time as dt_time,
)

import pytest

from pandas._libs.tslibs import (
    Timedelta,
    Timestamp,
)

            

Reported by Pylint.

                  Timedelta,
    Timestamp,
)
from pandas._libs.tslibs.offsets import (
    BDay,
    BusinessHour,
    Nano,
)


            

Reported by Pylint.

                  Timedelta,
    Timestamp,
)
from pandas._libs.tslibs.offsets import (
    BDay,
    BusinessHour,
    Nano,
)


            

Reported by Pylint.

              class TestBusinessHour(Base):
    _offset = BusinessHour

    def setup_method(self, method):
        self.d = datetime(2014, 7, 1, 10, 00)

        self.offset1 = BusinessHour()
        self.offset2 = BusinessHour(n=3)


            

Reported by Pylint.

                  def setup_method(self, method):
        self.d = datetime(2014, 7, 1, 10, 00)

        self.offset1 = BusinessHour()
        self.offset2 = BusinessHour(n=3)

        self.offset3 = BusinessHour(n=-1)
        self.offset4 = BusinessHour(n=-4)


            

Reported by Pylint.

                      self.d = datetime(2014, 7, 1, 10, 00)

        self.offset1 = BusinessHour()
        self.offset2 = BusinessHour(n=3)

        self.offset3 = BusinessHour(n=-1)
        self.offset4 = BusinessHour(n=-4)

        from datetime import time as dt_time

            

Reported by Pylint.

                      self.offset1 = BusinessHour()
        self.offset2 = BusinessHour(n=3)

        self.offset3 = BusinessHour(n=-1)
        self.offset4 = BusinessHour(n=-4)

        from datetime import time as dt_time

        self.offset5 = BusinessHour(start=dt_time(11, 0), end=dt_time(14, 30))

            

Reported by Pylint.

                      self.offset2 = BusinessHour(n=3)

        self.offset3 = BusinessHour(n=-1)
        self.offset4 = BusinessHour(n=-4)

        from datetime import time as dt_time

        self.offset5 = BusinessHour(start=dt_time(11, 0), end=dt_time(14, 30))
        self.offset6 = BusinessHour(start="20:00", end="05:00")

            

Reported by Pylint.

                      self.offset3 = BusinessHour(n=-1)
        self.offset4 = BusinessHour(n=-4)

        from datetime import time as dt_time

        self.offset5 = BusinessHour(start=dt_time(11, 0), end=dt_time(14, 30))
        self.offset6 = BusinessHour(start="20:00", end="05:00")
        self.offset7 = BusinessHour(n=-2, start=dt_time(21, 30), end=dt_time(6, 30))
        self.offset8 = BusinessHour(start=["09:00", "13:00"], end=["12:00", "17:00"])

            

Reported by Pylint.

                      self.offset3 = BusinessHour(n=-1)
        self.offset4 = BusinessHour(n=-4)

        from datetime import time as dt_time

        self.offset5 = BusinessHour(start=dt_time(11, 0), end=dt_time(14, 30))
        self.offset6 = BusinessHour(start="20:00", end="05:00")
        self.offset7 = BusinessHour(n=-2, start=dt_time(21, 30), end=dt_time(6, 30))
        self.offset8 = BusinessHour(start=["09:00", "13:00"], end=["12:00", "17:00"])

            

Reported by Pylint.

              """

import numpy as np
import pytest

import pandas.util._test_decorators as td

import pandas as pd
from pandas import (

            

Reported by Pylint.

                      )

        expected = pd.concat(
            [df_orig, DataFrame({"A": 7}, index=dates[-1:] + dates.freq)], sort=True
        )
        df = df_orig.copy()
        df.loc[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)
        df = df_orig.copy()

            

Reported by Pylint.

                      )

        expected = pd.concat(
            [df_orig, DataFrame({"A": 7}, index=dates[-1:] + dates.freq)], sort=True
        )
        df = df_orig.copy()
        df.loc[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)
        df = df_orig.copy()

            

Reported by Pylint.

                          [df_orig, DataFrame({"A": 7}, index=dates[-1:] + dates.freq)], sort=True
        )
        df = df_orig.copy()
        df.loc[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)
        df = df_orig.copy()
        df.at[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)


            

Reported by Pylint.

