"""
This file is very long and growing, but it was decided to not split it yet, as
it's still manageable (2020-03-17, ~1.1k LoC). See gh-31989

Instead of splitting it was decided to define sections here:
- Configuration / Settings
- Autouse fixtures
- Common arguments
- Missing values & co.
- Classes
- Indices
- Series'
- DataFrames
- Operators & Operations
- Data sets/files
- Time zones
- Dtypes
- Misc
"""

from __future__ import annotations

from collections import abc
import contextlib
from datetime import (
    UTC,
    date,
    datetime,
    time,
    timedelta,
    timezone,
)
from decimal import Decimal
import gc
import operator
import os
from typing import (
    TYPE_CHECKING,
    Any,
)
import uuid

from dateutil.tz import (
    tzlocal,
    tzutc,
)
import numpy as np
import pytest

from pandas._config import using_string_dtype

from pandas.compat._optional import import_optional_dependency
import pandas.util._test_decorators as td

from pandas.core.dtypes.dtypes import (
    DatetimeTZDtype,
    IntervalDtype,
)

import pandas as pd
import pandas._testing as tm
from pandas.core import ops
from pandas.core.indexes.api import (
    Index,
    MultiIndex,
)

if TYPE_CHECKING:
    from collections.abc import (
        Callable,
        Hashable,
        Iterator,
    )

try:
    import pyarrow as pa
except ImportError:
    has_pyarrow = False
else:
    del pa
    has_pyarrow = True

pytz = import_optional_dependency("pytz", errors="ignore")


# ----------------------------------------------------------------
# Configuration / Settings
# ----------------------------------------------------------------
# pytest

PANDAS_MARKERS = [
    "single_cpu: tests that should run on a single cpu only",
    "slow: mark a test as slow",
    "network: mark a test as network",
    "db: tests requiring a database (mysql or postgres)",
    "clipboard: mark a pd.read_clipboard test",
    "arm_slow: mark a test as slow for arm64 architecture",
    "high_memory: mark a test as requiring >5GB of memory",
]


def pytest_addoption(parser) -> None:
    parser.addoption(
        "--no-strict-data-files",
        action="store_false",
        dest="strict_data_files",
        default=True,
        help="Don't fail if a test is skipped for missing data file.",
    )
    parser.addoption(
        "--run-high-memory",
        action="store_true",
        help="Run tests marked high_memory (>5GB of memory).",
    )


def pytest_configure(config) -> None:
    for marker in PANDAS_MARKERS:
        config.addinivalue_line("markers", marker)


def pytest_sessionstart(session):
    import doctest
    import inspect

    # https://github.com/pandas-dev/pandas/pull/62988
    # When we modify the __module__ of a class, the __module__ on the methods
    # of that class do not change. When these two disagree, doctests would not
    # typically run. We hack `DocTestFinder` to avoid this.
    orig = doctest.DocTestFinder._from_module  # type: ignore[attr-defined]

    def _from_module(self, module, object):
        # When . is in __qualname__, object is a method of a class.
        if inspect.isfunction(object) and "." in object.__qualname__:
            # We only get here when the class that the method is on is from the
            # appropriate module. So ignore checking the __module__ of the method
            # itself and run the doctest.
            return True
        return orig(self, module, object)

    doctest.DocTestFinder._from_module = _from_module  # type: ignore[attr-defined]


def ignore_doctest_warning(item: pytest.Item, path: str, message: str) -> None:
    """Ignore doctest warning.

    Parameters
    ----------
    item : pytest.Item
        pytest test item.
    path : str
        Module path to Python object, e.g. "pandas.DataFrame.append". A
        warning will be filtered when item.name ends with in given path. So it is
        sufficient to specify e.g. "DataFrame.append".
    message : str
        Message to be filtered.
    """
    if item.name.endswith(path):
        item.add_marker(pytest.mark.filterwarnings(f"ignore:{message}"))


def pytest_collection_modifyitems(items, config) -> None:
    if not config.getoption("--run-high-memory"):
        skip_high_memory = pytest.mark.skip(reason="need --run-high-memory to run")
        for item in items:
            if "high_memory" in item.keywords:
                item.add_marker(skip_high_memory)

    is_doctest = config.getoption("--doctest-modules") or config.getoption(
        "--doctest-cython", default=False
    )

    # Warnings from doctests that can be ignored; place reason in comment above.
    # Each entry specifies (path, message) - see the ignore_doctest_warning function
    ignored_doctest_warnings = [
        ("api.interchange.from_dataframe", "The DataFrame Interchange Protocol"),
        ("is_int64_dtype", "is_int64_dtype is deprecated"),
        ("is_interval_dtype", "is_interval_dtype is deprecated"),
        ("is_period_dtype", "is_period_dtype is deprecated"),
        ("is_datetime64tz_dtype", "is_datetime64tz_dtype is deprecated"),
        ("is_categorical_dtype", "is_categorical_dtype is deprecated"),
        ("is_sparse", "is_sparse is deprecated"),
        ("CategoricalDtype._from_values_or_dtype", "Constructing a Categorical"),
        ("DataFrame.__dataframe__", "The DataFrame Interchange Protocol"),
        ("DataFrameGroupBy.corrwith", "DataFrameGroupBy.corrwith is deprecated"),
        ("to_pytimedelta", "The behavior of TimedeltaProperties.to_pytimedelta"),
        # Docstring divides by zero to show nan result
        ("Series.autocorr", "invalid value encountered in divide"),
        ("Series.corr", "invalid value encountered in divide"),
        # Docstring divides by zero to show behavior difference
        ("missing.mask_zero_div_zero", "divide by zero encountered"),
        ("Timestamp.utcfromtimestamp", "Timestamp.utcfromtimestamp is deprecated"),
        ("BaseOffset.name.__get__", "The 'name' property is deprecated"),
        (
            # matches both DatetimeProperties.freq and TimedeltaProperties.freq
            "Properties.freq",
            "A future version of pandas will return a BaseOffset",
        ),
    ]

    if is_doctest:
        for item in items:
            for path, message in ignored_doctest_warnings:
                ignore_doctest_warning(item, path, message)


# ----------------------------------------------------------------
# Autouse fixtures
# ----------------------------------------------------------------


# https://github.com/pytest-dev/pytest/issues/11873
# Would like to avoid autouse=True, but cannot as of pytest 8.0.0
@pytest.fixture(autouse=True)
def add_doctest_imports(doctest_namespace) -> None:
    """
    Make `np` and `pd` names available for doctests.
    """
    doctest_namespace["np"] = np
    doctest_namespace["pd"] = pd


# ----------------------------------------------------------------
# Common arguments
# ----------------------------------------------------------------
@pytest.fixture(params=[0, 1, "index", "columns"], ids=lambda x: f"axis={x!r}")
def axis(request):
    """
    Fixture for returning the axis numbers of a DataFrame.
    """
    return request.param


