import warnings

import numpy as np
import pytest

import pandas.util._test_decorators as td

import pandas as pd
from pandas import (

            

Reported by Pylint.

              
        ser = df["A"]  # populate item_cache
        if not using_array_manager:
            assert len(df._mgr.blocks) == 2

        _ = df.corr()

        # Check that the corr didn't break link between ser and df
        ser.values[0] = 99

            

Reported by Pylint.

              import warnings

import numpy as np
import pytest

import pandas.util._test_decorators as td

import pandas as pd
from pandas import (

            

Reported by Pylint.

              import pandas._testing as tm


class TestDataFrameCov:
    def test_cov(self, float_frame, float_string_frame):
        # min_periods no NAs (corner case)
        expected = float_frame.cov()
        result = float_frame.cov(min_periods=len(float_frame))


            

Reported by Pylint.

              

class TestDataFrameCov:
    def test_cov(self, float_frame, float_string_frame):
        # min_periods no NAs (corner case)
        expected = float_frame.cov()
        result = float_frame.cov(min_periods=len(float_frame))

        tm.assert_frame_equal(expected, result)

            

Reported by Pylint.

              

class TestDataFrameCov:
    def test_cov(self, float_frame, float_string_frame):
        # min_periods no NAs (corner case)
        expected = float_frame.cov()
        result = float_frame.cov(min_periods=len(float_frame))

        tm.assert_frame_equal(expected, result)

            

Reported by Pylint.

                      tm.assert_frame_equal(expected, result)

        result = float_frame.cov(min_periods=len(float_frame) + 1)
        assert isna(result.values).all()

        # with NAs
        frame = float_frame.copy()
        frame["A"][:5] = np.nan
        frame["B"][5:10] = np.nan

            

Reported by Bandit.

                      tm.assert_frame_equal(result, expected)

        # Single column frame
        df = DataFrame(np.linspace(0.0, 1.0, 10))
        result = df.cov()
        expected = DataFrame(
            np.cov(df.values.T).reshape((1, 1)), index=df.columns, columns=df.columns
        )
        tm.assert_frame_equal(result, expected)

            

Reported by Pylint.

                      tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize("test_ddof", [None, 0, 1, 2, 3])
    def test_cov_ddof(self, test_ddof):
        # GH#34611
        np_array1 = np.random.rand(10)
        np_array2 = np.random.rand(10)
        df = DataFrame({0: np_array1, 1: np_array2})
        result = df.cov(ddof=test_ddof)

            

Reported by Pylint.

                      tm.assert_frame_equal(result, expected)

    @pytest.mark.parametrize("test_ddof", [None, 0, 1, 2, 3])
    def test_cov_ddof(self, test_ddof):
        # GH#34611
        np_array1 = np.random.rand(10)
        np_array2 = np.random.rand(10)
        df = DataFrame({0: np_array1, 1: np_array2})
        result = df.cov(ddof=test_ddof)

            

Reported by Pylint.

              import operator

import numpy as np
import pytest

from pandas import (
    DataFrame,
    DatetimeIndex,
    Index,

            

Reported by Pylint.

                      assert result == s.iloc[0]

        with pytest.raises(KeyError, match=r"^'2004-12-31'$"):
            s["2004-12-31"]

    def test_partial_slice_daily(self):
        rng = date_range(freq="H", start=datetime(2005, 1, 31), periods=500)
        s = Series(np.arange(len(rng)), index=rng)


            

Reported by Pylint.

                      tm.assert_series_equal(result, s.iloc[:24])

        with pytest.raises(KeyError, match=r"^'2004-12-31 00'$"):
            s["2004-12-31 00"]

    def test_partial_slice_hourly(self):
        rng = date_range(freq="T", start=datetime(2005, 1, 1, 20, 0, 0), periods=500)
        s = Series(np.arange(len(rng)), index=rng)


            

Reported by Pylint.

