Tests for the str accessors are in pandas/tests/strings/test_string_array.py
"""
import numpy as np
import pytest

import pandas.util._test_decorators as td

from pandas.core.dtypes.common import is_dtype_equal


            

Reported by Pylint.

                  result = cls._from_sequence(nan_arr, copy=copy)

    if cls is ArrowStringArray:
        import pyarrow as pa

        expected = cls(pa.array(na_arr, type=pa.string(), from_pandas=True))
    else:
        expected = cls(na_arr)


            

Reported by Pylint.

              @td.skip_if_no("pyarrow")
def test_arrow_array(dtype):
    # protocol added in 0.15.0
    import pyarrow as pa

    data = pd.array(["a", "b", "c"], dtype=dtype)
    arr = pa.array(data)
    expected = pa.array(list(data), type=pa.string(), from_pandas=True)
    if dtype.storage == "pyarrow":

            

Reported by Pylint.

              @td.skip_if_no("pyarrow")
def test_arrow_roundtrip(dtype, string_storage2):
    # roundtrip possible from arrow 1.0.0
    import pyarrow as pa

    data = pd.array(["a", "b", None], dtype=dtype)
    df = pd.DataFrame({"a": data})
    table = pa.table(df)
    assert table.field("a").type == "string"

            

Reported by Pylint.

              @td.skip_if_no("pyarrow")
def test_arrow_load_from_zero_chunks(dtype, string_storage2):
    # GH-41040
    import pyarrow as pa

    data = pd.array([], dtype=dtype)
    df = pd.DataFrame({"a": data})
    table = pa.table(df)
    assert table.field("a").type == "string"

            

Reported by Pylint.

              

@pytest.fixture
def cls(dtype):
    return dtype.construct_array_type()


def test_repr(dtype):
    df = pd.DataFrame({"A": pd.array(["a", pd.NA, "b"], dtype=dtype)})

            

Reported by Pylint.

                  return dtype.construct_array_type()


def test_repr(dtype):
    df = pd.DataFrame({"A": pd.array(["a", pd.NA, "b"], dtype=dtype)})
    expected = "      A\n0     a\n1  <NA>\n2     b"
    assert repr(df) == expected

    expected = "0       a\n1    <NA>\n2       b\nName: A, dtype: string"

            

Reported by Pylint.

                  assert repr(df.A.array) == expected


def test_none_to_nan(cls):
    a = cls._from_sequence(["a", None, "b"])
    assert a[1] is not None
    assert a[1] is pd.NA



            

Reported by Pylint.

              

def test_none_to_nan(cls):
    a = cls._from_sequence(["a", None, "b"])
    assert a[1] is not None
    assert a[1] is pd.NA


def test_setitem_validates(cls):

            

Reported by Pylint.

                  assert a[1] is pd.NA


def test_setitem_validates(cls):
    arr = cls._from_sequence(["a", "b"])

    if cls is pd.arrays.StringArray:
        msg = "Cannot set non-string value '10' into a StringArray."
    else:

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas.core.dtypes.common import is_categorical_dtype

from pandas import (
    CategoricalIndex,
    DataFrame,
    Index,

            

Reported by Pylint.

              @pytest.mark.parametrize("b_dtype", ["category", "int64"])
def test_categoricals(a_dtype, b_dtype):
    # https://github.com/pandas-dev/pandas/issues/37465
    g = np.random.RandomState(25982704)
    a = Series(g.randint(0, 3, size=100)).astype(a_dtype)
    b = Series(g.randint(0, 2, size=100)).astype(b_dtype)
    result = crosstab(a, b, margins=True, dropna=False)
    columns = Index([0, 1, "All"], dtype="object", name="col_0")
    index = Index([0, 1, 2, "All"], dtype="object", name="row_0")

            

Reported by Pylint.

              

class TestCrosstab:
    def setup_method(self, method):
        df = DataFrame(
            {
                "A": [
                    "foo",
                    "foo",

            

Reported by Pylint.

