import operator

import numpy as np
import pytest

from pandas.errors import OutOfBoundsTimedelta

import pandas as pd
from pandas import (

            

Reported by Pylint.

                          ]
        )
        with pytest.raises(TypeError, match=msg):
            td // np.datetime64("2016-01-01", dtype="datetime64[us]")

    def test_td_floordiv_numeric_scalar(self):
        # GH#18846
        td = Timedelta(hours=3, minutes=4)


            

Reported by Pylint.

                  def test_td_add_timestamp_overflow(self):
        msg = "int too (large|big) to convert"
        with pytest.raises(OverflowError, match=msg):
            Timestamp("1700-01-01") + Timedelta(13 * 19999, unit="D")

        with pytest.raises(OutOfBoundsTimedelta, match=msg):
            Timestamp("1700-01-01") + timedelta(days=13 * 19999)

    @pytest.mark.parametrize("op", [operator.add, ops.radd])

            

Reported by Pylint.

                          Timestamp("1700-01-01") + Timedelta(13 * 19999, unit="D")

        with pytest.raises(OutOfBoundsTimedelta, match=msg):
            Timestamp("1700-01-01") + timedelta(days=13 * 19999)

    @pytest.mark.parametrize("op", [operator.add, ops.radd])
    def test_td_add_td(self, op):
        td = Timedelta(10, unit="d")


            

Reported by Pylint.

                      msg = "unsupported operand type"
        for other in [2, 2.0, np.int64(2), np.float64(2)]:
            with pytest.raises(TypeError, match=msg):
                td + other
            with pytest.raises(TypeError, match=msg):
                other + td
            with pytest.raises(TypeError, match=msg):
                td - other
            with pytest.raises(TypeError, match=msg):

            

Reported by Pylint.

                          with pytest.raises(TypeError, match=msg):
                td + other
            with pytest.raises(TypeError, match=msg):
                other + td
            with pytest.raises(TypeError, match=msg):
                td - other
            with pytest.raises(TypeError, match=msg):
                other - td


            

Reported by Pylint.

                          with pytest.raises(TypeError, match=msg):
                other + td
            with pytest.raises(TypeError, match=msg):
                td - other
            with pytest.raises(TypeError, match=msg):
                other - td

    def test_td_rsub_nat(self):
        td = Timedelta(10, unit="d")

            

Reported by Pylint.

                          with pytest.raises(TypeError, match=msg):
                td - other
            with pytest.raises(TypeError, match=msg):
                other - td

    def test_td_rsub_nat(self):
        td = Timedelta(10, unit="d")
        result = NaT - td
        assert result is NaT

            

Reported by Pylint.

                      arr = np.array([now, Timedelta("1D"), np.timedelta64(2, "h")])
        msg = r"unsupported operand type\(s\) for \-: 'Timedelta' and 'Timestamp'"
        with pytest.raises(TypeError, match=msg):
            Timedelta("1D") - arr

    @pytest.mark.parametrize("op", [operator.add, ops.radd])
    def test_td_add_timedeltalike_object_dtype_array(self, op):
        # GH#21980
        arr = np.array([Timestamp("20130101 9:01"), Timestamp("20121230 9:02")])

            

Reported by Pylint.

                      res = op(arr, Timedelta("1D"))
        tm.assert_numpy_array_equal(res, exp)

    # TODO: moved from index tests following #24365, may need de-duplication
    def test_ops_ndarray(self):
        td = Timedelta("1 day")

        # timedelta, timedelta
        other = pd.to_timedelta(["1 day"]).values

            

Reported by Pylint.

              import operator

import numpy as np
import pytest

import pandas.util._test_decorators as td

import pandas as pd
from pandas import (

            

Reported by Pylint.

                  return request.param


def skip_if_no_pandas_parser(parser):
    if parser != "pandas":
        pytest.skip(f"cannot evaluate with parser {repr(parser)}")


class TestCompat:

            

Reported by Pylint.