                          [df_orig, DataFrame({"A": 7}, index=dates[-1:] + dates.freq)], sort=True
        )
        df = df_orig.copy()
        df.loc[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)
        df = df_orig.copy()
        df.at[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)


            

Reported by Pylint.

                      df.loc[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)
        df = df_orig.copy()
        df.at[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)

        exp_other = DataFrame({0: 7}, index=dates[-1:] + dates.freq)
        expected = pd.concat([df_orig, exp_other], axis=1)


            

Reported by Pylint.

                      df.loc[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)
        df = df_orig.copy()
        df.at[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)

        exp_other = DataFrame({0: 7}, index=dates[-1:] + dates.freq)
        expected = pd.concat([df_orig, exp_other], axis=1)


            

Reported by Pylint.

                      df.at[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)

        exp_other = DataFrame({0: 7}, index=dates[-1:] + dates.freq)
        expected = pd.concat([df_orig, exp_other], axis=1)

        df = df_orig.copy()
        df.loc[dates[-1] + dates.freq, 0] = 7
        tm.assert_frame_equal(df, expected)

            

Reported by Pylint.

                      df.at[dates[-1] + dates.freq, "A"] = 7
        tm.assert_frame_equal(df, expected)

        exp_other = DataFrame({0: 7}, index=dates[-1:] + dates.freq)
        expected = pd.concat([df_orig, exp_other], axis=1)

        df = df_orig.copy()
        df.loc[dates[-1] + dates.freq, 0] = 7
        tm.assert_frame_equal(df, expected)

            

Reported by Pylint.

                      expected = pd.concat([df_orig, exp_other], axis=1)

        df = df_orig.copy()
        df.loc[dates[-1] + dates.freq, 0] = 7
        tm.assert_frame_equal(df, expected)
        df = df_orig.copy()
        df.at[dates[-1] + dates.freq, 0] = 7
        tm.assert_frame_equal(df, expected)


            

Reported by Pylint.

              import sys

import numpy as np
import pytest

import pandas._config.config as cf

from pandas.compat import (
    is_platform_windows,

            

Reported by Pylint.

                      rs = self.dtc.convert(Index([ts - Day(1), ts]), None, None)
        assert rs[1] == xp

        rs = self.dtc.convert(Index([ts - Day(1), ts]).to_pydatetime(), None, None)
        assert rs[1] == xp

    def test_conversion_float(self):
        rtol = 0.5 * 10 ** -9


            

Reported by Pylint.

              
    def test_registry_resets(self):
        units = pytest.importorskip("matplotlib.units")
        dates = pytest.importorskip("matplotlib.dates")

        # make a copy, to reset to
        original = dict(units.registry)

        try:

            

Reported by Pylint.

              

class TestDateTimeConverter:
    def setup_method(self, method):
        self.dtc = converter.DatetimeConverter()
        self.tc = converter.TimeFormatter(None)

    def test_convert_accepts_unicode(self):
        r1 = self.dtc.convert("12:22", None, None)

            

Reported by Pylint.

              
class TestDateTimeConverter:
    def setup_method(self, method):
        self.dtc = converter.DatetimeConverter()
        self.tc = converter.TimeFormatter(None)

    def test_convert_accepts_unicode(self):
        r1 = self.dtc.convert("12:22", None, None)
        r2 = self.dtc.convert("12:22", None, None)

            

Reported by Pylint.

              class TestDateTimeConverter:
    def setup_method(self, method):
        self.dtc = converter.DatetimeConverter()
        self.tc = converter.TimeFormatter(None)

    def test_convert_accepts_unicode(self):
        r1 = self.dtc.convert("12:22", None, None)
        r2 = self.dtc.convert("12:22", None, None)
        assert r1 == r2, "DatetimeConverter.convert should accept unicode"

            

Reported by Pylint.

                      for freq in ("B", "L", "S"):
            dateindex = tm.makeDateIndex(k=10, freq=freq)
            rs = self.dtc.convert(dateindex, None, None)
            xp = converter.dates.date2num(dateindex._mpl_repr())
            tm.assert_almost_equal(rs, xp, rtol=rtol)

    def test_resolution(self):
        def _assert_less(ts1, ts2):
            val1 = self.dtc.convert(ts1, None, None)

            

Reported by Pylint.