              
        assert s["2005-1-1 20:00"] == s.iloc[0]
        with pytest.raises(KeyError, match=r"^'2004-12-31 00:15'$"):
            s["2004-12-31 00:15"]

    def test_partial_slice_minutely(self):
        rng = date_range(freq="S", start=datetime(2005, 1, 1, 23, 59, 0), periods=500)
        s = Series(np.arange(len(rng)), index=rng)


            

Reported by Pylint.

              
        assert s[Timestamp("2005-1-1 23:59:00")] == s.iloc[0]
        with pytest.raises(KeyError, match=r"^'2004-12-31 00:00:00'$"):
            s["2004-12-31 00:00:00"]

    def test_partial_slice_second_precision(self):
        rng = date_range(
            start=datetime(2005, 1, 1, 0, 0, 59, microsecond=999990),
            periods=20,

            

Reported by Pylint.

              
        assert s[Timestamp("2005-1-1 00:00:59.999990")] == s.iloc[0]
        with pytest.raises(KeyError, match="2005-1-1 00:00:00"):
            s["2005-1-1 00:00:00"]

    def test_partial_slicing_dataframe(self):
        # GH14856
        # Test various combinations of string slicing resolution vs.
        # index resolution

            

Reported by Pylint.

                              assert result == expected
                msg = fr"^'{ts_string}'$"
                with pytest.raises(KeyError, match=msg):
                    df[ts_string]

            # Timestamp with resolution less precise than index
            for fmt in formats[:rnum]:
                for element, theslice in [[0, slice(None, 1)], [1, slice(1, None)]]:
                    ts_string = index[element].strftime(fmt)

            

Reported by Pylint.

                              assert result == 2
                msg = fr"^'{ts_string}'$"
                with pytest.raises(KeyError, match=msg):
                    df[ts_string]

            # Not compatible with existing key
            # Should raise KeyError
            for fmt, res in list(zip(formats, resolutions))[rnum + 1 :]:
                ts = index[1] + Timedelta("1 " + res)

            

Reported by Pylint.

                              ts_string = ts.strftime(fmt)
                msg = fr"^'{ts_string}'$"
                with pytest.raises(KeyError, match=msg):
                    df["a"][ts_string]
                with pytest.raises(KeyError, match=msg):
                    df[ts_string]

    def test_partial_slicing_with_multiindex(self):


            

Reported by Pylint.

                              with pytest.raises(KeyError, match=msg):
                    df["a"][ts_string]
                with pytest.raises(KeyError, match=msg):
                    df[ts_string]

    def test_partial_slicing_with_multiindex(self):

        # GH 4758
        # partial string indexing with a multi-index buggy

            

Reported by Pylint.

              import re

import numpy as np
import pytest

from pandas.errors import InvalidIndexError

from pandas import (
    NA,

            

Reported by Pylint.

                      tm.assert_numpy_array_equal(result_indexer, expected_indexer)
        tm.assert_numpy_array_equal(result_missing, expected_missing)

        # TODO we may also want to test get_indexer for the case when
        # the intervals are duplicated, decreasing, non-monotonic, etc..

    def test_get_indexer_non_monotonic(self):
        # GH 16410
        idx1 = IntervalIndex.from_tuples([(2, 3), (4, 5), (0, 1)])

            

Reported by Pylint.

                      index = IntervalIndex([np.nan, np.nan])
        other = IntervalIndex([np.nan])

        assert not index._index_as_unique

        result = index.get_indexer_for(other)
        expected = np.array([0, 1], dtype=np.intp)
        tm.assert_numpy_array_equal(result, expected)


            

Reported by Pylint.

              import re

import numpy as np
import pytest

from pandas.errors import InvalidIndexError

from pandas import (
    NA,

            

Reported by Pylint.

              import pandas._testing as tm


class TestGetLoc:
    @pytest.mark.parametrize("side", ["right", "left", "both", "neither"])
    def test_get_loc_interval(self, closed, side):

        idx = IntervalIndex.from_tuples([(0, 1), (2, 3)], closed=closed)


            

Reported by Pylint.

