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repo_id stringlengths 12 110 | file_path stringlengths 24 164 | content stringlengths 3 89.3M | __index_level_0__ int64 0 0 |
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public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/dtypes/test_common.py | from __future__ import annotations
import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas.core.dtypes.astype import astype_array
import pandas.core.dtypes.common as com
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
CategoricalDtypeType,
DatetimeTZDtype,
Ext... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/dtypes/test_inference.py | """
These the test the public routines exposed in types/common.py
related to inference and not otherwise tested in types/test_common.py
"""
import collections
from collections import namedtuple
from collections.abc import Iterator
from datetime import (
date,
datetime,
time,
timedelta,
)
from decimal i... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_downcast.py | import decimal
import numpy as np
import pytest
from pandas.core.dtypes.cast import maybe_downcast_to_dtype
from pandas import (
Series,
Timedelta,
)
import pandas._testing as tm
@pytest.mark.parametrize(
"arr,dtype,expected",
[
(
np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995]),... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_construct_object_arr.py | import pytest
from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
@pytest.mark.parametrize("datum1", [1, 2.0, "3", (4, 5), [6, 7], None])
@pytest.mark.parametrize("datum2", [8, 9.0, "10", (11, 12), [13, 14], None])
def test_cast_1d_array(datum1, datum2):
data = [datum1, datum2]
result... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_infer_datetimelike.py | import numpy as np
import pytest
from pandas import (
DataFrame,
NaT,
Series,
Timestamp,
)
@pytest.mark.parametrize(
"data,exp_size",
[
# see gh-16362.
([[NaT, "a", "b", 0], [NaT, "b", "c", 1]], 8),
([[NaT, "a", 0], [NaT, "b", 1]], 6),
],
)
def test_maybe_infer_to_... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_infer_dtype.py | from datetime import (
date,
datetime,
timedelta,
)
import numpy as np
import pytest
from pandas.core.dtypes.cast import (
infer_dtype_from,
infer_dtype_from_array,
infer_dtype_from_scalar,
)
from pandas.core.dtypes.common import is_dtype_equal
from pandas import (
Categorical,
Interv... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_find_common_type.py | import numpy as np
import pytest
from pandas.core.dtypes.cast import find_common_type
from pandas.core.dtypes.common import pandas_dtype
from pandas.core.dtypes.dtypes import (
CategoricalDtype,
DatetimeTZDtype,
IntervalDtype,
PeriodDtype,
)
from pandas import (
Categorical,
Index,
)
@pytest... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_can_hold_element.py | import numpy as np
from pandas.core.dtypes.cast import can_hold_element
def test_can_hold_element_range(any_int_numpy_dtype):
# GH#44261
dtype = np.dtype(any_int_numpy_dtype)
arr = np.array([], dtype=dtype)
rng = range(2, 127)
assert can_hold_element(arr, rng)
# negatives -> can't be held b... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_construct_from_scalar.py | import numpy as np
import pytest
from pandas.core.dtypes.cast import construct_1d_arraylike_from_scalar
from pandas.core.dtypes.dtypes import CategoricalDtype
from pandas import (
Categorical,
Timedelta,
)
import pandas._testing as tm
def test_cast_1d_array_like_from_scalar_categorical():
# see gh-19565... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_promote.py | """
These test the method maybe_promote from core/dtypes/cast.py
"""
import datetime
from decimal import Decimal
import numpy as np
import pytest
from pandas._libs.tslibs import NaT
from pandas.core.dtypes.cast import maybe_promote
from pandas.core.dtypes.common import is_scalar
from pandas.core.dtypes.dtypes impor... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_maybe_box_native.py | from datetime import datetime
import numpy as np
import pytest
from pandas.core.dtypes.cast import maybe_box_native
from pandas import (
Interval,
Period,
Timedelta,
Timestamp,
)
@pytest.mark.parametrize(
"obj,expected_dtype",
[
(b"\x00\x10", bytes),
(int(4), int),
(... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_dict_compat.py | import numpy as np
from pandas.core.dtypes.cast import dict_compat
from pandas import Timestamp
def test_dict_compat():
data_datetime64 = {np.datetime64("1990-03-15"): 1, np.datetime64("2015-03-15"): 2}
data_unchanged = {1: 2, 3: 4, 5: 6}
expected = {Timestamp("1990-3-15"): 1, Timestamp("2015-03-15"): 2... | 0 |
public_repos/pandas/pandas/tests/dtypes | public_repos/pandas/pandas/tests/dtypes/cast/test_construct_ndarray.py | import numpy as np
import pytest
import pandas._testing as tm