@pytest.fixture(params=[True, False])
def observed(request):
    """
    Pass in the observed keyword to groupby for [True, False]
    This indicates whether categoricals should return values for
    values which are not in the grouper [False / None], or only values which
    appear in the grouper [True]. [None] is supported for future compatibility
    if we decide to change the default (and would need to warn if this
    parameter is not passed).
    """
    return request.param


@pytest.fixture(params=[True, False, None])
def ordered(request):
    """
    Boolean 'ordered' parameter for Categorical.
    """
    return request.param


@pytest.fixture(params=[True, False])
def dropna(request):
    """
    Boolean 'dropna' parameter.
    """
    return request.param


@pytest.fixture(params=[True, False])
def sort(request):
    """
    Boolean 'sort' parameter.
    """
    return request.param


@pytest.fixture(params=[True, False])
def skipna(request):
    """
    Boolean 'skipna' parameter.
    """
    return request.param


@pytest.fixture(params=["first", "last", False])
def keep(request):
    """
    Valid values for the 'keep' parameter used in
    .duplicated or .drop_duplicates
    """
    return request.param


@pytest.fixture(params=["both", "neither", "left", "right"])
def inclusive_endpoints_fixture(request):
    """
    Fixture for trying all interval 'inclusive' parameters.
    """
    return request.param


@pytest.fixture(params=["left", "right", "both", "neither"])
def closed(request):
    """
    Fixture for trying all interval closed parameters.
    """
    return request.param


@pytest.fixture(params=["left", "right", "both", "neither"])
def other_closed(request):
    """
    Secondary closed fixture to allow parametrizing over all pairs of closed.
    """
    return request.param


@pytest.fixture(
    params=[
        None,
        "gzip",
        "bz2",
        "zip",
        "xz",
        "tar",
        pytest.param("zstd", marks=td.skip_if_no("zstandard")),
    ]
)
def compression(request):
    """
    Fixture for trying common compression types in compression tests.
    """
    return request.param


@pytest.fixture(
    params=[
        "gzip",
        "bz2",
        "zip",
        "xz",
        "tar",
        pytest.param("zstd", marks=td.skip_if_no("zstandard")),
    ]
)
def compression_only(request):
    """
    Fixture for trying common compression types in compression tests excluding
    uncompressed case.
    """
    return request.param


@pytest.fixture(params=[True, False])
def writable(request):
    """
    Fixture that an array is writable.
    """
    return request.param


@pytest.fixture(params=["inner", "outer", "left", "right"])
def join_type(request):
    """
    Fixture for trying all types of join operations.
    """
    return request.param


@pytest.fixture(params=["nlargest", "nsmallest"])
def nselect_method(request):
    """
    Fixture for trying all nselect methods.
    """
    return request.param


@pytest.fixture(params=[None, "ignore"])
def na_action(request):
    """
    Fixture for 'na_action' argument in map.
    """
    return request.param


@pytest.fixture(params=[True, False])
def ascending(request):
    """
    Fixture for 'ascending' argument in sort_values/sort_index/rank.
    """
    return request.param


@pytest.fixture(params=["average", "min", "max", "first", "dense"])
def rank_method(request):
    """
    Fixture for 'rank' argument in rank.
    """
    return request.param


@pytest.fixture(params=[True, False])
def as_index(request):
    """
    Fixture for 'as_index' argument in groupby.
    """
    return request.param


@pytest.fixture(params=[True, False])
def cache(request):
    """
    Fixture for 'cache' argument in to_datetime.
    """
    return request.param


@pytest.fixture(params=[True, False])
def parallel(request):
    """
    Fixture for parallel keyword argument for numba.jit.
    """
    return request.param


# Can parameterize nogil over True | False, but limiting per
# https://github.com/pandas-dev/pandas/pull/41971#issuecomment-860607472


@pytest.fixture(params=[False])
def nogil(request):
    """
    Fixture for nogil keyword argument for numba.jit.
    """
    return request.param


# ----------------------------------------------------------------
# Missing values & co.
# ----------------------------------------------------------------
@pytest.fixture(params=tm.NULL_OBJECTS, ids=lambda x: type(x).__name__)
def nulls_fixture(request):
    """
    Fixture for each null type in pandas.
    """
    return request.param


nulls_fixture2 = nulls_fixture  # Generate cartesian product of nulls_fixture


@pytest.fixture(params=[None, np.nan, pd.NaT])
def unique_nulls_fixture(request):
    """
    Fixture for each null type in pandas, each null type exactly once.
    """
    return request.param


# Generate cartesian product of unique_nulls_fixture:
unique_nulls_fixture2 = unique_nulls_fixture


@pytest.fixture(params=tm.NP_NAT_OBJECTS, ids=lambda x: type(x).__name__)
def np_nat_fixture(request):
    """
    Fixture for each NaT type in numpy.
    """
    return request.param


# Generate cartesian product of np_nat_fixture:
np_nat_fixture2 = np_nat_fixture


# ----------------------------------------------------------------
# Classes
# ----------------------------------------------------------------


@pytest.fixture(params=[pd.DataFrame, pd.Series])
def frame_or_series(request):
    """
    Fixture to parametrize over DataFrame and Series.
    """
    return request.param


@pytest.fixture(params=[Index, pd.Series], ids=["index", "series"])
def index_or_series(request):
    """
    Fixture to parametrize over Index and Series, made necessary by a mypy
    bug, giving an error:

    List item 0 has incompatible type "Type[Series]"; expected "Type[PandasObject]"

    See GH#29725
    """
    return request.param


@pytest.fixture(params=[Index, pd.Series, pd.array], ids=["index", "series", "array"])
def index_or_series_or_array(request):
    """
    Fixture to parametrize over Index, Series, and ExtensionArray
    """
    return request.param


@pytest.fixture(
    params=[Index, pd.Series, pd.DataFrame, pd.array], ids=lambda x: x.__name__
)
def box_with_array(request):
    """
    Fixture to test behavior for Index, Series, DataFrame, and pandas Array
    classes
    """
    return request.param


box_with_array2 = box_with_array


@pytest.fixture
def dict_subclass() -> type[dict[Any, Any]]:
    """
    Fixture for a dictionary subclass.
    """

    class TestSubDict(dict[Any, Any]):
        def __init__(self, *args, **kwargs) -> None:
            dict.__init__(self, *args, **kwargs)

    return TestSubDict


@pytest.fixture
def non_dict_mapping_subclass() -> type[abc.Mapping[Any, Any]]:
    """
    Fixture for a non-mapping dictionary subclass.
    """

    class TestNonDictMapping(abc.Mapping[Any, Any]):
        def __init__(self, underlying_dict) -> None:
            self._data = underlying_dict

        def __getitem__(self, key):
            return self._data.__getitem__(key)

        def __iter__(self) -> Iterator[Any]:
            return self._data.__iter__()

        def __len__(self) -> int:
            return self._data.__len__()

    return TestNonDictMapping


# ----------------------------------------------------------------
# Indices
# ----------------------------------------------------------------
@pytest.fixture
def multiindex_year_month_day_dataframe_random_data():
    """
    DataFrame with 3 level MultiIndex (year, month, day) covering
    first 100 business days from 2000-01-01 with random data
    """
    tdf = pd.DataFrame(
        np.random.default_rng(2).standard_normal((100, 4)),
        columns=Index(list("ABCD")),
        index=pd.date_range("2000-01-01", periods=100, freq="B"),
    )
    ymd = tdf.groupby([lambda x: x.year, lambda x: x.month, lambda x: x.day]).sum()
    # use int64 Index, to make sure things work
    ymd.index = ymd.index.set_levels([lev.astype("i8") for lev in ymd.index.levels])
    ymd.index.set_names(["year", "month", "day"], inplace=True)
    return ymd