              
class TestGetLoc:
    @pytest.mark.parametrize("side", ["right", "left", "both", "neither"])
    def test_get_loc_interval(self, closed, side):

        idx = IntervalIndex.from_tuples([(0, 1), (2, 3)], closed=closed)

        for bound in [[0, 1], [1, 2], [2, 3], [3, 4], [0, 2], [2.5, 3], [-1, 4]]:
            # if get_loc is supplied an interval, it should only search

            

Reported by Pylint.

              
class TestGetLoc:
    @pytest.mark.parametrize("side", ["right", "left", "both", "neither"])
    def test_get_loc_interval(self, closed, side):

        idx = IntervalIndex.from_tuples([(0, 1), (2, 3)], closed=closed)

        for bound in [[0, 1], [1, 2], [2, 3], [3, 4], [0, 2], [2.5, 3], [-1, 4]]:
            # if get_loc is supplied an interval, it should only search

            

Reported by Pylint.

                          msg = re.escape(f"Interval({bound[0]}, {bound[1]}, closed='{side}')")
            if closed == side:
                if bound == [0, 1]:
                    assert idx.get_loc(Interval(0, 1, closed=side)) == 0
                elif bound == [2, 3]:
                    assert idx.get_loc(Interval(2, 3, closed=side)) == 1
                else:
                    with pytest.raises(KeyError, match=msg):
                        idx.get_loc(Interval(*bound, closed=side))

            

Reported by Bandit.

                              if bound == [0, 1]:
                    assert idx.get_loc(Interval(0, 1, closed=side)) == 0
                elif bound == [2, 3]:
                    assert idx.get_loc(Interval(2, 3, closed=side)) == 1
                else:
                    with pytest.raises(KeyError, match=msg):
                        idx.get_loc(Interval(*bound, closed=side))
            else:
                with pytest.raises(KeyError, match=msg):

            

Reported by Bandit.

                                  idx.get_loc(Interval(*bound, closed=side))

    @pytest.mark.parametrize("scalar", [-0.5, 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5])
    def test_get_loc_scalar(self, closed, scalar):

        # correct = {side: {query: answer}}.
        # If query is not in the dict, that query should raise a KeyError
        correct = {
            "right": {0.5: 0, 1: 0, 2.5: 1, 3: 1},

            

Reported by Pylint.

              )

import numpy as np
import pytest

from pandas._libs.tslibs.offsets import (
    ApplyTypeError,
    BMonthEnd,
    CDay,

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas._libs.tslibs.offsets import (
    ApplyTypeError,
    BMonthEnd,
    CDay,
)
from pandas.compat import np_datetime64_compat

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas._libs.tslibs.offsets import (
    ApplyTypeError,
    BMonthEnd,
    CDay,
)
from pandas.compat import np_datetime64_compat

            

Reported by Pylint.

              class TestCustomBusinessDay(Base):
    _offset = CDay

    def setup_method(self, method):
        self.d = datetime(2008, 1, 1)
        self.nd = np_datetime64_compat("2008-01-01 00:00:00Z")

        self.offset = CDay()
        self.offset1 = self.offset

            

Reported by Pylint.

              
    def setup_method(self, method):
        self.d = datetime(2008, 1, 1)
        self.nd = np_datetime64_compat("2008-01-01 00:00:00Z")

        self.offset = CDay()
        self.offset1 = self.offset
        self.offset2 = CDay(2)


            

Reported by Pylint.

                      self.d = datetime(2008, 1, 1)
        self.nd = np_datetime64_compat("2008-01-01 00:00:00Z")

        self.offset = CDay()
        self.offset1 = self.offset
        self.offset2 = CDay(2)

    def test_different_normalize_equals(self):
        # GH#21404 changed __eq__ to return False when `normalize` does not match

            

Reported by Pylint.

                      self.nd = np_datetime64_compat("2008-01-01 00:00:00Z")

        self.offset = CDay()
        self.offset1 = self.offset
        self.offset2 = CDay(2)

    def test_different_normalize_equals(self):
        # GH#21404 changed __eq__ to return False when `normalize` does not match
        offset = self._offset()

            

Reported by Pylint.

              
        self.offset = CDay()
        self.offset1 = self.offset
        self.offset2 = CDay(2)

    def test_different_normalize_equals(self):
        # GH#21404 changed __eq__ to return False when `normalize` does not match
        offset = self._offset()
        offset2 = self._offset(normalize=True)

            

Reported by Pylint.