                          }
        )

        self.df = df.append(df, ignore_index=True)

    def test_crosstab_single(self):
        df = self.df
        result = crosstab(df["A"], df["C"])
        expected = df.groupby(["A", "C"]).size().unstack()

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas.core.dtypes.common import is_categorical_dtype

from pandas import (
    CategoricalIndex,
    DataFrame,
    Index,

            

Reported by Pylint.

              import pandas._testing as tm


class TestCrosstab:
    def setup_method(self, method):
        df = DataFrame(
            {
                "A": [
                    "foo",

            

Reported by Pylint.

              import pandas._testing as tm


class TestCrosstab:
    def setup_method(self, method):
        df = DataFrame(
            {
                "A": [
                    "foo",

            

Reported by Pylint.

              

class TestCrosstab:
    def setup_method(self, method):
        df = DataFrame(
            {
                "A": [
                    "foo",
                    "foo",

            

Reported by Pylint.

              
class TestCrosstab:
    def setup_method(self, method):
        df = DataFrame(
            {
                "A": [
                    "foo",
                    "foo",
                    "foo",

            

Reported by Pylint.

                          }
        )

        self.df = df.append(df, ignore_index=True)

    def test_crosstab_single(self):
        df = self.df
        result = crosstab(df["A"], df["C"])
        expected = df.groupby(["A", "C"]).size().unstack()

            

Reported by Pylint.

              )

import numpy as np
import pytest

from pandas.compat import (
    is_platform_arm,
    is_platform_mac,
)

            

Reported by Pylint.

                  date_today = datetime.now()
    days = date_range(date_today, date_today + timedelta(365), freq="D")

    npr = np.random.RandomState(seed=421)

    data = npr.randint(1, high=100, size=len(days))
    df = DataFrame({"DateCol": days, "metric": data})

    df.set_index("DateCol", inplace=True)

            

Reported by Pylint.

                          start = np.empty(num_values, dtype=np.int64)
            end = np.empty(num_values, dtype=np.int64)
            for i in range(num_values):
                if self.use_expanding[i]:
                    start[i] = 0
                    end[i] = i + 1
                else:
                    start[i] = i
                    end[i] = i + self.window_size

            

Reported by Pylint.

              def test_doc_string():

    df = DataFrame({"B": [0, 1, 2, np.nan, 4]})
    df
    df.rolling(2).sum()
    df.rolling(2, min_periods=1).sum()


def test_constructor(frame_or_series):

            

Reported by Pylint.

              def test_rolling_axis_sum(axis_frame):
    # see gh-23372.
    df = DataFrame(np.ones((10, 20)))
    axis = df._get_axis_number(axis_frame)

    if axis == 0:
        expected = DataFrame({i: [np.nan] * 2 + [3.0] * 8 for i in range(20)})
    else:
        # axis == 1

            

Reported by Pylint.

                  # see gh-26055
    df = DataFrame({"x": range(3), "y": range(3)})

    axis = df._get_axis_number(axis_frame)

    if axis in [0, "index"]:
        expected = DataFrame({"x": [1.0, 2.0, 2.0], "y": [1.0, 2.0, 2.0]})
    else:
        expected = DataFrame({"x": [1.0, 1.0, 1.0], "y": [2.0, 2.0, 2.0]})

            

Reported by Pylint.

                  ].agg("max")

    index = days.rename("DateCol")
    index = index._with_freq(None)
    expected = Series(expected_data, index=index, name="metric")
    tm.assert_series_equal(result, expected)


def test_min_periods1():

            

Reported by Pylint.

                  df = DataFrame({"values": np.arange(len(use_expanding)) ** 2})

    class CustomIndexer(BaseIndexer):
        def get_window_bounds(self, num_values, min_periods, center, closed):
            start = np.empty(num_values, dtype=np.int64)
            end = np.empty(num_values, dtype=np.int64)
            for i in range(num_values):
                if self.use_expanding[i]:
                    start[i] = 0

            

Reported by Pylint.

              

@pytest.mark.parametrize("window", [1, "1d"])
def test_rolling_descending_date_order_with_offset(window, frame_or_series):
    # GH#40002
    idx = date_range(start="2020-01-01", end="2020-01-03", freq="1d")
    obj = frame_or_series(range(1, 4), index=idx)
    result = obj.rolling("1d", closed="left").sum()
    expected = frame_or_series([np.nan, 1, 2], index=idx)

            

Reported by Pylint.

              from datetime import (
    datetime,
    timedelta,
)

import numpy as np
import pytest

from pandas.compat import (

            

Reported by Pylint.

              
import numpy as np

from pandas._libs import lib
from pandas._libs.tslibs import (
    BaseOffset,
    IncompatibleFrequency,
    NaT,
    Period,

            

Reported by Pylint.