              

class TestCompat:
    def setup_method(self, method):
        self.df = DataFrame({"A": [1, 2, 3]})
        self.expected1 = self.df[self.df.A > 0]
        self.expected2 = self.df.A + 1

    def test_query_default(self):

            

Reported by Pylint.

              
class TestCompat:
    def setup_method(self, method):
        self.df = DataFrame({"A": [1, 2, 3]})
        self.expected1 = self.df[self.df.A > 0]
        self.expected2 = self.df.A + 1

    def test_query_default(self):


            

Reported by Pylint.

              class TestCompat:
    def setup_method(self, method):
        self.df = DataFrame({"A": [1, 2, 3]})
        self.expected1 = self.df[self.df.A > 0]
        self.expected2 = self.df.A + 1

    def test_query_default(self):

        # GH 12749

            

Reported by Pylint.

                  def setup_method(self, method):
        self.df = DataFrame({"A": [1, 2, 3]})
        self.expected1 = self.df[self.df.A > 0]
        self.expected2 = self.df.A + 1

    def test_query_default(self):

        # GH 12749
        # this should always work, whether NUMEXPR_INSTALLED or not

            

Reported by Pylint.

                      df.iloc[0] = 2
        m = df.mean()

        base = DataFrame(  # noqa
            np.tile(m.values, n).reshape(n, -1), columns=list("abcd")
        )

        expected = eval(f"base {op_str} df")


            

Reported by Pylint.

                          np.tile(m.values, n).reshape(n, -1), columns=list("abcd")
        )

        expected = eval(f"base {op_str} df")

        # ops as strings
        result = eval(f"m {op_str} df")
        tm.assert_frame_equal(result, expected)


            

Reported by Bandit.

                          np.tile(m.values, n).reshape(n, -1), columns=list("abcd")
        )

        expected = eval(f"base {op_str} df")

        # ops as strings
        result = eval(f"m {op_str} df")
        tm.assert_frame_equal(result, expected)


            

Reported by Pylint.

                      expected = eval(f"base {op_str} df")

        # ops as strings
        result = eval(f"m {op_str} df")
        tm.assert_frame_equal(result, expected)

        # these are commutative
        if op in ["+", "*"]:
            result = getattr(df, op)(m)

            

Reported by Bandit.

              from io import StringIO

import numpy as np
import pytest

from pandas.compat import IS64
from pandas.errors import PerformanceWarning

import pandas as pd

            

Reported by Pylint.

                      ]
    )

    def func(dataf):
        return dataf["val2"] - dataf["val2"].mean()

    with tm.assert_produces_warning(FutureWarning):
        result = df2.groupby("val1", squeeze=True).apply(func)
    assert isinstance(result, Series)

            

Reported by Pylint.

                  result = df.groupby("A").apply(f)[["B"]]
    tm.assert_frame_equal(result, expected)

    def f(grp):
        if grp.name == "Tiger":
            return None
        return grp.iloc[0]

    result = df.groupby("A").apply(f)[["B"]]

            

Reported by Pylint.

                  e.loc["Tiger"] = np.nan
    tm.assert_frame_equal(result, e)

    def f(grp):
        if grp.name == "Pony":
            return None
        return grp.iloc[0]

    result = df.groupby("A").apply(f)[["B"]]

            

Reported by Pylint.

                  tm.assert_frame_equal(result, e)

    # 5592 revisited, with datetimes
    def f(grp):
        if grp.name == "Pony":
            return None
        return grp.iloc[0]

    result = df.groupby("A").apply(f)[["C"]]

            

Reported by Pylint.

                  tm.assert_frame_equal(result, e)

    # scalar outputs
    def f(grp):
        if grp.name == "Pony":
            return None
        return grp.iloc[0].loc["C"]

    result = df.groupby("A").apply(f)

            

Reported by Pylint.

              
    grouped = data.groupby(lambda x: x // 3)

    for k, v in grouped:
        assert len(v) == 3

    agged = grouped.aggregate(np.mean)
    assert agged[1] == 1


            

Reported by Pylint.

                  }
    expected = DataFrame(ex_data).T
    if not as_index:
        # TODO: try to get this more consistent?
        expected.index = Index(range(2))

    tm.assert_frame_equal(res, expected)



            

Reported by Pylint.

              
    # transform propagate
    transformed = grouped.transform(lambda x: x.mean())
    for name, group in grouped:
        mean = group.mean()
        for idx in group.index:
            tm.assert_series_equal(transformed.xs(idx), mean, check_names=False)

    # iterate

            

Reported by Pylint.

                  tm.assert_frame_equal(groupedT.transform(tf).T, grouped.transform(tf))

    # iterate
    for k, v in grouped:
        assert len(v.columns) == 2


def test_frame_set_name_single(df):
    grouped = df.groupby("A")

            

Reported by Pylint.