@pytest.fixture
def lexsorted_two_level_string_multiindex() -> MultiIndex:
    """
    2-level MultiIndex, lexsorted, with string names.
    """
    return MultiIndex(
        levels=[["foo", "bar", "baz", "qux"], ["one", "two", "three"]],
        codes=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]],
        names=["first", "second"],
    )


@pytest.fixture
def multiindex_dataframe_random_data(
    lexsorted_two_level_string_multiindex,
) -> pd.DataFrame:
    """DataFrame with 2 level MultiIndex with random data"""
    index = lexsorted_two_level_string_multiindex
    return pd.DataFrame(
        np.random.default_rng(2).standard_normal((10, 3)),
        index=index,
        columns=Index(["A", "B", "C"], name="exp"),
    )


def _create_multiindex():
    """
    MultiIndex used to test the general functionality of this object
    """

    # See Also: tests.multi.conftest.idx
    major_axis = Index(["foo", "bar", "baz", "qux"])
    minor_axis = Index(["one", "two"])

    major_codes = np.array([0, 0, 1, 2, 3, 3])
    minor_codes = np.array([0, 1, 0, 1, 0, 1])
    index_names = ["first", "second"]
    return MultiIndex(
        levels=[major_axis, minor_axis],
        codes=[major_codes, minor_codes],
        names=index_names,
        verify_integrity=False,
    )


def _create_mi_with_dt64tz_level():
    """
    MultiIndex with a level that is a tzaware DatetimeIndex.
    """
    # GH#8367 round trip with pickle
    return MultiIndex.from_product(
        [[1, 2], ["a", "b"], pd.date_range("20130101", periods=3, tz="US/Eastern")],
        names=["one", "two", "three"],
    )


indices_dict = {
    "object": Index([f"pandas_{i}" for i in range(10)], dtype=object),
    "string": Index([f"pandas_{i}" for i in range(10)], dtype="str"),
    "datetime": pd.date_range("2020-01-01", periods=10),
    "datetime-tz": pd.date_range("2020-01-01", periods=10, tz="US/Pacific"),
    "period": pd.period_range("2020-01-01", periods=10, freq="D"),
    "timedelta": pd.timedelta_range(start="1 day", periods=10, freq="D"),
    "range": pd.RangeIndex(10),
    "int8": Index(np.arange(10), dtype="int8"),
    "int16": Index(np.arange(10), dtype="int16"),
    "int32": Index(np.arange(10), dtype="int32"),
    "int64": Index(np.arange(10), dtype="int64"),
    "uint8": Index(np.arange(10), dtype="uint8"),
    "uint16": Index(np.arange(10), dtype="uint16"),
    "uint32": Index(np.arange(10), dtype="uint32"),
    "uint64": Index(np.arange(10), dtype="uint64"),
    "float32": Index(np.arange(10), dtype="float32"),
    "float64": Index(np.arange(10), dtype="float64"),
    "bool-object": Index([True, False] * 5, dtype=object),
    "bool-dtype": Index([True, False] * 5, dtype=bool),
    "complex64": Index(
        np.arange(10, dtype="complex64") + 1.0j * np.arange(10, dtype="complex64")
    ),
    "complex128": Index(
        np.arange(10, dtype="complex128") + 1.0j * np.arange(10, dtype="complex128")
    ),
    "categorical": pd.CategoricalIndex(list("abcd") * 2),
    "interval": pd.IntervalIndex.from_breaks(np.linspace(0, 100, num=11)),
    "empty": Index([]),
    "tuples": MultiIndex.from_tuples(
        zip(["foo", "bar", "baz"], [1, 2, 3], strict=True)
    ),
    "mi-with-dt64tz-level": _create_mi_with_dt64tz_level(),
    "multi": _create_multiindex(),
    "mixed-int-string": Index([0, "a", 1, "b", 2, "c"]),
    "repeats": Index([0, 0, 1, 1, 2, 2]),
    "nullable_int": Index(np.arange(10), dtype="Int64"),
    "nullable_uint": Index(np.arange(10), dtype="UInt16"),
    "nullable_float": Index(np.arange(10), dtype="Float32"),
    "nullable_bool": Index(np.arange(10).astype(bool), dtype="boolean"),
    "string-python": Index(
        pd.array([f"pandas_{i}" for i in range(10)], dtype="string[python]")
    ),
}
if has_pyarrow:
    idx = Index(pd.array([f"pandas_{i}" for i in range(10)], dtype="string[pyarrow]"))
    indices_dict["string-pyarrow"] = idx


@pytest.fixture(params=indices_dict.keys())
def index(request):
    """
    Fixture for many "simple" kinds of indices.

    These indices are unlikely to cover corner cases, e.g.
        - no names
        - no NaTs/NaNs
        - no values near implementation bounds
        - ...
    """
    # copy to avoid mutation, e.g. setting .name
    return indices_dict[request.param].copy(deep=False)


@pytest.fixture(params=[key for key in indices_dict if key != "mixed-int-string"])
def index_sortable(request):
    """
    index fixture, but excluding types that are not orderable.
    """
    return indices_dict[request.param].copy(deep=False)


@pytest.fixture(
    params=[
        key for key, value in indices_dict.items() if not isinstance(value, MultiIndex)
    ]
)
def index_flat(request):
    """
    index fixture, but excluding MultiIndex cases.
    """
    key = request.param
    return indices_dict[key].copy(deep=False)


@pytest.fixture(
    params=[
        key
        for key, value in indices_dict.items()
        if not isinstance(value, MultiIndex) and key != "mixed-int-string"
    ]
)
def index_flat_sortable(request):
    """
    index_flat fixture, but excluding types that are not orderable.
    """
    return indices_dict[request.param].copy(deep=False)


# Aliases so we can test with the cartesian product of index_flat
index_flat2 = index_flat
index_flat2_sortable = index_flat_sortable


@pytest.fixture(
    params=[
        key
        for key, value in indices_dict.items()
        if not (
            key.startswith(("int", "uint", "float"))
            or key in ["range", "empty", "repeats", "bool-dtype"]
        )
        and not isinstance(value, MultiIndex)
    ]
)
def index_with_missing(request):
    """
    Fixture for indices with missing values.

    Integer-dtype and empty cases are excluded because they cannot hold missing
    values.

