tzlocal,
)
import numpy as np
import pytz

try:
    from pandas._libs.tslibs import ints_to_pydatetime
except ImportError:
    from pandas._libs.tslib import ints_to_pydatetime

            

Reported by Pylint.

                      _tzs,
    )
    param_names = ["box", "size", "tz"]
    # TODO: fold? freq?

    def setup(self, box, size, tz):
        if box == "date" and tz is not None:
            # tz is ignored, so avoid running redundant benchmarks
            raise NotImplementedError  # skip benchmark

            

Reported by Pylint.

                          raise NotImplementedError  # skip benchmark

        arr = np.random.randint(0, 10, size=size, dtype="i8")
        self.i8data = arr

    def time_ints_to_pydatetime(self, box, size, tz):
        ints_to_pydatetime(self.i8data, tz, box=box)

            

Reported by Pylint.

                      arr = np.random.randint(0, 10, size=size, dtype="i8")
        self.i8data = arr

    def time_ints_to_pydatetime(self, box, size, tz):
        ints_to_pydatetime(self.i8data, tz, box=box)

            

Reported by Pylint.

              _sizes = [0, 1, 100, 10 ** 4, 10 ** 6]


class TimeIntsToPydatetime:
    params = (
        ["time", "date", "datetime", "timestamp"],
        _sizes,
        _tzs,
    )

            

Reported by Pylint.

                  param_names = ["box", "size", "tz"]
    # TODO: fold? freq?

    def setup(self, box, size, tz):
        if box == "date" and tz is not None:
            # tz is ignored, so avoid running redundant benchmarks
            raise NotImplementedError  # skip benchmark
        if size == 10 ** 6 and tz is _tzs[-1]:
            # This is cumbersomely-slow, so skip to trim runtime

            

Reported by Pylint.

                  param_names = ["box", "size", "tz"]
    # TODO: fold? freq?

    def setup(self, box, size, tz):
        if box == "date" and tz is not None:
            # tz is ignored, so avoid running redundant benchmarks
            raise NotImplementedError  # skip benchmark
        if size == 10 ** 6 and tz is _tzs[-1]:
            # This is cumbersomely-slow, so skip to trim runtime

            

Reported by Pylint.

                      arr = np.random.randint(0, 10, size=size, dtype="i8")
        self.i8data = arr

    def time_ints_to_pydatetime(self, box, size, tz):
        ints_to_pydatetime(self.i8data, tz, box=box)

            

Reported by Pylint.

                      arr = np.random.randint(0, 10, size=size, dtype="i8")
        self.i8data = arr

    def time_ints_to_pydatetime(self, box, size, tz):
        ints_to_pydatetime(self.i8data, tz, box=box)

            

Reported by Pylint.

              except ImportError:
    from pandas._libs.tslibs.resolution import get_resolution

from .tslib import (
    _sizes,
    _tzs,
    tzlocal_obj,
)


            

Reported by Pylint.

              
        arr = np.random.randint(0, 10, size=size, dtype="i8")
        arr = arr.view(f"M8[{unit}]").astype("M8[ns]").view("i8")
        self.i8data = arr

    def time_get_resolution(self, unit, size, tz):
        get_resolution(self.i8data, tz)

            

Reported by Pylint.

                      arr = arr.view(f"M8[{unit}]").astype("M8[ns]").view("i8")
        self.i8data = arr

    def time_get_resolution(self, unit, size, tz):
        get_resolution(self.i8data, tz)

            

Reported by Pylint.

                      arr = arr.view(f"M8[{unit}]").astype("M8[ns]").view("i8")
        self.i8data = arr

    def time_get_resolution(self, unit, size, tz):
        get_resolution(self.i8data, tz)

            

Reported by Pylint.

              )


class TimeResolution:
    params = (
        ["D", "h", "m", "s", "us", "ns"],
        _sizes,
        _tzs,
    )

            

Reported by Pylint.

                  )
    param_names = ["unit", "size", "tz"]

    def setup(self, unit, size, tz):
        if size == 10 ** 6 and tz is tzlocal_obj:
            # tzlocal is cumbersomely slow, so skip to keep runtime in check
            raise NotImplementedError

        arr = np.random.randint(0, 10, size=size, dtype="i8")

            

Reported by Pylint.

