import numpy as np
import pytest

from pandas import (
    Categorical,
    CategoricalIndex,
    Index,
    MultiIndex,
)

            

Reported by Pylint.

              
        # invalid comparisons
        with pytest.raises(ValueError, match="Lengths must match"):
            ci1 == Index(["a", "b", "c"])

        msg = "Categoricals can only be compared if 'categories' are the same"
        with pytest.raises(TypeError, match=msg):
            ci1 == ci2
        with pytest.raises(TypeError, match=msg):

            

Reported by Pylint.

              
        msg = "Categoricals can only be compared if 'categories' are the same"
        with pytest.raises(TypeError, match=msg):
            ci1 == ci2
        with pytest.raises(TypeError, match=msg):
            ci1 == Categorical(ci1.values, ordered=False)
        with pytest.raises(TypeError, match=msg):
            ci1 == Categorical(ci1.values, categories=list("abc"))


            

Reported by Pylint.

                      with pytest.raises(TypeError, match=msg):
            ci1 == ci2
        with pytest.raises(TypeError, match=msg):
            ci1 == Categorical(ci1.values, ordered=False)
        with pytest.raises(TypeError, match=msg):
            ci1 == Categorical(ci1.values, categories=list("abc"))

        # tests
        # make sure that we are testing for category inclusion properly

            

Reported by Pylint.

                      with pytest.raises(TypeError, match=msg):
            ci1 == Categorical(ci1.values, ordered=False)
        with pytest.raises(TypeError, match=msg):
            ci1 == Categorical(ci1.values, categories=list("abc"))

        # tests
        # make sure that we are testing for category inclusion properly
        ci = CategoricalIndex(list("aabca"), categories=["c", "a", "b"])
        assert not ci.equals(list("aabca"))

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas import (
    Categorical,
    CategoricalIndex,
    Index,
    MultiIndex,
)

            

Reported by Pylint.

              )


class TestEquals:
    def test_equals_categorical(self):
        ci1 = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
        ci2 = CategoricalIndex(["a", "b"], categories=["a", "b", "c"], ordered=True)

        assert ci1.equals(ci1)

            

Reported by Pylint.

              

class TestEquals:
    def test_equals_categorical(self):
        ci1 = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
        ci2 = CategoricalIndex(["a", "b"], categories=["a", "b", "c"], ordered=True)

        assert ci1.equals(ci1)
        assert not ci1.equals(ci2)

            

Reported by Pylint.

              

class TestEquals:
    def test_equals_categorical(self):
        ci1 = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
        ci2 = CategoricalIndex(["a", "b"], categories=["a", "b", "c"], ordered=True)

        assert ci1.equals(ci1)
        assert not ci1.equals(ci2)

            

Reported by Pylint.

                      ci1 = CategoricalIndex(["a", "b"], categories=["a", "b"], ordered=True)
        ci2 = CategoricalIndex(["a", "b"], categories=["a", "b", "c"], ordered=True)

        assert ci1.equals(ci1)
        assert not ci1.equals(ci2)
        assert ci1.equals(ci1.astype(object))
        assert ci1.astype(object).equals(ci1)

        assert (ci1 == ci1).all()

            

Reported by Bandit.

              from datetime import datetime

import numpy as np
import pytest

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

            

Reported by Pylint.

              
class TestResetIndex:
    def test_reset_index_dti_round_trip(self):
        dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None)
        d1 = DataFrame({"v": np.random.rand(len(dti))}, index=dti)
        d2 = d1.reset_index()
        assert d2.dtypes[0] == np.dtype("M8[ns]")
        d3 = d2.set_index("index")
        tm.assert_frame_equal(d1, d3, check_names=False)

            

Reported by Pylint.

              
class TestResetIndex:
    def test_reset_index_dti_round_trip(self):
        dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None)
        d1 = DataFrame({"v": np.random.rand(len(dti))}, index=dti)
        d2 = d1.reset_index()
        assert d2.dtypes[0] == np.dtype("M8[ns]")
        d3 = d2.set_index("index")
        tm.assert_frame_equal(d1, d3, check_names=False)