    MultiIndex is excluded because isna() is not defined for MultiIndex.
    """
    ind = indices_dict[request.param]
    if request.param in ["tuples", "mi-with-dt64tz-level", "multi"]:
        # For setting missing values in the top level of MultiIndex
        vals = ind.tolist()
        vals[0] = (None, *vals[0][1:])
        vals[-1] = (None, *vals[-1][1:])
        return MultiIndex.from_tuples(vals)
    else:
        vals = ind._values.copy()
        vals[0] = None
        vals[-1] = None
        return type(ind)(vals, copy=False)


@pytest.fixture(
    params=[
        key
        for key, value in indices_dict.items()
        if not (
            key.startswith(("int", "uint", "float"))
            or key in ["range", "empty", "repeats", "bool-dtype", "mixed-int-string"]
        )
        and not isinstance(value, MultiIndex)
    ]
)
def index_with_missing_sortable(request):
    """
    index_with_missing fixture, but excluding types that are not orderable.
    """
    ind = indices_dict[request.param]
    vals = ind._values.copy()
    vals[0] = None
    vals[-1] = None
    return type(ind)(vals, copy=False)


# ----------------------------------------------------------------
# Series'
# ----------------------------------------------------------------
@pytest.fixture
def string_series() -> pd.Series:
    """
    Fixture for Series of floats with Index of unique strings
    """
    return pd.Series(
        np.arange(30, dtype=np.float64) * 1.1,
        index=Index([f"i_{i}" for i in range(30)]),
        name="series",
    )


@pytest.fixture
def object_series() -> pd.Series:
    """
    Fixture for Series of dtype object with Index of unique strings
    """
    data = [f"foo_{i}" for i in range(30)]
    index = Index([f"bar_{i}" for i in range(30)])
    return pd.Series(data, index=index, name="objects", dtype=object)


@pytest.fixture
def datetime_series() -> pd.Series:
    """
    Fixture for Series of floats with DatetimeIndex
    """
    return pd.Series(
        np.random.default_rng(2).standard_normal(30),
        index=pd.date_range("2000-01-01", periods=30, freq="B"),
        name="ts",
    )


def _create_series(index):
    """Helper for the _series dict"""
    size = len(index)
    data = np.random.default_rng(2).standard_normal(size)
    return pd.Series(data, index=index, name="a", copy=False)


_series = {
    f"series-with-{index_id}-index": _create_series(index)
    for index_id, index in indices_dict.items()
}


@pytest.fixture
def series_with_simple_index(index) -> pd.Series:
    """
    Fixture for tests on series with changing types of indices.
    """
    return _create_series(index)


_narrow_series = {
    f"{dtype.__name__}-series": pd.Series(
        range(30), index=[f"i-{i}" for i in range(30)], name="a", dtype=dtype
    )
    for dtype in tm.NARROW_NP_DTYPES
}


_index_or_series_objs = {**indices_dict, **_series, **_narrow_series}


@pytest.fixture(params=_index_or_series_objs.keys())
def index_or_series_obj(request):
    """
    Fixture for tests on indexes, series and series with a narrow dtype
    copy to avoid mutation, e.g. setting .name
    """
    return _index_or_series_objs[request.param].copy(deep=False)


_index_or_series_objs_orderable = {
    key: value
    for key, value in _index_or_series_objs.items()
    if "mixed-int-string" not in key
}


@pytest.fixture(params=_index_or_series_objs_orderable.keys())
def index_or_series_obj_orderable(request):
    """
    index_or_series_obj fixture, but excluding types that are not orderable.
    """
    return _index_or_series_objs_orderable[request.param].copy(deep=False)


_typ_objects_series = {
    f"{dtype.__name__}-series": pd.Series(dtype) for dtype in tm.PYTHON_DATA_TYPES
}


_index_or_series_memory_objs = {
    **indices_dict,
    **_series,
    **_narrow_series,
    **_typ_objects_series,
}


@pytest.fixture(params=_index_or_series_memory_objs.keys())
def index_or_series_memory_obj(request):
    """
    Fixture for tests on indexes, series, series with a narrow dtype and
    series with empty objects type
    copy to avoid mutation, e.g. setting .name
    """
    return _index_or_series_memory_objs[request.param].copy(deep=False)


# ----------------------------------------------------------------
# DataFrames
# ----------------------------------------------------------------
@pytest.fixture
def int_frame() -> pd.DataFrame:
    """
    Fixture for DataFrame of ints with index of unique strings

    Columns are ['A', 'B', 'C', 'D']
    """
    return pd.DataFrame(
        np.ones((30, 4), dtype=np.int64),
        index=Index([f"foo_{i}" for i in range(30)]),
        columns=Index(list("ABCD")),
    )


@pytest.fixture
def float_frame() -> pd.DataFrame:
    """
    Fixture for DataFrame of floats with index of unique strings

    Columns are ['A', 'B', 'C', 'D'].
    """
    return pd.DataFrame(
        np.random.default_rng(2).standard_normal((30, 4)),
        index=Index([f"foo_{i}" for i in range(30)]),
        columns=Index(list("ABCD")),
    )


@pytest.fixture
def rand_series_with_duplicate_datetimeindex() -> pd.Series:
    """
    Fixture for Series with a DatetimeIndex that has duplicates.
    """
    dates = [
        datetime(2000, 1, 2),
        datetime(2000, 1, 2),
        datetime(2000, 1, 2),
        datetime(2000, 1, 3),
        datetime(2000, 1, 3),
        datetime(2000, 1, 3),
        datetime(2000, 1, 4),
        datetime(2000, 1, 4),
        datetime(2000, 1, 4),
        datetime(2000, 1, 5),
    ]

    return pd.Series(np.random.default_rng(2).standard_normal(len(dates)), index=dates)


# ----------------------------------------------------------------
# Scalars
# ----------------------------------------------------------------
@pytest.fixture(
    params=[
        (pd.Interval(left=0, right=5), IntervalDtype("int64", "right")),
        (pd.Interval(left=0.1, right=0.5), IntervalDtype("float64", "right")),
        (pd.Period("2012-01", freq="M"), "period[M]"),
        (pd.Period("2012-02-01", freq="D"), "period[D]"),
        (
            pd.Timestamp("2011-01-01", tz="US/Eastern").as_unit("s"),
            DatetimeTZDtype(unit="s", tz="US/Eastern"),
        ),
        (pd.Timedelta(seconds=500), "timedelta64[us]"),
    ]
)
def ea_scalar_and_dtype(request):
    """
    Fixture that tests each scalar and datetime type.
    """
    return request.param


# ----------------------------------------------------------------
# Operators & Operations
# ----------------------------------------------------------------


@pytest.fixture(params=tm.arithmetic_dunder_methods)
def all_arithmetic_operators(request):
    """
    Fixture for dunder names for common arithmetic operations.
    """
    return request.param


@pytest.fixture(
    params=[
        operator.add,
        ops.radd,
        operator.sub,
        ops.rsub,
        operator.mul,
        ops.rmul,
        operator.truediv,
        ops.rtruediv,
        operator.floordiv,
        ops.rfloordiv,
        operator.mod,
        ops.rmod,
        operator.pow,
        ops.rpow,
        operator.eq,
        operator.ne,
        operator.lt,
        operator.le,
        operator.gt,
        operator.ge,
        operator.and_,
        ops.rand_,
        operator.xor,
        ops.rxor,
        operator.or_,
        ops.ror_,
    ]
)
def all_binary_operators(request):
    """
    Fixture for operator and roperator arithmetic, comparison, and logical ops.
    """
    return request.param


@pytest.fixture(
    params=[
        operator.add,
        ops.radd,
        operator.sub,
        ops.rsub,
        operator.mul,
        ops.rmul,
        operator.truediv,
        ops.rtruediv,
        operator.floordiv,
        ops.rfloordiv,
        operator.mod,
        ops.rmod,
        operator.pow,
        ops.rpow,
    ]
)
def all_arithmetic_functions(request):
    """
    Fixture for operator and roperator arithmetic functions.