              )

import numpy as np
import pytest

from pandas._config import get_option

from pandas.compat import is_platform_windows
from pandas.compat.pyarrow import (

            

Reported by Pylint.

                      Expected partition names.
    """
    if pa_version_under5p0:
        import pyarrow.parquet as pq

        dataset = pq.ParquetDataset(path, validate_schema=False)
        assert len(dataset.partitions.partition_names) == len(expected)
        assert dataset.partitions.partition_names == set(expected)
    else:

            

Reported by Pylint.

                      assert len(dataset.partitions.partition_names) == len(expected)
        assert dataset.partitions.partition_names == set(expected)
    else:
        import pyarrow.dataset as ds

        dataset = ds.dataset(path, partitioning="hive")
        assert dataset.partitioning.schema.names == expected



            

Reported by Pylint.

                      self.check_error_on_write(df, engine, ValueError, msg)

    def test_use_nullable_dtypes(self, engine):
        import pyarrow.parquet as pq

        if engine == "fastparquet":
            # We are manually disabling fastparquet's
            # nullable dtype support pending discussion
            pytest.skip("Fastparquet nullable dtype support is disabled")

            

Reported by Pylint.

              
        # additional supported types for pyarrow
        dti = pd.date_range("20130101", periods=3, tz="Europe/Brussels")
        dti = dti._with_freq(None)  # freq doesn't round-trip
        df["datetime_tz"] = dti
        df["bool_with_none"] = [True, None, True]

        check_round_trip(df, pa)


            

Reported by Pylint.

              
        # additional supported types for pyarrow
        dti = pd.date_range("20130101", periods=3, tz="Europe/Brussels")
        dti = dti._with_freq(None)  # freq doesn't round-trip
        df["datetime_tz"] = dti
        df["bool_with_none"] = [True, None, True]

        check_round_trip(df, pa)


            

Reported by Pylint.

                      check_round_trip(df, pa)

    def test_write_with_schema(self, pa):
        import pyarrow

        df = pd.DataFrame({"x": [0, 1]})
        schema = pyarrow.schema([pyarrow.field("x", type=pyarrow.bool_())])
        out_df = df.astype(bool)
        check_round_trip(df, pa, write_kwargs={"schema": schema}, expected=out_df)

            

Reported by Pylint.

                      df = df_full

        dti = pd.date_range("20130101", periods=3, tz="US/Eastern")
        dti = dti._with_freq(None)  # freq doesn't round-trip
        df["datetime_tz"] = dti
        df["timedelta"] = pd.timedelta_range("1 day", periods=3)
        check_round_trip(df, fp)

    @pytest.mark.skip(reason="not supported")

            

Reported by Pylint.

                      df = df_full

        dti = pd.date_range("20130101", periods=3, tz="US/Eastern")
        dti = dti._with_freq(None)  # freq doesn't round-trip
        df["datetime_tz"] = dti
        df["timedelta"] = pd.timedelta_range("1 day", periods=3)
        check_round_trip(df, fp)

    @pytest.mark.skip(reason="not supported")

            

Reported by Pylint.

                              compression=None,
            )
            assert os.path.exists(path)
            import fastparquet

            actual_partition_cols = fastparquet.ParquetFile(path, False).cats
            assert len(actual_partition_cols) == 2

    def test_partition_cols_string(self, fp, df_full):

            

Reported by Pylint.

              import re

import numpy as np
import pytest

from pandas._libs.internals import BlockPlacement
import pandas.util._test_decorators as td

from pandas.core.dtypes.common import is_scalar

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas._libs.internals import BlockPlacement
import pandas.util._test_decorators as td

from pandas.core.dtypes.common import is_scalar

import pandas as pd

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas._libs.internals import BlockPlacement
import pandas.util._test_decorators as td

from pandas.core.dtypes.common import is_scalar

import pandas as pd

            

Reported by Pylint.