              )
from pandas.tests.tseries.offsets.test_offsets import _ApplyCases

from pandas.tseries import offsets as offsets
from pandas.tseries.holiday import USFederalHolidayCalendar


class TestCustomBusinessDay(Base):
    _offset = CDay

            

Reported by Pylint.

              from pandas.tseries.holiday import USFederalHolidayCalendar


class TestCustomBusinessDay(Base):
    _offset = CDay

    def setup_method(self, method):
        self.d = datetime(2008, 1, 1)
        self.nd = np_datetime64_compat("2008-01-01 00:00:00Z")

            

Reported by Pylint.

              
import numpy as np
from numpy import iinfo
import pytest

from pandas.compat import is_platform_arm

import pandas as pd
from pandas import (

            

Reported by Pylint.

              @pytest.mark.parametrize(
    "errors,exp_data", [("ignore", [1, -3.14, "apple"]), ("coerce", [1, -3.14, np.nan])]
)
def test_ignore_error(errors, exp_data):
    ser = Series([1, -3.14, "apple"])
    result = to_numeric(ser, errors=errors)

    expected = Series(exp_data)
    tm.assert_series_equal(result, expected)

            

Reported by Pylint.

                      ("coerce", [1.0, 0.0, np.nan]),
    ],
)
def test_bool_handling(errors, exp):
    ser = Series([True, False, "apple"])

    if isinstance(exp, str):
        with pytest.raises(ValueError, match=exp):
            to_numeric(ser, errors=errors)

            

Reported by Pylint.

                  tm.assert_series_equal(result, expected)


def test_type_check(errors):
    # see gh-11776
    df = DataFrame({"a": [1, -3.14, 7], "b": ["4", "5", "6"]})
    kwargs = {"errors": errors} if errors is not None else {}
    with pytest.raises(TypeError, match="1-d array"):
        to_numeric(df, **kwargs)

            

Reported by Pylint.

              

@pytest.mark.parametrize("val", [1, 1.1, 20001])
def test_scalar(val, signed, transform):
    val = -val if signed else val
    assert to_numeric(transform(val)) == float(val)


def test_really_large_scalar(large_val, signed, transform, errors):

            

Reported by Pylint.

              

@pytest.mark.parametrize("val", [1, 1.1, 20001])
def test_scalar(val, signed, transform):
    val = -val if signed else val
    assert to_numeric(transform(val)) == float(val)


def test_really_large_scalar(large_val, signed, transform, errors):

            

Reported by Pylint.

                  assert to_numeric(transform(val)) == float(val)


def test_really_large_scalar(large_val, signed, transform, errors):
    # see gh-24910
    kwargs = {"errors": errors} if errors is not None else {}
    val = -large_val if signed else large_val

    val = transform(val)

            

Reported by Pylint.

                  assert to_numeric(transform(val)) == float(val)


def test_really_large_scalar(large_val, signed, transform, errors):
    # see gh-24910
    kwargs = {"errors": errors} if errors is not None else {}
    val = -large_val if signed else large_val

    val = transform(val)

            

Reported by Pylint.

                  assert to_numeric(transform(val)) == float(val)


def test_really_large_scalar(large_val, signed, transform, errors):
    # see gh-24910
    kwargs = {"errors": errors} if errors is not None else {}
    val = -large_val if signed else large_val

    val = transform(val)

            

Reported by Pylint.

                  assert to_numeric(transform(val)) == float(val)


def test_really_large_scalar(large_val, signed, transform, errors):
    # see gh-24910
    kwargs = {"errors": errors} if errors is not None else {}
    val = -large_val if signed else large_val

    val = transform(val)

            

Reported by Pylint.

              import re

import numpy as np
import pytest

from pandas import (
    Categorical,
    CategoricalIndex,
    DataFrame,

            

Reported by Pylint.