                  )
    param_names = ["unit", "size", "tz"]

    def setup(self, unit, size, tz):
        if size == 10 ** 6 and tz is tzlocal_obj:
            # tzlocal is cumbersomely slow, so skip to keep runtime in check
            raise NotImplementedError

        arr = np.random.randint(0, 10, size=size, dtype="i8")

            

Reported by Pylint.

                      arr = arr.view(f"M8[{unit}]").astype("M8[ns]").view("i8")
        self.i8data = arr

    def time_get_resolution(self, unit, size, tz):
        get_resolution(self.i8data, tz)

            

Reported by Pylint.

                      arr = arr.view(f"M8[{unit}]").astype("M8[ns]").view("i8")
        self.i8data = arr

    def time_get_resolution(self, unit, size, tz):
        get_resolution(self.i8data, tz)

            

Reported by Pylint.

              import os

from pandas import read_sas


class SAS:

    params = ["sas7bdat", "xport"]
    param_names = ["format"]

            

Reported by Pylint.

                  params = ["sas7bdat", "xport"]
    param_names = ["format"]

    def setup(self, format):
        # Read files that are located in 'pandas/tests/io/sas/data'
        files = {"sas7bdat": "test1.sas7bdat", "xport": "paxraw_d_short.xpt"}
        file = files[format]
        paths = [
            os.path.dirname(__file__),

            

Reported by Pylint.

                          "data",
            file,
        ]
        self.f = os.path.join(*paths)

    def time_read_sas(self, format):
        read_sas(self.f, format=format)

            

Reported by Pylint.

                      ]
        self.f = os.path.join(*paths)

    def time_read_sas(self, format):
        read_sas(self.f, format=format)

            

Reported by Pylint.

              import os

from pandas import read_sas


class SAS:

    params = ["sas7bdat", "xport"]
    param_names = ["format"]

            

Reported by Pylint.

              from pandas import read_sas


class SAS:

    params = ["sas7bdat", "xport"]
    param_names = ["format"]

    def setup(self, format):

            

Reported by Pylint.

                  params = ["sas7bdat", "xport"]
    param_names = ["format"]

    def setup(self, format):
        # Read files that are located in 'pandas/tests/io/sas/data'
        files = {"sas7bdat": "test1.sas7bdat", "xport": "paxraw_d_short.xpt"}
        file = files[format]
        paths = [
            os.path.dirname(__file__),

            

Reported by Pylint.

                          "data",
            file,
        ]
        self.f = os.path.join(*paths)

    def time_read_sas(self, format):
        read_sas(self.f, format=format)

            

Reported by Pylint.

                      ]
        self.f = os.path.join(*paths)

    def time_read_sas(self, format):
        read_sas(self.f, format=format)

            

Reported by Pylint.

              import os
import tempfile

import pytest

from pandas.io.pytables import HDFStore

tables = pytest.importorskip("tables")
# set these parameters so we don't have file sharing

            

Reported by Pylint.

              
# contextmanager to ensure the file cleanup
@contextmanager
def ensure_clean_store(path, mode="a", complevel=None, complib=None, fletcher32=False):

    try:

        # put in the temporary path if we don't have one already
        if not len(os.path.dirname(path)):

            

Reported by Pylint.

              from contextlib import contextmanager
import os
import tempfile

import pytest

from pandas.io.pytables import HDFStore

tables = pytest.importorskip("tables")

            

Reported by Pylint.

              tables.parameters.MAX_THREADS = 1


def safe_remove(path):
    if path is not None:
        try:
            os.remove(path)  # noqa: PDF008
        except OSError:
            pass

            

Reported by Pylint.

                          pass


def safe_close(store):
    try:
        if store is not None:
            store.close()
    except OSError:
        pass

            

Reported by Pylint.

              
# contextmanager to ensure the file cleanup
@contextmanager
def ensure_clean_store(path, mode="a", complevel=None, complib=None, fletcher32=False):

    try:

        # put in the temporary path if we don't have one already
        if not len(os.path.dirname(path)):

            

Reported by Pylint.

                  try:

        # put in the temporary path if we don't have one already
        if not len(os.path.dirname(path)):
            path = create_tempfile(path)

        store = HDFStore(
            path, mode=mode, complevel=complevel, complib=complib, fletcher32=False
        )

            

Reported by Pylint.

                      yield store
    finally:
        safe_close(store)
        if mode == "w" or mode == "a":
            safe_remove(path)


@contextmanager
def ensure_clean_path(path):

            

Reported by Pylint.

                          filenames = [create_tempfile(path)]
            yield filenames[0]
    finally:
        for f in filenames:
            safe_remove(f)


def _maybe_remove(store, key):
    """

            

Reported by Pylint.

              from io import StringIO

import numpy as np
import pytest

from pandas import DataFrame
import pandas._testing as tm



            

Reported by Pylint.

