import numpy as np
import pytest

from pandas import (
    DataFrame,
    Index,
    MultiIndex,
)
import pandas._testing as tm

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas import (
    DataFrame,
    Index,
    MultiIndex,
)
import pandas._testing as tm

            

Reported by Pylint.

              import pandas._testing as tm


class TestDataFrameRenameAxis:
    def test_rename_axis_inplace(self, float_frame):
        # GH#15704
        expected = float_frame.rename_axis("foo")
        result = float_frame.copy()
        return_value = no_return = result.rename_axis("foo", inplace=True)

            

Reported by Pylint.

              

class TestDataFrameRenameAxis:
    def test_rename_axis_inplace(self, float_frame):
        # GH#15704
        expected = float_frame.rename_axis("foo")
        result = float_frame.copy()
        return_value = no_return = result.rename_axis("foo", inplace=True)
        assert return_value is None

            

Reported by Pylint.

              

class TestDataFrameRenameAxis:
    def test_rename_axis_inplace(self, float_frame):
        # GH#15704
        expected = float_frame.rename_axis("foo")
        result = float_frame.copy()
        return_value = no_return = result.rename_axis("foo", inplace=True)
        assert return_value is None

            

Reported by Pylint.

                      expected = float_frame.rename_axis("foo")
        result = float_frame.copy()
        return_value = no_return = result.rename_axis("foo", inplace=True)
        assert return_value is None

        assert no_return is None
        tm.assert_frame_equal(result, expected)

        expected = float_frame.rename_axis("bar", axis=1)

            

Reported by Bandit.

                      return_value = no_return = result.rename_axis("foo", inplace=True)
        assert return_value is None

        assert no_return is None
        tm.assert_frame_equal(result, expected)

        expected = float_frame.rename_axis("bar", axis=1)
        result = float_frame.copy()
        return_value = no_return = result.rename_axis("bar", axis=1, inplace=True)

            

Reported by Bandit.

                      expected = float_frame.rename_axis("bar", axis=1)
        result = float_frame.copy()
        return_value = no_return = result.rename_axis("bar", axis=1, inplace=True)
        assert return_value is None

        assert no_return is None
        tm.assert_frame_equal(result, expected)

    def test_rename_axis_raises(self):

            

Reported by Bandit.

                      return_value = no_return = result.rename_axis("bar", axis=1, inplace=True)
        assert return_value is None

        assert no_return is None
        tm.assert_frame_equal(result, expected)

    def test_rename_axis_raises(self):
        # GH#17833
        df = DataFrame({"A": [1, 2], "B": [1, 2]})

            

Reported by Bandit.

                      assert no_return is None
        tm.assert_frame_equal(result, expected)

    def test_rename_axis_raises(self):
        # GH#17833
        df = DataFrame({"A": [1, 2], "B": [1, 2]})
        with pytest.raises(ValueError, match="Use `.rename`"):
            df.rename_axis(id, axis=0)


            

Reported by Pylint.

              import numpy as np
import pytest

import pandas as pd
import pandas._testing as tm
from pandas.api.types import is_sparse
from pandas.tests.extension.base.base import BaseExtensionTests



            

Reported by Pylint.

              
        result = ser.fillna(fill_value)
        expected = pd.Series(
            data_missing._from_sequence(
                [fill_value, fill_value], dtype=data_missing.dtype
            )
        )
        self.assert_series_equal(result, expected)


            

Reported by Pylint.

              
        result = pd.Series(data_missing).fillna(method=fillna_method)
        expected = pd.Series(
            data_missing._from_sequence(
                [fill_value, fill_value], dtype=data_missing.dtype
            )
        )

        self.assert_series_equal(result, expected)

            

Reported by Pylint.

              
        expected = pd.DataFrame(
            {
                "A": data_missing._from_sequence(
                    [fill_value, fill_value], dtype=data_missing.dtype
                ),
                "B": [1, 2],
            }
        )

            

Reported by Pylint.

              import numpy as np
import pytest

import pandas as pd
import pandas._testing as tm
from pandas.api.types import is_sparse
from pandas.tests.extension.base.base import BaseExtensionTests



            

Reported by Pylint.

              from pandas.tests.extension.base.base import BaseExtensionTests


class BaseMissingTests(BaseExtensionTests):
    def test_isna(self, data_missing):
        expected = np.array([True, False])

        result = pd.isna(data_missing)
        tm.assert_numpy_array_equal(result, expected)

            

Reported by Pylint.

