from datetime import datetime

import numpy as np
import pytest

from pandas import (
    Index,
    RangeIndex,
    Series,

            

Reported by Pylint.

                          r"(RangeIndex.)?from_range\(\) got an unexpected keyword argument( 'copy')?"
        )
        with pytest.raises(TypeError, match=msg):
            RangeIndex.from_range(range(10), copy=True)

    def test_constructor_name(self):
        # GH#12288
        orig = RangeIndex(10)
        orig.name = "original"

            

Reported by Pylint.

                      expected = Index(np.arange(start, stop, step, dtype=np.int64), name=name)
        assert isinstance(result, RangeIndex)
        assert result.name is name
        assert result._range == range(start, stop, step)
        tm.assert_index_equal(result, expected)

    def test_constructor_invalid_args(self):
        msg = "RangeIndex\\(\\.\\.\\.\\) must be called with integers"
        with pytest.raises(TypeError, match=msg):

            

Reported by Pylint.

              from datetime import datetime

import numpy as np
import pytest

from pandas import (
    Index,
    RangeIndex,
    Series,

            

Reported by Pylint.

              import pandas._testing as tm


class TestRangeIndexConstructors:
    @pytest.mark.parametrize("name", [None, "foo"])
    @pytest.mark.parametrize(
        "args, kwargs, start, stop, step",
        [
            ((5,), {}, 0, 5, 1),

            

Reported by Pylint.

                          ((0, 0), {}, 0, 0, 1),
            ((), {"start": 0}, 0, 0, 1),
            ((), {"stop": 0}, 0, 0, 1),
        ],
    )
    def test_constructor(self, args, kwargs, start, stop, step, name):
        result = RangeIndex(*args, name=name, **kwargs)
        expected = Index(np.arange(start, stop, step, dtype=np.int64), name=name)
        assert isinstance(result, RangeIndex)

            

Reported by Pylint.

                          ((0, 0), {}, 0, 0, 1),
            ((), {"start": 0}, 0, 0, 1),
            ((), {"stop": 0}, 0, 0, 1),
        ],
    )
    def test_constructor(self, args, kwargs, start, stop, step, name):
        result = RangeIndex(*args, name=name, **kwargs)
        expected = Index(np.arange(start, stop, step, dtype=np.int64), name=name)
        assert isinstance(result, RangeIndex)

            

Reported by Pylint.

                          ((0, 0), {}, 0, 0, 1),
            ((), {"start": 0}, 0, 0, 1),
            ((), {"stop": 0}, 0, 0, 1),
        ],
    )
    def test_constructor(self, args, kwargs, start, stop, step, name):
        result = RangeIndex(*args, name=name, **kwargs)
        expected = Index(np.arange(start, stop, step, dtype=np.int64), name=name)
        assert isinstance(result, RangeIndex)

            

Reported by Pylint.

                  def test_constructor(self, args, kwargs, start, stop, step, name):
        result = RangeIndex(*args, name=name, **kwargs)
        expected = Index(np.arange(start, stop, step, dtype=np.int64), name=name)
        assert isinstance(result, RangeIndex)
        assert result.name is name
        assert result._range == range(start, stop, step)
        tm.assert_index_equal(result, expected)

    def test_constructor_invalid_args(self):

            

Reported by Bandit.

                      result = RangeIndex(*args, name=name, **kwargs)
        expected = Index(np.arange(start, stop, step, dtype=np.int64), name=name)
        assert isinstance(result, RangeIndex)
        assert result.name is name
        assert result._range == range(start, stop, step)
        tm.assert_index_equal(result, expected)

    def test_constructor_invalid_args(self):
        msg = "RangeIndex\\(\\.\\.\\.\\) must be called with integers"

            

Reported by Bandit.

              import pytest

from pandas import Series

pytest.importorskip("matplotlib")
from pandas.plotting._matplotlib.style import get_standard_colors

pytestmark = pytest.mark.slow


            

Reported by Pylint.

              import pytest

from pandas import Series

pytest.importorskip("matplotlib")
from pandas.plotting._matplotlib.style import get_standard_colors

pytestmark = pytest.mark.slow


            

Reported by Pylint.

              from pandas import Series

pytest.importorskip("matplotlib")
from pandas.plotting._matplotlib.style import get_standard_colors

pytestmark = pytest.mark.slow


class TestGetStandardColors:

            

Reported by Pylint.

              pytestmark = pytest.mark.slow


class TestGetStandardColors:
    @pytest.mark.parametrize(
        "num_colors, expected",
        [
            (3, ["red", "green", "blue"]),
            (5, ["red", "green", "blue", "red", "green"]),

            

Reported by Pylint.