                      2018-01-01 01:00:00   NaN
        Freq: H, dtype: float64
        """
        return self._selected_obj.groupby(self.groupby).transform(arg, *args, **kwargs)

    def _downsample(self, f):
        raise AbstractMethodError(self)

    def _upsample(self, f, limit=None, fill_value=None):

            

Reported by Pylint.

                      # error: Cannot determine type of 'loffset'
        needs_offset = (
            isinstance(
                self.loffset,  # type: ignore[has-type]
                (DateOffset, timedelta, np.timedelta64),
            )
            and isinstance(result.index, DatetimeIndex)
            and len(result.index) > 0
        )

            

Reported by Pylint.

              
        if needs_offset:
            # error: Cannot determine type of 'loffset'
            result.index = result.index + self.loffset  # type: ignore[has-type]

        self.loffset = None
        return result

    def _get_resampler_for_grouping(self, groupby):

            

Reported by Pylint.

                      """
        nv.validate_resampler_func("std", args, kwargs)
        # error: Unexpected keyword argument "ddof" for "_downsample"
        return self._downsample("std", ddof=ddof)  # type: ignore[call-arg]

    def var(self, ddof=1, *args, **kwargs):
        """
        Compute variance of groups, excluding missing values.


            

Reported by Pylint.

                      """
        nv.validate_resampler_func("var", args, kwargs)
        # error: Unexpected keyword argument "ddof" for "_downsample"
        return self._downsample("var", ddof=ddof)  # type: ignore[call-arg]

    @doc(GroupBy.size)
    def size(self):
        result = self._downsample("size")
        if not len(self.ax):

            

Reported by Pylint.

                      if not len(self.ax):
            from pandas import Series

            if self._selected_obj.ndim == 1:
                name = self._selected_obj.name
            else:
                name = None
            result = Series([], index=result.index, dtype="int64", name=name)
        return result

            

Reported by Pylint.

                          from pandas import Series

            if self._selected_obj.ndim == 1:
                name = self._selected_obj.name
            else:
                name = None
            result = Series([], index=result.index, dtype="int64", name=name)
        return result


            

Reported by Pylint.

                  def count(self):
        result = self._downsample("count")
        if not len(self.ax):
            if self._selected_obj.ndim == 1:
                result = type(self._selected_obj)(
                    [], index=result.index, dtype="int64", name=self._selected_obj.name
                )
            else:
                from pandas import DataFrame

            

Reported by Pylint.

                      if not len(self.ax):
            if self._selected_obj.ndim == 1:
                result = type(self._selected_obj)(
                    [], index=result.index, dtype="int64", name=self._selected_obj.name
                )
            else:
                from pandas import DataFrame

                result = DataFrame(

            

Reported by Pylint.

              from warnings import catch_warnings

import numpy as np
import pytest

import pandas as pd
from pandas import (
    DataFrame,
    Index,

            

Reported by Pylint.

                      # These are actual copies.
        result = concat([df, df2, df3], axis=1, copy=True)

        for arr in result._mgr.arrays:
            assert arr.base is None

        # These are the same.
        result = concat([df, df2, df3], axis=1, copy=False)


            

Reported by Pylint.

                      # These are the same.
        result = concat([df, df2, df3], axis=1, copy=False)

        for arr in result._mgr.arrays:
            if arr.dtype.kind == "f":
                assert arr.base is df._mgr.arrays[0].base
            elif arr.dtype.kind in ["i", "u"]:
                assert arr.base is df2._mgr.arrays[0].base
            elif arr.dtype == object:

            

Reported by Pylint.

              
        for arr in result._mgr.arrays:
            if arr.dtype.kind == "f":
                assert arr.base is df._mgr.arrays[0].base
            elif arr.dtype.kind in ["i", "u"]:
                assert arr.base is df2._mgr.arrays[0].base
            elif arr.dtype == object:
                if using_array_manager:
                    # we get the same array object, which has no base

            

Reported by Pylint.