              

class BaseMissingTests(BaseExtensionTests):
    def test_isna(self, data_missing):
        expected = np.array([True, False])

        result = pd.isna(data_missing)
        tm.assert_numpy_array_equal(result, expected)


            

Reported by Pylint.

                      self.assert_series_equal(result, expected)

    @pytest.mark.parametrize("na_func", ["isna", "notna"])
    def test_isna_returns_copy(self, data_missing, na_func):
        result = pd.Series(data_missing)
        expected = result.copy()
        mask = getattr(result, na_func)()
        if is_sparse(mask):
            mask = np.array(mask)

            

Reported by Pylint.

                      mask[:] = True
        self.assert_series_equal(result, expected)

    def test_dropna_array(self, data_missing):
        result = data_missing.dropna()
        expected = data_missing[[1]]
        self.assert_extension_array_equal(result, expected)

    def test_dropna_series(self, data_missing):

            

Reported by Pylint.

                      expected = data_missing[[1]]
        self.assert_extension_array_equal(result, expected)

    def test_dropna_series(self, data_missing):
        ser = pd.Series(data_missing)
        result = ser.dropna()
        expected = ser.iloc[[1]]
        self.assert_series_equal(result, expected)


            

Reported by Pylint.

                  op_desc = _op_descriptions[op_name]

    op_desc_op = op_desc["op"]
    assert op_desc_op is not None  # for mypy
    if op_name.startswith("r"):
        equiv = "other " + op_desc_op + " " + typ
    elif op_name == "divmod":
        equiv = f"{op_name}({typ}, other)"
    else:

            

Reported by Bandit.

                  return doc


_common_examples_algebra_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a    1.0

            

Reported by Pylint.

              e    NaN
dtype: float64"""

_common_examples_comparison_SERIES = """
Examples
--------
>>> a = pd.Series([1, 1, 1, np.nan, 1], index=['a', 'b', 'c', 'd', 'e'])
>>> a
a    1.0

            

Reported by Pylint.

              f    1.0
dtype: float64"""

_add_example_SERIES = (
    _common_examples_algebra_SERIES
    + """
>>> a.add(b, fill_value=0)
a    2.0
b    1.0

            

Reported by Pylint.

              """
)

_sub_example_SERIES = (
    _common_examples_algebra_SERIES
    + """
>>> a.subtract(b, fill_value=0)
a    0.0
b    1.0

            

Reported by Pylint.

              """
)

_mul_example_SERIES = (
    _common_examples_algebra_SERIES
    + """
>>> a.multiply(b, fill_value=0)
a    1.0
b    0.0

            

Reported by Pylint.

              """
)

_div_example_SERIES = (
    _common_examples_algebra_SERIES
    + """
>>> a.divide(b, fill_value=0)
a    1.0
b    inf

            

Reported by Pylint.

              """
)

_floordiv_example_SERIES = (
    _common_examples_algebra_SERIES
    + """
>>> a.floordiv(b, fill_value=0)
a    1.0
b    NaN

            

Reported by Pylint.

              """
)

_divmod_example_SERIES = (
    _common_examples_algebra_SERIES
    + """
>>> a.divmod(b, fill_value=0)
(a    1.0
 b    NaN

            

Reported by Pylint.

              """
)

_mod_example_SERIES = (
    _common_examples_algebra_SERIES
    + """
>>> a.mod(b, fill_value=0)
a    0.0
b    NaN

            

Reported by Pylint.

              
# flake8: noqa

from pandas.io.clipboards import read_clipboard
from pandas.io.excel import (
    ExcelFile,
    ExcelWriter,
    read_excel,
)

            

Reported by Pylint.

              # flake8: noqa

from pandas.io.clipboards import read_clipboard
from pandas.io.excel import (
    ExcelFile,
    ExcelWriter,
    read_excel,
)
from pandas.io.feather_format import read_feather

            

Reported by Pylint.

