + assert result[(0, 0)] == result[(0, 1)] + assert result[(1, 0)] == result[(1, 1)] + + +@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"]) +def test_background_gradient_color(styler, f): + result = getattr(styler, f)(subset=IndexSlice[1, "A"])._compute().ctx + if f == "background_gradient": + assert result[(1, 0)] == [("background-color", "#fff7fb"), ("color", "#000000")] + elif f == "text_gradient": + assert result[(1, 0)] == [("color", "#fff7fb")] + + +@pytest.mark.parametrize( + "axis, expected", + [ + (0, ["low", "low", "high", "high"]), + (1, ["low", "high", "low", "high"]), + (None, ["low", "mid", "mid", "high"]), + ], +) +@pytest.mark.parametrize("f", ["background_gradient", "text_gradient"]) +def test_background_gradient_axis(styler, axis, expected, f): + if f == "background_gradient": + colors = { + "low": [("background-color", "#f7fbff"), ("color", "#000000")], + "mid": [("background-color", "#abd0e6"), ("color", "#000000")], + "high": [("background-color", "#08306b"), ("color", "#f1f1f1")], + } + elif f == "text_gradient": + colors = { + "low": [("color", "#f7fbff")], + "mid": [("color", "#abd0e6")], + "high": [("color", "#08306b")], + } + result = getattr(styler, f)(cmap="Blues", axis=axis)._compute().ctx + for i, cell in enumerate([(0, 0), (0, 1), (1, 0), (1, 1)]): + assert result[cell] == colors[expected[i]] + + +@pytest.mark.parametrize( + "cmap, expected", + [ + ( + "PuBu", + { + (4, 5): [("background-color", "#86b0d3"), ("color", "#000000")], + (4, 6): [("background-color", "#83afd3"), ("color", "#f1f1f1")], + }, + ), + ( + "YlOrRd", + { + (4, 8): [("background-color", "#fd913e"), ("color", "#000000")], + (4, 9): [("background-color", "#fd8f3d"), ("color", "#f1f1f1")], + }, + ), + ( + None, + { + (7, 0): [("background-color", "#48c16e"), ("color", "#f1f1f1")], + (7, 1): [("background-color", "#4cc26c"), ("color", "#000000")], + }, + ), + ], +) +def test_text_color_threshold(cmap, expected): + # GH 39888 + df = DataFrame(np.arange(100).reshape(10, 10)) + result = df.style.background_gradient(cmap=cmap, axis=None)._compute().ctx + for k in expected.keys(): + assert result[k] == expected[k] + + +def test_background_gradient_vmin_vmax(): + # GH 12145 + df = DataFrame(range(5)) + ctx = df.style.background_gradient(vmin=1, vmax=3)._compute().ctx + assert ctx[(0, 0)] == ctx[(1, 0)] + assert ctx[(4, 0)] == ctx[(3, 0)] + + +def test_background_gradient_int64(): + # GH 28869 + df1 = Series(range(3)).to_frame() + df2 = Series(range(3), dtype="Int64").to_frame() + ctx1 = df1.style.background_gradient()._compute().ctx + ctx2 = df2.style.background_gradient()._compute().ctx + assert ctx2[(0, 0)] == ctx1[(0, 0)] + assert ctx2[(1, 0)] == ctx1[(1, 0)] + assert ctx2[(2, 0)] == ctx1[(2, 0)] + + +@pytest.mark.parametrize( + "axis, gmap, expected", + [ + ( + 0, + [1, 2], + { + (0, 0): [("background-color", "#fff7fb"), ("color", "#000000")], + (1, 0): [("background-color", "#023858"), ("color", "#f1f1f1")], + (0, 1): [("background-color", "#fff7fb"), ("color", "#000000")], + (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")], + }, + ), + ( + 1, + [1, 2], + { + (0, 0): [("background-color", "#fff7fb"), ("color", "#000000")], + (1, 0): [("background-color", "#fff7fb"), ("color", "#000000")], + (0, 1): [("background-color", "#023858"), ("color", "#f1f1f1")], + (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")], + }, + ), + ( + None, + np.array([[2, 1], [1, 2]]), + { + (0, 0): [("background-color", "#023858"), ("color", "#f1f1f1")], + (1, 0): [("background-color", "#fff7fb"), ("color", "#000000")], + (0, 1): [("background-color", "#fff7fb"), ("color", "#000000")], + (1, 1): [("background-color", "#023858"), ("color", "#f1f1f1")], + }, + ), + ], +) +def test_background_gradient_gmap_array(styler_blank, axis, gmap, expected): + # tests when gmap is given as a sequence and converted to ndarray + result = styler_blank.background_gradient(axis=axis, gmap=gmap)._compute().ctx + assert result == expected + + +@pytest.mark.parametrize( + "gmap, axis", [([1, 2, 3], 0), ([1, 2], 1), (np.array([[1, 2], [1, 2]]), None)] +) +def test_background_gradient_gmap_array_raises(gmap, axis): + # test when gmap as converted ndarray is bad shape + df = DataFrame([[0, 0, 0], [0, 0, 0]]) + msg = "supplied 'gmap' is not correct shape" + with pytest.raises(ValueError, match=msg): + df.style.background_gradient(gmap=gmap, axis=axis)._compute() + + +@pytest.mark.parametrize( + "gmap", + [ + DataFrame( # reverse the columns + [[2, 1], [1, 2]], columns=["B", "A"], index=["X", "Y"] + ), + DataFrame( # reverse the index + [[2, 1], [1, 2]], columns=["A", "B"], index=["Y", "X"] + ), + DataFrame( # reverse the index and columns + [[1, 2], [2, 1]], columns=["B", "A"], index=["Y", "X"] + ), + DataFrame( # add unnecessary columns + [[1, 2, 3], [2, 1, 3]], columns=["A", "B", "C"], index=["X", "Y"] + ), + DataFrame( # add unnecessary index + [[1, 2], [2, 1], [3, 3]], columns=["A", "B"], index=["X", "Y", "Z"] + ), + ], +) +@pytest.mark.parametrize( + "subset, exp_gmap", # exp_gmap is underlying map DataFrame should conform to + [ + (None, [[1, 2], [2, 1]]), + (["A"], [[1], [2]]), # slice only column "A" in data and gmap + (["B", "A"], [[2, 1], [1, 2]]), # reverse the columns in data + (IndexSlice["X", :], [[1, 2]]), # slice only index "X" in data and gmap + (IndexSlice[["Y", "X"], :], [[2, 1], [1, 2]]), # reverse the index in data + ], +) +def test_background_gradient_gmap_dataframe_align(styler_blank, gmap, subset, exp_gmap): + # test gmap given as DataFrame that it aligns to the data including subset + expected = styler_blank.background_gradient(axis=None, gmap=exp_gmap, subset=subset) + result = styler_blank.background_gradient(axis=None, gmap=gmap, subset=subset) + assert expected._compute().ctx == result._compute().ctx + + +@pytest.mark.parametrize( + "gmap, axis, exp_gmap", + [ + (Series([2, 1], index=["Y", "X"]), 0, [[1, 1], [2, 2]]), # reverse the index + (Series([2, 1], index=["B", "A"]), 1, [[1, 2], [1, 2]]), # reverse the cols + (Series([1, 2, 3], index=["X", "Y", "Z"]), 0, [[1, 1], [2, 2]]), # add idx + (Series([1, 2, 3], index=["A", "B", "C"]), 1, [[1, 2], [1, 2]]), # add col + ], +) +def test_background_gradient_gmap_series_align(styler_blank, gmap, axis, exp_gmap): + # test gmap given as Series that it aligns to the data including subset + expected = styler_blank.background_gradient(axis=None, gmap=exp_gmap)._compute() + result = styler_blank.background_gradient(axis=axis, gmap=gmap)._compute() + assert expected.ctx == result.ctx + + +@pytest.mark.parametrize("axis", [1, 0]) +def test_background_gradient_gmap_wrong_dataframe(styler_blank, axis): + # test giving a gmap in DataFrame but with wrong axis + gmap = DataFrame([[1, 2], [2, 1]], columns=["A", "B"], index=["X", "Y"]) + msg = "'gmap' is a DataFrame but underlying data for operations is a Series" + with pytest.raises(ValueError, match=msg): + styler_blank.background_gradient(gmap=gmap, axis=axis)._compute() + + +def test_background_gradient_gmap_wrong_series(styler_blank): + # test giving a gmap in Series form but with wrong axis + msg = "'gmap' is a Series but underlying data for operations is a DataFrame" + gmap = Series([1, 2], index=["X", "Y"]) + with pytest.raises(ValueError, match=msg): + styler_blank.background_gradient(gmap=gmap, axis=None)._compute() + + +def test_background_gradient_nullable_dtypes(): + # GH 50712 + df1 = DataFrame([[1], [0], [np.nan]], dtype=float) + df2 = DataFrame([[1], [0], [None]], dtype="Int64") + + ctx1 = df1.style.background_gradient()._compute().ctx + ctx2 = df2.style.background_gradient()._compute().ctx + assert ctx1 == ctx2 + + +@pytest.mark.parametrize( + "cmap", + ["PuBu", mpl.colormaps["PuBu"]], +) +def test_bar_colormap(cmap): + data = DataFrame([[1, 2], [3, 4]]) + ctx = data.style.bar(cmap=cmap, axis=None)._compute().ctx + pubu_colors = { + (0, 0): "#d0d1e6", + (1, 0): "#056faf", + (0, 1): "#73a9cf", + (1, 1): "#023858", + } + for k, v in pubu_colors.items(): + assert v in ctx[k][1][1] + + +def test_bar_color_raises(df): + msg = "`color` must be string or list or tuple of 2 strings" + with pytest.raises(ValueError, match=msg): + df.style.bar(color={"a", "b"}).to_html() + with pytest.raises(ValueError, match=msg): + df.style.bar(color=["a", "b", "c"]).to_html() + + msg = "`color` and `cmap` cannot both be given" + with pytest.raises(ValueError, match=msg): + df.style.bar(color="something", cmap="something else").to_html() + + +@pytest.mark.parametrize("plot_method", ["scatter", "hexbin"]) +def test_pass_colormap_instance(df, plot_method): + # https://github.com/pandas-dev/pandas/issues/49374 + cmap = mpl.colors.ListedColormap([[1, 1, 1], [0, 0, 0]]) + df["c"] = df.A + df.B + kwargs = {"x": "A", "y": "B", "c": "c", "colormap": cmap} + if plot_method == "hexbin": + kwargs["C"] = kwargs.pop("c") + getattr(df.plot, plot_method)(**kwargs) diff --git a/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_non_unique.py b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_non_unique.py new file mode 100644 index 0000000000000000000000000000000000000000..e4d31fe21f2c9cf3454a67f8c7443382f7f1c0ef --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_non_unique.py @@ -0,0 +1,140 @@ +from textwrap import dedent + +import pytest + +from pandas import ( + DataFrame, + IndexSlice, +) + +pytest.importorskip("jinja2") + +from pandas.io.formats.style import Styler + + +@pytest.fixture +def df(): + return DataFrame( + [[1, 2, 3], [4, 5, 6], [7, 8, 9]], + index=["i", "j", "j"], + columns=["c", "d", "d"], + dtype=float, + ) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0) + + +def test_format_non_unique(df): + # GH 41269 + + # test dict + html = df.style.format({"d": "{:.1f}"}).to_html() + for val in ["1.000000<", "4.000000<", "7.000000<"]: + assert val in html + for val in ["2.0<", "3.0<", "5.0<", "6.0<", "8.0<", "9.0<"]: + assert val in html + + # test subset + html = df.style.format(precision=1, subset=IndexSlice["j", "d"]).to_html() + for val in ["1.000000<", "4.000000<", "7.000000<", "2.000000<", "3.000000<"]: + assert val in html + for val in ["5.0<", "6.0<", "8.0<", "9.0<"]: + assert val in html + + +@pytest.mark.parametrize("func", ["apply", "map"]) +def test_apply_map_non_unique_raises(df, func): + # GH 41269 + if func == "apply": + op = lambda s: ["color: red;"] * len(s) + else: + op = lambda v: "color: red;" + + with pytest.raises(KeyError, match="`Styler.apply` and `.map` are not"): + getattr(df.style, func)(op)._compute() + + +def test_table_styles_dict_non_unique_index(styler): + styles = styler.set_table_styles( + {"j": [{"selector": "td", "props": "a: v;"}]}, axis=1 + ).table_styles + assert styles == [ + {"selector": "td.row1", "props": [("a", "v")]}, + {"selector": "td.row2", "props": [("a", "v")]}, + ] + + +def test_table_styles_dict_non_unique_columns(styler): + styles = styler.set_table_styles( + {"d": [{"selector": "td", "props": "a: v;"}]}, axis=0 + ).table_styles + assert styles == [ + {"selector": "td.col1", "props": [("a", "v")]}, + {"selector": "td.col2", "props": [("a", "v")]}, + ] + + +def test_tooltips_non_unique_raises(styler): + # ttips has unique keys + ttips = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "b"]) + styler.set_tooltips(ttips=ttips) # OK + + # ttips has non-unique columns + ttips = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "c"], index=["a", "b"]) + with pytest.raises(KeyError, match="Tooltips render only if `ttips` has unique"): + styler.set_tooltips(ttips=ttips) + + # ttips has non-unique index + ttips = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "a"]) + with pytest.raises(KeyError, match="Tooltips render only if `ttips` has unique"): + styler.set_tooltips(ttips=ttips) + + +def test_set_td_classes_non_unique_raises(styler): + # classes has unique keys + classes = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "b"]) + styler.set_td_classes(classes=classes) # OK + + # classes has non-unique columns + classes = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "c"], index=["a", "b"]) + with pytest.raises(KeyError, match="Classes render only if `classes` has unique"): + styler.set_td_classes(classes=classes) + + # classes has non-unique index + classes = DataFrame([["1", "2"], ["3", "4"]], columns=["c", "d"], index=["a", "a"]) + with pytest.raises(KeyError, match="Classes render only if `classes` has unique"): + styler.set_td_classes(classes=classes) + + +def test_hide_columns_non_unique(styler): + ctx = styler.hide(["d"], axis="columns")._translate(True, True) + + assert ctx["head"][0][1]["display_value"] == "c" + assert ctx["head"][0][1]["is_visible"] is True + + assert ctx["head"][0][2]["display_value"] == "d" + assert ctx["head"][0][2]["is_visible"] is False + + assert ctx["head"][0][3]["display_value"] == "d" + assert ctx["head"][0][3]["is_visible"] is False + + assert ctx["body"][0][1]["is_visible"] is True + assert ctx["body"][0][2]["is_visible"] is False + assert ctx["body"][0][3]["is_visible"] is False + + +def test_latex_non_unique(styler): + result = styler.to_latex() + assert result == dedent( + """\ + \\begin{tabular}{lrrr} + & c & d & d \\\\ + i & 1.000000 & 2.000000 & 3.000000 \\\\ + j & 4.000000 & 5.000000 & 6.000000 \\\\ + j & 7.000000 & 8.000000 & 9.000000 \\\\ + \\end{tabular} + """ + ) diff --git a/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_style.py b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_style.py new file mode 100644 index 0000000000000000000000000000000000000000..d78403771e227dd97eba2a130b4e60a0303c4400 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_style.py @@ -0,0 +1,1602 @@ +import contextlib +import copy +import re +from textwrap import dedent + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + IndexSlice, + MultiIndex, + Series, + option_context, +) +import pandas._testing as tm + +jinja2 = pytest.importorskip("jinja2") +from pandas.io.formats.style import ( # isort:skip + Styler, +) +from pandas.io.formats.style_render import ( + _get_level_lengths, + _get_trimming_maximums, + maybe_convert_css_to_tuples, + non_reducing_slice, +) + + +@pytest.fixture +def mi_df(): + return DataFrame( + [[1, 2], [3, 4]], + index=MultiIndex.from_product([["i0"], ["i1_a", "i1_b"]]), + columns=MultiIndex.from_product([["c0"], ["c1_a", "c1_b"]]), + dtype=int, + ) + + +@pytest.fixture +def mi_styler(mi_df): + return Styler(mi_df, uuid_len=0) + + +@pytest.fixture +def mi_styler_comp(mi_styler): + # comprehensively add features to mi_styler + mi_styler = mi_styler._copy(deepcopy=True) + mi_styler.css = {**mi_styler.css, "row": "ROW", "col": "COL"} + mi_styler.uuid_len = 5 + mi_styler.uuid = "abcde" + mi_styler.set_caption("capt") + mi_styler.set_table_styles([{"selector": "a", "props": "a:v;"}]) + mi_styler.hide(axis="columns") + mi_styler.hide([("c0", "c1_a")], axis="columns", names=True) + mi_styler.hide(axis="index") + mi_styler.hide([("i0", "i1_a")], axis="index", names=True) + mi_styler.set_table_attributes('class="box"') + other = mi_styler.data.agg(["mean"]) + other.index = MultiIndex.from_product([[""], other.index]) + mi_styler.concat(other.style) + mi_styler.format(na_rep="MISSING", precision=3) + mi_styler.format_index(precision=2, axis=0) + mi_styler.format_index(precision=4, axis=1) + mi_styler.highlight_max(axis=None) + mi_styler.map_index(lambda x: "color: white;", axis=0) + mi_styler.map_index(lambda x: "color: black;", axis=1) + mi_styler.set_td_classes( + DataFrame( + [["a", "b"], ["a", "c"]], index=mi_styler.index, columns=mi_styler.columns + ) + ) + mi_styler.set_tooltips( + DataFrame( + [["a2", "b2"], ["a2", "c2"]], + index=mi_styler.index, + columns=mi_styler.columns, + ) + ) + mi_styler.format_index_names(escape="html", axis=0) + mi_styler.format_index_names(escape="html", axis=1) + return mi_styler + + +@pytest.fixture +def blank_value(): + return " " + + +@pytest.fixture +def df(): + df = DataFrame({"A": [0, 1], "B": np.random.default_rng(2).standard_normal(2)}) + return df + + +@pytest.fixture +def styler(df): + df = DataFrame({"A": [0, 1], "B": np.random.default_rng(2).standard_normal(2)}) + return Styler(df) + + +@pytest.mark.parametrize( + "sparse_columns, exp_cols", + [ + ( + True, + [ + {"is_visible": True, "attributes": 'colspan="2"', "value": "c0"}, + {"is_visible": False, "attributes": "", "value": "c0"}, + ], + ), + ( + False, + [ + {"is_visible": True, "attributes": "", "value": "c0"}, + {"is_visible": True, "attributes": "", "value": "c0"}, + ], + ), + ], +) +def test_mi_styler_sparsify_columns(mi_styler, sparse_columns, exp_cols): + exp_l1_c0 = {"is_visible": True, "attributes": "", "display_value": "c1_a"} + exp_l1_c1 = {"is_visible": True, "attributes": "", "display_value": "c1_b"} + + ctx = mi_styler._translate(True, sparse_columns) + + assert exp_cols[0].items() <= ctx["head"][0][2].items() + assert exp_cols[1].items() <= ctx["head"][0][3].items() + assert exp_l1_c0.items() <= ctx["head"][1][2].items() + assert exp_l1_c1.items() <= ctx["head"][1][3].items() + + +@pytest.mark.parametrize( + "sparse_index, exp_rows", + [ + ( + True, + [ + {"is_visible": True, "attributes": 'rowspan="2"', "value": "i0"}, + {"is_visible": False, "attributes": "", "value": "i0"}, + ], + ), + ( + False, + [ + {"is_visible": True, "attributes": "", "value": "i0"}, + {"is_visible": True, "attributes": "", "value": "i0"}, + ], + ), + ], +) +def test_mi_styler_sparsify_index(mi_styler, sparse_index, exp_rows): + exp_l1_r0 = {"is_visible": True, "attributes": "", "display_value": "i1_a"} + exp_l1_r1 = {"is_visible": True, "attributes": "", "display_value": "i1_b"} + + ctx = mi_styler._translate(sparse_index, True) + + assert exp_rows[0].items() <= ctx["body"][0][0].items() + assert exp_rows[1].items() <= ctx["body"][1][0].items() + assert exp_l1_r0.items() <= ctx["body"][0][1].items() + assert exp_l1_r1.items() <= ctx["body"][1][1].items() + + +def test_mi_styler_sparsify_options(mi_styler): + with option_context("styler.sparse.index", False): + html1 = mi_styler.to_html() + with option_context("styler.sparse.index", True): + html2 = mi_styler.to_html() + + assert html1 != html2 + + with option_context("styler.sparse.columns", False): + html1 = mi_styler.to_html() + with option_context("styler.sparse.columns", True): + html2 = mi_styler.to_html() + + assert html1 != html2 + + +@pytest.mark.parametrize( + "rn, cn, max_els, max_rows, max_cols, exp_rn, exp_cn", + [ + (100, 100, 100, None, None, 12, 6), # reduce to (12, 6) < 100 elements + (1000, 3, 750, None, None, 250, 3), # dynamically reduce rows to 250, keep cols + (4, 1000, 500, None, None, 4, 125), # dynamically reduce cols to 125, keep rows + (1000, 3, 750, 10, None, 10, 3), # overwrite above dynamics with max_row + (4, 1000, 500, None, 5, 4, 5), # overwrite above dynamics with max_col + (100, 100, 700, 50, 50, 25, 25), # rows cols below given maxes so < 700 elmts + ], +) +def test_trimming_maximum(rn, cn, max_els, max_rows, max_cols, exp_rn, exp_cn): + rn, cn = _get_trimming_maximums( + rn, cn, max_els, max_rows, max_cols, scaling_factor=0.5 + ) + assert (rn, cn) == (exp_rn, exp_cn) + + +@pytest.mark.parametrize( + "option, val", + [ + ("styler.render.max_elements", 6), + ("styler.render.max_rows", 3), + ], +) +def test_render_trimming_rows(option, val): + # test auto and specific trimming of rows + df = DataFrame(np.arange(120).reshape(60, 2)) + with option_context(option, val): + ctx = df.style._translate(True, True) + assert len(ctx["head"][0]) == 3 # index + 2 data cols + assert len(ctx["body"]) == 4 # 3 data rows + trimming row + assert len(ctx["body"][0]) == 3 # index + 2 data cols + + +@pytest.mark.parametrize( + "option, val", + [ + ("styler.render.max_elements", 6), + ("styler.render.max_columns", 2), + ], +) +def test_render_trimming_cols(option, val): + # test auto and specific trimming of cols + df = DataFrame(np.arange(30).reshape(3, 10)) + with option_context(option, val): + ctx = df.style._translate(True, True) + assert len(ctx["head"][0]) == 4 # index + 2 data cols + trimming col + assert len(ctx["body"]) == 3 # 3 data rows + assert len(ctx["body"][0]) == 4 # index + 2 data cols + trimming col + + +def test_render_trimming_mi(): + midx = MultiIndex.from_product([[1, 2], [1, 2, 3]]) + df = DataFrame(np.arange(36).reshape(6, 6), columns=midx, index=midx) + with option_context("styler.render.max_elements", 4): + ctx = df.style._translate(True, True) + + assert len(ctx["body"][0]) == 5 # 2 indexes + 2 data cols + trimming row + assert {"attributes": 'rowspan="2"'}.items() <= ctx["body"][0][0].items() + assert {"class": "data row0 col_trim"}.items() <= ctx["body"][0][4].items() + assert {"class": "data row_trim col_trim"}.items() <= ctx["body"][2][4].items() + assert len(ctx["body"]) == 3 # 2 data rows + trimming row + + +def test_render_empty_mi(): + # GH 43305 + df = DataFrame(index=MultiIndex.from_product([["A"], [0, 1]], names=[None, "one"])) + expected = dedent( + """\ + > + + +   + one + + + """ + ) + assert expected in df.style.to_html() + + +@pytest.mark.parametrize("comprehensive", [True, False]) +@pytest.mark.parametrize("render", [True, False]) +@pytest.mark.parametrize("deepcopy", [True, False]) +def test_copy(comprehensive, render, deepcopy, mi_styler, mi_styler_comp): + styler = mi_styler_comp if comprehensive else mi_styler + styler.uuid_len = 5 + + s2 = copy.deepcopy(styler) if deepcopy else copy.copy(styler) # make copy and check + assert s2 is not styler + + if render: + styler.to_html() + + excl = [ + "cellstyle_map", # render time vars.. + "cellstyle_map_columns", + "cellstyle_map_index", + "template_latex", # render templates are class level + "template_html", + "template_html_style", + "template_html_table", + ] + if not deepcopy: # check memory locations are equal for all included attributes + for attr in [a for a in styler.