                          (7, ["red", "green", "blue", "red", "green", "blue", "red"]),
            (2, ["red", "green"]),
            (1, ["red"]),
        ],
    )
    def test_default_colors_named_from_prop_cycle(self, num_colors, expected):
        import matplotlib as mpl
        from matplotlib.pyplot import cycler


            

Reported by Pylint.

                          (7, ["red", "green", "blue", "red", "green", "blue", "red"]),
            (2, ["red", "green"]),
            (1, ["red"]),
        ],
    )
    def test_default_colors_named_from_prop_cycle(self, num_colors, expected):
        import matplotlib as mpl
        from matplotlib.pyplot import cycler


            

Reported by Pylint.

                      ],
    )
    def test_default_colors_named_from_prop_cycle(self, num_colors, expected):
        import matplotlib as mpl
        from matplotlib.pyplot import cycler

        mpl_params = {
            "axes.prop_cycle": cycler(color=["red", "green", "blue"]),
        }

            

Reported by Pylint.

                  )
    def test_default_colors_named_from_prop_cycle(self, num_colors, expected):
        import matplotlib as mpl
        from matplotlib.pyplot import cycler

        mpl_params = {
            "axes.prop_cycle": cycler(color=["red", "green", "blue"]),
        }
        with mpl.rc_context(rc=mpl_params):

            

Reported by Pylint.

                      }
        with mpl.rc_context(rc=mpl_params):
            result = get_standard_colors(num_colors=num_colors)
            assert result == expected

    @pytest.mark.parametrize(
        "num_colors, expected",
        [
            (1, ["b"]),

            

Reported by Bandit.

                          (4, ["b", "g", "r", "y"]),
            (5, ["b", "g", "r", "y", "b"]),
            (7, ["b", "g", "r", "y", "b", "g", "r"]),
        ],
    )
    def test_default_colors_named_from_prop_cycle_string(self, num_colors, expected):
        import matplotlib as mpl
        from matplotlib.pyplot import cycler


            

Reported by Pylint.

              from string import ascii_lowercase

import numpy as np
import pytest

import pandas as pd
import pandas._testing as tm



            

Reported by Pylint.

                      ],
    )
    @pytest.mark.parametrize("n", range(1, 11))
    def test_nlargest_n(self, df_strings, nselect_method, n, order):
        # GH#10393
        df = df_strings
        if "b" in order:

            error_msg = (

            

Reported by Pylint.

                  @pytest.mark.parametrize(
        "columns", [["group", "category_string"], ["group", "string"]]
    )
    def test_nlargest_error(self, df_main_dtypes, nselect_method, columns):
        df = df_main_dtypes
        col = columns[1]
        error_msg = (
            f"Column '{col}' has dtype {df[col].dtype}, "
            f"cannot use method '{nselect_method}' with this dtype"

            

Reported by Pylint.

                      with pytest.raises(TypeError, match=error_msg):
            getattr(df, nselect_method)(2, columns)

    def test_nlargest_all_dtypes(self, df_main_dtypes):
        df = df_main_dtypes
        df.nsmallest(2, list(set(df) - {"category_string", "string"}))
        df.nlargest(2, list(set(df) - {"category_string", "string"}))

    def test_nlargest_duplicates_on_starter_columns(self):

            

Reported by Pylint.

                      [["a", "b", "c"], ["c", "b", "a"], ["a"], ["b"], ["a", "b"], ["c", "b"]],
    )
    @pytest.mark.parametrize("n", range(1, 6))
    def test_nlargest_n_duplicate_index(self, df_duplicates, n, order):
        # GH#13412

        df = df_duplicates
        result = df.nsmallest(n, order)
        expected = df.sort_values(order).head(n)

            

Reported by Pylint.