            

Reported by Pylint.

              
class TestResetIndex:
    def test_reset_index_dti_round_trip(self):
        dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None)
        d1 = DataFrame({"v": np.random.rand(len(dti))}, index=dti)
        d2 = d1.reset_index()
        assert d2.dtypes[0] == np.dtype("M8[ns]")
        d3 = d2.set_index("index")
        tm.assert_frame_equal(d1, d3, check_names=False)

            

Reported by Pylint.

              from datetime import datetime

import numpy as np
import pytest

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

            

Reported by Pylint.

              import pandas._testing as tm


class TestResetIndex:
    def test_reset_index_dti_round_trip(self):
        dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None)
        d1 = DataFrame({"v": np.random.rand(len(dti))}, index=dti)
        d2 = d1.reset_index()
        assert d2.dtypes[0] == np.dtype("M8[ns]")

            

Reported by Pylint.

              

class TestResetIndex:
    def test_reset_index_dti_round_trip(self):
        dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None)
        d1 = DataFrame({"v": np.random.rand(len(dti))}, index=dti)
        d2 = d1.reset_index()
        assert d2.dtypes[0] == np.dtype("M8[ns]")
        d3 = d2.set_index("index")

            

Reported by Pylint.

              

class TestResetIndex:
    def test_reset_index_dti_round_trip(self):
        dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None)
        d1 = DataFrame({"v": np.random.rand(len(dti))}, index=dti)
        d2 = d1.reset_index()
        assert d2.dtypes[0] == np.dtype("M8[ns]")
        d3 = d2.set_index("index")

            

Reported by Pylint.

              class TestResetIndex:
    def test_reset_index_dti_round_trip(self):
        dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None)
        d1 = DataFrame({"v": np.random.rand(len(dti))}, index=dti)
        d2 = d1.reset_index()
        assert d2.dtypes[0] == np.dtype("M8[ns]")
        d3 = d2.set_index("index")
        tm.assert_frame_equal(d1, d3, check_names=False)


            

Reported by Pylint.

                  def test_reset_index_dti_round_trip(self):
        dti = date_range(start="1/1/2001", end="6/1/2001", freq="D")._with_freq(None)
        d1 = DataFrame({"v": np.random.rand(len(dti))}, index=dti)
        d2 = d1.reset_index()
        assert d2.dtypes[0] == np.dtype("M8[ns]")
        d3 = d2.set_index("index")
        tm.assert_frame_equal(d1, d3, check_names=False)

        # GH#2329

            

Reported by Pylint.

              import operator

import numpy as np
import pytest

from pandas.compat import np_version_under1p20

import pandas as pd
import pandas._testing as tm

            

Reported by Pylint.

              def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
    dtype = any_signed_int_ea_dtype
    arr = pd.array(source, dtype=dtype)
    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
    neg_target = pd.array(neg_target, dtype=dtype)
    abs_target = pd.array(abs_target, dtype=dtype)

    tm.assert_extension_array_equal(neg_result, neg_target)
    tm.assert_extension_array_equal(pos_result, arr)

            

Reported by Pylint.

              def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
    dtype = any_signed_int_ea_dtype
    arr = pd.array(source, dtype=dtype)
    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
    neg_target = pd.array(neg_target, dtype=dtype)
    abs_target = pd.array(abs_target, dtype=dtype)

    tm.assert_extension_array_equal(neg_result, neg_target)
    tm.assert_extension_array_equal(pos_result, arr)

            

Reported by Pylint.

              def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
    dtype = any_signed_int_ea_dtype
    arr = pd.array(source, dtype=dtype)
    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
    neg_target = pd.array(neg_target, dtype=dtype)
    abs_target = pd.array(abs_target, dtype=dtype)

    tm.assert_extension_array_equal(neg_result, neg_target)
    tm.assert_extension_array_equal(pos_result, arr)