__dict__ if (not callable(a) and a not in excl)]: + assert id(getattr(s2, attr)) == id(getattr(styler, attr)) + else: # check memory locations are different for nested or mutable vars + shallow = [ + "data", + "columns", + "index", + "uuid_len", + "uuid", + "caption", + "cell_ids", + "hide_index_", + "hide_columns_", + "hide_index_names", + "hide_column_names", + "table_attributes", + ] + for attr in shallow: + assert id(getattr(s2, attr)) == id(getattr(styler, attr)) + + for attr in [ + a + for a in styler.__dict__ + if (not callable(a) and a not in excl and a not in shallow) + ]: + if getattr(s2, attr) is None: + assert id(getattr(s2, attr)) == id(getattr(styler, attr)) + else: + assert id(getattr(s2, attr)) != id(getattr(styler, attr)) + + +@pytest.mark.parametrize("deepcopy", [True, False]) +def test_inherited_copy(mi_styler, deepcopy): + # Ensure that the inherited class is preserved when a Styler object is copied. + # GH 52728 + class CustomStyler(Styler): + pass + + custom_styler = CustomStyler(mi_styler.data) + custom_styler_copy = ( + copy.deepcopy(custom_styler) if deepcopy else copy.copy(custom_styler) + ) + assert isinstance(custom_styler_copy, CustomStyler) + + +def test_clear(mi_styler_comp): + # NOTE: if this test fails for new features then 'mi_styler_comp' should be updated + # to ensure proper testing of the 'copy', 'clear', 'export' methods with new feature + # GH 40675 + styler = mi_styler_comp + styler._compute() # execute applied methods + + clean_copy = Styler(styler.data, uuid=styler.uuid) + + excl = [ + "data", + "index", + "columns", + "uuid", + "uuid_len", # uuid is set to be the same on styler and clean_copy + "cell_ids", + "cellstyle_map", # execution time only + "cellstyle_map_columns", # execution time only + "cellstyle_map_index", # execution time only + "template_latex", # render templates are class level + "template_html", + "template_html_style", + "template_html_table", + ] + # tests vars are not same vals on obj and clean copy before clear (except for excl) + for attr in [a for a in styler.__dict__ if not (callable(a) or a in excl)]: + res = getattr(styler, attr) == getattr(clean_copy, attr) + if hasattr(res, "__iter__") and len(res) > 0: + assert not all(res) # some element in iterable differs + elif hasattr(res, "__iter__") and len(res) == 0: + pass # empty array + else: + assert not res # explicit var differs + + # test vars have same vales on obj and clean copy after clearing + styler.clear() + for attr in [a for a in styler.__dict__ if not callable(a)]: + res = getattr(styler, attr) == getattr(clean_copy, attr) + assert all(res) if hasattr(res, "__iter__") else res + + +def test_export(mi_styler_comp, mi_styler): + exp_attrs = [ + "_todo", + "hide_index_", + "hide_index_names", + "hide_columns_", + "hide_column_names", + "table_attributes", + "table_styles", + "css", + ] + for attr in exp_attrs: + check = getattr(mi_styler, attr) == getattr(mi_styler_comp, attr) + assert not ( + all(check) if (hasattr(check, "__iter__") and len(check) > 0) else check + ) + + export = mi_styler_comp.export() + used = mi_styler.use(export) + for attr in exp_attrs: + check = getattr(used, attr) == getattr(mi_styler_comp, attr) + assert all(check) if (hasattr(check, "__iter__") and len(check) > 0) else check + + used.to_html() + + +def test_hide_raises(mi_styler): + msg = "`subset` and `level` cannot be passed simultaneously" + with pytest.raises(ValueError, match=msg): + mi_styler.hide(axis="index", subset="something", level="something else") + + msg = "`level` must be of type `int`, `str` or list of such" + with pytest.raises(ValueError, match=msg): + mi_styler.hide(axis="index", level={"bad": 1, "type": 2}) + + +@pytest.mark.parametrize("level", [1, "one", [1], ["one"]]) +def test_hide_index_level(mi_styler, level): + mi_styler.index.names, mi_styler.columns.names = ["zero", "one"], ["zero", "one"] + ctx = mi_styler.hide(axis="index", level=level)._translate(False, True) + assert len(ctx["head"][0]) == 3 + assert len(ctx["head"][1]) == 3 + assert len(ctx["head"][2]) == 4 + assert ctx["head"][2][0]["is_visible"] + assert not ctx["head"][2][1]["is_visible"] + + assert ctx["body"][0][0]["is_visible"] + assert not ctx["body"][0][1]["is_visible"] + assert ctx["body"][1][0]["is_visible"] + assert not ctx["body"][1][1]["is_visible"] + + +@pytest.mark.parametrize("level", [1, "one", [1], ["one"]]) +@pytest.mark.parametrize("names", [True, False]) +def test_hide_columns_level(mi_styler, level, names): + mi_styler.columns.names = ["zero", "one"] + if names: + mi_styler.index.names = ["zero", "one"] + ctx = mi_styler.hide(axis="columns", level=level)._translate(True, False) + assert len(ctx["head"]) == (2 if names else 1) + + +@pytest.mark.parametrize("method", ["map", "apply"]) +@pytest.mark.parametrize("axis", ["index", "columns"]) +def test_apply_map_header(method, axis): + # GH 41893 + df = DataFrame({"A": [0, 0], "B": [1, 1]}, index=["C", "D"]) + func = { + "apply": lambda s: ["attr: val" if ("A" in v or "C" in v) else "" for v in s], + "map": lambda v: "attr: val" if ("A" in v or "C" in v) else "", + } + + # test execution added to todo + result = getattr(df.style, f"{method}_index")(func[method], axis=axis) + assert len(result._todo) == 1 + assert len(getattr(result, f"ctx_{axis}")) == 0 + + # test ctx object on compute + result._compute() + expected = { + (0, 0): [("attr", "val")], + } + assert getattr(result, f"ctx_{axis}") == expected + + +@pytest.mark.parametrize("method", ["apply", "map"]) +@pytest.mark.parametrize("axis", ["index", "columns"]) +def test_apply_map_header_mi(mi_styler, method, axis): + # GH 41893 + func = { + "apply": lambda s: ["attr: val;" if "b" in v else "" for v in s], + "map": lambda v: "attr: val" if "b" in v else "", + } + result = getattr(mi_styler, f"{method}_index")(func[method], axis=axis)._compute() + expected = {(1, 1): [("attr", "val")]} + assert getattr(result, f"ctx_{axis}") == expected + + +def test_apply_map_header_raises(mi_styler): + # GH 41893 + with pytest.raises(ValueError, match="No axis named bad for object type DataFrame"): + mi_styler.map_index(lambda v: "attr: val;", axis="bad")._compute() + + +class TestStyler: + def test_init_non_pandas(self): + msg = "``data`` must be a Series or DataFrame" + with pytest.raises(TypeError, match=msg): + Styler([1, 2, 3]) + + def test_init_series(self): + result = Styler(Series([1, 2])) + assert result.data.ndim == 2 + + def test_repr_html_ok(self, styler): + styler._repr_html_() + + def test_repr_html_mathjax(self, styler): + # gh-19824 / 41395 + assert "tex2jax_ignore" not in styler._repr_html_() + assert "mathjax_ignore" not in styler._repr_html_() + + with option_context("styler.html.mathjax", False): + assert "tex2jax_ignore" in styler._repr_html_() + assert "mathjax_ignore" in styler._repr_html_() + + def test_update_ctx(self, styler): + styler._update_ctx(DataFrame({"A": ["color: red", "color: blue"]})) + expected = {(0, 0): [("color", "red")], (1, 0): [("color", "blue")]} + assert styler.ctx == expected + + def test_update_ctx_flatten_multi_and_trailing_semi(self, styler): + attrs = DataFrame({"A": ["color: red; foo: bar", "color:blue ; foo: baz;"]}) + styler._update_ctx(attrs) + expected = { + (0, 0): [("color", "red"), ("foo", "bar")], + (1, 0): [("color", "blue"), ("foo", "baz")], + } + assert styler.ctx == expected + + def test_render(self): + df = DataFrame({"A": [0, 1]}) + style = lambda x: Series(["color: red", "color: blue"], name=x.name) + s = Styler(df, uuid="AB").apply(style) + s.to_html() + # it worked? + + def test_multiple_render(self, df): + # GH 39396 + s = Styler(df, uuid_len=0).map(lambda x: "color: red;", subset=["A"]) + s.to_html() # do 2 renders to ensure css styles not duplicated + assert ( + '" in s.to_html() + ) + + def test_render_empty_dfs(self): + empty_df = DataFrame() + es = Styler(empty_df) + es.to_html() + # An index but no columns + DataFrame(columns=["a"]).style.to_html() + # A column but no index + DataFrame(index=["a"]).style.to_html() + # No IndexError raised? + + def test_render_double(self): + df = DataFrame({"A": [0, 1]}) + style = lambda x: Series( + ["color: red; border: 1px", "color: blue; border: 2px"], name=x.name + ) + s = Styler(df, uuid="AB").apply(style) + s.to_html() + # it worked? + + def test_set_properties(self): + df = DataFrame({"A": [0, 1]}) + result = df.style.set_properties(color="white", size="10px")._compute().ctx + # order is deterministic + v = [("color", "white"), ("size", "10px")] + expected = {(0, 0): v, (1, 0): v} + assert result.keys() == expected.keys() + for v1, v2 in zip(result.values(), expected.values()): + assert sorted(v1) == sorted(v2) + + def test_set_properties_subset(self): + df = DataFrame({"A": [0, 1]}) + result = ( + df.style.set_properties(subset=IndexSlice[0, "A"], color="white") + ._compute() + .ctx + ) + expected = {(0, 0): [("color", "white")]} + assert result == expected + + def test_empty_index_name_doesnt_display(self, blank_value): + # https://github.com/pandas-dev/pandas/pull/12090#issuecomment-180695902 + df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]}) + result = df.style._translate(True, True) + assert len(result["head"]) == 1 + expected = { + "class": "blank level0", + "type": "th", + "value": blank_value, + "is_visible": True, + "display_value": blank_value, + } + assert expected.items() <= result["head"][0][0].items() + + def test_index_name(self): + # https://github.com/pandas-dev/pandas/issues/11655 + df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]}) + result = df.set_index("A").style._translate(True, True) + expected = { + "class": "index_name level0", + "type": "th", + "value": "A", + "is_visible": True, + "display_value": "A", + } + assert expected.items() <= result["head"][1][0].items() + + def test_numeric_columns(self): + # https://github.com/pandas-dev/pandas/issues/12125 + # smoke test for _translate + df = DataFrame({0: [1, 2, 3]}) + df.style._translate(True, True) + + def test_apply_axis(self): + df = DataFrame({"A": [0, 0], "B": [1, 1]}) + f = lambda x: [f"val: {x.max()}" for v in x] + result = df.style.apply(f, axis=1) + assert len(result._todo) == 1 + assert len(result.ctx) == 0 + result._compute() + expected = { + (0, 0): [("val", "1")], + (0, 1): [("val", "1")], + (1, 0): [("val", "1")], + (1, 1): [("val", "1")], + } + assert result.ctx == expected + + result = df.style.apply(f, axis=0) + expected = { + (0, 0): [("val", "0")], + (0, 1): [("val", "1")], + (1, 0): [("val", "0")], + (1, 1): [("val", "1")], + } + result._compute() + assert result.ctx == expected + result = df.style.apply(f) # default + result._compute() + assert result.ctx == expected + + @pytest.mark.parametrize("axis", [0, 1]) + def test_apply_series_return(self, axis): + # GH 42014 + df = DataFrame([[1, 2], [3, 4]], index=["X", "Y"], columns=["X", "Y"]) + + # test Series return where len(Series) < df.index or df.columns but labels OK + func = lambda s: Series(["color: red;"], index=["Y"]) + result = df.style.apply(func, axis=axis)._compute().ctx + assert result[(1, 1)] == [("color", "red")] + assert result[(1 - axis, axis)] == [("color", "red")] + + # test Series return where labels align but different order + func = lambda s: Series(["color: red;", "color: blue;"], index=["Y", "X"]) + result = df.style.apply(func, axis=axis)._compute().ctx + assert result[(0, 0)] == [("color", "blue")] + assert result[(1, 1)] == [("color", "red")] + assert result[(1 - axis, axis)] == [("color", "red")] + assert result[(axis, 1 - axis)] == [("color", "blue")] + + @pytest.mark.parametrize("index", [False, True]) + @pytest.mark.parametrize("columns", [False, True]) + def test_apply_dataframe_return(self, index, columns): + # GH 42014 + df = DataFrame([[1, 2], [3, 4]], index=["X", "Y"], columns=["X", "Y"]) + idxs = ["X", "Y"] if index else ["Y"] + cols = ["X", "Y"] if columns else ["Y"] + df_styles = DataFrame("color: red;", index=idxs, columns=cols) + result = df.style.apply(lambda x: df_styles, axis=None)._compute().ctx + + assert result[(1, 1)] == [("color", "red")] # (Y,Y) styles always present + assert (result[(0, 1)] == [("color", "red")]) is index # (X,Y) only if index + assert (result[(1, 0)] == [("color", "red")]) is columns # (Y,X) only if cols + assert (result[(0, 0)] == [("color", "red")]) is (index and columns) # (X,X) + + @pytest.mark.parametrize( + "slice_", + [ + IndexSlice[:], + IndexSlice[:, ["A"]], + IndexSlice[[1], :], + IndexSlice[[1], ["A"]], + IndexSlice[:2, ["A", "B"]], + ], + ) + @pytest.mark.parametrize("axis", [0, 1]) + def test_apply_subset(self, slice_, axis, df): + def h(x, color="bar"): + return Series(f"color: {color}", index=x.index, name=x.name) + + result = df.style.apply(h, axis=axis, subset=slice_, color="baz")._compute().ctx + expected = { + (r, c): [("color", "baz")] + for r, row in enumerate(df.index) + for c, col in enumerate(df.columns) + if row in df.loc[slice_].index and col in df.loc[slice_].columns + } + assert result == expected + + @pytest.mark.parametrize( + "slice_", + [ + IndexSlice[:], + IndexSlice[:, ["A"]], + IndexSlice[[1], :], + IndexSlice[[1], ["A"]], + IndexSlice[:2, ["A", "B"]], + ], + ) + def test_map_subset(self, slice_, df): + result = df.style.map(lambda x: "color:baz;", subset=slice_)._compute().ctx + expected = { + (r, c): [("color", "baz")] + for r, row in enumerate(df.index) + for c, col in enumerate(df.columns) + if row in df.loc[slice_].index and col in df.loc[slice_].columns + } + assert result == expected + + @pytest.mark.parametrize( + "slice_", + [ + IndexSlice[:, IndexSlice["x", "A"]], + IndexSlice[:, IndexSlice[:, "A"]], + IndexSlice[:, IndexSlice[:, ["A", "C"]]], # missing col element + IndexSlice[IndexSlice["a", 1], :], + IndexSlice[IndexSlice[:, 1], :], + IndexSlice[IndexSlice[:, [1, 3]], :], # missing row element + IndexSlice[:, ("x", "A")], + IndexSlice[("a", 1), :], + ], + ) + def test_map_subset_multiindex(self, slice_): + # GH 19861 + # edited for GH 33562 + if ( + isinstance(slice_[-1], tuple) + and isinstance(slice_[-1][-1], list) + and "C" in slice_[-1][-1] + ): + ctx = pytest.raises(KeyError, match="C") + elif ( + isinstance(slice_[0], tuple) + and isinstance(slice_[0][1], list) + and 3 in slice_[0][1] + ): + ctx = pytest.raises(KeyError, match="3") + else: + ctx = contextlib.nullcontext() + + idx = MultiIndex.from_product([["a", "b"], [1, 2]]) + col = MultiIndex.from_product([["x", "y"], ["A", "B"]]) + df = DataFrame(np.random.default_rng(2).random((4, 4)), columns=col, index=idx) + + with ctx: + df.style.map(lambda x: "color: red;", subset=slice_).to_html() + + def test_map_subset_multiindex_code(self): + # https://github.com/pandas-dev/pandas/issues/25858 + # Checks styler.map works with multindex when codes are provided + codes = np.array([[0, 0, 1, 1], [0, 1, 0, 1]]) + columns = MultiIndex( + levels=[["a", "b"], ["%", "#"]], codes=codes, names=["", ""] + ) + df = DataFrame( + [[1, -1, 1, 1], [-1, 1, 1, 1]], index=["hello", "world"], columns=columns + ) + pct_subset = IndexSlice[:, IndexSlice[:, "%":"%"]] + + def color_negative_red(val): + color = "red" if val < 0 else "black" + return f"color: {color}" + + df.loc[pct_subset] + df.style.map(color_negative_red, subset=pct_subset) + + @pytest.mark.parametrize( + "stylefunc", ["background_gradient", "bar", "text_gradient"] + ) + def test_subset_for_boolean_cols(self, stylefunc): + # GH47838 + df = DataFrame( + [ + [1, 2], + [3, 4], + ], + columns=[False, True], + ) + styled = getattr(df.style, stylefunc)() + styled._compute() + assert set(styled.ctx) == {(0, 0), (0, 1), (1, 0), (1, 1)} + + def test_empty(self): + df = DataFrame({"A": [1, 0]}) + s = df.style + s.ctx = {(0, 0): [("color", "red")], (1, 0): [("", "")]} + + result = s._translate(True, True)["cellstyle"] + expected = [ + {"props": [("color", "red")], "selectors": ["row0_col0"]}, + {"props": [("", "")], "selectors": ["row1_col0"]}, + ] + assert result == expected + + def test_duplicate(self): + df = DataFrame({"A": [1, 0]}) + s = df.style + s.ctx = {(0, 0): [("color", "red")], (1, 0): [("color", "red")]} + + result = s._translate(True, True)["cellstyle"] + expected = [ + {"props": [("color", "red")], "selectors": ["row0_col0", "row1_col0"]} + ] + assert result == expected + + def test_init_with_na_rep(self): + # GH 21527 28358 + df = DataFrame([[None, None], [1.1, 1.2]], columns=["A", "B"]) + + ctx = Styler(df, na_rep="NA")._translate(True, True) + assert ctx["body"][0][1]["display_value"] == "NA" + assert ctx["body"][0][2]["display_value"] == "NA" + + def test_caption(self, df): + styler = Styler(df, caption="foo") + result = styler.to_html() + assert all(["caption" in result, "foo" in result]) + + styler = df.style + result = styler.set_caption("baz") + assert styler is result + assert styler.caption == "baz" + + def test_uuid(self, df): + styler = Styler(df, uuid="abc123") + result = styler.to_html() + assert "abc123" in result + + styler = df.style + result = styler.set_uuid("aaa") + assert result is styler + assert result.uuid == "aaa" + + def test_unique_id(self): + # See https://github.com/pandas-dev/pandas/issues/16780 + df = DataFrame({"a": [1, 3, 5, 6], "b": [2, 4, 12, 21]}) + result = df.style.to_html(uuid="test") + assert "test" in result + ids = re.findall('id="(.*?)"', result) + assert np.unique(ids).size == len(ids) + + def test_table_styles(self, df): + style = [{"selector": "th", "props": [("foo", "bar")]}] # default format + styler = Styler(df, table_styles=style) + result = " ".join(styler.to_html().split()) + assert "th { foo: bar; }" in result + + styler = df.style + result = styler.set_table_styles(style) + assert styler is result + assert styler.table_styles == style + + # GH 39563 + style = [{"selector": "th", "props": "foo:bar;"}] # css string format + styler = df.style.set_table_styles(style) + result = " ".join(styler.to_html().split()) + assert "th { foo: bar; }" in result + + def test_table_styles_multiple(self, df): + ctx = df.style.set_table_styles( + [ + {"selector": "th,td", "props": "color:red;"}, + {"selector": "tr", "props": "color:green;"}, + ] + )._translate(True, True)["table_styles"] + assert ctx == [ + {"selector": "th", "props": [("color", "red")]}, + {"selector": "td", "props": [("color", "red")]}, + {"selector": "tr", "props": [("color", "green")]}, + ] + + def test_table_styles_dict_multiple_selectors(self, df): + # GH 44011 + result = df.style.set_table_styles( + { + "B": [ + {"selector": "th,td", "props": [("border-left", "2px solid black")]} + ] + } + )._translate(True, True)["table_styles"] + + expected = [ + {"selector": "th.col1", "props": [("border-left", "2px solid black")]}, + {"selector": "td.col1", "props": [("border-left", "2px solid black")]}, + ] + + assert result == expected + + def test_maybe_convert_css_to_tuples(self): + expected = [("a", "b"), ("c", "d e")] + assert maybe_convert_css_to_tuples("a:b;c:d e;") == expected + assert maybe_convert_css_to_tuples("a: b ;c: d e ") == expected + expected = [] + assert maybe_convert_css_to_tuples("") == expected + + # issue #59623 + expected = [("a", "b"), ("c", "url('data:123')")] + assert maybe_convert_css_to_tuples("a:b;c: url('data:123');") == expected + + # if no value, return attr and empty string + expected = [("a", ""), ("c", "")] + assert maybe_convert_css_to_tuples("a:;c: ") == expected + + def test_maybe_convert_css_to_tuples_err(self): + msg = ( + "Styles supplied as string must follow CSS rule formats, " + "for example 'attr: val;'. 