              

class TestPeriodConverter:
    def setup_method(self, method):
        self.pc = converter.PeriodConverter()

        class Axis:
            pass


            

Reported by Pylint.

              
class TestPeriodConverter:
    def setup_method(self, method):
        self.pc = converter.PeriodConverter()

        class Axis:
            pass

        self.axis = Axis()

            

Reported by Pylint.

                      class Axis:
            pass

        self.axis = Axis()
        self.axis.freq = "D"

    def test_convert_accepts_unicode(self):
        r1 = self.pc.convert("2012-1-1", None, self.axis)
        r2 = self.pc.convert("2012-1-1", None, self.axis)

            

Reported by Pylint.

              
import numpy as np

from pandas._libs import (
    Timedelta,
    hashtable as libhashtable,
    join as libjoin,
    lib,
)

            

Reported by Pylint.

              
import numpy as np

from pandas._libs import (
    Timedelta,
    hashtable as libhashtable,
    join as libjoin,
    lib,
)

            

Reported by Pylint.

              
import numpy as np

from pandas._libs import (
    Timedelta,
    hashtable as libhashtable,
    join as libjoin,
    lib,
)

            

Reported by Pylint.

                      self,
        index: Index,
        other_index: Index,
        indexer: npt.NDArray[np.intp],
        how: str = "left",
    ) -> Index:
        """
        Create a join index by rearranging one index to match another


            

Reported by Pylint.

              
def get_join_indexers(
    left_keys, right_keys, sort: bool = False, how: str = "inner", **kwargs
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
    """

    Parameters
    ----------
    left_keys : ndarray, Index, Series

            

Reported by Pylint.

              
def get_join_indexers(
    left_keys, right_keys, sort: bool = False, how: str = "inner", **kwargs
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp]]:
    """

    Parameters
    ----------
    left_keys : ndarray, Index, Series

            

Reported by Pylint.

                  right: MultiIndex,
    dropped_level_names,
    join_index: Index,
    lindexer: npt.NDArray[np.intp],
    rindexer: npt.NDArray[np.intp],
) -> tuple[list[Index], npt.NDArray[np.intp], list[Hashable]]:
    """
    *this is an internal non-public method*


            

Reported by Pylint.

                  dropped_level_names,
    join_index: Index,
    lindexer: npt.NDArray[np.intp],
    rindexer: npt.NDArray[np.intp],
) -> tuple[list[Index], npt.NDArray[np.intp], list[Hashable]]:
    """
    *this is an internal non-public method*

    Returns the levels, labels and names of a multi-index to multi-index join.

            

Reported by Pylint.

                  join_index: Index,
    lindexer: npt.NDArray[np.intp],
    rindexer: npt.NDArray[np.intp],
) -> tuple[list[Index], npt.NDArray[np.intp], list[Hashable]]:
    """
    *this is an internal non-public method*

    Returns the levels, labels and names of a multi-index to multi-index join.
    Depending on the type of join, this method restores the appropriate

            

Reported by Pylint.

              
def _factorize_keys(
    lk: ArrayLike, rk: ArrayLike, sort: bool = True, how: str = "inner"
) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:
    """
    Encode left and right keys as enumerated types.

    This is used to get the join indexers to be used when merging DataFrames.


            

Reported by Pylint.

              import numpy as np
import pytest

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

            

Reported by Pylint.

              

class TestJoin:
    def setup_method(self, method):
        # aggregate multiple columns
        self.df = DataFrame(
            {
                "key1": get_test_data(),
                "key2": get_test_data(),

            

Reported by Pylint.

              class TestJoin:
    def setup_method(self, method):
        # aggregate multiple columns
        self.df = DataFrame(
            {
                "key1": get_test_data(),
                "key2": get_test_data(),
                "data1": np.random.randn(N),
                "data2": np.random.randn(N),

            

Reported by Pylint.

                      )

        # exclude a couple keys for fun
        self.df = self.df[self.df["key2"] > 1]

        self.df2 = DataFrame(
            {
                "key1": get_test_data(n=N // 5),
                "key2": get_test_data(ngroups=NGROUPS // 2, n=N // 5),

            

Reported by Pylint.

                      # exclude a couple keys for fun
        self.df = self.df[self.df["key2"] > 1]

        self.df2 = DataFrame(
            {
                "key1": get_test_data(n=N // 5),
                "key2": get_test_data(ngroups=NGROUPS // 2, n=N // 5),
                "value": np.random.randn(N // 5),
            }

            

Reported by Pylint.