            

Reported by Pylint.

              def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
    dtype = any_signed_int_ea_dtype
    arr = pd.array(source, dtype=dtype)
    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
    neg_target = pd.array(neg_target, dtype=dtype)
    abs_target = pd.array(abs_target, dtype=dtype)

    tm.assert_extension_array_equal(neg_result, neg_target)
    tm.assert_extension_array_equal(pos_result, arr)

            

Reported by Pylint.

              def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
    dtype = any_signed_int_ea_dtype
    arr = pd.array(source, dtype=dtype)
    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
    neg_target = pd.array(neg_target, dtype=dtype)
    abs_target = pd.array(abs_target, dtype=dtype)

    tm.assert_extension_array_equal(neg_result, neg_target)
    tm.assert_extension_array_equal(pos_result, arr)

            

Reported by Pylint.

              def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
    dtype = any_signed_int_ea_dtype
    arr = pd.array(source, dtype=dtype)
    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
    neg_target = pd.array(neg_target, dtype=dtype)
    abs_target = pd.array(abs_target, dtype=dtype)

    tm.assert_extension_array_equal(neg_result, neg_target)
    tm.assert_extension_array_equal(pos_result, arr)

            

Reported by Pylint.

              def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
    dtype = any_signed_int_ea_dtype
    arr = pd.array(source, dtype=dtype)
    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
    neg_target = pd.array(neg_target, dtype=dtype)
    abs_target = pd.array(abs_target, dtype=dtype)

    tm.assert_extension_array_equal(neg_result, neg_target)
    tm.assert_extension_array_equal(pos_result, arr)

            

Reported by Pylint.

              def test_unary_int_operators(any_signed_int_ea_dtype, source, neg_target, abs_target):
    dtype = any_signed_int_ea_dtype
    arr = pd.array(source, dtype=dtype)
    neg_result, pos_result, abs_result = -arr, +arr, abs(arr)
    neg_target = pd.array(neg_target, dtype=dtype)
    abs_target = pd.array(abs_target, dtype=dtype)

    tm.assert_extension_array_equal(neg_result, neg_target)
    tm.assert_extension_array_equal(pos_result, arr)

            

Reported by Pylint.

              
    op = all_arithmetic_operators
    s = pd.Series(data)
    ops = getattr(s, op)

    # invalid scalars
    msg = (
        r"(:?can only perform ops with numeric values)"
        r"|(:?IntegerArray cannot perform the operation mod)"

            

Reported by Pylint.

              import numpy as np
import pytest

import pandas.util._test_decorators as td

from pandas import (
    DataFrame,
    Series,
    date_range,

            

Reported by Pylint.

              import numpy as np
import pytest

import pandas.util._test_decorators as td

from pandas import (
    DataFrame,
    Series,
    date_range,

            

Reported by Pylint.

              import pandas._testing as tm


def test_centered_axis_validation():

    # ok
    Series(np.ones(10)).rolling(window=3, center=True, axis=0).mean()

    # bad axis

            

Reported by Pylint.

              

@td.skip_if_no_scipy
def test_cmov_mean():
    # GH 8238
    vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48, 10.63, 14.48])
    result = Series(vals).rolling(5, center=True).mean()
    expected_values = [
        np.nan,

            

Reported by Pylint.

              

@td.skip_if_no_scipy
def test_cmov_window():
    # GH 8238
    vals = np.array([6.95, 15.21, 4.72, 9.12, 13.81, 13.49, 16.68, 9.48, 10.63, 14.48])
    result = Series(vals).rolling(5, win_type="boxcar", center=True).mean()
    expected_values = [
        np.nan,

            

Reported by Pylint.