from pandas.core.construction import sanitize_array
@pytest.mark.parametrize(
"values, dtype, expected",
[
([1, 2, 3], None, np.array([1, 2, 3], dtype=np.int64)),
(np.array([1, 2, 3]), None, np.array([1, 2, 3])),
(["1", "2"... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_reductions.py | import numpy as np
import pytest
import pandas as pd
from pandas import Series
import pandas._testing as tm
@pytest.mark.parametrize("operation, expected", [("min", "a"), ("max", "b")])
def test_reductions_series_strings(operation, expected):
# GH#31746
ser = Series(["a", "b"], dtype="string")
res_operat... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_validate.py | import pytest
@pytest.mark.parametrize(
"func",
[
"reset_index",
"_set_name",
"sort_values",
"sort_index",
"rename",
"dropna",
"drop_duplicates",
],
)
@pytest.mark.parametrize("inplace", [1, "True", [1, 2, 3], 5.0])
def test_validate_bool_args(string... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_logical_ops.py | from datetime import datetime
import operator
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
Series,
bdate_range,
)
import pandas._testing as tm
from pandas.core import ops
class TestSeriesLogicalOps:
@pytest.mark.filterwarnings("ignore:Downcasting object dtype arrays:Fu... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_subclass.py | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
pytestmark = pytest.mark.filterwarnings(
"ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
)
class TestSeriesSubclassing:
@pytest.mark.parametrize(
"idx_method, indexer, exp_data, exp_idx"... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_npfuncs.py | """
Tests for np.foo applied to Series, not necessarily ufuncs.
"""
import numpy as np
import pytest
from pandas import Series
import pandas._testing as tm
class TestPtp:
def test_ptp(self):
# GH#21614
N = 1000
arr = np.random.default_rng(2).standard_normal(N)
ser = Series(arr)
... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_formats.py | from datetime import (
datetime,
timedelta,
)
import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
DataFrame,
Index,
Series,
date_range,
option_context,
period_range,
timedelta_range,
)
import pandas._testing as tm
class TestSeriesRepr:
d... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_missing.py | from datetime import timedelta
import numpy as np
import pytest
from pandas._libs import iNaT
import pandas as pd
from pandas import (
Categorical,
Index,
NaT,
Series,
isna,
)
import pandas._testing as tm
class TestSeriesMissingData:
def test_categorical_nan_handling(self):
# NaNs a... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_arithmetic.py | from datetime import (
date,
timedelta,
timezone,
)
from decimal import Decimal
import operator
import numpy as np
import pytest
from pandas._libs import lib
from pandas._libs.tslibs import IncompatibleFrequency
import pandas as pd
from pandas import (
Categorical,
DatetimeTZDtype,
Index,
... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_api.py | import inspect
import pydoc
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
Series,
date_range,
)
import pandas._testing as tm
class TestSeriesMisc:
def test_tab_completion(self):
# GH 9910
s = Series(list("abcd"))
# Series of s... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_constructors.py | from collections import OrderedDict
from collections.abc import Iterator
from datetime import (
datetime,
timedelta,
)
from dateutil.tz import tzoffset
import numpy as np
from numpy import ma
import pytest
from pandas._libs import (
iNaT,
lib,
)
from pandas.errors import IntCastingNaNError
import pand... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_iteration.py | class TestIteration:
def test_keys(self, datetime_series):
assert datetime_series.keys() is datetime_series.index
def test_iter_datetimes(self, datetime_series):
for i, val in enumerate(datetime_series):
# pylint: disable-next=unnecessary-list-index-lookup
assert val == ... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_ufunc.py | from collections import deque
import re
import string
import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
import pandas._testing as tm
from pandas.arrays import SparseArray
@pytest.fixture(params=[np.add, np.logaddexp])
def ufunc(request):
# dunder op
return reque... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_unary.py | import pytest
from pandas import Series
import pandas._testing as tm
class TestSeriesUnaryOps:
# __neg__, __pos__, __invert__
def test_neg(self):
ser = tm.makeStringSeries()
ser.name = "series"
tm.assert_series_equal(-ser, -1 * ser)
def test_invert(self):
ser = tm.makeSt... | 0 |
public_repos/pandas/pandas/tests | public_repos/pandas/pandas/tests/series/test_cumulative.py | """
Tests for Series cumulative operations.