              
        # internals...
        c = Categorical([1, 2, 3, 4, 1], categories=[1, 2, 3, 4], ordered=True)
        tm.assert_numpy_array_equal(c._codes, np.array([0, 1, 2, 3, 0], dtype=np.int8))
        tm.assert_index_equal(c.categories, Index([1, 2, 3, 4]))

        exp = np.array([1, 2, 3, 4, 1], dtype=np.int64)
        tm.assert_numpy_array_equal(np.asarray(c), exp)


            

Reported by Pylint.

                      c = c.set_categories([4, 3, 2, 1])

        # positions are changed
        tm.assert_numpy_array_equal(c._codes, np.array([3, 2, 1, 0, 3], dtype=np.int8))

        # categories are now in new order
        tm.assert_index_equal(c.categories, Index([4, 3, 2, 1]))

        # output is the same

            

Reported by Pylint.

              
    def test_set_categories_private(self):
        cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"])
        cat._set_categories(["a", "c", "d", "e"])
        expected = Categorical(["a", "c", "d"], categories=list("acde"))
        tm.assert_categorical_equal(cat, expected)

        # fastpath
        cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"])

            

Reported by Pylint.

              
        # fastpath
        cat = Categorical(["a", "b", "c"], categories=["a", "b", "c", "d"])
        cat._set_categories(["a", "c", "d", "e"], fastpath=True)
        expected = Categorical(["a", "c", "d"], categories=list("acde"))
        tm.assert_categorical_equal(cat, expected)

    def test_remove_categories(self):
        cat = Categorical(["a", "b", "c", "a"], ordered=True)

            

Reported by Pylint.

              
    def test_codes_setter_deprecated(self):
        cat = Categorical([1, 2, 3, 1, 2, 3, 3, 2, 1, 1, 1])
        new_codes = cat._codes + 1
        with tm.assert_produces_warning(FutureWarning):
            # GH#40606
            cat._codes = new_codes

        assert cat._codes is new_codes

            

Reported by Pylint.

                      new_codes = cat._codes + 1
        with tm.assert_produces_warning(FutureWarning):
            # GH#40606
            cat._codes = new_codes

        assert cat._codes is new_codes


class TestPrivateCategoricalAPI:

            

Reported by Pylint.

                          # GH#40606
            cat._codes = new_codes

        assert cat._codes is new_codes


class TestPrivateCategoricalAPI:
    def test_codes_immutable(self):


            

Reported by Pylint.

                      c[4] = "a"
        exp = np.array([0, 1, 2, 0, 0], dtype="int8")
        tm.assert_numpy_array_equal(c.codes, exp)
        c._codes[4] = 2
        exp = np.array([0, 1, 2, 0, 2], dtype="int8")
        tm.assert_numpy_array_equal(c.codes, exp)

    @pytest.mark.parametrize(
        "codes, old, new, expected",

            

Reported by Pylint.

              import re

import numpy as np
import pytest

from pandas import (
    Categorical,
    CategoricalIndex,
    DataFrame,

            

Reported by Pylint.

              import numpy as np
import pytest

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

            

Reported by Pylint.

                      level_name = frame.index.names[level]

        if axis == 0:
            frame = frame
        else:
            frame = frame.T

        grouped = frame.groupby(level=level, axis=axis, sort=sort)


            

Reported by Pylint.

                          frame = frame.sort_index(level=level, axis=axis)

        # for good measure, groupby detail
        level_index = frame._get_axis(axis).levels[level].rename(level_name)

        tm.assert_index_equal(leftside._get_axis(axis), level_index)
        tm.assert_index_equal(rightside._get_axis(axis), level_index)

        tm.assert_frame_equal(leftside, rightside)

            

Reported by Pylint.