              

@pytest.fixture
def df_duplicates():
    return pd.DataFrame(
        {"a": [1, 2, 3, 4, 4], "b": [1, 1, 1, 1, 1], "c": [0, 1, 2, 5, 4]},
        index=[0, 0, 1, 1, 1],
    )


            

Reported by Pylint.

              

@pytest.fixture
def df_strings():
    return pd.DataFrame(
        {
            "a": np.random.permutation(10),
            "b": list(ascii_lowercase[:10]),
            "c": np.random.permutation(10).astype("float64"),

            

Reported by Pylint.

              

@pytest.fixture
def df_main_dtypes():
    return pd.DataFrame(
        {
            "group": [1, 1, 2],
            "int": [1, 2, 3],
            "float": [4.0, 5.0, 6.0],

            

Reported by Pylint.

                  )


class TestNLargestNSmallest:

    # ----------------------------------------------------------------------
    # Top / bottom
    @pytest.mark.parametrize(
        "order",

            

Reported by Pylint.

                          # dups!
            ["b", "c", "c"],
        ],
    )
    @pytest.mark.parametrize("n", range(1, 11))
    def test_nlargest_n(self, df_strings, nselect_method, n, order):
        # GH#10393
        df = df_strings
        if "b" in order:

            

Reported by Pylint.

              import numpy as np
import pytest

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

from pandas import (
    DataFrame,
    Series,

            

Reported by Pylint.

              @td.skip_if_no_scipy
def test_win_type_not_implemented():
    class CustomIndexer(BaseIndexer):
        def get_window_bounds(self, num_values, min_periods, center, closed):
            return np.array([0, 1]), np.array([1, 2])

    df = DataFrame({"values": range(2)})
    indexer = CustomIndexer()
    with pytest.raises(NotImplementedError, match="BaseIndexer subclasses not"):

            

Reported by Pylint.

              import numpy as np
import pytest

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

from pandas import (
    DataFrame,
    Series,

            

Reported by Pylint.

              

@td.skip_if_no_scipy
def test_constructor(frame_or_series):
    # GH 12669
    c = frame_or_series(range(5)).rolling

    # valid
    c(win_type="boxcar", window=2, min_periods=1)

            

Reported by Pylint.

              @td.skip_if_no_scipy
def test_constructor(frame_or_series):
    # GH 12669
    c = frame_or_series(range(5)).rolling

    # valid
    c(win_type="boxcar", window=2, min_periods=1)
    c(win_type="boxcar", window=2, min_periods=1, center=True)
    c(win_type="boxcar", window=2, min_periods=1, center=False)

            

Reported by Pylint.

              
@pytest.mark.parametrize("w", [2.0, "foo", np.array([2])])
@td.skip_if_no_scipy
def test_invalid_constructor(frame_or_series, w):
    # not valid

    c = frame_or_series(range(5)).rolling
    with pytest.raises(ValueError, match="min_periods must be an integer"):
        c(win_type="boxcar", window=2, min_periods=w)

            

Reported by Pylint.

              
@pytest.mark.parametrize("w", [2.0, "foo", np.array([2])])
@td.skip_if_no_scipy
def test_invalid_constructor(frame_or_series, w):
    # not valid

    c = frame_or_series(range(5)).rolling
    with pytest.raises(ValueError, match="min_periods must be an integer"):
        c(win_type="boxcar", window=2, min_periods=w)

            

Reported by Pylint.

              def test_invalid_constructor(frame_or_series, w):
    # not valid

    c = frame_or_series(range(5)).rolling
    with pytest.raises(ValueError, match="min_periods must be an integer"):
        c(win_type="boxcar", window=2, min_periods=w)
    with pytest.raises(ValueError, match="center must be a boolean"):
        c(win_type="boxcar", window=2, min_periods=1, center=w)


            

Reported by Pylint.

              
@pytest.mark.parametrize("wt", ["foobar", 1])
@td.skip_if_no_scipy
def test_invalid_constructor_wintype(frame_or_series, wt):
    c = frame_or_series(range(5)).rolling
    with pytest.raises(ValueError, match="Invalid win_type"):
        c(win_type=wt, window=2)



            

Reported by Pylint.