    Notes
    -----
    This does not include divmod and rdivmod, which return a tuple and so
    cannot be used interchangeably with the other operators.
    """
    return request.param


@pytest.fixture(params=tm.numeric_reductions)
def all_numeric_reductions(request):
    """
    Fixture for numeric reduction names.
    """
    return request.param


@pytest.fixture(params=tm.boolean_reductions)
def all_boolean_reductions(request):
    """
    Fixture for boolean reduction names.
    """
    return request.param


@pytest.fixture(params=tm.all_reductions)
def all_reductions(request):
    """
    Fixture for all (boolean + numeric) reduction names.
    """
    return request.param


@pytest.fixture(params=tm.comparison_ops)
def comparison_op(request):
    """
    Fixture for operator module comparison functions.
    """
    return request.param


@pytest.fixture(params=["__le__", "__lt__", "__ge__", "__gt__"])
def compare_operators_no_eq_ne(request):
    """
    Fixture for dunder names for compare operations except == and !=

    * >=
    * >
    * <
    * <=
    """
    return request.param


@pytest.fixture(
    params=["__and__", "__rand__", "__or__", "__ror__", "__xor__", "__rxor__"]
)
def all_logical_operators(request):
    """
    Fixture for dunder names for common logical operations

    * |
    * &
    * ^
    """
    return request.param


@pytest.fixture(params=tm.numeric_accumulations)
def all_numeric_accumulations(request):
    """
    Fixture for numeric accumulation names
    """
    return request.param


# ----------------------------------------------------------------
# Data sets/files
# ----------------------------------------------------------------
@pytest.fixture
def strict_data_files(pytestconfig):
    """
    Returns the configuration for the test setting `--no-strict-data-files`.
    """
    return pytestconfig.getoption("strict_data_files")


@pytest.fixture
def datapath(strict_data_files: str) -> Callable[..., str]:
    """
    Get the path to a data file.

    Parameters
    ----------
    path : str
        Path to the file, relative to ``pandas/tests/``

    Returns
    -------
    path including ``pandas/tests``.

    Raises
    ------
    ValueError
        If the path doesn't exist and the --no-strict-data-files option is not set.
    """
    BASE_PATH = os.path.join(os.path.dirname(__file__), "tests")

    def deco(*args):
        path = os.path.join(BASE_PATH, *args)
        if not os.path.exists(path):
            if strict_data_files:
                raise ValueError(
                    f"Could not find file {path} and --no-strict-data-files is not set."
                )
            pytest.skip(f"Could not find {path}.")
        return path

    return deco


# ----------------------------------------------------------------
# Time zones
# ----------------------------------------------------------------
TIMEZONES = [
    None,
    "UTC",
    "US/Eastern",
    "Asia/Tokyo",
    "dateutil/US/Pacific",
    "dateutil/Asia/Singapore",
    "+01:15",
    "-02:15",
    "UTC+01:15",
    "UTC-02:15",
    tzutc(),
    tzlocal(),
    UTC,
    timezone(timedelta(hours=1)),
    timezone(timedelta(hours=-1), name="foo"),
]
if pytz is not None:
    TIMEZONES.extend(
        (
            pytz.FixedOffset(300),
            pytz.FixedOffset(0),
            pytz.FixedOffset(-300),
            pytz.timezone("US/Pacific"),
            pytz.timezone("UTC"),
        )
    )
TIMEZONE_IDS = [repr(i) for i in TIMEZONES]


@td.parametrize_fixture_doc(str(TIMEZONE_IDS))
@pytest.fixture(params=TIMEZONES, ids=TIMEZONE_IDS)
def tz_naive_fixture(request):
    """
    Fixture for trying timezones including default (None): {0}
    """
    return request.param


@td.parametrize_fixture_doc(str(TIMEZONE_IDS[1:]))
@pytest.fixture(params=TIMEZONES[1:], ids=TIMEZONE_IDS[1:])
def tz_aware_fixture(request):
    """
    Fixture for trying explicit timezones: {0}
    """
    return request.param


_UTCS = ["utc", "dateutil/UTC", tzutc(), UTC]

if pytz is not None:
    _UTCS.append(pytz.utc)


@pytest.fixture(params=_UTCS)
def utc_fixture(request):
    """
    Fixture to provide variants of UTC timezone strings and tzinfo objects.
    """
    return request.param


utc_fixture2 = utc_fixture


@pytest.fixture(params=["s", "ms", "us", "ns"])
def unit(request):
    """
    datetime64 units we support.
    """
    return request.param


unit2 = unit


# ----------------------------------------------------------------
# Dtypes
# ----------------------------------------------------------------
@pytest.fixture(params=tm.STRING_DTYPES)
def string_dtype(request):
    """
    Parametrized fixture for string dtypes.

    * str
    * 'str'
    * 'U'
    """
    return request.param


@pytest.fixture(
    params=[
        ("python", pd.NA),
        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
        ("python", np.nan),
    ],
    ids=[
        "string=string[python]",
        "string=string[pyarrow]",
        "string=str[pyarrow]",
        "string=str[python]",
    ],
)
def string_dtype_no_object(request):
    """
    Parametrized fixture for string dtypes.
    * 'string[python]' (NA variant)
    * 'string[pyarrow]' (NA variant)
    * 'str' (NaN variant, with pyarrow)
    * 'str' (NaN variant, without pyarrow)
    """
    # need to instantiate the StringDtype here instead of in the params
    # to avoid importing pyarrow during test collection
    storage, na_value = request.param
    return pd.StringDtype(storage, na_value)


@pytest.fixture(
    params=[
        "string[python]",
        pytest.param("string[pyarrow]", marks=td.skip_if_no("pyarrow")),
    ]
)
def nullable_string_dtype(request):
    """
    Parametrized fixture for string dtypes.

    * 'string[python]'
    * 'string[pyarrow]'
    """
    return request.param


@pytest.fixture(
    params=[
        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
    ]
)
def pyarrow_string_dtype(request):
    """
    Parametrized fixture for string dtypes backed by Pyarrow.