                      # for good measure, groupby detail
        level_index = frame._get_axis(axis).levels[level].rename(level_name)

        tm.assert_index_equal(leftside._get_axis(axis), level_index)
        tm.assert_index_equal(rightside._get_axis(axis), level_index)

        tm.assert_frame_equal(leftside, rightside)

    def test_std_var_pass_ddof(self):

            

Reported by Pylint.

                      level_index = frame._get_axis(axis).levels[level].rename(level_name)

        tm.assert_index_equal(leftside._get_axis(axis), level_index)
        tm.assert_index_equal(rightside._get_axis(axis), level_index)

        tm.assert_frame_equal(leftside, rightside)

    def test_std_var_pass_ddof(self):
        index = MultiIndex.from_arrays(

            

Reported by Pylint.

              
        for meth in ["var", "std"]:
            ddof = 4
            alt = lambda x: getattr(x, meth)(ddof=ddof)

            with tm.assert_produces_warning(FutureWarning):
                result = getattr(df[0], meth)(level=0, ddof=ddof)
            expected = df[0].groupby(level=0).agg(alt)
            tm.assert_series_equal(result, expected)

            

Reported by Pylint.

              
        for meth in ["var", "std"]:
            ddof = 4
            alt = lambda x: getattr(x, meth)(ddof=ddof)

            with tm.assert_produces_warning(FutureWarning):
                result = getattr(df[0], meth)(level=0, ddof=ddof)
            expected = df[0].groupby(level=0).agg(alt)
            tm.assert_series_equal(result, expected)

            

Reported by Pylint.

              
        expected = ymd.groupby([k1, k2]).mean()

        # TODO groupby with level_values drops names
        tm.assert_frame_equal(result, expected, check_names=False)
        assert result.index.names == ymd.index.names[:2]

        result2 = ymd.groupby(level=ymd.index.names[:2]).mean()
        tm.assert_frame_equal(result, result2)

            

Reported by Pylint.

                      )
        df = DataFrame(np.random.randn(4, 4), index=index, columns=index)
        df["Totals", ""] = df.sum(1)
        df = df._consolidate()

    def test_level_with_tuples(self):
        index = MultiIndex(
            levels=[[("foo", "bar", 0), ("foo", "baz", 0), ("foo", "qux", 0)], [0, 1]],
            codes=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],

            

Reported by Pylint.

                      tm.assert_series_equal(result2, expected)

        with pytest.raises(KeyError, match=r"^\(\('foo', 'bar', 0\), 2\)$"):
            series[("foo", "bar", 0), 2]

        result = frame.loc[("foo", "bar", 0)]
        result2 = frame.xs(("foo", "bar", 0))
        expected = frame[:2]
        expected.index = expected.index.droplevel(0)

            

Reported by Pylint.

              from pandas.io.formats.style import Styler

loader = jinja2.PackageLoader("pandas", "io/formats/templates")
env = jinja2.Environment(loader=loader, trim_blocks=True)


@pytest.fixture
def styler():
    return Styler(DataFrame([[2.61], [2.69]], index=["a", "b"], columns=["A"]))

            

Reported by Bandit.

              from textwrap import dedent

import numpy as np
import pytest

from pandas import (
    DataFrame,
    MultiIndex,
    option_context,

            

Reported by Pylint.

                  assert "{% include html_table_tpl %}" in result


def test_exclude_styles(styler):
    result = styler.to_html(exclude_styles=True, doctype_html=True)
    expected = dedent(
        """\
        <!DOCTYPE html>
        <html>

            

Reported by Pylint.

                  assert result == expected


def test_w3_html_format(styler):
    styler.set_uuid("").set_table_styles(
        [{"selector": "th", "props": "att2:v2;"}]
    ).applymap(lambda x: "att1:v1;").set_table_attributes(
        'class="my-cls1" style="attr3:v3;"'
    ).set_td_classes(

            

Reported by Pylint.