              
@pytest.mark.parametrize("wt", ["foobar", 1])
@td.skip_if_no_scipy
def test_invalid_constructor_wintype(frame_or_series, wt):
    c = frame_or_series(range(5)).rolling
    with pytest.raises(ValueError, match="Invalid win_type"):
        c(win_type=wt, window=2)



            

Reported by Pylint.

              import numpy as np
import pytest

from pandas._libs.missing import is_matching_na

import pandas as pd
from pandas import Index
import pandas._testing as tm


            

Reported by Pylint.

              import numpy as np
import pytest

from pandas._libs.missing import is_matching_na

import pandas as pd
from pandas import Index
import pandas._testing as tm


            

Reported by Pylint.

              import numpy as np
import pytest

from pandas._libs.missing import is_matching_na

import pandas as pd
from pandas import Index
import pandas._testing as tm


            

Reported by Pylint.

              import numpy as np
import pytest

from pandas._libs.missing import is_matching_na

import pandas as pd
from pandas import Index
import pandas._testing as tm


            

Reported by Pylint.

              import pandas._testing as tm


class TestGetLoc:
    def test_get_loc_raises_object_nearest(self):
        index = Index(["a", "c"])
        with pytest.raises(TypeError, match="unsupported operand type"):
            with tm.assert_produces_warning(FutureWarning, match="deprecated"):
                index.get_loc("a", method="nearest")

            

Reported by Pylint.

              

class TestGetLoc:
    def test_get_loc_raises_object_nearest(self):
        index = Index(["a", "c"])
        with pytest.raises(TypeError, match="unsupported operand type"):
            with tm.assert_produces_warning(FutureWarning, match="deprecated"):
                index.get_loc("a", method="nearest")


            

Reported by Pylint.

              

class TestGetLoc:
    def test_get_loc_raises_object_nearest(self):
        index = Index(["a", "c"])
        with pytest.raises(TypeError, match="unsupported operand type"):
            with tm.assert_produces_warning(FutureWarning, match="deprecated"):
                index.get_loc("a", method="nearest")


            

Reported by Pylint.

                          with tm.assert_produces_warning(FutureWarning, match="deprecated"):
                index.get_loc("a", method="nearest")

    def test_get_loc_raises_object_tolerance(self):
        index = Index(["a", "c"])
        with pytest.raises(TypeError, match="unsupported operand type"):
            with tm.assert_produces_warning(FutureWarning, match="deprecated"):
                index.get_loc("a", method="pad", tolerance="invalid")


            

Reported by Pylint.

                          with tm.assert_produces_warning(FutureWarning, match="deprecated"):
                index.get_loc("a", method="nearest")

    def test_get_loc_raises_object_tolerance(self):
        index = Index(["a", "c"])
        with pytest.raises(TypeError, match="unsupported operand type"):
            with tm.assert_produces_warning(FutureWarning, match="deprecated"):
                index.get_loc("a", method="pad", tolerance="invalid")


            

Reported by Pylint.

                              index.get_loc("a", method="pad", tolerance="invalid")


class TestGetIndexer:
    @pytest.mark.parametrize(
        "method,expected",
        [
            ("pad", np.array([-1, 0, 1, 1], dtype=np.intp)),
            ("backfill", np.array([0, 0, 1, -1], dtype=np.intp)),

            

Reported by Pylint.

              import numpy as np
import pytest

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

            

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.

                          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] = max(i + end_value, 1)
                else:
                    start[i] = i
                    end[i] = i + self.window_size

            

Reported by Pylint.

              
def test_bad_get_window_bounds_signature():
    class BadIndexer(BaseIndexer):
        def get_window_bounds(self):
            return None

    indexer = BadIndexer()
    with pytest.raises(ValueError, match="BadIndexer does not implement"):
        Series(range(5)).rolling(indexer)

            

Reported by Pylint.

                  df = DataFrame({"values": range(5)})

    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.

                  df = DataFrame({"values": range(5)})

    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 center and min_periods == 1 and closed == "both" and i == 2:
                    start[i] = 0

            

Reported by Pylint.