              # flake8: noqa

from pandas.io.clipboards import read_clipboard
from pandas.io.excel import (
    ExcelFile,
    ExcelWriter,
    read_excel,
)
from pandas.io.feather_format import read_feather

            

Reported by Pylint.

              # flake8: noqa

from pandas.io.clipboards import read_clipboard
from pandas.io.excel import (
    ExcelFile,
    ExcelWriter,
    read_excel,
)
from pandas.io.feather_format import read_feather

            

Reported by Pylint.

                  ExcelWriter,
    read_excel,
)
from pandas.io.feather_format import read_feather
from pandas.io.gbq import read_gbq
from pandas.io.html import read_html
from pandas.io.json import read_json
from pandas.io.orc import read_orc
from pandas.io.parquet import read_parquet

            

Reported by Pylint.

                  read_excel,
)
from pandas.io.feather_format import read_feather
from pandas.io.gbq import read_gbq
from pandas.io.html import read_html
from pandas.io.json import read_json
from pandas.io.orc import read_orc
from pandas.io.parquet import read_parquet
from pandas.io.parsers import (

            

Reported by Pylint.

              )
from pandas.io.feather_format import read_feather
from pandas.io.gbq import read_gbq
from pandas.io.html import read_html
from pandas.io.json import read_json
from pandas.io.orc import read_orc
from pandas.io.parquet import read_parquet
from pandas.io.parsers import (
    read_csv,

            

Reported by Pylint.

              from pandas.io.feather_format import read_feather
from pandas.io.gbq import read_gbq
from pandas.io.html import read_html
from pandas.io.json import read_json
from pandas.io.orc import read_orc
from pandas.io.parquet import read_parquet
from pandas.io.parsers import (
    read_csv,
    read_fwf,

            

Reported by Pylint.

              from pandas.io.gbq import read_gbq
from pandas.io.html import read_html
from pandas.io.json import read_json
from pandas.io.orc import read_orc
from pandas.io.parquet import read_parquet
from pandas.io.parsers import (
    read_csv,
    read_fwf,
    read_table,

            

Reported by Pylint.

              from pandas.io.html import read_html
from pandas.io.json import read_json
from pandas.io.orc import read_orc
from pandas.io.parquet import read_parquet
from pandas.io.parsers import (
    read_csv,
    read_fwf,
    read_table,
)

            

Reported by Pylint.

              import pytest

from pandas import (
    DataFrame,
    Index,
    Series,
)
import pandas._testing as tm


            

Reported by Pylint.

              import pytest

from pandas import (
    DataFrame,
    Index,
    Series,
)
import pandas._testing as tm


            

Reported by Pylint.

              

@pytest.mark.parametrize("n, frac", [(2, None), (None, 0.2)])
def test_groupby_sample_balanced_groups_shape(n, frac):
    values = [1] * 10 + [2] * 10
    df = DataFrame({"a": values, "b": values})

    result = df.groupby("a").sample(n=n, frac=frac)
    values = [1] * 2 + [2] * 2

            

Reported by Pylint.

              

@pytest.mark.parametrize("n, frac", [(2, None), (None, 0.2)])
def test_groupby_sample_balanced_groups_shape(n, frac):
    values = [1] * 10 + [2] * 10
    df = DataFrame({"a": values, "b": values})

    result = df.groupby("a").sample(n=n, frac=frac)
    values = [1] * 2 + [2] * 2

            

Reported by Pylint.

              @pytest.mark.parametrize("n, frac", [(2, None), (None, 0.2)])
def test_groupby_sample_balanced_groups_shape(n, frac):
    values = [1] * 10 + [2] * 10
    df = DataFrame({"a": values, "b": values})

    result = df.groupby("a").sample(n=n, frac=frac)
    values = [1] * 2 + [2] * 2
    expected = DataFrame({"a": values, "b": values}, index=result.index)
    tm.assert_frame_equal(result, expected)

            

Reported by Pylint.

                  tm.assert_series_equal(result, expected)


def test_groupby_sample_unbalanced_groups_shape():
    values = [1] * 10 + [2] * 20
    df = DataFrame({"a": values, "b": values})

    result = df.groupby("a").sample(n=5)
    values = [1] * 5 + [2] * 5

            

Reported by Pylint.