    * 'str[pyarrow]'
    * 'string[pyarrow]'
    """
    return pd.StringDtype(*request.param)


@pytest.fixture(
    params=[
        "python",
        pytest.param("pyarrow", marks=td.skip_if_no("pyarrow")),
    ]
)
def string_storage(request):
    """
    Parametrized fixture for pd.options.mode.string_storage.

    * 'python'
    * 'pyarrow'
    """
    return request.param


@pytest.fixture(
    params=[
        ("python", pd.NA),
        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
        ("python", np.nan),
    ],
    ids=[
        "string=string[python]",
        "string=string[pyarrow]",
        "string=str[pyarrow]",
        "string=str[python]",
    ],
)
def string_dtype_arguments(request):
    """
    Parametrized fixture for StringDtype storage and na_value.

    * 'python' + pd.NA
    * 'pyarrow' + pd.NA
    * 'pyarrow' + np.nan
    """
    return request.param


@pytest.fixture(
    params=[
        "numpy_nullable",
        pytest.param("pyarrow", marks=td.skip_if_no("pyarrow")),
    ]
)
def dtype_backend(request):
    """
    Parametrized fixture for pd.options.mode.string_storage.

    * 'python'
    * 'pyarrow'
    """
    return request.param


# Alias so we can test with cartesian product of string_storage
string_storage2 = string_storage
string_dtype_arguments2 = string_dtype_arguments


@pytest.fixture(params=tm.BYTES_DTYPES)
def bytes_dtype(request):
    """
    Parametrized fixture for bytes dtypes.

    * bytes
    * 'bytes'
    """
    return request.param


@pytest.fixture(params=tm.OBJECT_DTYPES)
def object_dtype(request):
    """
    Parametrized fixture for object dtypes.

    * object
    * 'object'
    """
    return request.param


@pytest.fixture(
    params=[
        np.dtype("object"),
        ("python", pd.NA),
        pytest.param(("pyarrow", pd.NA), marks=td.skip_if_no("pyarrow")),
        pytest.param(("pyarrow", np.nan), marks=td.skip_if_no("pyarrow")),
        ("python", np.nan),
    ],
    ids=[
        "string=object",
        "string=string[python]",
        "string=string[pyarrow]",
        "string=str[pyarrow]",
        "string=str[python]",
    ],
)
def any_string_dtype(request):
    """
    Parametrized fixture for string dtypes.
    * 'object'
    * 'string[python]' (NA variant)
    * 'string[pyarrow]' (NA variant)
    * 'str' (NaN variant, with pyarrow)
    * 'str' (NaN variant, without pyarrow)
    """
    if isinstance(request.param, np.dtype):
        return request.param
    else:
        # need to instantiate the StringDtype here instead of in the params
        # to avoid importing pyarrow during test collection
        storage, na_value = request.param
        return pd.StringDtype(storage, na_value)


any_string_dtype2 = any_string_dtype


@pytest.fixture(params=tm.DATETIME64_DTYPES)
def datetime64_dtype(request):
    """
    Parametrized fixture for datetime64 dtypes.

    * 'datetime64[ns]'
    * 'M8[ns]'
    """
    return request.param


@pytest.fixture(params=tm.TIMEDELTA64_DTYPES)
def timedelta64_dtype(request):
    """
    Parametrized fixture for timedelta64 dtypes.

    * 'timedelta64[ns]'
    * 'm8[ns]'
    """
    return request.param


@pytest.fixture
def fixed_now_ts() -> pd.Timestamp:
    """
    Fixture emits fixed Timestamp.now()
    """
    return pd.Timestamp(  # pyright: ignore[reportReturnType]
        year=2021, month=1, day=1, hour=12, minute=4, second=13, microsecond=22
    )


@pytest.fixture(params=tm.FLOAT_NUMPY_DTYPES)
def float_numpy_dtype(request):
    """
    Parameterized fixture for float dtypes.

    * float
    * 'float32'
    * 'float64'
    """
    return request.param


@pytest.fixture(params=tm.FLOAT_EA_DTYPES)
def float_ea_dtype(request):
    """
    Parameterized fixture for float dtypes.

    * 'Float32'
    * 'Float64'
    """
    return request.param


@pytest.fixture(params=tm.ALL_FLOAT_DTYPES)
def any_float_dtype(request):
    """
    Parameterized fixture for float dtypes.

    * float
    * 'float32'
    * 'float64'
    * 'Float32'
    * 'Float64'
    """
    return request.param


@pytest.fixture(params=tm.COMPLEX_DTYPES)
def complex_dtype(request):
    """
    Parameterized fixture for complex dtypes.

    * complex
    * 'complex64'
    * 'complex128'
    """
    return request.param


@pytest.fixture(params=tm.COMPLEX_FLOAT_DTYPES)
def complex_or_float_dtype(request):
    """
    Parameterized fixture for complex and numpy float dtypes.

    * complex
    * 'complex64'
    * 'complex128'
    * float
    * 'float32'
    * 'float64'
    """
    return request.param


@pytest.fixture(params=tm.SIGNED_INT_NUMPY_DTYPES)
def any_signed_int_numpy_dtype(request):
    """
    Parameterized fixture for signed integer dtypes.

    * int
    * 'int8'
    * 'int16'
    * 'int32'
    * 'int64'
    """
    return request.param


@pytest.fixture(params=tm.UNSIGNED_INT_NUMPY_DTYPES)
def any_unsigned_int_numpy_dtype(request):
    """
    Parameterized fixture for unsigned integer dtypes.

    * 'uint8'
    * 'uint16'
    * 'uint32'
    * 'uint64'
    """
    return request.param


@pytest.fixture(params=tm.ALL_INT_NUMPY_DTYPES)
def any_int_numpy_dtype(request):
    """
    Parameterized fixture for any integer dtype.

    * int
    * 'int8'
    * 'uint8'
    * 'int16'
    * 'uint16'
    * 'int32'
    * 'uint32'
    * 'int64'
    * 'uint64'
    """
    return request.param


@pytest.fixture(params=tm.ALL_INT_EA_DTYPES)
def any_int_ea_dtype(request):
    """
    Parameterized fixture for any nullable integer dtype.

    * 'UInt8'
    * 'Int8'
    * 'UInt16'
    * 'Int16'
    * 'UInt32'
    * 'Int32'
    * 'UInt64'
    * 'Int64'
    """
    return request.param


@pytest.fixture(params=tm.ALL_INT_DTYPES)
def any_int_dtype(request):
    """
    Parameterized fixture for any nullable integer dtype.

    * int
    * 'int8'
    * 'uint8'
    * 'int16'
    * 'uint16'
    * 'int32'
    * 'uint32'
    * 'int64'
    * 'uint64'
    * 'UInt8'
    * 'Int8'
    * 'UInt16'
    * 'Int16'
    * 'UInt32'
    * 'Int32'
    * 'UInt64'
    * 'Int64'
    """
    return request.param


@pytest.fixture(params=tm.ALL_INT_EA_DTYPES + tm.FLOAT_EA_DTYPES)
def any_numeric_ea_dtype(request):
    """
    Parameterized fixture for any nullable integer dtype and
    any float ea dtypes.