              )

# this file contains BlockManager specific tests
# TODO(ArrayManager) factor out interleave_dtype tests
pytestmark = td.skip_array_manager_invalid_test


@pytest.fixture(params=[new_block, make_block])
def block_maker(request):

            

Reported by Pylint.

                      assert m is not None, f"incompatible typestr -> {typestr}"
        tz = m.groups()[0]
        assert num_items == 1, "must have only 1 num items for a tz-aware"
        values = DatetimeIndex(np.arange(N) * 1e9, tz=tz)._data
        values = ensure_block_shape(values, ndim=len(shape))
    elif typestr in ("timedelta", "td", "m8[ns]"):
        values = (mat * 1).astype("m8[ns]")
    elif typestr in ("category",):
        values = Categorical([1, 1, 2, 2, 3, 3, 3, 3, 4, 4])

            

Reported by Pylint.

                  elif typestr in ("category2",):
        values = Categorical(["a", "a", "a", "a", "b", "b", "c", "c", "c", "d"])
    elif typestr in ("sparse", "sparse_na"):
        # FIXME: doesn't support num_rows != 10
        assert shape[-1] == 10
        assert all(s == 1 for s in shape[:-1])
        if typestr.endswith("_na"):
            fill_value = np.nan
        else:

            

Reported by Pylint.

              

class TestBlock:
    def setup_method(self, method):
        self.fblock = create_block("float", [0, 2, 4])
        self.cblock = create_block("complex", [7])
        self.oblock = create_block("object", [1, 3])
        self.bool_block = create_block("bool", [5])


            

Reported by Pylint.

              
class TestBlock:
    def setup_method(self, method):
        self.fblock = create_block("float", [0, 2, 4])
        self.cblock = create_block("complex", [7])
        self.oblock = create_block("object", [1, 3])
        self.bool_block = create_block("bool", [5])

    def test_constructor(self):

            

Reported by Pylint.

              class TestBlock:
    def setup_method(self, method):
        self.fblock = create_block("float", [0, 2, 4])
        self.cblock = create_block("complex", [7])
        self.oblock = create_block("object", [1, 3])
        self.bool_block = create_block("bool", [5])

    def test_constructor(self):
        int32block = create_block("i4", [0])

            

Reported by Pylint.

                  def setup_method(self, method):
        self.fblock = create_block("float", [0, 2, 4])
        self.cblock = create_block("complex", [7])
        self.oblock = create_block("object", [1, 3])
        self.bool_block = create_block("bool", [5])

    def test_constructor(self):
        int32block = create_block("i4", [0])
        assert int32block.dtype == np.int32

            

Reported by Pylint.

              import re

import numpy as np
import pytest

from pandas.core.dtypes.common import (
    is_categorical_dtype,
    is_object_dtype,
)

            

Reported by Pylint.

              

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

            

Reported by Pylint.

              class TestMerge:
    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.

                          }
        )

        self.left = DataFrame(
            {"key": ["a", "b", "c", "d", "e", "e", "a"], "v1": np.random.randn(7)}
        )
        self.right = DataFrame({"v2": np.random.randn(4)}, index=["d", "b", "c", "a"])

    def test_merge_inner_join_empty(self):

            

Reported by Pylint.

                      self.left = DataFrame(
            {"key": ["a", "b", "c", "d", "e", "e", "a"], "v1": np.random.randn(7)}
        )
        self.right = DataFrame({"v2": np.random.randn(4)}, index=["d", "b", "c", "a"])

    def test_merge_inner_join_empty(self):
        # GH 15328
        df_empty = DataFrame()
        df_a = DataFrame({"a": [1, 2]}, index=[0, 1], dtype="int64")

            

Reported by Pylint.

                  def test_merge_non_string_columns(self):
        # https://github.com/pandas-dev/pandas/issues/17962
        # Checks that method runs for non string column names
        left = DataFrame(
            {0: [1, 0, 1, 0], 1: [0, 1, 0, 0], 2: [0, 0, 2, 0], 3: [1, 0, 0, 3]}
        )

        right = left.astype(float)
        expected = left

            

Reported by Pylint.

                          {0: [1, 0, 1, 0], 1: [0, 1, 0, 0], 2: [0, 0, 2, 0], 3: [1, 0, 0, 3]}
        )

        right = left.astype(float)
        expected = left
        result = merge(left, right)
        tm.assert_frame_equal(expected, result)

    def test_merge_index_as_on_arg(self):

            

Reported by Pylint.

                  def test_merge_index_as_on_arg(self):
        # GH14355

        left = self.df.set_index("key1")
        right = self.df2.set_index("key1")
        result = merge(left, right, on="key1")
        expected = merge(self.df, self.df2, on="key1").set_index("key1")
        tm.assert_frame_equal(result, expected)


            

Reported by Pylint.