                          if arr.dtype.kind == "f":
                assert arr.base is df._mgr.arrays[0].base
            elif arr.dtype.kind in ["i", "u"]:
                assert arr.base is df2._mgr.arrays[0].base
            elif arr.dtype == object:
                if using_array_manager:
                    # we get the same array object, which has no base
                    assert arr is df3._mgr.arrays[0]
                else:

            

Reported by Pylint.

                          elif arr.dtype == object:
                if using_array_manager:
                    # we get the same array object, which has no base
                    assert arr is df3._mgr.arrays[0]
                else:
                    assert arr.base is not None

        # Float block was consolidated.
        df4 = DataFrame(np.random.randn(4, 1))

            

Reported by Pylint.

                      # Float block was consolidated.
        df4 = DataFrame(np.random.randn(4, 1))
        result = concat([df, df2, df3, df4], axis=1, copy=False)
        for arr in result._mgr.arrays:
            if arr.dtype.kind == "f":
                if using_array_manager:
                    # this is a view on some array in either df or df4
                    assert any(
                        np.shares_memory(arr, other)

            

Reported by Pylint.

                                  # this is a view on some array in either df or df4
                    assert any(
                        np.shares_memory(arr, other)
                        for other in df._mgr.arrays + df4._mgr.arrays
                    )
                else:
                    # the block was consolidated, so we got a copy anyway
                    assert arr.base is None
            elif arr.dtype.kind in ["i", "u"]:

            

Reported by Pylint.

                                  # this is a view on some array in either df or df4
                    assert any(
                        np.shares_memory(arr, other)
                        for other in df._mgr.arrays + df4._mgr.arrays
                    )
                else:
                    # the block was consolidated, so we got a copy anyway
                    assert arr.base is None
            elif arr.dtype.kind in ["i", "u"]:

            

Reported by Pylint.

                                  # the block was consolidated, so we got a copy anyway
                    assert arr.base is None
            elif arr.dtype.kind in ["i", "u"]:
                assert arr.base is df2._mgr.arrays[0].base
            elif arr.dtype == object:
                # this is a view on df3
                assert any(np.shares_memory(arr, other) for other in df3._mgr.arrays)

    def test_concat_with_group_keys(self):

            

Reported by Pylint.

              )

import numpy as np
import pytest

from pandas.core.dtypes.common import is_scalar

from pandas import (
    DataFrame,

            

Reported by Pylint.

              class Generic:
    @property
    def _ndim(self):
        return self._typ._AXIS_LEN

    def _axes(self):
        """return the axes for my object typ"""
        return self._typ._AXIS_ORDERS


            

Reported by Pylint.

              
    def _axes(self):
        """return the axes for my object typ"""
        return self._typ._AXIS_ORDERS

    def _construct(self, shape, value=None, dtype=None, **kwargs):
        """
        construct an object for the given shape
        if value is specified use that if its a scalar

            

Reported by Pylint.

                                  dtype = np.float64

                    # remove the info axis
                    kwargs.pop(self._typ._info_axis_name, None)
                else:
                    arr = np.empty(shape, dtype=dtype)
                    arr.fill(value)
            else:
                fshape = np.prod(shape)

            

Reported by Pylint.

                              arr = np.repeat(arr, new_shape).reshape(shape)
        else:
            arr = np.random.randn(*shape)
        return self._typ(arr, dtype=dtype, **kwargs)

    def _compare(self, result, expected):
        self._comparator(result, expected)

    def test_rename(self):

            

Reported by Pylint.

                      return self._typ(arr, dtype=dtype, **kwargs)

    def _compare(self, result, expected):
        self._comparator(result, expected)

    def test_rename(self):

        # single axis
        idx = list("ABCD")

            

Reported by Pylint.

              
        n = 4
        kwargs = {
            self._typ._get_axis_name(i): list(range(n)) for i in range(self._ndim)
        }

        # get the numeric data
        o = self._construct(n, **kwargs)
        result = o._get_numeric_data()

            

Reported by Pylint.

                      # GH 4633
        # look at the boolean/nonzero behavior for objects
        obj = self._construct(shape=4)
        msg = f"The truth value of a {self._typ.__name__} is ambiguous"
        with pytest.raises(ValueError, match=msg):
            bool(obj == 0)
        with pytest.raises(ValueError, match=msg):
            bool(obj == 1)
        with pytest.raises(ValueError, match=msg):

            

Reported by Pylint.