                      )

        index, data = tm.getMixedTypeDict()
        self.target = DataFrame(data, index=index)

        # Join on string value
        self.source = DataFrame(
            {"MergedA": data["A"], "MergedD": data["D"]}, index=data["C"]
        )

            

Reported by Pylint.

                      self.target = DataFrame(data, index=index)

        # Join on string value
        self.source = DataFrame(
            {"MergedA": data["A"], "MergedD": data["D"]}, index=data["C"]
        )

    def test_left_outer_join(self):
        joined_key2 = merge(self.df, self.df2, on="key2")

            

Reported by Pylint.

              import numpy as np
import pytest

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

            

Reported by Pylint.

              a_ = np.array


class TestJoin:
    def setup_method(self, method):
        # aggregate multiple columns
        self.df = DataFrame(
            {
                "key1": get_test_data(),

            

Reported by Pylint.

              a_ = np.array


class TestJoin:
    def setup_method(self, method):
        # aggregate multiple columns
        self.df = DataFrame(
            {
                "key1": get_test_data(),

            

Reported by Pylint.

              import unicodedata

import numpy as np
import pytest

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

            

Reported by Pylint.

                  def test_datetimeindex_accessors2(self):
        dti = date_range(freq="BQ-FEB", start=datetime(1998, 1, 1), periods=4)

        assert sum(dti.is_quarter_start) == 0
        assert sum(dti.is_quarter_end) == 4
        assert sum(dti.is_year_start) == 0
        assert sum(dti.is_year_end) == 1

    def test_datetimeindex_accessors3(self):

            

Reported by Pylint.

                  def test_datetimeindex_accessors2(self):
        dti = date_range(freq="BQ-FEB", start=datetime(1998, 1, 1), periods=4)

        assert sum(dti.is_quarter_start) == 0
        assert sum(dti.is_quarter_end) == 4
        assert sum(dti.is_year_start) == 0
        assert sum(dti.is_year_end) == 1

    def test_datetimeindex_accessors3(self):

            

Reported by Pylint.

                      dti = date_range(freq="BQ-FEB", start=datetime(1998, 1, 1), periods=4)

        assert sum(dti.is_quarter_start) == 0
        assert sum(dti.is_quarter_end) == 4
        assert sum(dti.is_year_start) == 0
        assert sum(dti.is_year_end) == 1

    def test_datetimeindex_accessors3(self):
        # Ensure is_start/end accessors throw ValueError for CustomBusinessDay,

            

Reported by Pylint.

                      dti = date_range(freq="BQ-FEB", start=datetime(1998, 1, 1), periods=4)

        assert sum(dti.is_quarter_start) == 0
        assert sum(dti.is_quarter_end) == 4
        assert sum(dti.is_year_start) == 0
        assert sum(dti.is_year_end) == 1

    def test_datetimeindex_accessors3(self):
        # Ensure is_start/end accessors throw ValueError for CustomBusinessDay,

            

Reported by Pylint.

              
        assert sum(dti.is_quarter_start) == 0
        assert sum(dti.is_quarter_end) == 4
        assert sum(dti.is_year_start) == 0
        assert sum(dti.is_year_end) == 1

    def test_datetimeindex_accessors3(self):
        # Ensure is_start/end accessors throw ValueError for CustomBusinessDay,
        bday_egypt = offsets.CustomBusinessDay(weekmask="Sun Mon Tue Wed Thu")

            

Reported by Pylint.

              
        assert sum(dti.is_quarter_start) == 0
        assert sum(dti.is_quarter_end) == 4
        assert sum(dti.is_year_start) == 0
        assert sum(dti.is_year_end) == 1

    def test_datetimeindex_accessors3(self):
        # Ensure is_start/end accessors throw ValueError for CustomBusinessDay,
        bday_egypt = offsets.CustomBusinessDay(weekmask="Sun Mon Tue Wed Thu")

            

Reported by Pylint.

                      assert sum(dti.is_quarter_start) == 0
        assert sum(dti.is_quarter_end) == 4
        assert sum(dti.is_year_start) == 0
        assert sum(dti.is_year_end) == 1

    def test_datetimeindex_accessors3(self):
        # Ensure is_start/end accessors throw ValueError for CustomBusinessDay,
        bday_egypt = offsets.CustomBusinessDay(weekmask="Sun Mon Tue Wed Thu")
        dti = date_range(datetime(2013, 4, 30), periods=5, freq=bday_egypt)