              
from pandas._config import get_option

from pandas._libs import (
    algos as libalgos,
    index as libindex,
    lib,
)
import pandas._libs.join as libjoin

            

Reported by Pylint.

              
from pandas._config import get_option

from pandas._libs import (
    algos as libalgos,
    index as libindex,
    lib,
)
import pandas._libs.join as libjoin

            

Reported by Pylint.

              
from pandas._config import get_option

from pandas._libs import (
    algos as libalgos,
    index as libindex,
    lib,
)
import pandas._libs.join as libjoin

            

Reported by Pylint.

                  index as libindex,
    lib,
)
import pandas._libs.join as libjoin
from pandas._libs.lib import (
    is_datetime_array,
    no_default,
)
from pandas._libs.tslibs import (

            

Reported by Pylint.

                  index as libindex,
    lib,
)
import pandas._libs.join as libjoin
from pandas._libs.lib import (
    is_datetime_array,
    no_default,
)
from pandas._libs.tslibs import (

            

Reported by Pylint.

                  lib,
)
import pandas._libs.join as libjoin
from pandas._libs.lib import (
    is_datetime_array,
    no_default,
)
from pandas._libs.tslibs import (
    IncompatibleFrequency,

            

Reported by Pylint.

                  lib,
)
import pandas._libs.join as libjoin
from pandas._libs.lib import (
    is_datetime_array,
    no_default,
)
from pandas._libs.tslibs import (
    IncompatibleFrequency,

            

Reported by Pylint.

                  #  given the dtypes of the passed arguments

    @final
    def _left_indexer_unique(self: _IndexT, other: _IndexT) -> npt.NDArray[np.intp]:
        # Caller is responsible for ensuring other.dtype == self.dtype
        sv = self._get_join_target()
        ov = other._get_join_target()
        return libjoin.left_join_indexer_unique(sv, ov)


            

Reported by Pylint.

                  @final
    def _left_indexer(
        self: _IndexT, other: _IndexT
    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
        # Caller is responsible for ensuring other.dtype == self.dtype
        sv = self._get_join_target()
        ov = other._get_join_target()
        joined_ndarray, lidx, ridx = libjoin.left_join_indexer(sv, ov)
        joined = self._from_join_target(joined_ndarray)

            

Reported by Pylint.

                  @final
    def _left_indexer(
        self: _IndexT, other: _IndexT
    ) -> tuple[ArrayLike, npt.NDArray[np.intp], npt.NDArray[np.intp]]:
        # Caller is responsible for ensuring other.dtype == self.dtype
        sv = self._get_join_target()
        ov = other._get_join_target()
        joined_ndarray, lidx, ridx = libjoin.left_join_indexer(sv, ov)
        joined = self._from_join_target(joined_ndarray)

            

Reported by Pylint.

              )

import numpy as np
import pytest

from pandas.errors import (
    OutOfBoundsDatetime,
    PerformanceWarning,
)

            

Reported by Pylint.

                              ["-2H", "-4H", "-6H", "-8H", "-10H"], freq="-2H", name="x"
            )
            tm.assert_index_equal(result, exp)
            assert result.freq == "-2H"

        idx = TimedeltaIndex(["-2H", "-1H", "0H", "1H", "2H"], freq="H", name="x")
        for result in [abs(idx), np.absolute(idx)]:
            assert isinstance(result, TimedeltaIndex)
            exp = TimedeltaIndex(["2H", "1H", "0H", "1H", "2H"], freq=None, name="x")

            

Reported by Pylint.

                              ["-2H", "-4H", "-6H", "-8H", "-10H"], freq="-2H", name="x"
            )
            tm.assert_index_equal(result, exp)
            assert result.freq == "-2H"

        idx = TimedeltaIndex(["-2H", "-1H", "0H", "1H", "2H"], freq="H", name="x")
        for result in [abs(idx), np.absolute(idx)]:
            assert isinstance(result, TimedeltaIndex)
            exp = TimedeltaIndex(["2H", "1H", "0H", "1H", "2H"], freq=None, name="x")

            

Reported by Pylint.

                      dti = pd.date_range("20130101", periods=3)
        ts = Timestamp("20130101")
        dt = ts.to_pydatetime()
        dti_tz = pd.date_range("20130101", periods=3).tz_localize("US/Eastern")
        ts_tz = Timestamp("20130101").tz_localize("US/Eastern")
        ts_tz2 = Timestamp("20130101").tz_localize("CET")
        dt_tz = ts_tz.to_pydatetime()
        td = Timedelta("1 days")