                          return self._construct(shape=3, value=1, dtype=dtype)

        msg = (
            "compound dtypes are not implemented "
            f"in the {self._typ.__name__} constructor"
        )

        with pytest.raises(NotImplementedError, match=msg):
            f([("A", "datetime64[h]"), ("B", "str"), ("C", "int32")])

            

Reported by Pylint.

              class Generic:
    @property
    def _ndim(self):
        return self._typ._AXIS_LEN

    def _axes(self):
        """return the axes for my object typ"""
        return self._typ._AXIS_ORDERS


            

Reported by Pylint.

              import string

import numpy as np
import pytest

import pandas.util._test_decorators as td

from pandas import (
    DataFrame,

            

Reported by Pylint.

              
        # When ax is supplied and required number of axes is 1,
        # passed ax should be used:
        fig, ax = self.plt.subplots()
        axes = df.boxplot("Col1", by="X", ax=ax)
        ax_axes = ax.axes
        assert ax_axes is axes

        fig, ax = self.plt.subplots()

            

Reported by Pylint.

                      ax_axes = ax.axes
        assert ax_axes is axes

        fig, ax = self.plt.subplots()
        axes = df.groupby("Y").boxplot(ax=ax, return_type="axes")
        ax_axes = ax.axes
        assert ax_axes is axes["A"]

        # Multiple columns with an ax argument should use same figure

            

Reported by Pylint.

                      assert ax_axes is axes["A"]

        # Multiple columns with an ax argument should use same figure
        fig, ax = self.plt.subplots()
        with tm.assert_produces_warning(UserWarning):
            axes = df.boxplot(
                column=["Col1", "Col2"], by="X", ax=ax, return_type="axes"
            )
        assert axes["Col1"].get_figure() is fig

            

Reported by Pylint.

              
        # When by is None, check that all relevant lines are present in the
        # dict
        fig, ax = self.plt.subplots()
        d = df.boxplot(ax=ax, return_type="dict")
        lines = list(itertools.chain.from_iterable(d.values()))
        assert len(ax.get_lines()) == len(lines)

    def test_boxplot_return_type_none(self):

            

Reported by Pylint.

                  def test_boxplot_return_type_none(self):
        # GH 12216; return_type=None & by=None -> axes
        result = self.hist_df.boxplot()
        assert isinstance(result, self.plt.Axes)

    def test_boxplot_return_type_legacy(self):
        # API change in https://github.com/pandas-dev/pandas/pull/7096
        import matplotlib as mpl  # noqa


            

Reported by Pylint.

                      gb = df.groupby("gender")

        res = gb.plot()
        assert len(self.plt.get_fignums()) == 2
        assert len(res) == 2
        tm.close()

        res = gb.boxplot(return_type="axes")
        assert len(self.plt.get_fignums()) == 1

            

Reported by Pylint.

                      tm.close()

        res = gb.boxplot(return_type="axes")
        assert len(self.plt.get_fignums()) == 1
        assert len(res) == 2
        tm.close()

        # now works with GH 5610 as gender is excluded
        res = df.groupby("gender").hist()

            

Reported by Pylint.

                          box = _check_plot_works(
                df.groupby("gender").boxplot, column="height", return_type="dict"
            )
        self._check_axes_shape(self.plt.gcf().axes, axes_num=2, layout=(1, 2))

        with tm.assert_produces_warning(UserWarning):
            box = _check_plot_works(
                df.groupby("category").boxplot, column="height", return_type="dict"
            )

            

Reported by Pylint.

                          box = _check_plot_works(
                df.groupby("category").boxplot, column="height", return_type="dict"
            )
        self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2))

        # GH 6769
        with tm.assert_produces_warning(UserWarning):
            box = _check_plot_works(
                df.groupby("classroom").boxplot, column="height", return_type="dict"

            

Reported by Pylint.

              import itertools

import numpy as np
import pytest

from pandas.errors import PerformanceWarning
import pandas.util._test_decorators as td

import pandas as pd

            

Reported by Pylint.

                      assert df["B"]._values.freq is None

        # check that the DatetimeIndex was not altered in place
        assert dti.freq == "D"
        assert dti[1] == ts

    def test_cast_internals(self, float_frame):
        casted = DataFrame(float_frame._mgr, dtype=int)
        expected = DataFrame(float_frame._series, dtype=int)

            

Reported by Pylint.

                      assert df["B"]._values.freq is None

        # check that the DatetimeIndex was not altered in place
        assert dti.freq == "D"
        assert dti[1] == ts

    def test_cast_internals(self, float_frame):
        casted = DataFrame(float_frame._mgr, dtype=int)
        expected = DataFrame(float_frame._series, dtype=int)

            

Reported by Pylint.