See also
--------
tests.frame.test_cumulative
"""
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
methods = {
"cumsum": np.cumsum,
"cumprod": np.cumprod,
"cummin": np.minimum.accumulate,
"cummax": np.maximum.accumulate,
}
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/accessors/test_dt_accessor.py | import calendar
from datetime import (
date,
datetime,
time,
)
import locale
import unicodedata
import numpy as np
import pytest
import pytz
from pandas._libs.tslibs.timezones import maybe_get_tz
from pandas.errors import SettingWithCopyError
from pandas.core.dtypes.common import (
is_integer_dtype,
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/accessors/test_struct_accessor.py | import re
import pytest
from pandas import (
ArrowDtype,
DataFrame,
Index,
Series,
)
import pandas._testing as tm
pa = pytest.importorskip("pyarrow")
def test_struct_accessor_dtypes():
ser = Series(
[],
dtype=ArrowDtype(
pa.struct(
[
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/accessors/test_str_accessor.py | import pytest
from pandas import Series
import pandas._testing as tm
class TestStrAccessor:
def test_str_attribute(self):
# GH#9068
methods = ["strip", "rstrip", "lstrip"]
ser = Series([" jack", "jill ", " jesse ", "frank"])
for method in methods:
expected = Series([ge... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/accessors/test_list_accessor.py | import re
import pytest
from pandas import (
ArrowDtype,
Series,
)
import pandas._testing as tm
pa = pytest.importorskip("pyarrow")
from pandas.compat import pa_version_under11p0
@pytest.mark.parametrize(
"list_dtype",
(
pa.list_(pa.int64()),
pa.list_(pa.int64(), list_size=3),
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/accessors/test_sparse_accessor.py | from pandas import Series
class TestSparseAccessor:
def test_sparse_accessor_updates_on_inplace(self):
ser = Series([1, 1, 2, 3], dtype="Sparse[int]")
return_value = ser.drop([0, 1], inplace=True)
assert return_value is None
assert ser.sparse.density == 1.0
| 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/accessors/test_cat_accessor.py | import numpy as np
import pytest
from pandas import (
Categorical,
DataFrame,
Index,
Series,
Timestamp,
date_range,
period_range,
timedelta_range,
)
import pandas._testing as tm
from pandas.core.arrays.categorical import CategoricalAccessor
from pandas.core.indexes.accessors import Prop... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_argsort.py | import numpy as np
import pytest
from pandas import (
Series,
Timestamp,
isna,
)
import pandas._testing as tm
class TestSeriesArgsort:
def test_argsort_axis(self):
# GH#54257
ser = Series(range(3))
msg = "No axis named 2 for object type Series"
with pytest.raises(Valu... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_truncate.py | from datetime import datetime
import pytest
import pandas as pd
from pandas import (
Series,
date_range,
)
import pandas._testing as tm
class TestTruncate:
def test_truncate_datetimeindex_tz(self):
# GH 9243
idx = date_range("4/1/2005", "4/30/2005", freq="D", tz="US/Pacific")
s =... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_is_monotonic.py | import numpy as np
from pandas import (
Series,
date_range,
)
class TestIsMonotonic:
def test_is_monotonic_numeric(self):
ser = Series(np.random.default_rng(2).integers(0, 10, size=1000))
assert not ser.is_monotonic_increasing
ser = Series(np.arange(1000))
assert ser.is_mo... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_between.py | import numpy as np
import pytest
from pandas import (
Series,
bdate_range,
date_range,
period_range,
)
import pandas._testing as tm
class TestBetween:
def test_between(self):
series = Series(date_range("1/1/2000", periods=10))