              

@td.skip_if_no_scipy
def test_cmov_window_corner():
    # GH 8238
    # all nan
    vals = Series([np.nan] * 10)
    result = vals.rolling(5, center=True, win_type="boxcar").mean()
    assert np.isnan(result).all()

            

Reported by Pylint.

                  # all nan
    vals = Series([np.nan] * 10)
    result = vals.rolling(5, center=True, win_type="boxcar").mean()
    assert np.isnan(result).all()

    # empty
    vals = Series([], dtype=object)
    result = vals.rolling(5, center=True, win_type="boxcar").mean()
    assert len(result) == 0

            

Reported by Bandit.

                  # empty
    vals = Series([], dtype=object)
    result = vals.rolling(5, center=True, win_type="boxcar").mean()
    assert len(result) == 0

    # shorter than window
    vals = Series(np.random.randn(5))
    result = vals.rolling(10, win_type="boxcar").mean()
    assert np.isnan(result).all()

            

Reported by Bandit.

                  # shorter than window
    vals = Series(np.random.randn(5))
    result = vals.rolling(10, win_type="boxcar").mean()
    assert np.isnan(result).all()
    assert len(result) == 5


@td.skip_if_no_scipy
@pytest.mark.parametrize(

            

Reported by Bandit.

                  vals = Series(np.random.randn(5))
    result = vals.rolling(10, win_type="boxcar").mean()
    assert np.isnan(result).all()
    assert len(result) == 5


@td.skip_if_no_scipy
@pytest.mark.parametrize(
    "f,xp",

            

Reported by Bandit.

                  # namedtuple to hold results
    BP = namedtuple("BP", ["ax", "lines"])

    def __init__(self, data, return_type="axes", **kwargs):
        # Do not call LinePlot.__init__ which may fill nan
        if return_type not in self._valid_return_types:
            raise ValueError("return_type must be {None, 'axes', 'dict', 'both'}")

        self.return_type = return_type

            

Reported by Pylint.

                          raise ValueError("return_type must be {None, 'axes', 'dict', 'both'}")

        self.return_type = return_type
        MPLPlot.__init__(self, data, **kwargs)

    def _args_adjust(self):
        if self.subplots:
            # Disable label ax sharing. Otherwise, all subplots shows last
            # column label

            

Reported by Pylint.

                              self.sharey = False

    @classmethod
    def _plot(cls, ax, y, column_num=None, return_type="axes", **kwds):
        if y.ndim == 2:
            y = [remove_na_arraylike(v) for v in y]
            # Boxplot fails with empty arrays, so need to add a NaN
            #   if any cols are empty
            # GH 8181

            

Reported by Pylint.

              
    def _make_plot(self):
        if self.subplots:
            self._return_obj = pd.Series(dtype=object)

            # Re-create iterated data if `by` is assigned by users
            data = (
                create_iter_data_given_by(self.data, self._kind)
                if self.by is not None

            

Reported by Pylint.

                              ax, y, column_num=0, return_type=self.return_type, **kwds
            )
            self.maybe_color_bp(bp)
            self._return_obj = ret

            labels = [left for left, _ in self._iter_data()]
            labels = [pprint_thing(left) for left in labels]
            if not self.use_index:
                labels = [pprint_thing(key) for key in range(len(labels))]

            

Reported by Pylint.

                  data,
    columns=None,
    by=None,
    numeric_only=True,
    grid=False,
    figsize=None,
    ax=None,
    layout=None,
    return_type=None,

            

Reported by Pylint.

                  if columns is None:
        if not isinstance(by, (list, tuple)):
            by = [by]
        columns = data._get_numeric_data().columns.difference(by)
    naxes = len(columns)
    fig, axes = create_subplots(
        naxes=naxes, sharex=True, sharey=True, figsize=figsize, ax=ax, layout=layout
    )


            

Reported by Pylint.

                  import matplotlib.pyplot as plt

    # validate return_type:
    if return_type not in BoxPlot._valid_return_types:
        raise ValueError("return_type must be {'axes', 'dict', 'both'}")

    if isinstance(data, pd.Series):
        data = data.to_frame("x")
        column = "x"

            

Reported by Pylint.