            

Reported by Pylint.

                      assert sum(dti.is_quarter_start) == 0
        assert sum(dti.is_quarter_end) == 4
        assert sum(dti.is_year_start) == 0
        assert sum(dti.is_year_end) == 1

    def test_datetimeindex_accessors3(self):
        # Ensure is_start/end accessors throw ValueError for CustomBusinessDay,
        bday_egypt = offsets.CustomBusinessDay(weekmask="Sun Mon Tue Wed Thu")
        dti = date_range(datetime(2013, 4, 30), periods=5, freq=bday_egypt)

            

Reported by Pylint.

                      dti = date_range(datetime(2013, 4, 30), periods=5, freq=bday_egypt)
        msg = "Custom business days is not supported by is_month_start"
        with pytest.raises(ValueError, match=msg):
            dti.is_month_start

    def test_datetimeindex_accessors4(self):
        dti = DatetimeIndex(["2000-01-01", "2000-01-02", "2000-01-03"])

        assert dti.is_month_start[0] == 1

            

Reported by Pylint.

              
from pandas._config import get_option

from pandas._libs import (
    lib,
    properties,
    reshape,
    tslibs,
)

            

Reported by Pylint.

              
from pandas._config import get_option

from pandas._libs import (
    lib,
    properties,
    reshape,
    tslibs,
)

            

Reported by Pylint.

              
from pandas._config import get_option

from pandas._libs import (
    lib,
    properties,
    reshape,
    tslibs,
)

            

Reported by Pylint.

                  reshape,
    tslibs,
)
from pandas._libs.lib import no_default
from pandas._typing import (
    AggFuncType,
    ArrayLike,
    Axis,
    Dtype,

            

Reported by Pylint.

                  reshape,
    tslibs,
)
from pandas._libs.lib import no_default
from pandas._typing import (
    AggFuncType,
    ArrayLike,
    Axis,
    Dtype,

            

Reported by Pylint.

                  # error: Incompatible types in assignment (expression has type "property",
    # base class "IndexOpsMixin" defined the type as "Callable[[IndexOpsMixin], bool]")
    hasnans = property(  # type: ignore[assignment]
        base.IndexOpsMixin.hasnans.func, doc=base.IndexOpsMixin.hasnans.__doc__
    )
    _mgr: SingleManager
    div: Callable[[Series, Any], Series]
    rdiv: Callable[[Series, Any], Series]


            

Reported by Pylint.

                      return self._mgr.internal_values()

    # error: Decorated property not supported
    @Appender(base.IndexOpsMixin.array.__doc__)  # type: ignore[misc]
    @property
    def array(self) -> ExtensionArray:
        return self._mgr.array_values()

    # ops

            

Reported by Pylint.

                              key = check_bool_indexer(self.index, key)
                key = np.asarray(key, dtype=bool)
                try:
                    self._where(~key, value, inplace=True)
                except InvalidIndexError:
                    self.iloc[key] = value
                return

            else:

            

Reported by Pylint.

                              return op(delegate, skipna=skipna, **kwds)

    def _reindex_indexer(
        self, new_index: Index | None, indexer: npt.NDArray[np.intp] | None, copy: bool
    ) -> Series:
        # Note: new_index is None iff indexer is None
        # if not None, indexer is np.intp
        if indexer is None:
            if copy:

            

Reported by Pylint.

              
        # Input is now list-like, so rely on "standard" construction:

        # TODO: passing np.float64 to not break anything yet. See GH-17261
        s = create_series_with_explicit_dtype(
            # error: Argument "index" to "create_series_with_explicit_dtype" has
            # incompatible type "Tuple[Any, ...]"; expected "Union[ExtensionArray,
            # ndarray, Index, None]"
            values,

            

Reported by Pylint.

              """ Test cases for misc plot functions """

import numpy as np
import pytest

import pandas.util._test_decorators as td

from pandas import (
    DataFrame,

            

Reported by Pylint.

              
        ax = None
        if pass_axis:
            _, ax = self.plt.subplots(3, 3)

        with tm.RNGContext(42):
            df = DataFrame(np.random.randn(100, 3))

        # we are plotting multiples on a sub-plot

            

Reported by Pylint.