left, right = series[[2, 7]]
result = series.... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_explode.py | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
def test_basic():
s = pd.Series([[0, 1, 2], np.nan, [], (3, 4)], index=list("abcd"), name="foo")
result = s.explode()
expected = pd.Series(
[0, 1, 2, np.nan, np.nan, 3, 4], index=list("aaabcdd"), dtype=object, name=... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_to_numpy.py | import numpy as np
import pytest
from pandas import (
NA,
Series,
)
import pandas._testing as tm
@pytest.mark.parametrize("dtype", ["int64", "float64"])
def test_to_numpy_na_value(dtype):
# GH#48951
ser = Series([1, 2, NA, 4])
result = ser.to_numpy(dtype=dtype, na_value=0)
expected = np.array... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_sort_values.py | import numpy as np
import pytest
from pandas import (
Categorical,
DataFrame,
Series,
)
import pandas._testing as tm
class TestSeriesSortValues:
def test_sort_values(self, datetime_series, using_copy_on_write):
# check indexes are reordered corresponding with the values
ser = Series([... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_dropna.py | import numpy as np
import pytest
from pandas import (
DatetimeIndex,
IntervalIndex,
NaT,
Period,
Series,
Timestamp,
)
import pandas._testing as tm
class TestDropna:
def test_dropna_empty(self):
ser = Series([], dtype=object)
assert len(ser.dropna()) == 0
return_va... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_is_unique.py | import numpy as np
import pytest
from pandas import Series
@pytest.mark.parametrize(
"data, expected",
[
(np.random.default_rng(2).integers(0, 10, size=1000), False),
(np.arange(1000), True),
([], True),
([np.nan], True),
(["foo", "bar", np.nan], True),
(["foo"... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_astype.py | from datetime import (
datetime,
timedelta,
)
from importlib import reload
import string
import sys
import numpy as np
import pytest
from pandas._libs.tslibs import iNaT
import pandas.util._test_decorators as td
from pandas import (
NA,
Categorical,
CategoricalDtype,
DatetimeTZDtype,
Inde... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_isin.py | import numpy as np
import pytest
import pandas as pd
from pandas import (
Series,
date_range,
)
import pandas._testing as tm
from pandas.core import algorithms
from pandas.core.arrays import PeriodArray
class TestSeriesIsIn:
def test_isin(self):
s = Series(["A", "B", "C", "a", "B", "B", "A", "C"]... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_describe.py | import numpy as np
import pytest
from pandas.compat.numpy import np_version_gte1p25
from pandas.core.dtypes.common import (
is_complex_dtype,
is_extension_array_dtype,
)
from pandas import (
NA,
Period,
Series,
Timedelta,
Timestamp,
date_range,
)
import pandas._testing as tm
class T... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_tolist.py | import pytest
import pandas.util._test_decorators as td
from pandas import (
Interval,
Period,
Series,
Timedelta,
Timestamp,
)
@pytest.mark.parametrize(
"values, dtype, expected_dtype",
(
([1], "int64", int),
([1], "Int64", int),
([1.0], "float64", float),
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_quantile.py | import numpy as np
import pytest
from pandas.core.dtypes.common import is_integer
import pandas as pd
from pandas import (
Index,
Series,
)
import pandas._testing as tm
from pandas.core.indexes.datetimes import Timestamp
class TestSeriesQuantile:
def test_quantile(self, datetime_series):
q = dat... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_get_numeric_data.py | from pandas import (
Index,
Series,
date_range,
)
import pandas._testing as tm
class TestGetNumericData:
def test_get_numeric_data_preserve_dtype(
self, using_copy_on_write, warn_copy_on_write
):
# get the numeric data
obj = Series([1, 2, 3])
result = obj._get_numer... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_head_tail.py | import pandas._testing as tm
def test_head_tail(string_series):
tm.assert_series_equal(string_series.head(), string_series[:5])
tm.assert_series_equal(string_series.head(0), string_series[0:0])
tm.assert_series_equal(string_series.tail(), string_series[-5:])
tm.assert_series_equal(string_series.tail(0... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_pct_change.py | import numpy as np
import pytest
from pandas import (
Series,
date_range,
)
import pandas._testing as tm
class TestSeriesPctChange:
def test_pct_change(self, datetime_series):
msg = (
"The 'fill_method' keyword being not None and the 'limit' keyword in "
"Series.pct_change... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_dtypes.py | import numpy as np
class TestSeriesDtypes:
def test_dtype(self, datetime_series):
assert datetime_series.dtype == np.dtype("float64")
assert datetime_series.dtypes == np.dtype("float64")
| 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_diff.py | import numpy as np
import pytest
from pandas import (
Series,
TimedeltaIndex,
date_range,
)
import pandas._testing as tm
class TestSeriesDiff:
def test_diff_np(self):
# TODO(__array_function__): could make np.diff return a Series
# matching ser.diff()
ser = Series(np.arange(... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_reindex_like.py | from datetime import datetime
import numpy as np
from pandas import Series
import pandas._testing as tm
def test_reindex_like(datetime_series):
other = datetime_series[::2]
tm.assert_series_equal(
datetime_series.reindex(other.index), datetime_series.reindex_like(other)
)
# GH#7179
day1... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_compare.py | import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
@pytest.mark.parametrize("align_axis", [0, 1, "index", "columns"])
def test_compare_axis(align_axis):
# GH#30429
s1 = pd.Series(["a", "b", "c"])
s2 = pd.Series(["x", "b", "z"])
result = s1.compare(s2, align_axis=align_... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_set_name.py | from datetime import datetime
from pandas import Series
class TestSetName:
def test_set_name(self):
ser = Series([1, 2, 3])
ser2 = ser._set_name("foo")
assert ser2.name == "foo"
assert ser.name is None
assert ser is not ser2
def test_set_name_attribute(self):
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_reindex.py | import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import (
NA,
Categorical,
Float64Dtype,
Index,
MultiIndex,
NaT,
Period,
PeriodIndex,
RangeIndex,
Series,
Timedelta,
Timestamp,
date_range,
isna,
)
import pandas._testing as tm... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_equals.py | from contextlib import nullcontext
import copy
import numpy as np
import pytest
from pandas._libs.missing import is_matching_na
from pandas.compat.numpy import np_version_gte1p25
from pandas.core.dtypes.common import is_float
from pandas import (
Index,
MultiIndex,
Series,
)
import pandas._testing as tm... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_drop.py | import pytest
from pandas import (
Index,
Series,
)
import pandas._testing as tm
from pandas.api.types import is_bool_dtype
@pytest.mark.parametrize(
"data, index, drop_labels, axis, expected_data, expected_index",
[
# Unique Index
([1, 2], ["one", "two"], ["two"], 0, [1], ["one"]),
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_item.py | """
Series.item method, mainly testing that we get python scalars as opposed to
numpy scalars.