                          rc = {"figure.figsize": figsize} if figsize is not None else {}
            with plt.rc_context(rc):
                ax = plt.gca()
        data = data._get_numeric_data()
        if columns is None:
            columns = data.columns
        else:
            data = data[columns]


            

Reported by Pylint.

              from __future__ import annotations

from collections import namedtuple
from typing import TYPE_CHECKING
import warnings

from matplotlib.artist import setp
import numpy as np


            

Reported by Pylint.

              
import numpy as np

import pandas._libs.lib as lib
import pandas._libs.ops as libops
import pandas._libs.parsers as parsers
from pandas._libs.parsers import STR_NA_VALUES
from pandas._libs.tslibs import parsing
from pandas._typing import (

            

Reported by Pylint.

              
import numpy as np

import pandas._libs.lib as lib
import pandas._libs.ops as libops
import pandas._libs.parsers as parsers
from pandas._libs.parsers import STR_NA_VALUES
from pandas._libs.tslibs import parsing
from pandas._typing import (

            

Reported by Pylint.

              import numpy as np

import pandas._libs.lib as lib
import pandas._libs.ops as libops
import pandas._libs.parsers as parsers
from pandas._libs.parsers import STR_NA_VALUES
from pandas._libs.tslibs import parsing
from pandas._typing import (
    ArrayLike,

            

Reported by Pylint.

              import numpy as np

import pandas._libs.lib as lib
import pandas._libs.ops as libops
import pandas._libs.parsers as parsers
from pandas._libs.parsers import STR_NA_VALUES
from pandas._libs.tslibs import parsing
from pandas._typing import (
    ArrayLike,

            

Reported by Pylint.

              
import pandas._libs.lib as lib
import pandas._libs.ops as libops
import pandas._libs.parsers as parsers
from pandas._libs.parsers import STR_NA_VALUES
from pandas._libs.tslibs import parsing
from pandas._typing import (
    ArrayLike,
    DtypeArg,

            

Reported by Pylint.

              
import pandas._libs.lib as lib
import pandas._libs.ops as libops
import pandas._libs.parsers as parsers
from pandas._libs.parsers import STR_NA_VALUES
from pandas._libs.tslibs import parsing
from pandas._typing import (
    ArrayLike,
    DtypeArg,

            

Reported by Pylint.

              import pandas._libs.lib as lib
import pandas._libs.ops as libops
import pandas._libs.parsers as parsers
from pandas._libs.parsers import STR_NA_VALUES
from pandas._libs.tslibs import parsing
from pandas._typing import (
    ArrayLike,
    DtypeArg,
    FilePathOrBuffer,

            

Reported by Pylint.

              import pandas._libs.lib as lib
import pandas._libs.ops as libops
import pandas._libs.parsers as parsers
from pandas._libs.parsers import STR_NA_VALUES
from pandas._libs.tslibs import parsing
from pandas._typing import (
    ArrayLike,
    DtypeArg,
    FilePathOrBuffer,

            

Reported by Pylint.

              import pandas._libs.ops as libops
import pandas._libs.parsers as parsers
from pandas._libs.parsers import STR_NA_VALUES
from pandas._libs.tslibs import parsing
from pandas._typing import (
    ArrayLike,
    DtypeArg,
    FilePathOrBuffer,
)

            

Reported by Pylint.

                          )

            if not is_object_dtype(values) and not known_cats:
                # TODO: this is for consistency with
                # c-parser which parses all categories
                # as strings

                values = astype_nansafe(values, np.dtype(str))


            

Reported by Pylint.

              import numpy as np
import pytest

from pandas import (
    DataFrame,
    date_range,
)
from pandas.tests.plotting.common import TestPlotBase


            

Reported by Pylint.

                  def test_missing_marker_multi_plots_on_same_ax(self):
        # GH 18222
        df = DataFrame(data=[[1, 1, 1, 1], [2, 2, 4, 8]], columns=["x", "r", "g", "b"])
        fig, ax = self.plt.subplots(nrows=1, ncols=3)
        # Left plot
        df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[0])
        df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[0])
        df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[0])
        self._check_legend_labels(ax[0], labels=["r", "g", "b"])

            

Reported by Pylint.