              
def test_groupby_sample_unbalanced_groups_shape():
    values = [1] * 10 + [2] * 20
    df = DataFrame({"a": values, "b": values})

    result = df.groupby("a").sample(n=5)
    values = [1] * 5 + [2] * 5
    expected = DataFrame({"a": values, "b": values}, index=result.index)
    tm.assert_frame_equal(result, expected)

            

Reported by Pylint.

                  tm.assert_series_equal(result, expected)


def test_groupby_sample_index_value_spans_groups():
    values = [1] * 3 + [2] * 3
    df = DataFrame({"a": values, "b": values}, index=[1, 2, 2, 2, 2, 2])

    result = df.groupby("a").sample(n=2)
    values = [1] * 2 + [2] * 2

            

Reported by Pylint.

              
def test_groupby_sample_index_value_spans_groups():
    values = [1] * 3 + [2] * 3
    df = DataFrame({"a": values, "b": values}, index=[1, 2, 2, 2, 2, 2])

    result = df.groupby("a").sample(n=2)
    values = [1] * 2 + [2] * 2
    expected = DataFrame({"a": values, "b": values}, index=result.index)
    tm.assert_frame_equal(result, expected)

            

Reported by Pylint.

                  tm.assert_series_equal(result, expected)


def test_groupby_sample_n_and_frac_raises():
    df = DataFrame({"a": [1, 2], "b": [1, 2]})
    msg = "Please enter a value for `frac` OR `n`, not both"

    with pytest.raises(ValueError, match=msg):
        df.groupby("a").sample(n=1, frac=1.0)

            

Reported by Pylint.

              import numpy as np

from pandas import (
    DataFrame,
    MultiIndex,
    Series,
)
import pandas._testing as tm


            

Reported by Pylint.

              import pandas._testing as tm


class TestDataFramePop:
    def test_pop(self, float_frame):
        float_frame.columns.name = "baz"

        float_frame.pop("A")
        assert "A" not in float_frame

            

Reported by Pylint.

              

class TestDataFramePop:
    def test_pop(self, float_frame):
        float_frame.columns.name = "baz"

        float_frame.pop("A")
        assert "A" not in float_frame


            

Reported by Pylint.

              

class TestDataFramePop:
    def test_pop(self, float_frame):
        float_frame.columns.name = "baz"

        float_frame.pop("A")
        assert "A" not in float_frame


            

Reported by Pylint.

                      float_frame.columns.name = "baz"

        float_frame.pop("A")
        assert "A" not in float_frame

        float_frame["foo"] = "bar"
        float_frame.pop("foo")
        assert "foo" not in float_frame
        assert float_frame.columns.name == "baz"

            

Reported by Bandit.

              
        float_frame["foo"] = "bar"
        float_frame.pop("foo")
        assert "foo" not in float_frame
        assert float_frame.columns.name == "baz"

        # gh-10912: inplace ops cause caching issue
        a = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"], index=["X", "Y"])
        b = a.pop("B")

            

Reported by Bandit.

                      float_frame["foo"] = "bar"
        float_frame.pop("foo")
        assert "foo" not in float_frame
        assert float_frame.columns.name == "baz"

        # gh-10912: inplace ops cause caching issue
        a = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"], index=["X", "Y"])
        b = a.pop("B")
        b += 1

            

Reported by Bandit.

                      assert float_frame.columns.name == "baz"

        # gh-10912: inplace ops cause caching issue
        a = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"], index=["X", "Y"])
        b = a.pop("B")
        b += 1

        # original frame
        expected = DataFrame([[1, 3], [4, 6]], columns=["A", "C"], index=["X", "Y"])

            

Reported by Pylint.

              
        # gh-10912: inplace ops cause caching issue
        a = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"], index=["X", "Y"])
        b = a.pop("B")
        b += 1

        # original frame
        expected = DataFrame([[1, 3], [4, 6]], columns=["A", "C"], index=["X", "Y"])
        tm.assert_frame_equal(a, expected)

            

Reported by Pylint.