            

Reported by Pylint.

                      dti = pd.date_range("20130101", periods=3)
        ts = Timestamp("20130101")
        dt = ts.to_pydatetime()
        dti_tz = pd.date_range("20130101", periods=3).tz_localize("US/Eastern")
        ts_tz = Timestamp("20130101").tz_localize("US/Eastern")
        ts_tz2 = Timestamp("20130101").tz_localize("CET")
        dt_tz = ts_tz.to_pydatetime()
        td = Timedelta("1 days")


            

Reported by Pylint.

              
        if freq == "D":
            expected = pd.tseries.offsets.Day(1)
            assert index.freq == expected
            assert shifted.freq == expected
            assert back.freq == expected
        else:  # freq == 'B'
            assert index.freq == pd.tseries.offsets.BusinessDay(1)
            assert shifted.freq is None

            

Reported by Pylint.

              
        if freq == "D":
            expected = pd.tseries.offsets.Day(1)
            assert index.freq == expected
            assert shifted.freq == expected
            assert back.freq == expected
        else:  # freq == 'B'
            assert index.freq == pd.tseries.offsets.BusinessDay(1)
            assert shifted.freq is None

            

Reported by Pylint.

                          assert shifted.freq == expected
            assert back.freq == expected
        else:  # freq == 'B'
            assert index.freq == pd.tseries.offsets.BusinessDay(1)
            assert shifted.freq is None
            assert back.freq == pd.tseries.offsets.BusinessDay(1)

        result = index - timedelta(1)
        expected = index + timedelta(-1)

            

Reported by Pylint.

                          assert shifted.freq == expected
            assert back.freq == expected
        else:  # freq == 'B'
            assert index.freq == pd.tseries.offsets.BusinessDay(1)
            assert shifted.freq is None
            assert back.freq == pd.tseries.offsets.BusinessDay(1)

        result = index - timedelta(1)
        expected = index + timedelta(-1)

            

Reported by Pylint.

                      result3 = rng - np.timedelta64(100000000)
        result4 = DatetimeIndex(s - offsets.Hour(1))

        assert result1.freq == rng.freq
        result1 = result1._with_freq(None)
        tm.assert_index_equal(result1, result4)

        assert result3.freq == rng.freq
        result3 = result3._with_freq(None)

            

Reported by Pylint.

              import re

import numpy as np
import pytest
import pytz

import pandas.util._test_decorators as td

import pandas as pd

            

Reported by Pylint.

              
import numpy as np
import pytest
import pytz

import pandas.util._test_decorators as td

import pandas as pd
from pandas import (

            

Reported by Pylint.

                  autouse=True, scope="module", params=[0, 1000000], ids=["numexpr", "python"]
)
def switch_numexpr_min_elements(request):
    _MIN_ELEMENTS = expr._MIN_ELEMENTS
    expr._MIN_ELEMENTS = request.param
    yield request.param
    expr._MIN_ELEMENTS = _MIN_ELEMENTS



            

Reported by Pylint.

              )
def switch_numexpr_min_elements(request):
    _MIN_ELEMENTS = expr._MIN_ELEMENTS
    expr._MIN_ELEMENTS = request.param
    yield request.param
    expr._MIN_ELEMENTS = _MIN_ELEMENTS


class DummyElement:

            

Reported by Pylint.

                  _MIN_ELEMENTS = expr._MIN_ELEMENTS
    expr._MIN_ELEMENTS = request.param
    yield request.param
    expr._MIN_ELEMENTS = _MIN_ELEMENTS


class DummyElement:
    def __init__(self, value, dtype):
        self.value = value

            

Reported by Pylint.

                  def __repr__(self) -> str:
        return str(self)

    def astype(self, dtype, copy=False):
        self.dtype = dtype
        return self

    def view(self, dtype):
        return type(self)(self.value.view(dtype), dtype)

            

Reported by Pylint.

                  def view(self, dtype):
        return type(self)(self.value.view(dtype), dtype)

    def any(self, axis=None):
        return bool(self.value)


# -------------------------------------------------------------------
# Comparisons

            

Reported by Pylint.

                      df = DataFrame(np.random.randn(6, 4), columns=list("ABCD"))
        msg = "The truth value of a DataFrame is ambiguous"
        with pytest.raises(ValueError, match=msg):
            df in [None]

    def test_comparison_invalid(self):
        def check(df, df2):

            for (x, y) in [(df, df2), (df2, df)]:

            

Reported by Pylint.