                          }
        )

        fig, ax = self.plt.subplots()
        for kind in "ABC":
            df.plot("X", kind, label=kind, ax=ax, style=".")

        self._check_legend_labels(ax, labels=["A", "B", "C"])
        self._check_legend_marker(ax, expected_markers=[".", ".", "."])

            

Reported by Pylint.

                  def test_missing_marker_multi_plots_on_same_ax(self):
        # GH 18222
        df = DataFrame(data=[[1, 1, 1, 1], [2, 2, 4, 8]], columns=["x", "r", "g", "b"])
        fig, ax = self.plt.subplots(nrows=1, ncols=3)
        # Left plot
        df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[0])
        df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[0])
        df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[0])
        self._check_legend_labels(ax[0], labels=["r", "g", "b"])

            

Reported by Pylint.

                          }
        )

        fig, ax = self.plt.subplots()
        for kind in "ABC":
            df.plot("X", kind, label=kind, ax=ax, style=".")

        self._check_legend_labels(ax, labels=["A", "B", "C"])
        self._check_legend_marker(ax, expected_markers=[".", ".", "."])

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas import (
    DataFrame,
    date_range,
)
from pandas.tests.plotting.common import TestPlotBase


            

Reported by Pylint.

              pytestmark = pytest.mark.slow


class TestFrameLegend(TestPlotBase):
    @pytest.mark.xfail(
        reason=(
            "Open bug in matplotlib "
            "https://github.com/matplotlib/matplotlib/issues/11357"
        )

            

Reported by Pylint.

                  @pytest.mark.xfail(
        reason=(
            "Open bug in matplotlib "
            "https://github.com/matplotlib/matplotlib/issues/11357"
        )
    )
    def test_mixed_yerr(self):
        # https://github.com/pandas-dev/pandas/issues/39522
        from matplotlib.collections import LineCollection

            

Reported by Pylint.

                  @pytest.mark.xfail(
        reason=(
            "Open bug in matplotlib "
            "https://github.com/matplotlib/matplotlib/issues/11357"
        )
    )
    def test_mixed_yerr(self):
        # https://github.com/pandas-dev/pandas/issues/39522
        from matplotlib.collections import LineCollection

            

Reported by Pylint.

                  )
    def test_mixed_yerr(self):
        # https://github.com/pandas-dev/pandas/issues/39522
        from matplotlib.collections import LineCollection
        from matplotlib.lines import Line2D

        df = DataFrame([{"x": 1, "a": 1, "b": 1}, {"x": 2, "a": 2, "b": 3}])

        ax = df.plot("x", "a", c="orange", yerr=0.1, label="orange")

            

Reported by Pylint.

              import numpy as np
import pytest

import pandas as pd
from pandas import (
    Int64Index,
    TimedeltaIndex,
    timedelta_range,
)

            

Reported by Pylint.

              
        result = ordered[:0].union(ordered)
        tm.assert_index_equal(result, ordered)
        assert result.freq == ordered.freq

    def test_union_bug_1730(self):

        rng_a = timedelta_range("1 day", periods=4, freq="3H")
        rng_b = timedelta_range("1 day", periods=4, freq="4H")

            

Reported by Pylint.

                          result = index_1 & index_2
        expected = timedelta_range("1 day 01:00:00", periods=3, freq="h")
        tm.assert_index_equal(result, expected)
        assert result.freq == expected.freq

    def test_intersection_equal(self, sort):
        # GH 24471 Test intersection outcome given the sort keyword
        # for equal indices intersection should return the original index
        first = timedelta_range("1 day", periods=4, freq="h")

            

Reported by Pylint.