    * 'UInt8'
    * 'Int8'
    * 'UInt16'
    * 'Int16'
    * 'UInt32'
    * 'Int32'
    * 'UInt64'
    * 'Int64'
    * 'Float32'
    * 'Float64'
    """
    return request.param


#  Unsupported operand types for + ("List[Union[str, ExtensionDtype, dtype[Any],
#  Type[object]]]" and "List[str]")
@pytest.fixture(
    params=tm.ALL_INT_EA_DTYPES
    + tm.FLOAT_EA_DTYPES
    + tm.ALL_INT_PYARROW_DTYPES_STR_REPR
    + tm.FLOAT_PYARROW_DTYPES_STR_REPR  # type: ignore[operator]
)
def any_numeric_ea_and_arrow_dtype(request):
    """
    Parameterized fixture for any nullable integer dtype and
    any float ea dtypes.

    * 'UInt8'
    * 'Int8'
    * 'UInt16'
    * 'Int16'
    * 'UInt32'
    * 'Int32'
    * 'UInt64'
    * 'Int64'
    * 'Float32'
    * 'Float64'
    * 'uint8[pyarrow]'
    * 'int8[pyarrow]'
    * 'uint16[pyarrow]'
    * 'int16[pyarrow]'
    * 'uint32[pyarrow]'
    * 'int32[pyarrow]'
    * 'uint64[pyarrow]'
    * 'int64[pyarrow]'
    * 'float32[pyarrow]'
    * 'float64[pyarrow]'
    """
    return request.param


@pytest.fixture(params=tm.SIGNED_INT_EA_DTYPES)
def any_signed_int_ea_dtype(request):
    """
    Parameterized fixture for any signed nullable integer dtype.

    * 'Int8'
    * 'Int16'
    * 'Int32'
    * 'Int64'
    """
    return request.param


@pytest.fixture(params=tm.ALL_REAL_NUMPY_DTYPES)
def any_real_numpy_dtype(request):
    """
    Parameterized fixture for any (purely) real numeric dtype.

    * int
    * 'int8'
    * 'uint8'
    * 'int16'
    * 'uint16'
    * 'int32'
    * 'uint32'
    * 'int64'
    * 'uint64'
    * float
    * 'float32'
    * 'float64'
    """
    return request.param


@pytest.fixture(params=tm.ALL_REAL_DTYPES)
def any_real_numeric_dtype(request):
    """
    Parameterized fixture for any (purely) real numeric dtype.

    * int
    * 'int8'
    * 'uint8'
    * 'int16'
    * 'uint16'
    * 'int32'
    * 'uint32'
    * 'int64'
    * 'uint64'
    * float
    * 'float32'
    * 'float64'

    and associated ea dtypes.
    """
    return request.param


@pytest.fixture(params=tm.ALL_NUMPY_DTYPES)
def any_numpy_dtype(request):
    """
    Parameterized fixture for all numpy dtypes.

    * bool
    * 'bool'
    * int
    * 'int8'
    * 'uint8'
    * 'int16'
    * 'uint16'
    * 'int32'
    * 'uint32'
    * 'int64'
    * 'uint64'
    * float
    * 'float32'
    * 'float64'
    * complex
    * 'complex64'
    * 'complex128'
    * str
    * 'str'
    * 'U'
    * bytes
    * 'bytes'
    * 'datetime64[ns]'
    * 'M8[ns]'
    * 'timedelta64[ns]'
    * 'm8[ns]'
    * object
    * 'object'
    """
    return request.param


@pytest.fixture(params=tm.ALL_REAL_NULLABLE_DTYPES)
def any_real_nullable_dtype(request):
    """
    Parameterized fixture for all real dtypes that can hold NA.

    * float
    * 'float32'
    * 'float64'
    * 'Float32'
    * 'Float64'
    * 'UInt8'
    * 'UInt16'
    * 'UInt32'
    * 'UInt64'
    * 'Int8'
    * 'Int16'
    * 'Int32'
    * 'Int64'
    * 'uint8[pyarrow]'
    * 'uint16[pyarrow]'
    * 'uint32[pyarrow]'
    * 'uint64[pyarrow]'
    * 'int8[pyarrow]'
    * 'int16[pyarrow]'
    * 'int32[pyarrow]'
    * 'int64[pyarrow]'
    * 'float[pyarrow]'
    * 'double[pyarrow]'
    """
    return request.param


@pytest.fixture(params=tm.ALL_NUMERIC_DTYPES)
def any_numeric_dtype(request):
    """
    Parameterized fixture for all numeric dtypes.

    * int
    * 'int8'
    * 'uint8'
    * 'int16'
    * 'uint16'
    * 'int32'
    * 'uint32'
    * 'int64'
    * 'uint64'
    * float
    * 'float32'
    * 'float64'
    * complex
    * 'complex64'
    * 'complex128'
    * 'UInt8'
    * 'Int8'
    * 'UInt16'
    * 'Int16'
    * 'UInt32'
    * 'Int32'
    * 'UInt64'
    * 'Int64'
    * 'Float32'
    * 'Float64'
    """
    return request.param


# categoricals are handled separately
_any_skipna_inferred_dtype = [
    ("string", ["a", np.nan, "c"]),
    ("string", ["a", pd.NA, "c"]),
    ("mixed", ["a", pd.NaT, "c"]),  # pd.NaT not considered valid by is_string_array
    ("bytes", [b"a", np.nan, b"c"]),
    ("empty", [np.nan, np.nan, np.nan]),
    ("empty", []),
    ("mixed-integer", ["a", np.nan, 2]),
    ("mixed", ["a", np.nan, 2.0]),
    ("floating", [1.0, np.nan, 2.0]),
    ("integer", [1, np.nan, 2]),
    ("mixed-integer-float", [1, np.nan, 2.0]),
    ("decimal", [Decimal(1), np.nan, Decimal(2)]),
    ("boolean", [True, np.nan, False]),
    ("boolean", [True, pd.NA, False]),
    ("datetime64", [np.datetime64("2013-01-01"), np.nan, np.datetime64("2018-01-01")]),
    ("datetime", [pd.Timestamp("20130101"), np.nan, pd.Timestamp("20180101")]),
    ("date", [date(2013, 1, 1), np.nan, date(2018, 1, 1)]),
    ("complex", [1 + 1j, np.nan, 2 + 2j]),
    # The following dtype is commented out due to GH 23554
    # ('timedelta64', [np.timedelta64(1, 'D'),
    #                  np.nan, np.timedelta64(2, 'D')]),
    ("timedelta", [timedelta(1), np.nan, timedelta(2)]),
    ("time", [time(1), np.nan, time(2)]),
    ("period", [pd.Period("2013"), pd.NaT, pd.Period("2018")]),
    ("interval", [pd.Interval(0, 1), np.nan, pd.Interval(0, 2)]),
]
ids = [
    pair[0] for pair in _any_skipna_inferred_dtype
]  # use inferred type as fixture-id


@pytest.fixture(params=_any_skipna_inferred_dtype, ids=ids)
def any_skipna_inferred_dtype(request):
    """
    Fixture for all inferred dtypes from _libs.lib.infer_dtype