              # structure


# TODO(ArrayManager) check which of those tests need to be rewritten to test the
# equivalent for ArrayManager
pytestmark = td.skip_array_manager_invalid_test


class TestDataFrameBlockInternals:

            

Reported by Pylint.

                      ts = dti[1]

        df = DataFrame({"B": dti})
        assert df["B"]._values.freq is None

        df.iloc[1, 0] = pd.NaT
        assert df["B"]._values.freq is None

        # check that the DatetimeIndex was not altered in place

            

Reported by Pylint.

                      assert df["B"]._values.freq is None

        df.iloc[1, 0] = pd.NaT
        assert df["B"]._values.freq is None

        # check that the DatetimeIndex was not altered in place
        assert dti.freq == "D"
        assert dti[1] == ts


            

Reported by Pylint.

                      assert dti[1] == ts

    def test_cast_internals(self, float_frame):
        casted = DataFrame(float_frame._mgr, dtype=int)
        expected = DataFrame(float_frame._series, dtype=int)
        tm.assert_frame_equal(casted, expected)

        casted = DataFrame(float_frame._mgr, dtype=np.int32)
        expected = DataFrame(float_frame._series, dtype=np.int32)

            

Reported by Pylint.

              
    def test_cast_internals(self, float_frame):
        casted = DataFrame(float_frame._mgr, dtype=int)
        expected = DataFrame(float_frame._series, dtype=int)
        tm.assert_frame_equal(casted, expected)

        casted = DataFrame(float_frame._mgr, dtype=np.int32)
        expected = DataFrame(float_frame._series, dtype=np.int32)
        tm.assert_frame_equal(casted, expected)

            

Reported by Pylint.

                      expected = DataFrame(float_frame._series, dtype=int)
        tm.assert_frame_equal(casted, expected)

        casted = DataFrame(float_frame._mgr, dtype=np.int32)
        expected = DataFrame(float_frame._series, dtype=np.int32)
        tm.assert_frame_equal(casted, expected)

    def test_consolidate(self, float_frame):
        float_frame["E"] = 7.0

            

Reported by Pylint.

                      tm.assert_frame_equal(casted, expected)

        casted = DataFrame(float_frame._mgr, dtype=np.int32)
        expected = DataFrame(float_frame._series, dtype=np.int32)
        tm.assert_frame_equal(casted, expected)

    def test_consolidate(self, float_frame):
        float_frame["E"] = 7.0
        consolidated = float_frame._consolidate()

            

Reported by Pylint.

              
import numpy as np

import pandas._libs.lib as lib
from pandas._typing import DtypeArg
from pandas.compat._optional import import_optional_dependency
from pandas.errors import AbstractMethodError

from pandas.core.dtypes.common import (

            

Reported by Pylint.

              
import numpy as np

import pandas._libs.lib as lib
from pandas._typing import DtypeArg
from pandas.compat._optional import import_optional_dependency
from pandas.errors import AbstractMethodError

from pandas.core.dtypes.common import (

            

Reported by Pylint.

                          # https://stackoverflow.com/a/67358288/6067848
            msg = r"""(\(1054, "Unknown column 'inf(e0)?' in 'field list'"\))(?#
            )|inf can not be used with MySQL"""
            err_text = str(err.orig)
            if re.search(msg, err_text):
                raise ValueError("inf cannot be used with MySQL") from err
            else:
                raise err


            

Reported by Pylint.