'err' was given." + ) + with pytest.raises(ValueError, match=msg): + maybe_convert_css_to_tuples("err") + + def test_table_attributes(self, df): + attributes = 'class="foo" data-bar' + styler = Styler(df, table_attributes=attributes) + result = styler.to_html() + assert 'class="foo" data-bar' in result + + result = df.style.set_table_attributes(attributes).to_html() + assert 'class="foo" data-bar' in result + + def test_apply_none(self): + def f(x): + return DataFrame( + np.where(x == x.max(), "color: red", ""), + index=x.index, + columns=x.columns, + ) + + result = DataFrame([[1, 2], [3, 4]]).style.apply(f, axis=None)._compute().ctx + assert result[(1, 1)] == [("color", "red")] + + def test_trim(self, df): + result = df.style.to_html() # trim=True + assert result.count("#") == 0 + + result = df.style.highlight_max().to_html() + assert result.count("#") == len(df.columns) + + def test_export(self, df, styler): + f = lambda x: "color: red" if x > 0 else "color: blue" + g = lambda x, z: f"color: {z}" + style1 = styler + style1.map(f).map(g, z="b").highlight_max()._compute() # = render + result = style1.export() + style2 = df.style + style2.use(result) + assert style1._todo == style2._todo + style2.to_html() + + def test_bad_apply_shape(self): + df = DataFrame([[1, 2], [3, 4]], index=["A", "B"], columns=["X", "Y"]) + + msg = "resulted in the apply method collapsing to a Series." + with pytest.raises(ValueError, match=msg): + df.style._apply(lambda x: "x") + + msg = "created invalid {} labels" + with pytest.raises(ValueError, match=msg.format("index")): + df.style._apply(lambda x: [""]) + + with pytest.raises(ValueError, match=msg.format("index")): + df.style._apply(lambda x: ["", "", "", ""]) + + with pytest.raises(ValueError, match=msg.format("index")): + df.style._apply(lambda x: Series(["a:v;", ""], index=["A", "C"]), axis=0) + + with pytest.raises(ValueError, match=msg.format("columns")): + df.style._apply(lambda x: ["", "", ""], axis=1) + + with pytest.raises(ValueError, match=msg.format("columns")): + df.style._apply(lambda x: Series(["a:v;", ""], index=["X", "Z"]), axis=1) + + msg = "returned ndarray with wrong shape" + with pytest.raises(ValueError, match=msg): + df.style._apply(lambda x: np.array([[""], [""]]), axis=None) + + def test_apply_bad_return(self): + def f(x): + return "" + + df = DataFrame([[1, 2], [3, 4]]) + msg = ( + "must return a DataFrame or ndarray when passed to `Styler.apply` " + "with axis=None" + ) + with pytest.raises(TypeError, match=msg): + df.style._apply(f, axis=None) + + @pytest.mark.parametrize("axis", ["index", "columns"]) + def test_apply_bad_labels(self, axis): + def f(x): + return DataFrame(**{axis: ["bad", "labels"]}) + + df = DataFrame([[1, 2], [3, 4]]) + msg = f"created invalid {axis} labels." + with pytest.raises(ValueError, match=msg): + df.style._apply(f, axis=None) + + def test_get_level_lengths(self): + index = MultiIndex.from_product([["a", "b"], [0, 1, 2]]) + expected = { + (0, 0): 3, + (0, 3): 3, + (1, 0): 1, + (1, 1): 1, + (1, 2): 1, + (1, 3): 1, + (1, 4): 1, + (1, 5): 1, + } + result = _get_level_lengths(index, sparsify=True, max_index=100) + tm.assert_dict_equal(result, expected) + + expected = { + (0, 0): 1, + (0, 1): 1, + (0, 2): 1, + (0, 3): 1, + (0, 4): 1, + (0, 5): 1, + (1, 0): 1, + (1, 1): 1, + (1, 2): 1, + (1, 3): 1, + (1, 4): 1, + (1, 5): 1, + } + result = _get_level_lengths(index, sparsify=False, max_index=100) + tm.assert_dict_equal(result, expected) + + def test_get_level_lengths_un_sorted(self): + index = MultiIndex.from_arrays([[1, 1, 2, 1], ["a", "b", "b", "d"]]) + expected = { + (0, 0): 2, + (0, 2): 1, + (0, 3): 1, + (1, 0): 1, + (1, 1): 1, + (1, 2): 1, + (1, 3): 1, + } + result = _get_level_lengths(index, sparsify=True, max_index=100) + tm.assert_dict_equal(result, expected) + + expected = { + (0, 0): 1, + (0, 1): 1, + (0, 2): 1, + (0, 3): 1, + (1, 0): 1, + (1, 1): 1, + (1, 2): 1, + (1, 3): 1, + } + result = _get_level_lengths(index, sparsify=False, max_index=100) + tm.assert_dict_equal(result, expected) + + def test_mi_sparse_index_names(self, blank_value): + # Test the class names and displayed value are correct on rendering MI names + df = DataFrame( + {"A": [1, 2]}, + index=MultiIndex.from_arrays( + [["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"] + ), + ) + result = df.style._translate(True, True) + head = result["head"][1] + expected = [ + { + "class": "index_name level0", + "display_value": "idx_level_0", + "is_visible": True, + }, + { + "class": "index_name level1", + "display_value": "idx_level_1", + "is_visible": True, + }, + { + "class": "blank col0", + "display_value": blank_value, + "is_visible": True, + }, + ] + for i, expected_dict in enumerate(expected): + assert expected_dict.items() <= head[i].items() + + def test_mi_sparse_column_names(self, blank_value): + df = DataFrame( + np.arange(16).reshape(4, 4), + index=MultiIndex.from_arrays( + [["a", "a", "b", "a"], [0, 1, 1, 2]], + names=["idx_level_0", "idx_level_1"], + ), + columns=MultiIndex.from_arrays( + [["C1", "C1", "C2", "C2"], [1, 0, 1, 0]], names=["colnam_0", "colnam_1"] + ), + ) + result = Styler(df, cell_ids=False)._translate(True, True) + + for level in [0, 1]: + head = result["head"][level] + expected = [ + { + "class": "blank", + "display_value": blank_value, + "is_visible": True, + }, + { + "class": f"index_name level{level}", + "display_value": f"colnam_{level}", + "is_visible": True, + }, + ] + for i, expected_dict in enumerate(expected): + assert expected_dict.items() <= head[i].items() + + def test_hide_column_headers(self, df, styler): + ctx = styler.hide(axis="columns")._translate(True, True) + assert len(ctx["head"]) == 0 # no header entries with an unnamed index + + df.index.name = "some_name" + ctx = df.style.hide(axis="columns")._translate(True, True) + assert len(ctx["head"]) == 1 + # index names still visible, changed in #42101, reverted in 43404 + + def test_hide_single_index(self, df): + # GH 14194 + # single unnamed index + ctx = df.style._translate(True, True) + assert ctx["body"][0][0]["is_visible"] + assert ctx["head"][0][0]["is_visible"] + ctx2 = df.style.hide(axis="index")._translate(True, True) + assert not ctx2["body"][0][0]["is_visible"] + assert not ctx2["head"][0][0]["is_visible"] + + # single named index + ctx3 = df.set_index("A").style._translate(True, True) + assert ctx3["body"][0][0]["is_visible"] + assert len(ctx3["head"]) == 2 # 2 header levels + assert ctx3["head"][0][0]["is_visible"] + + ctx4 = df.set_index("A").style.hide(axis="index")._translate(True, True) + assert not ctx4["body"][0][0]["is_visible"] + assert len(ctx4["head"]) == 1 # only 1 header levels + assert not ctx4["head"][0][0]["is_visible"] + + def test_hide_multiindex(self): + # GH 14194 + df = DataFrame( + {"A": [1, 2], "B": [1, 2]}, + index=MultiIndex.from_arrays( + [["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"] + ), + ) + ctx1 = df.style._translate(True, True) + # tests for 'a' and '0' + assert ctx1["body"][0][0]["is_visible"] + assert ctx1["body"][0][1]["is_visible"] + # check for blank header rows + assert len(ctx1["head"][0]) == 4 # two visible indexes and two data columns + + ctx2 = df.style.hide(axis="index")._translate(True, True) + # tests for 'a' and '0' + assert not ctx2["body"][0][0]["is_visible"] + assert not ctx2["body"][0][1]["is_visible"] + # check for blank header rows + assert len(ctx2["head"][0]) == 3 # one hidden (col name) and two data columns + assert not ctx2["head"][0][0]["is_visible"] + + def test_hide_columns_single_level(self, df): + # GH 14194 + # test hiding single column + ctx = df.style._translate(True, True) + assert ctx["head"][0][1]["is_visible"] + assert ctx["head"][0][1]["display_value"] == "A" + assert ctx["head"][0][2]["is_visible"] + assert ctx["head"][0][2]["display_value"] == "B" + assert ctx["body"][0][1]["is_visible"] # col A, row 1 + assert ctx["body"][1][2]["is_visible"] # col B, row 1 + + ctx = df.style.hide("A", axis="columns")._translate(True, True) + assert not ctx["head"][0][1]["is_visible"] + assert not ctx["body"][0][1]["is_visible"] # col A, row 1 + assert ctx["body"][1][2]["is_visible"] # col B, row 1 + + # test hiding multiple columns + ctx = df.style.hide(["A", "B"], axis="columns")._translate(True, True) + assert not ctx["head"][0][1]["is_visible"] + assert not ctx["head"][0][2]["is_visible"] + assert not ctx["body"][0][1]["is_visible"] # col A, row 1 + assert not ctx["body"][1][2]["is_visible"] # col B, row 1 + + def test_hide_columns_index_mult_levels(self): + # GH 14194 + # setup dataframe with multiple column levels and indices + i1 = MultiIndex.from_arrays( + [["a", "a"], [0, 1]], names=["idx_level_0", "idx_level_1"] + ) + i2 = MultiIndex.from_arrays( + [["b", "b"], [0, 1]], names=["col_level_0", "col_level_1"] + ) + df = DataFrame([[1, 2], [3, 4]], index=i1, columns=i2) + ctx = df.style._translate(True, True) + # column headers + assert ctx["head"][0][2]["is_visible"] + assert ctx["head"][1][2]["is_visible"] + assert ctx["head"][1][3]["display_value"] == "1" + # indices + assert ctx["body"][0][0]["is_visible"] + # data + assert ctx["body"][1][2]["is_visible"] + assert ctx["body"][1][2]["display_value"] == "3" + assert ctx["body"][1][3]["is_visible"] + assert ctx["body"][1][3]["display_value"] == "4" + + # hide top column level, which hides both columns + ctx = df.style.hide("b", axis="columns")._translate(True, True) + assert not ctx["head"][0][2]["is_visible"] # b + assert not ctx["head"][1][2]["is_visible"] # 0 + assert not ctx["body"][1][2]["is_visible"] # 3 + assert ctx["body"][0][0]["is_visible"] # index + + # hide first column only + ctx = df.style.hide([("b", 0)], axis="columns")._translate(True, True) + assert not ctx["head"][0][2]["is_visible"] # b + assert ctx["head"][0][3]["is_visible"] # b + assert not ctx["head"][1][2]["is_visible"] # 0 + assert not ctx["body"][1][2]["is_visible"] # 3 + assert ctx["body"][1][3]["is_visible"] + assert ctx["body"][1][3]["display_value"] == "4" + + # hide second column and index + ctx = df.style.hide([("b", 1)], axis=1).hide(axis=0)._translate(True, True) + assert not ctx["body"][0][0]["is_visible"] # index + assert len(ctx["head"][0]) == 3 + assert ctx["head"][0][1]["is_visible"] # b + assert ctx["head"][1][1]["is_visible"] # 0 + assert not ctx["head"][1][2]["is_visible"] # 1 + assert not ctx["body"][1][3]["is_visible"] # 4 + assert ctx["body"][1][2]["is_visible"] + assert ctx["body"][1][2]["display_value"] == "3" + + # hide top row level, which hides both rows so body empty + ctx = df.style.hide("a", axis="index")._translate(True, True) + assert ctx["body"] == [] + + # hide first row only + ctx = df.style.hide(("a", 0), axis="index")._translate(True, True) + for i in [0, 1, 2, 3]: + assert "row1" in ctx["body"][0][i]["class"] # row0 not included in body + assert ctx["body"][0][i]["is_visible"] + + def test_pipe(self, df): + def set_caption_from_template(styler, a, b): + return styler.set_caption(f"Dataframe with a = {a} and b = {b}") + + styler = df.style.pipe(set_caption_from_template, "A", b="B") + assert "Dataframe with a = A and b = B" in styler.to_html() + + # Test with an argument that is a (callable, keyword_name) pair. + def f(a, b, styler): + return (a, b, styler) + + styler = df.style + result = styler.pipe((f, "styler"), a=1, b=2) + assert result == (1, 2, styler) + + def test_no_cell_ids(self): + # GH 35588 + # GH 35663 + df = DataFrame(data=[[0]]) + styler = Styler(df, uuid="_", cell_ids=False) + styler.to_html() + s = styler.to_html() # render twice to ensure ctx is not updated + assert s.find('') != -1 + + @pytest.mark.parametrize( + "classes", + [ + DataFrame( + data=[["", "test-class"], [np.nan, None]], + columns=["A", "B"], + index=["a", "b"], + ), + DataFrame(data=[["test-class"]], columns=["B"], index=["a"]), + DataFrame(data=[["test-class", "unused"]], columns=["B", "C"], index=["a"]), + ], + ) + def test_set_data_classes(self, classes): + # GH 36159 + df = DataFrame(data=[[0, 1], [2, 3]], columns=["A", "B"], index=["a", "b"]) + s = Styler(df, uuid_len=0, cell_ids=False).set_td_classes(classes).to_html() + assert '0' in s + assert '1' in s + assert '2' in s + assert '3' in s + # GH 39317 + s = Styler(df, uuid_len=0, cell_ids=True).set_td_classes(classes).to_html() + assert '0' in s + assert '1' in s + assert '2' in s + assert '3' in s + + def test_set_data_classes_reindex(self): + # GH 39317 + df = DataFrame( + data=[[0, 1, 2], [3, 4, 5], [6, 7, 8]], columns=[0, 1, 2], index=[0, 1, 2] + ) + classes = DataFrame( + data=[["mi", "ma"], ["mu", "mo"]], + columns=[0, 2], + index=[0, 2], + ) + s = Styler(df, uuid_len=0).set_td_classes(classes).to_html() + assert '0' in s + assert '2' in s + assert '4' in s + assert '6' in s + assert '8' in s + + def test_chaining_table_styles(self): + # GH 35607 + df = DataFrame(data=[[0, 1], [1, 2]], columns=["A", "B"]) + styler = df.style.set_table_styles( + [{"selector": "", "props": [("background-color", "yellow")]}] + ).set_table_styles( + [{"selector": ".col0", "props": [("background-color", "blue")]}], + overwrite=False, + ) + assert len(styler.table_styles) == 2 + + def test_column_and_row_styling(self): + # GH 35607 + df = DataFrame(data=[[0, 1], [1, 2]], columns=["A", "B"]) + s = Styler(df, uuid_len=0) + s = s.set_table_styles({"A": [{"selector": "", "props": [("color", "blue")]}]}) + assert "#T_ .col0 {\n color: blue;\n}" in s.to_html() + s = s.set_table_styles( + {0: [{"selector": "", "props": [("color", "blue")]}]}, axis=1 + ) + assert "#T_ .row0 {\n color: blue;\n}" in s.to_html() + + @pytest.mark.parametrize("len_", [1, 5, 32, 33, 100]) + def test_uuid_len(self, len_): + # GH 36345 + df = DataFrame(data=[["A"]]) + s = Styler(df, uuid_len=len_, cell_ids=False).to_html() + strt = s.find('id="T_') + end = s[strt + 6 :].find('"') + if len_ > 32: + assert end == 32 + else: + assert end == len_ + + @pytest.mark.parametrize("len_", [-2, "bad", None]) + def test_uuid_len_raises(self, len_): + # GH 36345 + df = DataFrame(data=[["A"]]) + msg = "``uuid_len`` must be an integer in range \\[0, 32\\]." + with pytest.raises(TypeError, match=msg): + Styler(df, uuid_len=len_, cell_ids=False).to_html() + + @pytest.mark.parametrize( + "slc", + [ + IndexSlice[:, :], + IndexSlice[:, 1], + IndexSlice[1, :], + IndexSlice[[1], [1]], + IndexSlice[1, [1]], + IndexSlice[[1], 1], + IndexSlice[1], + IndexSlice[1, 1], + slice(None, None, None), + [0, 1], + np.array([0, 1]), + Series([0, 1]), + ], + ) + def test_non_reducing_slice(self, slc): + df = DataFrame([[0, 1], [2, 3]]) + + tslice_ = non_reducing_slice(slc) + assert isinstance(df.loc[tslice_], DataFrame) + + @pytest.mark.parametrize("box", [list, Series, np.array]) + def test_list_slice(self, box): + # like dataframe getitem + subset = box(["A"]) + + df = DataFrame({"A": [1, 2], "B": [3, 4]}, index=["A", "B"]) + expected = IndexSlice[:, ["A"]] + + result = non_reducing_slice(subset) + tm.assert_frame_equal(df.loc[result], df.loc[expected]) + + def test_non_reducing_slice_on_multiindex(self): + # GH 19861 + dic = { + ("a", "d"): [1, 4], + ("a", "c"): [2, 3], + ("b", "c"): [3, 2], + ("b", "d"): [4, 1], + } + df = DataFrame(dic, index=[0, 1]) + idx = IndexSlice + slice_ = idx[:, idx["b", "d"]] + tslice_ = non_reducing_slice(slice_) + + result = df.loc[tslice_] + expected = DataFrame({("b", "d"): [4, 1]}) + tm.assert_frame_equal(result, expected) + + @pytest.mark.parametrize( + "slice_", + [ + IndexSlice[:, :], + # check cols + IndexSlice[:, IndexSlice[["a"]]], # inferred deeper need list + IndexSlice[:, IndexSlice[["a"], ["c"]]], # inferred deeper need list + IndexSlice[:, IndexSlice["a", "c", :]], + IndexSlice[:, IndexSlice["a", :, "e"]], + IndexSlice[:, IndexSlice[:, "c", "e"]], + IndexSlice[:, IndexSlice["a", ["c", "d"], :]], # check list + IndexSlice[:, IndexSlice["a", ["c", "d", "-"], :]], # don't allow missing + IndexSlice[:, IndexSlice["a", ["c", "d", "-"], "e"]], # no slice + # check rows + IndexSlice[IndexSlice[["U"]], :], # inferred deeper need list + IndexSlice[IndexSlice[["U"], ["W"]], :], # inferred deeper need list + IndexSlice[IndexSlice["U", "W", :], :], + IndexSlice[IndexSlice["U", :, "Y"], :], + IndexSlice[IndexSlice[:, "W", "Y"], :], + IndexSlice[IndexSlice[:, "W", ["Y", "Z"]], :], # check list + IndexSlice[IndexSlice[:, "W", ["Y", "Z", "-"]], :], # don't allow missing + IndexSlice[IndexSlice["U", "W", ["Y", "Z", "-"]], :], # no slice + # check simultaneous + IndexSlice[IndexSlice[:, "W", "Y"], IndexSlice["a", "c", :]], + ], + ) + def test_non_reducing_multi_slice_on_multiindex(self, slice_): + # GH 33562 + cols = MultiIndex.from_product([["a", "b"], ["c", "d"], ["e", "f"]]) + idxs = MultiIndex.from_product([["U", "V"], ["W", "X"], ["Y", "Z"]]) + df = DataFrame(np.arange(64).reshape(8, 8), columns=cols, index=idxs) + + for lvl in [0, 1]: + key = slice_[lvl] + if isinstance(key, tuple): + for subkey in key: + if isinstance(subkey, list) and "-" in subkey: + # not present in the index level, raises KeyError since 2.0 + with pytest.raises(KeyError, match="-"): + df.loc[slice_] + return + + expected = df.loc[slice_] + result = df.loc[non_reducing_slice(slice_)] + tm.assert_frame_equal(result, expected) + + +def test_hidden_index_names(mi_df): + mi_df.index.names = ["Lev0", "Lev1"] + mi_styler = mi_df.style + ctx = mi_styler._translate(True, True) + assert len(ctx["head"]) == 3 # 2 column index levels + 1 index names row + + mi_styler.hide(axis="index", names=True) + ctx = mi_styler._translate(True, True) + assert len(ctx["head"]) == 2 # index names row is unparsed + for i in range(4): + assert ctx["body"][0][i]["is_visible"] # 2 index levels + 2 data values visible + + mi_styler.hide(axis="index", level=1) + ctx = mi_styler._translate(True, True) + assert len(ctx["head"]) == 2 # index names row is still hidden + assert ctx["body"][0][0]["is_visible"] is True + assert ctx["body"][0][1]["is_visible"] is False + + +def test_hidden_column_names(mi_df): + mi_df.columns.names = ["Lev0", "Lev1"] + mi_styler = mi_df.style + ctx = mi_styler._translate(True, True) + assert ctx["head"][0][1]["display_value"] == "Lev0" + assert ctx["head"][1][1]["display_value"] == "Lev1" + + mi_styler.hide(names=True, axis="columns") + ctx = mi_styler._translate(True, True) + assert ctx["head"][0][1]["display_value"] == " " + assert ctx["head"][1][1]["display_value"] == " " + + mi_styler.hide(level=0, axis="columns") + ctx = mi_styler._translate(True, True) + assert len(ctx["head"]) == 1 # no index names and only one visible column headers + assert ctx["head"][0][1]["display_value"] == " " + + +@pytest.mark.parametrize("caption", [1, ("a", "b", "c"), (1, "s")]) +def test_caption_raises(mi_styler, caption): + msg = "`caption` must be either a string or 2-tuple of strings." + with pytest.raises(ValueError, match=msg): + mi_styler.set_caption(caption) + + +def test_hiding_headers_over_index_no_sparsify(): + # GH 43464 + midx = MultiIndex.from_product([[1, 2], ["a", "a", "b"]]) + df = DataFrame(9, index=midx, columns=[0]) + ctx = df.style._translate(False, False) + assert len(ctx["body"]) == 6 + ctx = df.style.hide((1, "a"), axis=0)._translate(False, False) + assert len(ctx["body"]) == 4 + assert "row2" in ctx["body"][0][0]["class"] + + +def test_hiding_headers_over_columns_no_sparsify(): + # GH 43464 + midx = MultiIndex.from_product([[1, 2], ["a", "a", "b"]]) + df = DataFrame(9, columns=midx, index=[0]) + ctx = df.style._translate(False, False) + for ix in [(0, 1), (0, 2), (1, 1), (1, 2)]: + assert ctx["head"][ix[0]][ix[1]]["is_visible"] is True + ctx = df.style.hide((1, "a"), axis="columns")._translate(False, False) + for ix in [(0, 1), (0, 2), (1, 1), (1, 2)]: + assert ctx["head"][ix[0]][ix[1]]["is_visible"] is False + + +def test_get_level_lengths_mi_hidden(): + # GH 43464 + index = MultiIndex.from_arrays([[1, 1, 1, 2, 2, 2], ["a", "a", "b", "a", "a", "b"]]) + expected = { + (0, 2): 1, + (0, 3): 1, + (0, 4): 1, + (0, 5): 1, + (1, 2): 1, + (1, 3): 1, + (1, 4): 1, + (1, 5): 1, + } + result = _get_level_lengths( + index, + sparsify=False, + max_index=100, + hidden_elements=[0, 1, 0, 1], # hidden element can repeat if duplicated index + ) + tm.assert_dict_equal(result, expected) + + +def test_row_trimming_hide_index(): + # gh 43703 + df = DataFrame([[1], [2], [3], [4], [5]]) + with option_context("styler.render.max_rows", 2): + ctx = df.style.hide([0, 1], axis="index")._translate(True, True) + assert len(ctx["body"]) == 3 + for r, val in enumerate(["3", "4", "..."]): + assert ctx["body"][r][1]["display_value"] == val + + +def test_row_trimming_hide_index_mi(): + # gh 44247 + df = DataFrame([[1], [2], [3], [4], [5]]) + df.index = MultiIndex.from_product([[0], [0, 1, 2, 3, 4]]) + with option_context("styler.render.max_rows", 2): + ctx = df.style.hide([(0, 0), (0, 1)], axis="index")._translate(True, True) + assert len(ctx["body"]) == 3 + + # level 0 index headers (sparsified) + assert {"value": 0, "attributes": 'rowspan="2"', "is_visible": True}.items() <= ctx[ + "body" + ][0][0].items() + assert {"value": 0, "attributes": "", "is_visible": False}.items() <= ctx["body"][ + 1 + ][0].items() + assert {"value": "...", "is_visible": True}.items() <= ctx["body"][2][0].items() + + for r, val in enumerate(["2", "3", "..."]): + assert ctx["body"][r][1]["display_value"] == val # level 1 index headers + for r, val in enumerate(["3", "4", "..."]): + assert ctx["body"][r][2]["display_value"] == val # data values + + +def test_col_trimming_hide_columns(): + # gh 44272 + df = DataFrame([[1, 2, 3, 4, 5]]) + with option_context("styler.render.max_columns", 2): + ctx = df.style.hide([0, 1], axis="columns")._translate(True, True) + + assert len(ctx["head"][0]) == 6 # blank, [0, 1 (hidden)], [2 ,3 (visible)], + trim + for c, vals in enumerate([(1, False), (2, True), (3, True), ("...", True)]): + assert ctx["head"][0][c + 2]["value"] == vals[0] + assert ctx["head"][0][c + 2]["is_visible"] == vals[1] + + assert len(ctx["body"][0]) == 6 # index + 2 hidden + 2 visible + trimming col + + +def test_no_empty_apply(mi_styler): + # 45313 + mi_styler.apply(lambda s: ["a:v;"] * 2, subset=[False, False]) + mi_styler._compute() + + +@pytest.mark.parametrize("format", ["html", "latex", "string"]) +def test_output_buffer(mi_styler, format, temp_file): + # gh 47053 + getattr(mi_styler, f"to_{format}")(temp_file) diff --git