                      # gh-10912: inplace ops cause caching issue
        a = DataFrame([[1, 2, 3], [4, 5, 6]], columns=["A", "B", "C"], index=["X", "Y"])
        b = a.pop("B")
        b += 1

        # original frame
        expected = DataFrame([[1, 3], [4, 6]], columns=["A", "C"], index=["X", "Y"])
        tm.assert_frame_equal(a, expected)


            

Reported by Pylint.

              import numpy as np
import pytest

from pandas.core.dtypes.cast import find_common_type
from pandas.core.dtypes.dtypes import (
    CategoricalDtype,
    DatetimeTZDtype,
    IntervalDtype,
    PeriodDtype,

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas.core.dtypes.cast import find_common_type
from pandas.core.dtypes.dtypes import (
    CategoricalDtype,
    DatetimeTZDtype,
    IntervalDtype,
    PeriodDtype,

            

Reported by Pylint.

                      ),
        ((np.dtype("datetime64[ns]"), np.dtype("timedelta64[ns]")), object),
        ((np.dtype("datetime64[ns]"), np.int64), object),
    ],
)
def test_numpy_dtypes(source_dtypes, expected_common_dtype):
    assert find_common_type(source_dtypes) == expected_common_dtype



            

Reported by Pylint.

                  ],
)
def test_numpy_dtypes(source_dtypes, expected_common_dtype):
    assert find_common_type(source_dtypes) == expected_common_dtype


def test_raises_empty_input():
    with pytest.raises(ValueError, match="no types given"):
        find_common_type([])

            

Reported by Bandit.

                  assert find_common_type(source_dtypes) == expected_common_dtype


def test_raises_empty_input():
    with pytest.raises(ValueError, match="no types given"):
        find_common_type([])


@pytest.mark.parametrize(

            

Reported by Pylint.

                      ([CategoricalDtype()], "category"),
        ([object, CategoricalDtype()], object),
        ([CategoricalDtype(), CategoricalDtype()], "category"),
    ],
)
def test_categorical_dtype(dtypes, exp_type):
    assert find_common_type(dtypes) == exp_type



            

Reported by Pylint.

                  ],
)
def test_categorical_dtype(dtypes, exp_type):
    assert find_common_type(dtypes) == exp_type


def test_datetimetz_dtype_match():
    dtype = DatetimeTZDtype(unit="ns", tz="US/Eastern")
    assert find_common_type([dtype, dtype]) == "datetime64[ns, US/Eastern]"

            

Reported by Bandit.

                  assert find_common_type(dtypes) == exp_type


def test_datetimetz_dtype_match():
    dtype = DatetimeTZDtype(unit="ns", tz="US/Eastern")
    assert find_common_type([dtype, dtype]) == "datetime64[ns, US/Eastern]"


@pytest.mark.parametrize(

            

Reported by Pylint.

              
def test_datetimetz_dtype_match():
    dtype = DatetimeTZDtype(unit="ns", tz="US/Eastern")
    assert find_common_type([dtype, dtype]) == "datetime64[ns, US/Eastern]"


@pytest.mark.parametrize(
    "dtype2",
    [

            

Reported by Bandit.

                      np.dtype("datetime64[ns]"),
        object,
        np.int64,
    ],
)
def test_datetimetz_dtype_mismatch(dtype2):
    dtype = DatetimeTZDtype(unit="ns", tz="US/Eastern")
    assert find_common_type([dtype, dtype2]) == object
    assert find_common_type([dtype2, dtype]) == object

            

Reported by Pylint.

              import pytest

import pandas as pd
import pandas._testing as tm
from pandas.tests.extension.base.base import BaseExtensionTests


class BaseGroupbyTests(BaseExtensionTests):
    """Groupby-specific tests."""

            

Reported by Pylint.

                      _, uniques = pd.factorize(data_for_grouping, sort=True)

        if as_index:
            index = pd.Index._with_infer(uniques, name="B")
            expected = pd.Series([3.0, 1.0, 4.0], index=index, name="A")
            self.assert_series_equal(result, expected)
        else:
            expected = pd.DataFrame({"B": uniques, "A": [3.0, 1.0, 4.0]})
            self.assert_frame_equal(result, expected)

            

Reported by Pylint.

                      result = df.groupby("B", sort=False).A.mean()
        _, index = pd.factorize(data_for_grouping, sort=False)

        index = pd.Index._with_infer(index, name="B")
        expected = pd.Series([1.0, 3.0, 4.0], index=index, name="A")
        self.assert_series_equal(result, expected)

    def test_groupby_extension_transform(self, data_for_grouping):
        valid = data_for_grouping[~data_for_grouping.isna()]

            

Reported by Pylint.