              

def _handle_date_column(
    col, utc: bool | None = None, format: str | dict[str, Any] | None = None
):
    if isinstance(format, dict):
        # GH35185 Allow custom error values in parse_dates argument of
        # read_sql like functions.
        # Format can take on custom to_datetime argument values such as

            

Reported by Pylint.

              
    try:
        _is_table_name = pandas_sql.has_table(sql)
    except Exception:
        # using generic exception to catch errors from sql drivers (GH24988)
        _is_table_name = False

    if _is_table_name:
        pandas_sql.meta.reflect(bind=pandas_sql.connectable, only=[sql])

            

Reported by Pylint.

                  pass them between functions all the time.
    """

    # TODO: support for multiIndex

    def __init__(
        self,
        name: str,
        pandas_sql_engine,

            

Reported by Pylint.

                      data_list = [None] * ncols

        for i, (_, ser) in enumerate(temp.items()):
            vals = ser._values
            if vals.dtype.kind == "M":
                d = vals.to_pydatetime()
            elif vals.dtype.kind == "m":
                # store as integers, see GH#6921, GH#7076
                d = vals.view("i8").astype(object)

            

Reported by Pylint.

              
            assert isinstance(d, np.ndarray), type(d)

            if ser._can_hold_na:
                # Note: this will miss timedeltas since they are converted to int
                mask = isna(d)
                d[mask] = None

            # error: No overload variant of "__setitem__" of "list" matches

            

Reported by Pylint.

                      column_names_and_types = []
        if self.index is not None:
            for i, idx_label in enumerate(self.index):
                idx_type = dtype_mapper(self.frame.index._get_level_values(i))
                column_names_and_types.append((str(idx_label), idx_type, True))

        column_names_and_types += [
            (str(self.frame.columns[i]), dtype_mapper(self.frame.iloc[:, i]), False)
            for i in range(len(self.frame.columns))

            

Reported by Pylint.

                      if isinstance(sqltype, Float):
            return float
        elif isinstance(sqltype, Integer):
            # TODO: Refine integer size.
            return np.dtype("int64")
        elif isinstance(sqltype, TIMESTAMP):
            # we have a timezone capable type
            if not sqltype.timezone:
                return datetime

            

Reported by Pylint.

              
import numpy as np

import pandas as pd
from pandas import (
    DataFrame,
    MultiIndex,
    date_range,
    melt,

            

Reported by Pylint.

              import numpy as np

import pandas as pd
from pandas import (
    DataFrame,
    MultiIndex,
    date_range,
    melt,
    wide_to_long,

            

Reported by Pylint.

                  melt,
    wide_to_long,
)
from pandas.api.types import CategoricalDtype


class Melt:
    def setup(self):
        self.df = DataFrame(np.random.randn(10000, 3), columns=["A", "B", "C"])

            

Reported by Pylint.

                      self.series.explode()


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

            

Reported by Pylint.

              
class Melt:
    def setup(self):
        self.df = DataFrame(np.random.randn(10000, 3), columns=["A", "B", "C"])
        self.df["id1"] = np.random.randint(0, 10, 10000)
        self.df["id2"] = np.random.randint(100, 1000, 10000)

    def time_melt_dataframe(self):
        melt(self.df, id_vars=["id1", "id2"])

            

Reported by Pylint.

                          "variable": np.arange(50).repeat(N),
            "date": np.tile(index.values, 50),
        }
        self.df = DataFrame(data)

    def time_reshape_pivot_time_series(self):
        self.df.pivot("date", "variable", "value")



            

Reported by Pylint.

                  def setup(self):
        arrays = [np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)]
        index = MultiIndex.from_arrays(arrays)
        self.df = DataFrame(np.random.randn(10000, 4), index=index)
        self.udf = self.df.unstack(1)

    def time_stack(self):
        self.udf.stack()


            

Reported by Pylint.

                      arrays = [np.arange(100).repeat(100), np.roll(np.tile(np.arange(100), 100), 25)]
        index = MultiIndex.from_arrays(arrays)
        self.df = DataFrame(np.random.randn(10000, 4), index=index)
        self.udf = self.df.unstack(1)

    def time_stack(self):
        self.udf.stack()

    def time_unstack(self):

            

Reported by Pylint.

                      df = ser.unstack("bar")
        # roundtrips -> df.stack().equals(ser)

        self.ser = ser
        self.df = df

    def time_stack(self, dtype):
        self.df.stack()


            

Reported by Pylint.

                      # roundtrips -> df.stack().equals(ser)

        self.ser = ser
        self.df = df

    def time_stack(self, dtype):
        self.df.stack()

    def time_unstack_fast(self, dtype):

            

Reported by Pylint.