a/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_to_latex.py b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_to_latex.py new file mode 100644 index 0000000000000000000000000000000000000000..eb221686dd165be0dd55dac77c25f11c047f1f23 --- /dev/null +++ b/lib/python3.11/site-packages/pandas/tests/io/formats/style/test_to_latex.py @@ -0,0 +1,1091 @@ +from textwrap import dedent + +import numpy as np +import pytest + +from pandas import ( + DataFrame, + MultiIndex, + Series, + option_context, +) + +pytest.importorskip("jinja2") +from pandas.io.formats.style import Styler +from pandas.io.formats.style_render import ( + _parse_latex_cell_styles, + _parse_latex_css_conversion, + _parse_latex_header_span, + _parse_latex_table_styles, + _parse_latex_table_wrapping, +) + + +@pytest.fixture +def df(): + return DataFrame( + {"A": [0, 1], "B": [-0.61, -1.22], "C": Series(["ab", "cd"], dtype=object)} + ) + + +@pytest.fixture +def df_ext(): + return DataFrame( + {"A": [0, 1, 2], "B": [-0.61, -1.22, -2.22], "C": ["ab", "cd", "de"]} + ) + + +@pytest.fixture +def styler(df): + return Styler(df, uuid_len=0, precision=2) + + +def test_minimal_latex_tabular(styler): + expected = dedent( + """\ + \\begin{tabular}{lrrl} + & A & B & C \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{tabular} + """ + ) + assert styler.to_latex() == expected + + +def test_tabular_hrules(styler): + expected = dedent( + """\ + \\begin{tabular}{lrrl} + \\toprule + & A & B & C \\\\ + \\midrule + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\bottomrule + \\end{tabular} + """ + ) + assert styler.to_latex(hrules=True) == expected + + +def test_tabular_custom_hrules(styler): + styler.set_table_styles( + [ + {"selector": "toprule", "props": ":hline"}, + {"selector": "bottomrule", "props": ":otherline"}, + ] + ) # no midrule + expected = dedent( + """\ + \\begin{tabular}{lrrl} + \\hline + & A & B & C \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\otherline + \\end{tabular} + """ + ) + assert styler.to_latex() == expected + + +def test_column_format(styler): + # default setting is already tested in `test_latex_minimal_tabular` + styler.set_table_styles([{"selector": "column_format", "props": ":cccc"}]) + + assert "\\begin{tabular}{rrrr}" in styler.to_latex(column_format="rrrr") + styler.set_table_styles([{"selector": "column_format", "props": ":r|r|cc"}]) + assert "\\begin{tabular}{r|r|cc}" in styler.to_latex() + + +def test_siunitx_cols(styler): + expected = dedent( + """\ + \\begin{tabular}{lSSl} + {} & {A} & {B} & {C} \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{tabular} + """ + ) + assert styler.to_latex(siunitx=True) == expected + + +def test_position(styler): + assert "\\begin{table}[h!]" in styler.to_latex(position="h!") + assert "\\end{table}" in styler.to_latex(position="h!") + styler.set_table_styles([{"selector": "position", "props": ":b!"}]) + assert "\\begin{table}[b!]" in styler.to_latex() + assert "\\end{table}" in styler.to_latex() + + +@pytest.mark.parametrize("env", [None, "longtable"]) +def test_label(styler, env): + assert "\n\\label{text}" in styler.to_latex(label="text", environment=env) + styler.set_table_styles([{"selector": "label", "props": ":{more §text}"}]) + assert "\n\\label{more :text}" in styler.to_latex(environment=env) + + +def test_position_float_raises(styler): + msg = "`position_float` should be one of 'raggedright', 'raggedleft', 'centering'," + with pytest.raises(ValueError, match=msg): + styler.to_latex(position_float="bad_string") + + msg = "`position_float` cannot be used in 'longtable' `environment`" + with pytest.raises(ValueError, match=msg): + styler.to_latex(position_float="centering", environment="longtable") + + +@pytest.mark.parametrize("label", [(None, ""), ("text", "\\label{text}")]) +@pytest.mark.parametrize("position", [(None, ""), ("h!", "{table}[h!]")]) +@pytest.mark.parametrize("caption", [(None, ""), ("text", "\\caption{text}")]) +@pytest.mark.parametrize("column_format", [(None, ""), ("rcrl", "{tabular}{rcrl}")]) +@pytest.mark.parametrize("position_float", [(None, ""), ("centering", "\\centering")]) +def test_kwargs_combinations( + styler, label, position, caption, column_format, position_float +): + result = styler.to_latex( + label=label[0], + position=position[0], + caption=caption[0], + column_format=column_format[0], + position_float=position_float[0], + ) + assert label[1] in result + assert position[1] in result + assert caption[1] in result + assert column_format[1] in result + assert position_float[1] in result + + +def test_custom_table_styles(styler): + styler.set_table_styles( + [ + {"selector": "mycommand", "props": ":{myoptions}"}, + {"selector": "mycommand2", "props": ":{myoptions2}"}, + ] + ) + expected = dedent( + """\ + \\begin{table} + \\mycommand{myoptions} + \\mycommand2{myoptions2} + """ + ) + assert expected in styler.to_latex() + + +def test_cell_styling(styler): + styler.highlight_max(props="itshape:;Huge:--wrap;") + expected = dedent( + """\ + \\begin{tabular}{lrrl} + & A & B & C \\\\ + 0 & 0 & \\itshape {\\Huge -0.61} & ab \\\\ + 1 & \\itshape {\\Huge 1} & -1.22 & \\itshape {\\Huge cd} \\\\ + \\end{tabular} + """ + ) + assert expected == styler.to_latex() + + +def test_multiindex_columns(df): + cidx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df.columns = cidx + expected = dedent( + """\ + \\begin{tabular}{lrrl} + & \\multicolumn{2}{r}{A} & B \\\\ + & a & b & c \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{tabular} + """ + ) + s = df.style.format(precision=2) + assert expected == s.to_latex() + + # non-sparse + expected = dedent( + """\ + \\begin{tabular}{lrrl} + & A & A & B \\\\ + & a & b & c \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{tabular} + """ + ) + s = df.style.format(precision=2) + assert expected == s.to_latex(sparse_columns=False) + + +def test_multiindex_row(df_ext): + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index = ridx + expected = dedent( + """\ + \\begin{tabular}{llrrl} + & & A & B & C \\\\ + \\multirow[c]{2}{*}{A} & a & 0 & -0.61 & ab \\\\ + & b & 1 & -1.22 & cd \\\\ + B & c & 2 & -2.22 & de \\\\ + \\end{tabular} + """ + ) + styler = df_ext.style.format(precision=2) + result = styler.to_latex() + assert expected == result + + # non-sparse + expected = dedent( + """\ + \\begin{tabular}{llrrl} + & & A & B & C \\\\ + A & a & 0 & -0.61 & ab \\\\ + A & b & 1 & -1.22 & cd \\\\ + B & c & 2 & -2.22 & de \\\\ + \\end{tabular} + """ + ) + result = styler.to_latex(sparse_index=False) + assert expected == result + + +def test_multirow_naive(df_ext): + ridx = MultiIndex.from_tuples([("X", "x"), ("X", "y"), ("Y", "z")]) + df_ext.index = ridx + expected = dedent( + """\ + \\begin{tabular}{llrrl} + & & A & B & C \\\\ + X & x & 0 & -0.61 & ab \\\\ + & y & 1 & -1.22 & cd \\\\ + Y & z & 2 & -2.22 & de \\\\ + \\end{tabular} + """ + ) + styler = df_ext.style.format(precision=2) + result = styler.to_latex(multirow_align="naive") + assert expected == result + + +def test_multiindex_row_and_col(df_ext): + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index, df_ext.columns = ridx, cidx + expected = dedent( + """\ + \\begin{tabular}{llrrl} + & & \\multicolumn{2}{l}{Z} & Y \\\\ + & & a & b & c \\\\ + \\multirow[b]{2}{*}{A} & a & 0 & -0.61 & ab \\\\ + & b & 1 & -1.22 & cd \\\\ + B & c & 2 & -2.22 & de \\\\ + \\end{tabular} + """ + ) + styler = df_ext.style.format(precision=2) + result = styler.to_latex(multirow_align="b", multicol_align="l") + assert result == expected + + # non-sparse + expected = dedent( + """\ + \\begin{tabular}{llrrl} + & & Z & Z & Y \\\\ + & & a & b & c \\\\ + A & a & 0 & -0.61 & ab \\\\ + A & b & 1 & -1.22 & cd \\\\ + B & c & 2 & -2.22 & de \\\\ + \\end{tabular} + """ + ) + result = styler.to_latex(sparse_index=False, sparse_columns=False) + assert result == expected + + +@pytest.mark.parametrize( + "multicol_align, siunitx, header", + [ + ("naive-l", False, " & A & &"), + ("naive-r", False, " & & & A"), + ("naive-l", True, "{} & {A} & {} & {}"), + ("naive-r", True, "{} & {} & {} & {A}"), + ], +) +def test_multicol_naive(df, multicol_align, siunitx, header): + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("A", "c")]) + df.columns = ridx + level1 = " & a & b & c" if not siunitx else "{} & {a} & {b} & {c}" + col_format = "lrrl" if not siunitx else "lSSl" + expected = dedent( + f"""\ + \\begin{{tabular}}{{{col_format}}} + {header} \\\\ + {level1} \\\\ + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{{tabular}} + """ + ) + styler = df.style.format(precision=2) + result = styler.to_latex(multicol_align=multicol_align, siunitx=siunitx) + assert expected == result + + +def test_multi_options(df_ext): + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index, df_ext.columns = ridx, cidx + styler = df_ext.style.format(precision=2) + + expected = dedent( + """\ + & & \\multicolumn{2}{r}{Z} & Y \\\\ + & & a & b & c \\\\ + \\multirow[c]{2}{*}{A} & a & 0 & -0.61 & ab \\\\ + """ + ) + result = styler.to_latex() + assert expected in result + + with option_context("styler.latex.multicol_align", "l"): + assert " & & \\multicolumn{2}{l}{Z} & Y \\\\" in styler.to_latex() + + with option_context("styler.latex.multirow_align", "b"): + assert "\\multirow[b]{2}{*}{A} & a & 0 & -0.61 & ab \\\\" in styler.to_latex() + + +def test_multiindex_columns_hidden(): + df = DataFrame([[1, 2, 3, 4]]) + df.columns = MultiIndex.from_tuples([("A", 1), ("A", 2), ("A", 3), ("B", 1)]) + s = df.style + assert "{tabular}{lrrrr}" in s.to_latex() + s.set_table_styles([]) # reset the position command + s.hide([("A", 2)], axis="columns") + assert "{tabular}{lrrr}" in s.to_latex() + + +@pytest.mark.parametrize( + "option, value", + [ + ("styler.sparse.index", True), + ("styler.sparse.index", False), + ("styler.sparse.columns", True), + ("styler.sparse.columns", False), + ], +) +def test_sparse_options(df_ext, option, value): + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index, df_ext.columns = ridx, cidx + styler = df_ext.style + + latex1 = styler.to_latex() + with option_context(option, value): + latex2 = styler.to_latex() + assert (latex1 == latex2) is value + + +def test_hidden_index(styler): + styler.hide(axis="index") + expected = dedent( + """\ + \\begin{tabular}{rrl} + A & B & C \\\\ + 0 & -0.61 & ab \\\\ + 1 & -1.22 & cd \\\\ + \\end{tabular} + """ + ) + assert styler.to_latex() == expected + + +@pytest.mark.parametrize("environment", ["table", "figure*", None]) +def test_comprehensive(df_ext, environment): + # test as many low level features simultaneously as possible + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index, df_ext.columns = ridx, cidx + stlr = df_ext.style + stlr.set_caption("mycap") + stlr.set_table_styles( + [ + {"selector": "label", "props": ":{fig§item}"}, + {"selector": "position", "props": ":h!"