                      )

        ax = _check_plot_works(
            andrews_curves, frame=df, class_column="Name", colormap=cm.jet
        )
        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
        self._check_colors(
            ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
        )

            

Reported by Pylint.

                      ax = _check_plot_works(
            andrews_curves, frame=df, class_column="Name", colormap=cm.jet
        )
        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
        self._check_colors(
            ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
        )

        length = 10

            

Reported by Pylint.

                      )

        ax = _check_plot_works(
            andrews_curves, frame=df, class_column="Name", colormap=cm.jet
        )
        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
        self._check_colors(
            ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
        )

            

Reported by Pylint.

                      ax = _check_plot_works(
            andrews_curves, frame=df, class_column="Name", colormap=cm.jet
        )
        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
        self._check_colors(
            ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
        )

        colors = ["b", "g", "r"]

            

Reported by Pylint.

                      )

        ax = _check_plot_works(
            parallel_coordinates, frame=df, class_column="Name", colormap=cm.jet
        )
        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
        self._check_colors(
            ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
        )

            

Reported by Pylint.

                      ax = _check_plot_works(
            parallel_coordinates, frame=df, class_column="Name", colormap=cm.jet
        )
        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
        self._check_colors(
            ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
        )

        ax = _check_plot_works(

            

Reported by Pylint.

                      patches = [p for p in ax.patches[:20] if p.get_label() != ""]
        self._check_colors(patches, facecolors=cnames, mapping=df["Name"][:10])

        _check_plot_works(radviz, frame=df, class_column="Name", colormap=cm.jet)
        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
        patches = [p for p in ax.patches[:20] if p.get_label() != ""]
        self._check_colors(patches, facecolors=cmaps, mapping=df["Name"][:10])

        colors = [[0.0, 0.0, 1.0, 1.0], [0.0, 0.5, 1.0, 1.0], [1.0, 0.0, 0.0, 1.0]]

            

Reported by Pylint.

                      self._check_colors(patches, facecolors=cnames, mapping=df["Name"][:10])

        _check_plot_works(radviz, frame=df, class_column="Name", colormap=cm.jet)
        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
        patches = [p for p in ax.patches[:20] if p.get_label() != ""]
        self._check_colors(patches, facecolors=cmaps, mapping=df["Name"][:10])

        colors = [[0.0, 0.0, 1.0, 1.0], [0.0, 0.5, 1.0, 1.0], [1.0, 0.0, 0.0, 1.0]]
        df = DataFrame(

            

Reported by Pylint.

              import operator

import numpy as np
import pytest

from pandas.core.dtypes.common import is_bool_dtype

import pandas as pd
import pandas._testing as tm

            

Reported by Pylint.

                      expected = pd.Series(np.array([1, -1, 0], dtype=np.int64))
        self.assert_series_equal(result, expected)

    def test_argmin_argmax(self, data_for_sorting, data_missing_for_sorting, na_value):
        # GH 24382

        # data_for_sorting -> [B, C, A] with A < B < C
        assert data_for_sorting.argmax() == 1
        assert data_for_sorting.argmin() == 2

            

Reported by Pylint.

                  def test_argmin_argmax_all_na(self, method, data, na_value):
        # all missing with skipna=True is the same as empty
        err_msg = "attempt to get"
        data_na = type(data)._from_sequence([na_value, na_value], dtype=data.dtype)
        with pytest.raises(ValueError, match=err_msg):
            getattr(data_na, method)()

    @pytest.mark.parametrize(
        "op_name, skipna, expected",

            

Reported by Pylint.

                      self.assert_series_equal(result, expected)

    @pytest.mark.parametrize("ascending", [True, False])
    def test_sort_values_frame(self, data_for_sorting, ascending):
        df = pd.DataFrame({"A": [1, 2, 1], "B": data_for_sorting})
        result = df.sort_values(["A", "B"])
        expected = pd.DataFrame(
            {"A": [1, 1, 2], "B": data_for_sorting.take([2, 0, 1])}, index=[2, 0, 1]
        )

            

Reported by Pylint.