              

@pytest.mark.parametrize("na_values", [None, ["NaN"]])
def test_comment(all_parsers, na_values):
    parser = all_parsers
    data = """A,B,C
1,2.,4.#hello world
5.,NaN,10.0
"""

            

Reported by Pylint.

              
@pytest.mark.parametrize(
    "read_kwargs", [{}, {"lineterminator": "*"}, {"delim_whitespace": True}]
)
def test_line_comment(all_parsers, read_kwargs):
    parser = all_parsers
    data = """# empty
A,B,C
1,2.,4.#hello world

            

Reported by Pylint.

                  tm.assert_frame_equal(result, expected)


def test_comment_skiprows(all_parsers):
    parser = all_parsers
    data = """# empty
random line
# second empty line
1,2,3

            

Reported by Pylint.

                  tm.assert_frame_equal(result, expected)


def test_comment_header(all_parsers):
    parser = all_parsers
    data = """# empty
# second empty line
1,2,3
A,B,C

            

Reported by Pylint.

                  tm.assert_frame_equal(result, expected)


def test_comment_skiprows_header(all_parsers):
    parser = all_parsers
    data = """# empty
# second empty line
# third empty line
X,Y,Z

            

Reported by Pylint.

              

@pytest.mark.parametrize("comment_char", ["#", "~", "&", "^", "*", "@"])
def test_custom_comment_char(all_parsers, comment_char):
    parser = all_parsers
    data = "a,b,c\n1,2,3#ignore this!\n4,5,6#ignorethistoo"
    result = parser.read_csv(
        StringIO(data.replace("#", comment_char)), comment=comment_char
    )

            

Reported by Pylint.

              

@pytest.mark.parametrize("header", ["infer", None])
def test_comment_first_line(all_parsers, header):
    # see gh-4623
    parser = all_parsers
    data = "# notes\na,b,c\n# more notes\n1,2,3"

    if header is None:

            

Reported by Pylint.

                  tm.assert_frame_equal(result, expected)


def test_comment_char_in_default_value(all_parsers, request):
    # GH#34002
    if all_parsers.engine == "c":
        reason = "see gh-34002: works on the python engine but not the c engine"
        # NA value containing comment char is interpreted as comment
        request.node.add_marker(pytest.mark.xfail(reason=reason, raises=AssertionError))

            

Reported by Pylint.

              import pytest

from pandas.util._validators import (
    validate_bool_kwarg,
    validate_kwargs,
)

_fname = "func"


            

Reported by Pylint.

              import pytest

from pandas.util._validators import (
    validate_bool_kwarg,
    validate_kwargs,
)

_fname = "func"


            

Reported by Pylint.

                  validate_kwargs,
)

_fname = "func"


def test_bad_kwarg():
    good_arg = "f"
    bad_arg = good_arg + "o"

            

Reported by Pylint.

              _fname = "func"


def test_bad_kwarg():
    good_arg = "f"
    bad_arg = good_arg + "o"

    compat_args = {good_arg: "foo", bad_arg + "o": "bar"}
    kwargs = {good_arg: "foo", bad_arg: "bar"}

            

Reported by Pylint.

              

@pytest.mark.parametrize("i", range(1, 3))
def test_not_all_none(i):
    bad_arg = "foo"
    msg = (
        fr"the '{bad_arg}' parameter is not supported "
        fr"in the pandas implementation of {_fname}\(\)"
    )

            

Reported by Pylint.

                      validate_kwargs(_fname, kwargs, compat_args)


def test_validation():
    # No exceptions should be raised.
    compat_args = {"f": None, "b": 1, "ba": "s"}

    kwargs = {"f": None, "b": 1}
    validate_kwargs(_fname, kwargs, compat_args)

            

Reported by Pylint.

              
@pytest.mark.parametrize("name", ["inplace", "copy"])
@pytest.mark.parametrize("value", [1, "True", [1, 2, 3], 5.0])
def test_validate_bool_kwarg_fail(name, value):
    msg = (
        f'For argument "{name}" expected type bool, '
        f"received type {type(value).__name__}"
    )


            

Reported by Pylint.

              
@pytest.mark.parametrize("name", ["inplace", "copy"])
@pytest.mark.parametrize("value", [True, False, None])
def test_validate_bool_kwarg(name, value):
    assert validate_bool_kwarg(value, name) == value

            

Reported by Pylint.