              
    # Check that the rolling function output matches applying an alternative
    # function to the rolling window object
    expected2 = constructor(rolling.apply(lambda x: np_func(x, **np_kwargs)))
    tm.assert_equal(result, expected2)

    # Check that the function output matches applying an alternative function
    # if min_periods isn't specified
    # GH 39604: After count-min_periods deprecation, apply(lambda x: len(x))

            

Reported by Pylint.

                  min_periods = 0 if func == "count" else None
    rolling3 = constructor(values).rolling(window=indexer, min_periods=min_periods)
    result3 = getattr(rolling3, func)()
    expected3 = constructor(rolling3.apply(lambda x: np_func(x, **np_kwargs)))
    tm.assert_equal(result3, expected3)


@pytest.mark.parametrize("constructor", [Series, DataFrame])
def test_rolling_forward_skewness(constructor):

            

Reported by Pylint.

              def test_indexer_quantile_sum(end_value, values, func, args):
    # GH 37153
    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.

              import numpy as np
import pytest

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

            

Reported by Pylint.

              import sys

import numpy as np
import pytest

from pandas.compat import (
    IS64,
    PYPY,
)

            

Reported by Pylint.

              
    msg = f"index {size} is out of bounds for axis 0 with size {size}"
    with pytest.raises(IndexError, match=msg):
        index[size]
    msg = "single positional indexer is out-of-bounds"
    with pytest.raises(IndexError, match=msg):
        series.iloc[size]

            

Reported by Pylint.

                      index[size]
    msg = "single positional indexer is out-of-bounds"
    with pytest.raises(IndexError, match=msg):
        series.iloc[size]

            

Reported by Pylint.

              import sys

import numpy as np
import pytest

from pandas.compat import (
    IS64,
    PYPY,
)

            

Reported by Pylint.

                      ("truediv", "/"),
        ("floordiv", "//"),
    ],
)
@pytest.mark.parametrize("klass", [Series, DataFrame])
def test_binary_ops_docstring(klass, op_name, op):
    # not using the all_arithmetic_functions fixture with _get_opstr
    # as _get_opstr is used internally in the dynamic implementation of the docstring
    operand1 = klass.__name__.lower()

            

Reported by Pylint.

                      ("truediv", "/"),
        ("floordiv", "//"),
    ],
)
@pytest.mark.parametrize("klass", [Series, DataFrame])
def test_binary_ops_docstring(klass, op_name, op):
    # not using the all_arithmetic_functions fixture with _get_opstr
    # as _get_opstr is used internally in the dynamic implementation of the docstring
    operand1 = klass.__name__.lower()

            

Reported by Pylint.

                  operand1 = klass.__name__.lower()
    operand2 = "other"
    expected_str = " ".join([operand1, op, operand2])
    assert expected_str in getattr(klass, op_name).__doc__

    # reverse version of the binary ops
    expected_str = " ".join([operand2, op, operand1])
    assert expected_str in getattr(klass, "r" + op_name).__doc__


            

Reported by Bandit.

              
    # reverse version of the binary ops
    expected_str = " ".join([operand2, op, operand1])
    assert expected_str in getattr(klass, "r" + op_name).__doc__


def test_ndarray_compat_properties(index_or_series_obj):
    obj = index_or_series_obj


            

Reported by Bandit.

                  assert expected_str in getattr(klass, "r" + op_name).__doc__


def test_ndarray_compat_properties(index_or_series_obj):
    obj = index_or_series_obj

    # Check that we work.
    for p in ["shape", "dtype", "T", "nbytes"]:
        assert getattr(obj, p, None) is not None

            

Reported by Pylint.

                  obj = index_or_series_obj

    # Check that we work.
    for p in ["shape", "dtype", "T", "nbytes"]:
        assert getattr(obj, p, None) is not None

    # deprecated properties
    for p in ["strides", "itemsize", "base", "data"]:
        assert not hasattr(obj, p)

            

Reported by Pylint.

              )

import numpy as np
import pytest

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

            

Reported by Pylint.

              
@all_ts
@pytest.mark.parametrize("arg", ["mean", {"value": "mean"}, ["mean"]])
def test_resample_loffset_arg_type(frame, create_index, arg):
    # GH 13218, 15002
    df = frame
    expected_means = [df.values[i : i + 2].mean() for i in range(0, len(df.values), 2)]
    expected_index = create_index(df.index[0], periods=len(df.index) / 2, freq="2D")


            

Reported by Pylint.

                  with tm.assert_produces_warning(FutureWarning):
        expected = s.to_timestamp().resample(end_freq, base=base).mean()
    if end_freq == "M":
        # TODO: is non-tick the relevant characteristic? (GH 33815)
        expected.index = expected.index._with_freq(None)
    tm.assert_series_equal(result, expected)


def test_resample_base_with_timedeltaindex():

            

Reported by Pylint.

                      expected = s.to_timestamp().resample(end_freq, base=base).mean()
    if end_freq == "M":
        # TODO: is non-tick the relevant characteristic? (GH 33815)
        expected.index = expected.index._with_freq(None)
    tm.assert_series_equal(result, expected)


def test_resample_base_with_timedeltaindex():
    # GH 10530

            

Reported by Pylint.