}, + {"selector": "position_float", "props": ":centering"}, + {"selector": "column_format", "props": ":rlrlr"}, + {"selector": "toprule", "props": ":toprule"}, + {"selector": "midrule", "props": ":midrule"}, + {"selector": "bottomrule", "props": ":bottomrule"}, + {"selector": "rowcolors", "props": ":{3}{pink}{}"}, # custom command + ] + ) + stlr.highlight_max(axis=0, props="textbf:--rwrap;cellcolor:[rgb]{1,1,0.6}--rwrap") + stlr.highlight_max(axis=None, props="Huge:--wrap;", subset=[("Z", "a"), ("Z", "b")]) + + expected = ( + """\ +\\begin{table}[h!] +\\centering +\\caption{mycap} +\\label{fig:item} +\\rowcolors{3}{pink}{} +\\begin{tabular}{rlrlr} +\\toprule + & & \\multicolumn{2}{r}{Z} & Y \\\\ + & & a & b & c \\\\ +\\midrule +\\multirow[c]{2}{*}{A} & a & 0 & \\textbf{\\cellcolor[rgb]{1,1,0.6}{-0.61}} & ab \\\\ + & b & 1 & -1.22 & cd \\\\ +B & c & \\textbf{\\cellcolor[rgb]{1,1,0.6}{{\\Huge 2}}} & -2.22 & """ + """\ +\\textbf{\\cellcolor[rgb]{1,1,0.6}{de}} \\\\ +\\bottomrule +\\end{tabular} +\\end{table} +""" + ).replace("table", environment if environment else "table") + result = stlr.format(precision=2).to_latex(environment=environment) + assert result == expected + + +def test_environment_option(styler): + with option_context("styler.latex.environment", "bar-env"): + assert "\\begin{bar-env}" in styler.to_latex() + assert "\\begin{foo-env}" in styler.to_latex(environment="foo-env") + + +def test_parse_latex_table_styles(styler): + styler.set_table_styles( + [ + {"selector": "foo", "props": [("attr", "value")]}, + {"selector": "bar", "props": [("attr", "overwritten")]}, + {"selector": "bar", "props": [("attr", "baz"), ("attr2", "ignored")]}, + {"selector": "label", "props": [("", "{fig§item}")]}, + ] + ) + assert _parse_latex_table_styles(styler.table_styles, "bar") == "baz" + + # test '§' replaced by ':' [for CSS compatibility] + assert _parse_latex_table_styles(styler.table_styles, "label") == "{fig:item}" + + +def test_parse_latex_cell_styles_basic(): # test nesting + cell_style = [("itshape", "--rwrap"), ("cellcolor", "[rgb]{0,1,1}--rwrap")] + expected = "\\itshape{\\cellcolor[rgb]{0,1,1}{text}}" + assert _parse_latex_cell_styles(cell_style, "text") == expected + + +@pytest.mark.parametrize( + "wrap_arg, expected", + [ # test wrapping + ("", "\\ "), + ("--wrap", "{\\ }"), + ("--nowrap", "\\ "), + ("--lwrap", "{\\} "), + ("--dwrap", "{\\}{}"), + ("--rwrap", "\\{}"), + ], +) +def test_parse_latex_cell_styles_braces(wrap_arg, expected): + cell_style = [("", f"{wrap_arg}")] + assert _parse_latex_cell_styles(cell_style, "") == expected + + +def test_parse_latex_header_span(): + cell = {"attributes": 'colspan="3"', "display_value": "text", "cellstyle": []} + expected = "\\multicolumn{3}{Y}{text}" + assert _parse_latex_header_span(cell, "X", "Y") == expected + + cell = {"attributes": 'rowspan="5"', "display_value": "text", "cellstyle": []} + expected = "\\multirow[X]{5}{*}{text}" + assert _parse_latex_header_span(cell, "X", "Y") == expected + + cell = {"display_value": "text", "cellstyle": []} + assert _parse_latex_header_span(cell, "X", "Y") == "text" + + cell = {"display_value": "text", "cellstyle": [("bfseries", "--rwrap")]} + assert _parse_latex_header_span(cell, "X", "Y") == "\\bfseries{text}" + + +def test_parse_latex_table_wrapping(styler): + styler.set_table_styles( + [ + {"selector": "toprule", "props": ":value"}, + {"selector": "bottomrule", "props": ":value"}, + {"selector": "midrule", "props": ":value"}, + {"selector": "column_format", "props": ":value"}, + ] + ) + assert _parse_latex_table_wrapping(styler.table_styles, styler.caption) is False + assert _parse_latex_table_wrapping(styler.table_styles, "some caption") is True + styler.set_table_styles( + [ + {"selector": "not-ignored", "props": ":value"}, + ], + overwrite=False, + ) + assert _parse_latex_table_wrapping(styler.table_styles, None) is True + + +def test_short_caption(styler): + result = styler.to_latex(caption=("full cap", "short cap")) + assert "\\caption[short cap]{full cap}" in result + + +@pytest.mark.parametrize( + "css, expected", + [ + ([("color", "red")], [("color", "{red}")]), # test color and input format types + ( + [("color", "rgb(128, 128, 128 )")], + [("color", "[rgb]{0.502, 0.502, 0.502}")], + ), + ( + [("color", "rgb(128, 50%, 25% )")], + [("color", "[rgb]{0.502, 0.500, 0.250}")], + ), + ( + [("color", "rgba(128,128,128,1)")], + [("color", "[rgb]{0.502, 0.502, 0.502}")], + ), + ([("color", "#FF00FF")], [("color", "[HTML]{FF00FF}")]), + ([("color", "#F0F")], [("color", "[HTML]{FF00FF}")]), + ([("font-weight", "bold")], [("bfseries", "")]), # test font-weight and types + ([("font-weight", "bolder")], [("bfseries", "")]), + ([("font-weight", "normal")], []), + ([("background-color", "red")], [("cellcolor", "{red}--lwrap")]), + ( + [("background-color", "#FF00FF")], # test background-color command and wrap + [("cellcolor", "[HTML]{FF00FF}--lwrap")], + ), + ([("font-style", "italic")], [("itshape", "")]), # test font-style and types + ([("font-style", "oblique")], [("slshape", "")]), + ([("font-style", "normal")], []), + ([("color", "red /*--dwrap*/")], [("color", "{red}--dwrap")]), # css comments + ([("background-color", "red /* --dwrap */")], [("cellcolor", "{red}--dwrap")]), + ], +) +def test_parse_latex_css_conversion(css, expected): + result = _parse_latex_css_conversion(css) + assert result == expected + + +@pytest.mark.parametrize( + "env, inner_env", + [ + (None, "tabular"), + ("table", "tabular"), + ("longtable", "longtable"), + ], +) +@pytest.mark.parametrize( + "convert, exp", [(True, "bfseries"), (False, "font-weightbold")] +) +def test_parse_latex_css_convert_minimal(styler, env, inner_env, convert, exp): + # parameters ensure longtable template is also tested + styler.highlight_max(props="font-weight:bold;") + result = styler.to_latex(convert_css=convert, environment=env) + expected = dedent( + f"""\ + 0 & 0 & \\{exp} -0.61 & ab \\\\ + 1 & \\{exp} 1 & -1.22 & \\{exp} cd \\\\ + \\end{{{inner_env}}} + """ + ) + assert expected in result + + +def test_parse_latex_css_conversion_option(): + css = [("command", "option--latex--wrap")] + expected = [("command", "option--wrap")] + result = _parse_latex_css_conversion(css) + assert result == expected + + +def test_styler_object_after_render(styler): + # GH 42320 + pre_render = styler._copy(deepcopy=True) + styler.to_latex( + column_format="rllr", + position="h", + position_float="centering", + hrules=True, + label="my lab", + caption="my cap", + ) + + assert pre_render.table_styles == styler.table_styles + assert pre_render.caption == styler.caption + + +def test_longtable_comprehensive(styler): + result = styler.to_latex( + environment="longtable", hrules=True, label="fig:A", caption=("full", "short") + ) + expected = dedent( + """\ + \\begin{longtable}{lrrl} + \\caption[short]{full} \\label{fig:A} \\\\ + \\toprule + & A & B & C \\\\ + \\midrule + \\endfirsthead + \\caption[]{full} \\\\ + \\toprule + & A & B & C \\\\ + \\midrule + \\endhead + \\midrule + \\multicolumn{4}{r}{Continued on next page} \\\\ + \\midrule + \\endfoot + \\bottomrule + \\endlastfoot + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{longtable} + """ + ) + assert result == expected + + +def test_longtable_minimal(styler): + result = styler.to_latex(environment="longtable") + expected = dedent( + """\ + \\begin{longtable}{lrrl} + & A & B & C \\\\ + \\endfirsthead + & A & B & C \\\\ + \\endhead + \\multicolumn{4}{r}{Continued on next page} \\\\ + \\endfoot + \\endlastfoot + 0 & 0 & -0.61 & ab \\\\ + 1 & 1 & -1.22 & cd \\\\ + \\end{longtable} + """ + ) + assert result == expected + + +@pytest.mark.parametrize( + "sparse, exp, siunitx", + [ + (True, "{} & \\multicolumn{2}{r}{A} & {B}", True), + (False, "{} & {A} & {A} & {B}", True), + (True, " & \\multicolumn{2}{r}{A} & B", False), + (False, " & A & A & B", False), + ], +) +def test_longtable_multiindex_columns(df, sparse, exp, siunitx): + cidx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df.columns = cidx + with_si = "{} & {a} & {b} & {c} \\\\" + without_si = " & a & b & c \\\\" + expected = dedent( + f"""\ + \\begin{{longtable}}{{l{"SS" if siunitx else "rr"}l}} + {exp} \\\\ + {with_si if siunitx else without_si} + \\endfirsthead + {exp} \\\\ + {with_si if siunitx else without_si} + \\endhead + """ + ) + result = df.style.to_latex( + environment="longtable", sparse_columns=sparse, siunitx=siunitx + ) + assert expected in result + + +@pytest.mark.parametrize( + "caption, cap_exp", + [ + ("full", ("{full}", "")), + (("full", "short"), ("{full}", "[short]")), + ], +) +@pytest.mark.parametrize("label, lab_exp", [(None, ""), ("tab:A", " \\label{tab:A}")]) +def test_longtable_caption_label(styler, caption, cap_exp, label, lab_exp): + cap_exp1 = f"\\caption{cap_exp[1]}{cap_exp[0]}" + cap_exp2 = f"\\caption[]{cap_exp[0]}" + + expected = dedent( + f"""\ + {cap_exp1}{lab_exp} \\\\ + & A & B & C \\\\ + \\endfirsthead + {cap_exp2} \\\\ + """ + ) + assert expected in styler.to_latex( + environment="longtable", caption=caption, label=label + ) + + +@pytest.mark.parametrize("index", [True, False]) +@pytest.mark.parametrize( + "columns, siunitx", + [ + (True, True), + (True, False), + (False, False), + ], +) +def test_apply_map_header_render_mi(df_ext, index, columns, siunitx): + cidx = MultiIndex.from_tuples([("Z", "a"), ("Z", "b"), ("Y", "c")]) + ridx = MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "c")]) + df_ext.index, df_ext.columns = ridx, cidx + styler = df_ext.style + + func = lambda v: "bfseries: --rwrap" if "A" in v or "Z" in v or "c" in v else None + + if index: + styler.map_index(func, axis="index") + if columns: + styler.map_index(func, axis="columns") + + result = styler.to_latex(siunitx=siunitx) + + expected_index = dedent( + """\ + \\multirow[c]{2}{*}{\\bfseries{A}} & a & 0 & -0.610000 & ab \\\\ + \\bfseries{} & b & 1 & -1.220000 & cd \\\\ + B & \\bfseries{c} & 2 & -2.220000 & de \\\\ + """ + ) + assert (expected_index in result) is index + + exp_cols_si = dedent( + """\ + {} & {} & \\multicolumn{2}{r}{\\bfseries{Z}} & {Y} \\\\ + {} & {} & {a} & {b} & {\\bfseries{c}} \\\\ + """ + ) + exp_cols_no_si = """\ + & & \\multicolumn{2}{r}{\\bfseries{Z}} & Y \\\\ + & & a & b & \\bfseries{c} \\\\ +""" + assert ((exp_cols_si if siunitx else exp_cols_no_si) in result) is columns + + +def test_repr_option(styler): + assert "