"""
import pytest
from pandas import (
Series,
Timedelta,
Timestamp,
date_range,
)
class TestItem:
def test_item(self):
# We are testing that we get python scalars as opposed to numpy scalars
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_view.py | import numpy as np
import pytest
from pandas import (
Index,
Series,
array,
date_range,
)
import pandas._testing as tm
class TestView:
def test_view_i8_to_datetimelike(self):
dti = date_range("2000", periods=4, tz="US/Central")
ser = Series(dti.asi8)
result = ser.view(dti... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_rank.py | from itertools import chain
import operator
import numpy as np
import pytest
from pandas._libs.algos import (
Infinity,
NegInfinity,
)
import pandas.util._test_decorators as td
from pandas import (
NA,
NaT,
Series,
Timestamp,
date_range,
)
import pandas._testing as tm
from pandas.api.type... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_nunique.py | import numpy as np
from pandas import (
Categorical,
Series,
)
def test_nunique():
# basics.rst doc example
series = Series(np.random.default_rng(2).standard_normal(500))
series[20:500] = np.nan
series[10:20] = 5000
result = series.nunique()
assert result == 11
def test_nunique_cate... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_rename.py | from datetime import datetime
import re
import numpy as np
import pytest
from pandas import (
Index,
MultiIndex,
Series,
)
import pandas._testing as tm
class TestRename:
def test_rename(self, datetime_series):
ts = datetime_series
renamer = lambda x: x.strftime("%Y%m%d")
rena... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_repeat.py | import numpy as np
import pytest
from pandas import (
MultiIndex,
Series,
)
import pandas._testing as tm
class TestRepeat:
def test_repeat(self):
ser = Series(np.random.default_rng(2).standard_normal(3), index=["a", "b", "c"])
reps = ser.repeat(5)
exp = Series(ser.values.repeat(5... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_info.py | from io import StringIO
from string import ascii_uppercase
import textwrap
import numpy as np
import pytest
from pandas.compat import PYPY
from pandas import (
CategoricalIndex,
MultiIndex,
Series,
date_range,
)
def test_info_categorical_column_just_works():
n = 2500
data = np.array(list("a... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_unique.py | import numpy as np
from pandas import (
Categorical,
IntervalIndex,
Series,
date_range,
)
import pandas._testing as tm
class TestUnique:
def test_unique_uint64(self):
ser = Series([1, 2, 2**63, 2**63], dtype=np.uint64)
res = ser.unique()
exp = np.array([1, 2, 2**63], dtype... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_sort_index.py | import numpy as np
import pytest
from pandas import (
DatetimeIndex,
IntervalIndex,
MultiIndex,
Series,
)
import pandas._testing as tm
@pytest.fixture(params=["quicksort", "mergesort", "heapsort", "stable"])
def sort_kind(request):
return request.param
class TestSeriesSortIndex:
def test_so... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_size.py | import pytest
from pandas import Series
@pytest.mark.parametrize(
"data, index, expected",
[
([1, 2, 3], None, 3),
({"a": 1, "b": 2, "c": 3}, None, 3),
([1, 2, 3], ["x", "y", "z"], 3),
([1, 2, 3, 4, 5], ["x", "y", "z", "w", "n"], 5),
([1, 2, 3], None, 3),
([1, ... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_duplicated.py | import numpy as np
import pytest
from pandas import (
NA,
Categorical,
Series,
)
import pandas._testing as tm
@pytest.mark.parametrize(
"keep, expected",
[
("first", Series([False, False, True, False, True], name="name")),
("last", Series([True, True, False, False, False], name="n... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_to_csv.py | from datetime import datetime
from io import StringIO
import numpy as np
import pytest
import pandas as pd
from pandas import Series
import pandas._testing as tm
from pandas.io.common import get_handle
class TestSeriesToCSV:
def read_csv(self, path, **kwargs):
params = {"index_col": 0, "header": None}
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_value_counts.py | import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
CategoricalIndex,
Index,
Series,
)
import pandas._testing as tm
class TestSeriesValueCounts:
def test_value_counts_datetime(self, unit):
# most dtypes are tested in tests/base
values = [