                              ]
                msg = "|".join(msgs)
                with pytest.raises(TypeError, match=msg):
                    x >= y
                with pytest.raises(TypeError, match=msg):
                    x > y
                with pytest.raises(TypeError, match=msg):
                    x < y
                with pytest.raises(TypeError, match=msg):

            

Reported by Pylint.

                              with pytest.raises(TypeError, match=msg):
                    x >= y
                with pytest.raises(TypeError, match=msg):
                    x > y
                with pytest.raises(TypeError, match=msg):
                    x < y
                with pytest.raises(TypeError, match=msg):
                    x <= y


            

Reported by Pylint.

              import sys

import numpy as np
import pytest

from pandas.compat import (
    IS64,
    PY310,
    is_platform_windows,

            

Reported by Pylint.

                  def test_datetime_tz(self):
        # GH4377 df.to_json segfaults with non-ndarray blocks
        tz_range = pd.date_range("20130101", periods=3, tz="US/Eastern")
        tz_naive = tz_range.tz_convert("utc").tz_localize(None)

        df = DataFrame({"A": tz_range, "B": pd.date_range("20130101", periods=3)})

        df_naive = df.copy()
        df_naive["A"] = tz_naive

            

Reported by Pylint.

                  def test_datetime_tz(self):
        # GH4377 df.to_json segfaults with non-ndarray blocks
        tz_range = pd.date_range("20130101", periods=3, tz="US/Eastern")
        tz_naive = tz_range.tz_convert("utc").tz_localize(None)

        df = DataFrame({"A": tz_range, "B": pd.date_range("20130101", periods=3)})

        df_naive = df.copy()
        df_naive["A"] = tz_naive

            

Reported by Pylint.

              class TestPandasContainer:
    @pytest.fixture(autouse=True)
    def setup(self):
        self.categorical = _cat_frame.copy()

        yield

    @pytest.fixture
    def datetime_series(self):

            

Reported by Pylint.

                      #  since that doesn't round-trip, see GH#33711
        ser = tm.makeTimeSeries()
        ser.name = "ts"
        ser.index = ser.index._with_freq(None)
        return ser

    @pytest.fixture
    def datetime_frame(self):
        # Same as usual datetime_frame, but with index freq set to None,

            

Reported by Pylint.

                      # Same as usual datetime_frame, but with index freq set to None,
        #  since that doesn't round-trip, see GH#33711
        df = DataFrame(tm.getTimeSeriesData())
        df.index = df.index._with_freq(None)
        return df

    def test_frame_double_encoded_labels(self, orient):
        df = DataFrame(
            [["a", "b"], ["c", "d"]],

            

Reported by Pylint.

                              # orient == "values" by default will write Timestamp objects out
                # in milliseconds; these are internally stored in nanosecond,
                # so divide to get where we need
                # TODO: a to_epoch method would also solve; see GH 14772
                expected.iloc[:, 0] = expected.iloc[:, 0].view(np.int64) // 1000000
        elif orient == "split":
            expected = df

        tm.assert_frame_equal(result, expected)

            

Reported by Pylint.

                          and not dtype
            and orient != "split"
        ):
            # TODO: see what is causing roundtrip dtype loss
            expected = expected.astype(np.int32)

        assert_json_roundtrip_equal(result, expected, orient)

    @pytest.mark.parametrize("dtype", [None, np.float64, int, "U3"])

            

Reported by Pylint.

                          dtype=dtype,
        )

        # TODO: do we even need to support U3 dtypes?
        if numpy and dtype == "U3" and orient != "split":
            request.node.add_marker(
                pytest.mark.xfail(reason="Can't decode directly to array")
            )


            

Reported by Pylint.

                  @pytest.mark.parametrize("convert_axes", [True, False])
    @pytest.mark.parametrize("numpy", [True, False])
    def test_roundtrip_categorical(self, request, orient, convert_axes, numpy):
        # TODO: create a better frame to test with and improve coverage
        if orient in ("index", "columns"):
            request.node.add_marker(
                pytest.mark.xfail(
                    reason=f"Can't have duplicate index values for orient '{orient}')"
                )

            

Reported by Pylint.