              def test_colspan_w3():
    # GH 36223
    df = DataFrame(data=[[1, 2]], columns=[["l0", "l0"], ["l1a", "l1b"]])
    styler = Styler(df, uuid="_", cell_ids=False)
    assert '<th class="col_heading level0 col0" colspan="2">l0</th>' in styler.to_html()


def test_rowspan_w3():
    # GH 38533

            

Reported by Pylint.

              def test_rowspan_w3():
    # GH 38533
    df = DataFrame(data=[[1, 2]], index=[["l0", "l0"], ["l1a", "l1b"]])
    styler = Styler(df, uuid="_", cell_ids=False)
    assert '<th class="row_heading level0 row0" rowspan="2">l0</th>' in styler.to_html()


def test_styles(styler):
    styler.set_uuid("abc")

            

Reported by Pylint.

                  assert '<th class="row_heading level0 row0" rowspan="2">l0</th>' in styler.to_html()


def test_styles(styler):
    styler.set_uuid("abc")
    styler.set_table_styles([{"selector": "td", "props": "color: red;"}])
    result = styler.to_html(doctype_html=True)
    expected = dedent(
        """\

            

Reported by Pylint.

                  assert result == expected


def test_doctype(styler):
    result = styler.to_html(doctype_html=False)
    assert "<html>" not in result
    assert "<body>" not in result
    assert "<!DOCTYPE html>" not in result
    assert "<head>" not in result

            

Reported by Pylint.

                  assert "<head>" not in result


def test_block_names(tpl_style, tpl_table):
    # catch accidental removal of a block
    expected_style = {
        "before_style",
        "style",
        "table_styles",

            

Reported by Pylint.

                  assert "<head>" not in result


def test_block_names(tpl_style, tpl_table):
    # catch accidental removal of a block
    expected_style = {
        "before_style",
        "style",
        "table_styles",

            

Reported by Pylint.

              import itertools

import numpy as np
import pytest

from pandas.core.dtypes.common import (
    is_datetime64tz_dtype,
    is_interval_dtype,
    is_period_dtype,

            

Reported by Pylint.

                          dtype = result.dtype

        assert dtype == data.dtype
        if hasattr(result._mgr, "blocks"):
            assert isinstance(result._mgr.blocks[0], ExtensionBlock)
        assert isinstance(result._mgr.arrays[0], ExtensionArray)

    @pytest.mark.parametrize("in_frame", [True, False])
    def test_concat_all_na_block(self, data_missing, in_frame):

            

Reported by Pylint.

              
        assert dtype == data.dtype
        if hasattr(result._mgr, "blocks"):
            assert isinstance(result._mgr.blocks[0], ExtensionBlock)
        assert isinstance(result._mgr.arrays[0], ExtensionArray)

    @pytest.mark.parametrize("in_frame", [True, False])
    def test_concat_all_na_block(self, data_missing, in_frame):
        valid_block = pd.Series(data_missing.take([1, 1]), index=[0, 1])

            

Reported by Pylint.

                      assert dtype == data.dtype
        if hasattr(result._mgr, "blocks"):
            assert isinstance(result._mgr.blocks[0], ExtensionBlock)
        assert isinstance(result._mgr.arrays[0], ExtensionArray)

    @pytest.mark.parametrize("in_frame", [True, False])
    def test_concat_all_na_block(self, data_missing, in_frame):
        valid_block = pd.Series(data_missing.take([1, 1]), index=[0, 1])
        na_block = pd.Series(data_missing.take([0, 0]), index=[2, 3])

            

Reported by Pylint.

                      df2 = pd.DataFrame({"B": [1, 2, 3]}, index=[1, 2, 3])
        expected = pd.DataFrame(
            {
                "A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype),
                "B": [np.nan, 1, 2, 3],
            }
        )

        result = pd.concat([df1, df2], axis=1)

            

Reported by Pylint.

                      df2 = pd.DataFrame({"B": data[3:7]})
        expected = pd.DataFrame(
            {
                "A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype),
                "B": data[3:7],
            }
        )
        result = pd.concat([df1, df2], axis=1, copy=False)
        self.assert_frame_equal(result, expected)

            

Reported by Pylint.

                      r1, r2 = pd.Series(a).align(pd.Series(b, index=[1, 2, 3]))

        # Assumes that the ctor can take a list of scalars of the type
        e1 = pd.Series(data._from_sequence(list(a) + [na_value], dtype=data.dtype))
        e2 = pd.Series(data._from_sequence([na_value] + list(b), dtype=data.dtype))
        self.assert_series_equal(r1, e1)
        self.assert_series_equal(r2, e2)

    def test_align_frame(self, data, na_value):

            

Reported by Pylint.