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_values.py | import numpy as np
import pytest
from pandas import (
IntervalIndex,
Series,
period_range,
)
import pandas._testing as tm
class TestValues:
@pytest.mark.parametrize(
"data",
[
period_range("2000", periods=4),
IntervalIndex.from_breaks([1, 2, 3, 4]),
],
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_align.py | from datetime import timezone
import numpy as np
import pytest
import pandas as pd
from pandas import (
Series,
date_range,
period_range,
)
import pandas._testing as tm
@pytest.mark.parametrize(
"first_slice,second_slice",
[
[[2, None], [None, -5]],
[[None, 0], [None, -5]],
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_infer_objects.py | import numpy as np
from pandas import (
Series,
interval_range,
)
import pandas._testing as tm
class TestInferObjects:
def test_copy(self, index_or_series):
# GH#50096
# case where we don't need to do inference because it is already non-object
obj = index_or_series(np.array([1, 2,... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_fillna.py | from datetime import (
datetime,
timedelta,
timezone,
)
import numpy as np
import pytest
import pytz
from pandas import (
Categorical,
DataFrame,
DatetimeIndex,
NaT,
Period,
Series,
Timedelta,
Timestamp,
date_range,
isna,
)
import pandas._testing as tm
from pandas.c... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_searchsorted.py | import numpy as np
import pytest
import pandas as pd
from pandas import (
Series,
Timestamp,
date_range,
)
import pandas._testing as tm
from pandas.api.types import is_scalar
class TestSeriesSearchSorted:
def test_searchsorted(self):
ser = Series([1, 2, 3])
result = ser.searchsorted(... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_combine.py | from pandas import Series
import pandas._testing as tm
class TestCombine:
def test_combine_scalar(self):
# GH#21248
# Note - combine() with another Series is tested elsewhere because
# it is used when testing operators
ser = Series([i * 10 for i in range(5)])
result = ser.c... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_reset_index.py | from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
RangeIndex,
Series,
date_range,
)
import pandas._testing as tm
class TestResetIndex:
def test_reset_index_dti_round_trip(self):
dti = date_range(s... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_interpolate.py | import numpy as np
import pytest
import pandas.util._test_decorators as td
import pandas as pd
from pandas import (
Index,
MultiIndex,
Series,
date_range,
isna,
)
import pandas._testing as tm
@pytest.fixture(
params=[
"linear",
"index",
"values",
"nearest",
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_rename_axis.py | import pytest
from pandas import (
Index,
MultiIndex,
Series,
)
import pandas._testing as tm
class TestSeriesRenameAxis:
def test_rename_axis_mapper(self):
# GH 19978
mi = MultiIndex.from_product([["a", "b", "c"], [1, 2]], names=["ll", "nn"])
ser = Series(list(range(len(mi))),... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_clip.py | from datetime import datetime
import numpy as np
import pytest
import pandas as pd
from pandas import (
Series,
Timestamp,
isna,
notna,
)
import pandas._testing as tm
class TestSeriesClip:
def test_clip(self, datetime_series):
val = datetime_series.median()
assert datetime_serie... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_asof.py | import numpy as np
import pytest
from pandas._libs.tslibs import IncompatibleFrequency
from pandas import (
DatetimeIndex,
PeriodIndex,
Series,
Timestamp,
date_range,
isna,
notna,
offsets,
period_range,
)
import pandas._testing as tm
class TestSeriesAsof:
def test_asof_nanose... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_map.py | from collections import (
Counter,
defaultdict,
)
from decimal import Decimal
import math
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
isna,
timedelta_range,
)
import pandas._testing as tm
def test_series_map_box_time... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_combine_first.py | from datetime import datetime
import numpy as np
import pandas as pd
from pandas import (
Period,
Series,
date_range,
period_range,
to_datetime,
)
import pandas._testing as tm
class TestCombineFirst:
def test_combine_first_period_datetime(self):
# GH#3367
didx = date_range(st... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_isna.py | """
We also test Series.notna in this file.