                      )
        result = df.groupby("A").sum().columns

        if data_for_grouping.dtype._is_numeric:
            expected = pd.Index(["B", "C"])
        else:
            expected = pd.Index(["C"])

        tm.assert_index_equal(result, expected)

            

Reported by Pylint.

              import pytest

import pandas as pd
import pandas._testing as tm
from pandas.tests.extension.base.base import BaseExtensionTests


class BaseGroupbyTests(BaseExtensionTests):
    """Groupby-specific tests."""

            

Reported by Pylint.

              class BaseGroupbyTests(BaseExtensionTests):
    """Groupby-specific tests."""

    def test_grouping_grouper(self, data_for_grouping):
        df = pd.DataFrame(
            {"A": ["B", "B", None, None, "A", "A", "B", "C"], "B": data_for_grouping}
        )
        gr1 = df.groupby("A").grouper.groupings[0]
        gr2 = df.groupby("B").grouper.groupings[0]

            

Reported by Pylint.

              class BaseGroupbyTests(BaseExtensionTests):
    """Groupby-specific tests."""

    def test_grouping_grouper(self, data_for_grouping):
        df = pd.DataFrame(
            {"A": ["B", "B", None, None, "A", "A", "B", "C"], "B": data_for_grouping}
        )
        gr1 = df.groupby("A").grouper.groupings[0]
        gr2 = df.groupby("B").grouper.groupings[0]

            

Reported by Pylint.

                  """Groupby-specific tests."""

    def test_grouping_grouper(self, data_for_grouping):
        df = pd.DataFrame(
            {"A": ["B", "B", None, None, "A", "A", "B", "C"], "B": data_for_grouping}
        )
        gr1 = df.groupby("A").grouper.groupings[0]
        gr2 = df.groupby("B").grouper.groupings[0]


            

Reported by Pylint.

                      tm.assert_extension_array_equal(gr2.grouping_vector, data_for_grouping)

    @pytest.mark.parametrize("as_index", [True, False])
    def test_groupby_extension_agg(self, as_index, data_for_grouping):
        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
        result = df.groupby("B", as_index=as_index).A.mean()
        _, uniques = pd.factorize(data_for_grouping, sort=True)

        if as_index:

            

Reported by Pylint.

              
    @pytest.mark.parametrize("as_index", [True, False])
    def test_groupby_extension_agg(self, as_index, data_for_grouping):
        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
        result = df.groupby("B", as_index=as_index).A.mean()
        _, uniques = pd.factorize(data_for_grouping, sort=True)

        if as_index:
            index = pd.Index._with_infer(uniques, name="B")

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas._libs.tslibs import iNaT
from pandas._libs.tslibs.period import IncompatibleFrequency

from pandas.core.dtypes.base import _registry as registry
from pandas.core.dtypes.dtypes import PeriodDtype


            

Reported by Pylint.

              import pytest

from pandas._libs.tslibs import iNaT
from pandas._libs.tslibs.period import IncompatibleFrequency

from pandas.core.dtypes.base import _registry as registry
from pandas.core.dtypes.dtypes import PeriodDtype

import pandas as pd

            

Reported by Pylint.

              import pytest

from pandas._libs.tslibs import iNaT
from pandas._libs.tslibs.period import IncompatibleFrequency

from pandas.core.dtypes.base import _registry as registry
from pandas.core.dtypes.dtypes import PeriodDtype

import pandas as pd

            

Reported by Pylint.

                  arr = period_array(["2000", "2001"], freq="D")
    other = pd.Period("2000", freq="M")
    with pytest.raises(IncompatibleFrequency, match="freq"):
        arr - other


# ----------------------------------------------------------------------------
# Methods


            

Reported by Pylint.

              import numpy as np
import pytest

from pandas._libs.tslibs import iNaT
from pandas._libs.tslibs.period import IncompatibleFrequency

from pandas.core.dtypes.base import _registry as registry
from pandas.core.dtypes.dtypes import PeriodDtype


            

Reported by Pylint.