              
        # Assumes that the ctor can take a list of scalars of the type
        e1 = pd.Series(data._from_sequence(list(a) + [na_value], dtype=data.dtype))
        e2 = pd.Series(data._from_sequence([na_value] + list(b), dtype=data.dtype))
        self.assert_series_equal(r1, e1)
        self.assert_series_equal(r2, e2)

    def test_align_frame(self, data, na_value):
        a = data[:3]

            

Reported by Pylint.

              
        # Assumes that the ctor can take a list of scalars of the type
        e1 = pd.DataFrame(
            {"A": data._from_sequence(list(a) + [na_value], dtype=data.dtype)}
        )
        e2 = pd.DataFrame(
            {"A": data._from_sequence([na_value] + list(b), dtype=data.dtype)}
        )
        self.assert_frame_equal(r1, e1)

            

Reported by Pylint.

                          {"A": data._from_sequence(list(a) + [na_value], dtype=data.dtype)}
        )
        e2 = pd.DataFrame(
            {"A": data._from_sequence([na_value] + list(b), dtype=data.dtype)}
        )
        self.assert_frame_equal(r1, e1)
        self.assert_frame_equal(r2, e2)

    def test_align_series_frame(self, data, na_value):

            

Reported by Pylint.

              
from pandas._config import get_option

from pandas._libs import (
    NaT,
    algos as libalgos,
    hashtable as htable,
    lib,
)

            

Reported by Pylint.

              
from pandas._config import get_option

from pandas._libs import (
    NaT,
    algos as libalgos,
    hashtable as htable,
    lib,
)

            

Reported by Pylint.

              
from pandas._config import get_option

from pandas._libs import (
    NaT,
    algos as libalgos,
    hashtable as htable,
    lib,
)

            

Reported by Pylint.

                  hashtable as htable,
    lib,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.lib import no_default
from pandas._typing import (
    ArrayLike,
    Dtype,
    NpDtype,

            

Reported by Pylint.

                  hashtable as htable,
    lib,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.lib import no_default
from pandas._typing import (
    ArrayLike,
    Dtype,
    NpDtype,

            

Reported by Pylint.

                  lib,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.lib import no_default
from pandas._typing import (
    ArrayLike,
    Dtype,
    NpDtype,
    Ordered,

            

Reported by Pylint.

                  lib,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.lib import no_default
from pandas._typing import (
    ArrayLike,
    Dtype,
    NpDtype,
    Ordered,

            

Reported by Pylint.

                      codes = self.categories.get_indexer(value)
        return codes.astype(self._ndarray.dtype, copy=False)

    def _reverse_indexer(self) -> dict[Hashable, npt.NDArray[np.intp]]:
        """
        Compute the inverse of a categorical, returning
        a dict of categories -> indexers.

        *This is an internal function*

            

Reported by Pylint.

                          # the same (maybe up to ordering, depending on ordered)

            msg = "Categoricals can only be compared if 'categories' are the same."
            if not self._categories_match_up_to_permutation(other):
                raise TypeError(msg)

            if not self.ordered and not self.categories.equals(other.categories):
                # both unordered and different order
                other_codes = recode_for_categories(

            

Reported by Pylint.

                                  other.codes, other.categories, self.categories, copy=False
                )
            else:
                other_codes = other._codes

            ret = op(self._codes, other_codes)
            mask = (self._codes == -1) | (other_codes == -1)
            if mask.any():
                ret[mask] = fill_value

            

Reported by Pylint.