"""
import numpy as np
from pandas import (
Period,
Series,
)
import pandas._testing as tm
class TestIsna:
def test_isna_period_dtype(self):
# GH#13737
ser = Series([Period("2011-01", freq="M"), Period("NaT", freq="M")])
expected = Seri... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_drop_duplicates.py | import numpy as np
import pytest
import pandas as pd
from pandas import (
Categorical,
Series,
)
import pandas._testing as tm
@pytest.mark.parametrize(
"keep, expected",
[
("first", Series([False, False, False, False, True, True, False])),
("last", Series([False, True, True, False, Fa... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_replace.py | import re
import numpy as np
import pytest
import pandas as pd
import pandas._testing as tm
from pandas.core.arrays import IntervalArray
class TestSeriesReplace:
def test_replace_explicit_none(self):
# GH#36984 if the user explicitly passes value=None, give it to them
ser = pd.Series([0, 0, ""],... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_unstack.py | import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
MultiIndex,
Series,
)
import pandas._testing as tm
def test_unstack_preserves_object():
mi = MultiIndex.from_product([["bar", "foo"], ["one", "two"]])
ser = Series(np.arange(4.0), index=mi, dtype=object)
re... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_tz_localize.py | from datetime import timezone
import pytest
import pytz
from pandas._libs.tslibs import timezones
from pandas import (
DatetimeIndex,
NaT,
Series,
Timestamp,
date_range,
)
import pandas._testing as tm
class TestTZLocalize:
def test_series_tz_localize_ambiguous_bool(self):
# make sur... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_pop.py | from pandas import Series
import pandas._testing as tm
def test_pop():
# GH#6600
ser = Series([0, 4, 0], index=["A", "B", "C"], name=4)
result = ser.pop("B")
assert result == 4
expected = Series([0, 0], index=["A", "C"], name=4)
tm.assert_series_equal(ser, expected)
| 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_cov_corr.py | import math
import numpy as np
import pytest
import pandas as pd
from pandas import (
Series,
isna,
)
import pandas._testing as tm
class TestSeriesCov:
def test_cov(self, datetime_series):
# full overlap
tm.assert_almost_equal(
datetime_series.cov(datetime_series), datetime_s... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_count.py | import numpy as np
import pandas as pd
from pandas import (
Categorical,
Series,
)
import pandas._testing as tm
class TestSeriesCount:
def test_count(self, datetime_series):
assert datetime_series.count() == len(datetime_series)
datetime_series[::2] = np.nan
assert datetime_seri... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_convert_dtypes.py | from itertools import product
import numpy as np
import pytest
from pandas._libs import lib
import pandas as pd
import pandas._testing as tm
# Each test case consists of a tuple with the data and dtype to create the
# test Series, the default dtype for the expected result (which is valid
# for most cases), and the ... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_matmul.py | import operator
import numpy as np
import pytest
from pandas import (
DataFrame,
Series,
)
import pandas._testing as tm
class TestMatmul:
def test_matmul(self):
# matmul test is for GH#10259
a = Series(
np.random.default_rng(2).standard_normal(4), index=["p", "q", "r", "s"]
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_to_frame.py | import pytest
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
class TestToFrame:
def test_to_frame_respects_name_none(self):
# GH#44212 if we explicitly pass name=None, then that should be respected,
# not changed to 0
# GH-45448 this is first de... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_autocorr.py | import numpy as np
class TestAutoCorr:
def test_autocorr(self, datetime_series):
# Just run the function
corr1 = datetime_series.autocorr()
# Now run it with the lag parameter
corr2 = datetime_series.autocorr(lag=1)
# corr() with lag needs Series of at least length 2
... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_to_dict.py | import collections
import numpy as np
import pytest
from pandas import Series
import pandas._testing as tm
class TestSeriesToDict:
@pytest.mark.parametrize(
"mapping", (dict, collections.defaultdict(list), collections.OrderedDict)
)
def test_to_dict(self, mapping, datetime_series):
# GH#... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_add_prefix_suffix.py | import pytest
from pandas import Index
import pandas._testing as tm
def test_add_prefix_suffix(string_series):
with_prefix = string_series.add_prefix("foo#")
expected = Index([f"foo#{c}" for c in string_series.index])
tm.assert_index_equal(with_prefix.index, expected)
with_suffix = string_series.add... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_update.py | import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas import (
CategoricalDtype,
DataFrame,
NaT,
Series,
Timestamp,
)
import pandas._testing as tm
class TestUpdate:
def test_update(self, using_copy_on_write):
s = Series([1.5, np.nan, 3.0, 4.0, np.nan]... | 0 |
public_repos/pandas/pandas/tests/series | public_repos/pandas/pandas/tests/series/methods/test_copy.py | import numpy as np
import pytest
from pandas import (
Series,
Timestamp,
)
import pandas._testing as tm
class TestCopy:
@pytest.mark.parametrize("deep", ["default", None, False, True])
def test_copy(self, deep, using_copy_on_write, warn_copy_on_write):
ser = Series(np.arange(10), dtype="float... | 0 |
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