              # Dtype


def test_registered():
    assert PeriodDtype in registry.dtypes
    result = registry.find("Period[D]")
    expected = PeriodDtype("D")
    assert result == expected


            

Reported by Pylint.

              

def test_registered():
    assert PeriodDtype in registry.dtypes
    result = registry.find("Period[D]")
    expected = PeriodDtype("D")
    assert result == expected



            

Reported by Bandit.

                  assert PeriodDtype in registry.dtypes
    result = registry.find("Period[D]")
    expected = PeriodDtype("D")
    assert result == expected


# ----------------------------------------------------------------------------
# period_array


            

Reported by Bandit.

              # period_array


def test_asi8():
    result = period_array(["2000", "2001", None], freq="D").asi8
    expected = np.array([10957, 11323, iNaT])
    tm.assert_numpy_array_equal(result, expected)



            

Reported by Pylint.

                  tm.assert_numpy_array_equal(result, expected)


def test_take_raises():
    arr = period_array(["2000", "2001"], freq="D")
    with pytest.raises(IncompatibleFrequency, match="freq"):
        arr.take([0, -1], allow_fill=True, fill_value=pd.Period("2000", freq="W"))

    msg = "value should be a 'Period' or 'NaT'. Got 'str' instead"

            

Reported by Pylint.

              import numpy as np

from pandas import (
    DataFrame,
    date_range,
    read_stata,
)

from ..pandas_vb_common import (

            

Reported by Pylint.

                  read_stata,
)

from ..pandas_vb_common import (
    BaseIO,
    tm,
)



            

Reported by Pylint.

                      self.df.to_stata(self.fname, self.convert_dates)


from ..pandas_vb_common import setup  # noqa: F401 isort:skip

            

Reported by Pylint.

                  param_names = ["convert_dates"]

    def setup(self, convert_dates):
        self.fname = "__test__.dta"
        N = self.N = 100000
        C = self.C = 5
        self.df = DataFrame(
            np.random.randn(N, C),
            columns=[f"float{i}" for i in range(C)],

            

Reported by Pylint.

              
    def setup(self, convert_dates):
        self.fname = "__test__.dta"
        N = self.N = 100000
        C = self.C = 5
        self.df = DataFrame(
            np.random.randn(N, C),
            columns=[f"float{i}" for i in range(C)],
            index=date_range("20000101", periods=N, freq="H"),

            

Reported by Pylint.

                  def setup(self, convert_dates):
        self.fname = "__test__.dta"
        N = self.N = 100000
        C = self.C = 5
        self.df = DataFrame(
            np.random.randn(N, C),
            columns=[f"float{i}" for i in range(C)],
            index=date_range("20000101", periods=N, freq="H"),
        )

            

Reported by Pylint.

                      self.fname = "__test__.dta"
        N = self.N = 100000
        C = self.C = 5
        self.df = DataFrame(
            np.random.randn(N, C),
            columns=[f"float{i}" for i in range(C)],
            index=date_range("20000101", periods=N, freq="H"),
        )
        self.df["object"] = tm.makeStringIndex(self.N)

            

Reported by Pylint.

                          np.iinfo(np.int32).min, np.iinfo(np.int32).max - 27, N
        )
        self.df["float32_"] = np.array(np.random.randn(N), dtype=np.float32)
        self.convert_dates = {"index": convert_dates}
        self.df.to_stata(self.fname, self.convert_dates)

    def time_read_stata(self, convert_dates):
        read_stata(self.fname)


            

Reported by Pylint.

                      self.convert_dates = {"index": convert_dates}
        self.df.to_stata(self.fname, self.convert_dates)

    def time_read_stata(self, convert_dates):
        read_stata(self.fname)

    def time_write_stata(self, convert_dates):
        self.df.to_stata(self.fname, self.convert_dates)


            

Reported by Pylint.

                  def time_read_stata(self, convert_dates):
        read_stata(self.fname)

    def time_write_stata(self, convert_dates):
        self.df.to_stata(self.fname, self.convert_dates)


class StataMissing(Stata):
    def setup(self, convert_dates):

            

Reported by Pylint.