                  )
    expected = Series([1.0, 1.0, 1.0, 1.0], index=idx)

    expected.index._data.freq = "3s"
    tm.assert_series_equal(result, expected)

            

Reported by Pylint.

              from datetime import (
    datetime,
    timedelta,
)

import numpy as np
import pytest

import pandas as pd

            

Reported by Pylint.

              

@pytest.fixture
def create_index(_index_factory):
    def _create_index(*args, **kwargs):
        """return the _index_factory created using the args, kwargs"""
        return _index_factory(*args, **kwargs)

    return _create_index

            

Reported by Pylint.

              

# new test to check that all FutureWarning are triggered
def test_deprecating_on_loffset_and_base():
    # GH 31809

    idx = date_range("2001-01-01", periods=4, freq="T")
    df = DataFrame(data=4 * [range(2)], index=idx, columns=["a", "b"])


            

Reported by Pylint.

                  # GH 31809

    idx = date_range("2001-01-01", periods=4, freq="T")
    df = DataFrame(data=4 * [range(2)], index=idx, columns=["a", "b"])

    with tm.assert_produces_warning(FutureWarning):
        pd.Grouper(freq="10s", base=0)
    with tm.assert_produces_warning(FutureWarning):
        pd.Grouper(freq="10s", loffset="0s")

            

Reported by Pylint.

              
@all_ts
@pytest.mark.parametrize("arg", ["mean", {"value": "mean"}, ["mean"]])
def test_resample_loffset_arg_type(frame, create_index, arg):
    # GH 13218, 15002
    df = frame
    expected_means = [df.values[i : i + 2].mean() for i in range(0, len(df.values), 2)]
    expected_index = create_index(df.index[0], periods=len(df.index) / 2, freq="2D")


            

Reported by Pylint.

              import pytest

import pandas as pd


class TestFlags:
    def test_equality(self):
        a = pd.DataFrame().set_flags(allows_duplicate_labels=True).flags
        b = pd.DataFrame().set_flags(allows_duplicate_labels=False).flags

            

Reported by Pylint.

                      assert flags["allows_duplicate_labels"] is False

        with pytest.raises(KeyError, match="a"):
            flags["a"]

        with pytest.raises(ValueError, match="a"):
            flags["a"] = 10

            

Reported by Pylint.

              import pytest

import pandas as pd


class TestFlags:
    def test_equality(self):
        a = pd.DataFrame().set_flags(allows_duplicate_labels=True).flags
        b = pd.DataFrame().set_flags(allows_duplicate_labels=False).flags

            

Reported by Pylint.

              import pandas as pd


class TestFlags:
    def test_equality(self):
        a = pd.DataFrame().set_flags(allows_duplicate_labels=True).flags
        b = pd.DataFrame().set_flags(allows_duplicate_labels=False).flags

        assert a == a

            

Reported by Pylint.

              

class TestFlags:
    def test_equality(self):
        a = pd.DataFrame().set_flags(allows_duplicate_labels=True).flags
        b = pd.DataFrame().set_flags(allows_duplicate_labels=False).flags

        assert a == a
        assert b == b

            

Reported by Pylint.