                  @pytest.mark.parametrize("box", [pd.Series, lambda x: x])
    @pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
    def test_unique(self, data, box, method):
        duplicated = box(data._from_sequence([data[0], data[0]]))

        result = method(duplicated)

        assert len(result) == 1
        assert isinstance(result, type(data))

            

Reported by Pylint.

                  def test_factorize_empty(self, data):
        codes, uniques = pd.factorize(data[:0])
        expected_codes = np.array([], dtype=np.intp)
        expected_uniques = type(data)._from_sequence([], dtype=data[:0].dtype)

        tm.assert_numpy_array_equal(codes, expected_codes)
        self.assert_extension_array_equal(uniques, expected_uniques)

    def test_fillna_copy_frame(self, data_missing):

            

Reported by Pylint.

                      filled_val = ser[0]
        result = ser.fillna(filled_val)

        assert ser._values is not result._values
        assert ser._values is arr

    def test_fillna_length_mismatch(self, data_missing):
        msg = "Length of 'value' does not match."
        with pytest.raises(ValueError, match=msg):

            

Reported by Pylint.

                      filled_val = ser[0]
        result = ser.fillna(filled_val)

        assert ser._values is not result._values
        assert ser._values is arr

    def test_fillna_length_mismatch(self, data_missing):
        msg = "Length of 'value' does not match."
        with pytest.raises(ValueError, match=msg):

            

Reported by Pylint.

                      result = ser.fillna(filled_val)

        assert ser._values is not result._values
        assert ser._values is arr

    def test_fillna_length_mismatch(self, data_missing):
        msg = "Length of 'value' does not match."
        with pytest.raises(ValueError, match=msg):
            data_missing.fillna(data_missing.take([1]))

            

Reported by Pylint.

                      result = s1.combine(s2, lambda x1, x2: x1 + x2)
        with np.errstate(over="ignore"):
            expected = pd.Series(
                orig_data1._from_sequence(
                    [a + b for (a, b) in zip(list(orig_data1), list(orig_data2))]
                )
            )
        self.assert_series_equal(result, expected)


            

Reported by Pylint.

              import re

import numpy as np
import pytest

import pandas as pd
import pandas._testing as tm



            

Reported by Pylint.

              import re

import numpy as np
import pytest

import pandas as pd
import pandas._testing as tm



            

Reported by Pylint.

              import pandas._testing as tm


class TestSeriesReplace:
    def test_replace(self, datetime_series):
        N = 100
        ser = pd.Series(np.random.randn(N))
        ser[0:4] = np.nan
        ser[6:10] = 0

            

Reported by Pylint.

              import pandas._testing as tm


class TestSeriesReplace:
    def test_replace(self, datetime_series):
        N = 100
        ser = pd.Series(np.random.randn(N))
        ser[0:4] = np.nan
        ser[6:10] = 0

            

Reported by Pylint.

              

class TestSeriesReplace:
    def test_replace(self, datetime_series):
        N = 100
        ser = pd.Series(np.random.randn(N))
        ser[0:4] = np.nan
        ser[6:10] = 0


            

Reported by Pylint.

              

class TestSeriesReplace:
    def test_replace(self, datetime_series):
        N = 100
        ser = pd.Series(np.random.randn(N))
        ser[0:4] = np.nan
        ser[6:10] = 0


            

Reported by Pylint.

              
class TestSeriesReplace:
    def test_replace(self, datetime_series):
        N = 100
        ser = pd.Series(np.random.randn(N))
        ser[0:4] = np.nan
        ser[6:10] = 0

        # replace list with a single value

            

Reported by Pylint.

              
        # replace list with a single value
        return_value = ser.replace([np.nan], -1, inplace=True)
        assert return_value is None

        exp = ser.fillna(-1)
        tm.assert_series_equal(ser, exp)

        rs = ser.replace(0.0, np.nan)

            

Reported by Bandit.

                      exp = ser.fillna(-1)
        tm.assert_series_equal(ser, exp)

        rs = ser.replace(0.0, np.nan)
        ser[ser == 0.0] = np.nan
        tm.assert_series_equal(rs, ser)

        ser = pd.Series(np.fabs(np.random.randn(N)), tm.makeDateIndex(N), dtype=object)
        ser[:5] = np.nan

            

Reported by Pylint.

                      ser[20:30] = "bar"

        # replace list with a single value
        rs = ser.replace([np.nan, "foo", "bar"], -1)

        assert (rs[:5] == -1).all()
        assert (rs[6:10] == -1).all()
        assert (rs[20:30] == -1).all()
        assert (pd.isna(ser[:5])).all()

            

Reported by Pylint.