                          result = index_1 & index_2
        expected = timedelta_range("1 day 01:00:00", periods=3, freq="h")
        tm.assert_index_equal(result, expected)
        assert result.freq == expected.freq

    def test_intersection_equal(self, sort):
        # GH 24471 Test intersection outcome given the sort keyword
        # for equal indices intersection should return the original index
        first = timedelta_range("1 day", periods=4, freq="h")

            

Reported by Pylint.

                      right = tdi[:3]

        # Check that we are testing the desired code path
        assert left._can_fast_union(right)

        result = left.union(right)
        tm.assert_index_equal(result, tdi)

        result = left.union(right, sort=False)

            

Reported by Pylint.

              import numpy as np
import pytest

import pandas as pd
from pandas import (
    Int64Index,
    TimedeltaIndex,
    timedelta_range,
)

            

Reported by Pylint.

              from pandas.tseries.offsets import Hour


class TestTimedeltaIndex:
    def test_union(self):

        i1 = timedelta_range("1day", periods=5)
        i2 = timedelta_range("3day", periods=5)
        result = i1.union(i2)

            

Reported by Pylint.

              

class TestTimedeltaIndex:
    def test_union(self):

        i1 = timedelta_range("1day", periods=5)
        i2 = timedelta_range("3day", periods=5)
        result = i1.union(i2)
        expected = timedelta_range("1day", periods=7)

            

Reported by Pylint.

              

class TestTimedeltaIndex:
    def test_union(self):

        i1 = timedelta_range("1day", periods=5)
        i2 = timedelta_range("3day", periods=5)
        result = i1.union(i2)
        expected = timedelta_range("1day", periods=7)

            

Reported by Pylint.

              class TestTimedeltaIndex:
    def test_union(self):

        i1 = timedelta_range("1day", periods=5)
        i2 = timedelta_range("3day", periods=5)
        result = i1.union(i2)
        expected = timedelta_range("1day", periods=7)
        tm.assert_index_equal(result, expected)


            

Reported by Pylint.

              
"""
import numpy as np
import pytest

from pandas.core.dtypes.dtypes import DatetimeTZDtype

import pandas as pd
from pandas.core.arrays import DatetimeArray

            

Reported by Pylint.

              

@pytest.fixture
def data(dtype):
    data = DatetimeArray(pd.date_range("2000", periods=100, tz=dtype.tz), dtype=dtype)
    return data


@pytest.fixture

            

Reported by Pylint.

              
@pytest.fixture
def data(dtype):
    data = DatetimeArray(pd.date_range("2000", periods=100, tz=dtype.tz), dtype=dtype)
    return data


@pytest.fixture
def data_missing(dtype):

            

Reported by Pylint.

              

@pytest.fixture
def data_missing(dtype):
    return DatetimeArray(
        np.array(["NaT", "2000-01-01"], dtype="datetime64[ns]"), dtype=dtype
    )



            

Reported by Pylint.

              

@pytest.fixture
def data_for_sorting(dtype):
    a = pd.Timestamp("2000-01-01")
    b = pd.Timestamp("2000-01-02")
    c = pd.Timestamp("2000-01-03")
    return DatetimeArray(np.array([b, c, a], dtype="datetime64[ns]"), dtype=dtype)


            

Reported by Pylint.

              

@pytest.fixture
def data_missing_for_sorting(dtype):
    a = pd.Timestamp("2000-01-01")
    b = pd.Timestamp("2000-01-02")
    return DatetimeArray(np.array([b, "NaT", a], dtype="datetime64[ns]"), dtype=dtype)



            

Reported by Pylint.

              

@pytest.fixture
def data_for_grouping(dtype):
    """
    Expected to be like [B, B, NA, NA, A, A, B, C]

    Where A < B < C and NA is missing
    """

            

Reported by Pylint.