              

class TestFlags:
    def test_equality(self):
        a = pd.DataFrame().set_flags(allows_duplicate_labels=True).flags
        b = pd.DataFrame().set_flags(allows_duplicate_labels=False).flags

        assert a == a
        assert b == b

            

Reported by Pylint.

              
class TestFlags:
    def test_equality(self):
        a = pd.DataFrame().set_flags(allows_duplicate_labels=True).flags
        b = pd.DataFrame().set_flags(allows_duplicate_labels=False).flags

        assert a == a
        assert b == b
        assert a != b

            

Reported by Pylint.

              class TestFlags:
    def test_equality(self):
        a = pd.DataFrame().set_flags(allows_duplicate_labels=True).flags
        b = pd.DataFrame().set_flags(allows_duplicate_labels=False).flags

        assert a == a
        assert b == b
        assert a != b
        assert a != 2

            

Reported by Pylint.

                      a = pd.DataFrame().set_flags(allows_duplicate_labels=True).flags
        b = pd.DataFrame().set_flags(allows_duplicate_labels=False).flags

        assert a == a
        assert b == b
        assert a != b
        assert a != 2

    def test_set(self):

            

Reported by Bandit.

                      a = pd.DataFrame().set_flags(allows_duplicate_labels=True).flags
        b = pd.DataFrame().set_flags(allows_duplicate_labels=False).flags

        assert a == a
        assert b == b
        assert a != b
        assert a != 2

    def test_set(self):

            

Reported by Pylint.

                      expected = index.astype(object).union(index2.astype(object), sort=sort)
        tm.assert_index_equal(result, expected)

    # TODO: belongs elsewhere
    def test_union_dataframe_index(self):
        rng1 = period_range("1/1/1999", "1/1/2012", freq="M")
        s1 = pd.Series(np.random.randn(len(rng1)), rng1)

        rng2 = period_range("1/1/1980", "12/1/2001", freq="M")

            

Reported by Pylint.

              import numpy as np

import pandas as pd
from pandas import (
    PeriodIndex,
    date_range,
    period_range,
)
import pandas._testing as tm

            

Reported by Pylint.

                  return obj.take(np.random.permutation(len(obj)))


class TestPeriodIndex:
    def test_union(self, sort):
        # union
        other1 = period_range("1/1/2000", freq="D", periods=5)
        rng1 = period_range("1/6/2000", freq="D", periods=5)
        expected1 = PeriodIndex(

            

Reported by Pylint.

              

class TestPeriodIndex:
    def test_union(self, sort):
        # union
        other1 = period_range("1/1/2000", freq="D", periods=5)
        rng1 = period_range("1/6/2000", freq="D", periods=5)
        expected1 = PeriodIndex(
            [

            

Reported by Pylint.

              

class TestPeriodIndex:
    def test_union(self, sort):
        # union
        other1 = period_range("1/1/2000", freq="D", periods=5)
        rng1 = period_range("1/6/2000", freq="D", periods=5)
        expected1 = PeriodIndex(
            [

            

Reported by Pylint.

              

class TestPeriodIndex:
    def test_union(self, sort):
        # union
        other1 = period_range("1/1/2000", freq="D", periods=5)
        rng1 = period_range("1/6/2000", freq="D", periods=5)
        expected1 = PeriodIndex(
            [

            

Reported by Pylint.

                              expected = expected.sort_values()
            tm.assert_index_equal(result_union, expected)

    def test_union_misc(self, sort):
        index = period_range("1/1/2000", "1/20/2000", freq="D")

        result = index[:-5].union(index[10:], sort=sort)
        tm.assert_index_equal(result, index)


            

Reported by Pylint.

                              expected = expected.sort_values()
            tm.assert_index_equal(result_union, expected)

    def test_union_misc(self, sort):
        index = period_range("1/1/2000", "1/20/2000", freq="D")

        result = index[:-5].union(index[10:], sort=sort)
        tm.assert_index_equal(result, index)


            

Reported by Pylint.

                      result = _permute(index[:-5]).union(_permute(index[10:]), sort=sort)
        if sort is None:
            tm.assert_index_equal(result, index)
        assert tm.equalContents(result, index)

        # cast if different frequencies
        index = period_range("1/1/2000", "1/20/2000", freq="D")
        index2 = period_range("1/1/2000", "1/20/2000", freq="W-WED")
        result = index.union(index2, sort=sort)

            

Reported by Bandit.

                      tm.assert_index_equal(result, expected)

    # TODO: belongs elsewhere
    def test_union_dataframe_index(self):
        rng1 = period_range("1/1/1999", "1/1/2012", freq="M")
        s1 = pd.Series(np.random.randn(len(rng1)), rng1)

        rng2 = period_range("1/1/1980", "12/1/2001", freq="M")
        s2 = pd.Series(np.random.randn(len(rng2)), rng2)

            

Reported by Pylint.