    The covered (inferred) types are:
    * 'string'
    * 'empty'
    * 'bytes'
    * 'mixed'
    * 'mixed-integer'
    * 'mixed-integer-float'
    * 'floating'
    * 'integer'
    * 'decimal'
    * 'boolean'
    * 'datetime64'
    * 'datetime'
    * 'date'
    * 'timedelta'
    * 'time'
    * 'period'
    * 'interval'

    Returns
    -------
    inferred_dtype : str
        The string for the inferred dtype from _libs.lib.infer_dtype
    values : np.ndarray
        An array of object dtype that will be inferred to have
        `inferred_dtype`

    Examples
    --------
    >>> from pandas._libs import lib
    >>>
    >>> def test_something(any_skipna_inferred_dtype):
    ...     inferred_dtype, values = any_skipna_inferred_dtype
    ...     # will pass
    ...     assert lib.infer_dtype(values, skipna=True) == inferred_dtype
    """
    inferred_dtype, values = request.param
    values = np.array(values, dtype=object)  # object dtype to avoid casting

    # correctness of inference tested in tests/dtypes/test_inference.py
    return inferred_dtype, values


# ----------------------------------------------------------------
# Misc
# ----------------------------------------------------------------
@pytest.fixture
def ip():
    """
    Get an instance of IPython.InteractiveShell.

    Will raise a skip if IPython is not installed.
    """
    pytest.importorskip("IPython", minversion="6.0.0")
    from IPython.core.interactiveshell import InteractiveShell

    # GH#35711 make sure sqlite history file handle is not leaked.
    # Using :memory: avoids leaking a file on disk; disabling the history
    # manager entirely avoids leaking the underlying sqlite3.Connection.
    from traitlets.config import Config  # isort:skip

    c = Config()
    c.HistoryManager.hist_file = ":memory:"
    c.HistoryManager.enabled = False

    return InteractiveShell(config=c)


@pytest.fixture
def mpl_cleanup():
    """Uses mpl_cleanup_context to ensure matplotlib is cleaned up around a test."""
    with mpl_cleanup_context():
        yield


@contextlib.contextmanager
def mpl_cleanup_context():
    """
    Ensure Matplotlib is cleaned up around a test.

    Before a test is run:

    1) Set the backend to "template" to avoid requiring a GUI.

    After a test is run:

    1) Reset units registry
    2) Reset rc_context
    3) Close all figures

    See matplotlib/testing/decorators.py#L24.
    """
    mpl = pytest.importorskip("matplotlib")
    mpl_units = pytest.importorskip("matplotlib.units")
    plt = pytest.importorskip("matplotlib.pyplot")
    orig_units_registry = mpl_units.registry.copy()
    try:
        with mpl.rc_context():
            mpl.use("template")
            yield
    finally:
        mpl_units.registry.clear()
        mpl_units.registry.update(orig_units_registry)
        plt.close("all")
        # https://matplotlib.org/stable/users/prev_whats_new/whats_new_3.6.0.html#garbage-collection-is-no-longer-run-on-figure-close  # noqa: E501
        gc.collect(1)


@pytest.fixture(
    params=[
        getattr(pd.offsets, o)
        for o in pd.offsets.__all__
        if issubclass(getattr(pd.offsets, o), pd.offsets.Tick) and o != "Tick"
    ]
)
def tick_classes(request):
    """
    Fixture for Tick based datetime offsets available for a time series.
    """
    return request.param


@pytest.fixture(params=[None, lambda x: x])
def sort_by_key(request):
    """
    Simple fixture for testing keys in sorting methods.
    Tests None (no key) and the identity key.
    """
    return request.param


@pytest.fixture(
    params=[
        ("foo", None, None),
        ("Egon", "Venkman", None),
        ("NCC1701D", "NCC1701D", "NCC1701D"),
        # possibly-matching NAs
        (np.nan, np.nan, np.nan),
        (np.nan, pd.NaT, None),
        (np.nan, pd.NA, None),
        (pd.NA, pd.NA, pd.NA),
    ]
)
def names(request) -> tuple[Hashable, Hashable, Hashable]:
    """
    A 3-tuple of names, the first two for operands, the last for a result.
    """
    return request.param


@pytest.fixture(params=[tm.setitem, tm.loc, tm.iloc])
def indexer_sli(request):
    """
    Parametrize over __setitem__, loc.__setitem__, iloc.__setitem__
    """
    return request.param


@pytest.fixture(params=[tm.loc, tm.iloc])
def indexer_li(request):
    """
    Parametrize over loc.__getitem__, iloc.__getitem__
    """
    return request.param


@pytest.fixture(params=[tm.setitem, tm.iloc])
def indexer_si(request):
    """
    Parametrize over __setitem__, iloc.__setitem__
    """
    return request.param


@pytest.fixture(params=[tm.setitem, tm.loc])
def indexer_sl(request):
    """
    Parametrize over __setitem__, loc.__setitem__
    """
    return request.param


@pytest.fixture(params=[tm.at, tm.loc])
def indexer_al(request):
    """
    Parametrize over at.__setitem__, loc.__setitem__
    """
    return request.param


@pytest.fixture(params=[tm.iat, tm.iloc])
def indexer_ial(request):
    """
    Parametrize over iat.__setitem__, iloc.__setitem__
    """
    return request.param


@pytest.fixture(params=[True, False])
def performance_warning(request) -> Iterator[bool | type[Warning]]:
    """
    Fixture to check if performance warnings are enabled. Either produces
    ``PerformanceWarning`` if they are enabled, otherwise ``False``.
    """
    with pd.option_context("mode.performance_warnings", request.param):
        yield pd.errors.PerformanceWarning if request.param else False


@pytest.fixture
def using_infer_string() -> bool:
    """
    Fixture to check if infer string option is enabled.
    """
    return using_string_dtype()


@pytest.fixture
def using_python_scalars() -> bool:
    return pd.options.future.python_scalars is True


_warsaws: list[Any] = ["Europe/Warsaw", "dateutil/Europe/Warsaw"]
if pytz is not None:
    _warsaws.append(pytz.timezone("Europe/Warsaw"))


@pytest.fixture(params=_warsaws)
def warsaw(request) -> str:
    """
    tzinfo for Europe/Warsaw using pytz, dateutil, or zoneinfo.
    """
    return request.param


@pytest.fixture
def temp_file(tmp_path):
    """
    Generate a unique file for testing use. See link for removal policy.
    https://docs.pytest.org/en/7.1.x/how-to/tmp_path.html#the-default-base-temporary-directory
    """
    file_path = tmp_path / str(uuid.uuid4())
    file_path.touch()
    return file_path


@pytest.fixture(scope="session")
def monkeysession():
    with pytest.MonkeyPatch.context() as mp:
        yield mp


@pytest.fixture(params=[True, False])
def using_nan_is_na(request):
    opt = request.param
    with pd.option_context("future.distinguish_nan_and_na", not opt):
        yield opt