              

class TestConstructors(BaseDatetimeTests, base.BaseConstructorsTests):
    def test_series_constructor(self, data):
        # Series construction drops any .freq attr
        data = data._with_freq(None)
        super().test_series_constructor(data)



            

Reported by Pylint.

              class TestConstructors(BaseDatetimeTests, base.BaseConstructorsTests):
    def test_series_constructor(self, data):
        # Series construction drops any .freq attr
        data = data._with_freq(None)
        super().test_series_constructor(data)


class TestGetitem(BaseDatetimeTests, base.BaseGetitemTests):
    pass

            

Reported by Pylint.

              

class TestInterface(BaseDatetimeTests, base.BaseInterfaceTests):
    def test_array_interface(self, data):
        if data.tz:
            # np.asarray(DTA) is currently always tz-naive.
            pytest.skip("GH-23569")
        else:
            super().test_array_interface(data)

            

Reported by Pylint.

              import string

import numpy as np
import pytest

import pandas as pd
from pandas.core.arrays.string_ import StringDtype
from pandas.tests.extension import base


            

Reported by Pylint.

                      pytest.skip("chunked array n/a")

    def _split_array(arr):
        import pyarrow as pa

        arrow_array = arr._data
        split = len(arrow_array) // 2
        arrow_array = pa.chunked_array(
            [*arrow_array[:split].chunks, *arrow_array[split:].chunks]

            

Reported by Pylint.

                  def _split_array(arr):
        import pyarrow as pa

        arrow_array = arr._data
        split = len(arrow_array) // 2
        arrow_array = pa.chunked_array(
            [*arrow_array[:split].chunks, *arrow_array[split:].chunks]
        )
        assert arrow_array.num_chunks == 2

            

Reported by Pylint.

              

@pytest.fixture
def data(dtype, chunked):
    strings = np.random.choice(list(string.ascii_letters), size=100)
    while strings[0] == strings[1]:
        strings = np.random.choice(list(string.ascii_letters), size=100)

    arr = dtype.construct_array_type()._from_sequence(strings)

            

Reported by Pylint.

              

@pytest.fixture
def data(dtype, chunked):
    strings = np.random.choice(list(string.ascii_letters), size=100)
    while strings[0] == strings[1]:
        strings = np.random.choice(list(string.ascii_letters), size=100)

    arr = dtype.construct_array_type()._from_sequence(strings)

            

Reported by Pylint.

                  while strings[0] == strings[1]:
        strings = np.random.choice(list(string.ascii_letters), size=100)

    arr = dtype.construct_array_type()._from_sequence(strings)
    return split_array(arr) if chunked else arr


@pytest.fixture
def data_missing(dtype, chunked):

            

Reported by Pylint.

              

@pytest.fixture
def data_missing(dtype, chunked):
    """Length 2 array with [NA, Valid]"""
    arr = dtype.construct_array_type()._from_sequence([pd.NA, "A"])
    return split_array(arr) if chunked else arr



            

Reported by Pylint.

              

@pytest.fixture
def data_missing(dtype, chunked):
    """Length 2 array with [NA, Valid]"""
    arr = dtype.construct_array_type()._from_sequence([pd.NA, "A"])
    return split_array(arr) if chunked else arr



            

Reported by Pylint.

              @pytest.fixture
def data_missing(dtype, chunked):
    """Length 2 array with [NA, Valid]"""
    arr = dtype.construct_array_type()._from_sequence([pd.NA, "A"])
    return split_array(arr) if chunked else arr


@pytest.fixture
def data_for_sorting(dtype, chunked):

            

Reported by Pylint.

              

@pytest.fixture
def data_for_sorting(dtype, chunked):
    arr = dtype.construct_array_type()._from_sequence(["B", "C", "A"])
    return split_array(arr) if chunked else arr


@pytest.fixture

            

Reported by Pylint.