              @pytest.mark.parametrize("name", ["inplace", "copy"])
@pytest.mark.parametrize("value", [True, False, None])
def test_validate_bool_kwarg(name, value):
    assert validate_bool_kwarg(value, name) == value

            

Reported by Bandit.

              from pandas._libs.tslibs.offsets import (  # noqa:F401
    FY5253,
    BaseOffset,
    BDay,
    BMonthBegin,
    BMonthEnd,
    BQuarterBegin,
    BQuarterEnd,
    BusinessDay,

            

Reported by Pylint.

              from pandas._libs.tslibs.offsets import (  # noqa:F401
    FY5253,
    BaseOffset,
    BDay,
    BMonthBegin,
    BMonthEnd,
    BQuarterBegin,
    BQuarterEnd,
    BusinessDay,

            

Reported by Pylint.

              from pandas._libs.tslibs.offsets import (  # noqa:F401
    FY5253,
    BaseOffset,
    BDay,
    BMonthBegin,
    BMonthEnd,
    BQuarterBegin,
    BQuarterEnd,
    BusinessDay,

            

Reported by Pylint.

              from pandas._libs.tslibs.offsets import (  # noqa:F401
    FY5253,
    BaseOffset,
    BDay,
    BMonthBegin,
    BMonthEnd,
    BQuarterBegin,
    BQuarterEnd,
    BusinessDay,

            

Reported by Pylint.

              from pandas._libs.tslibs.offsets import (  # noqa:F401
    FY5253,
    BaseOffset,
    BDay,
    BMonthBegin,
    BMonthEnd,
    BQuarterBegin,
    BQuarterEnd,
    BusinessDay,

            

Reported by Pylint.

              from pandas._libs.tslibs.offsets import (  # noqa:F401
    FY5253,
    BaseOffset,
    BDay,
    BMonthBegin,
    BMonthEnd,
    BQuarterBegin,
    BQuarterEnd,
    BusinessDay,

            

Reported by Pylint.

              from pandas._libs.tslibs.offsets import (  # noqa:F401
    FY5253,
    BaseOffset,
    BDay,
    BMonthBegin,
    BMonthEnd,
    BQuarterBegin,
    BQuarterEnd,
    BusinessDay,

            

Reported by Pylint.

              from pandas._libs.tslibs.offsets import (  # noqa:F401
    FY5253,
    BaseOffset,
    BDay,
    BMonthBegin,
    BMonthEnd,
    BQuarterBegin,
    BQuarterEnd,
    BusinessDay,

            

Reported by Pylint.

              from pandas._libs.tslibs.offsets import (  # noqa:F401
    FY5253,
    BaseOffset,
    BDay,
    BMonthBegin,
    BMonthEnd,
    BQuarterBegin,
    BQuarterEnd,
    BusinessDay,

            

Reported by Pylint.

              import operator

import numpy as np
import pytest

from pandas import (
    DataFrame,
    Series,
)

            

Reported by Pylint.

              import operator

import numpy as np
import pytest

from pandas import (
    DataFrame,
    Series,
)

            

Reported by Pylint.

              import pandas._testing as tm


class TestMatmul:
    def test_matmul(self):
        # matmul test is for GH#10259
        a = Series(np.random.randn(4), index=["p", "q", "r", "s"])
        b = DataFrame(
            np.random.randn(3, 4), index=["1", "2", "3"], columns=["p", "q", "r", "s"]

            

Reported by Pylint.

              import pandas._testing as tm


class TestMatmul:
    def test_matmul(self):
        # matmul test is for GH#10259
        a = Series(np.random.randn(4), index=["p", "q", "r", "s"])
        b = DataFrame(
            np.random.randn(3, 4), index=["1", "2", "3"], columns=["p", "q", "r", "s"]

            

Reported by Pylint.

              

class TestMatmul:
    def test_matmul(self):
        # matmul test is for GH#10259
        a = Series(np.random.randn(4), index=["p", "q", "r", "s"])
        b = DataFrame(
            np.random.randn(3, 4), index=["1", "2", "3"], columns=["p", "q", "r", "s"]
        ).T

            

Reported by Pylint.

              

class TestMatmul:
    def test_matmul(self):
        # matmul test is for GH#10259
        a = Series(np.random.randn(4), index=["p", "q", "r", "s"])
        b = DataFrame(
            np.random.randn(3, 4), index=["1", "2", "3"], columns=["p", "q", "r", "s"]
        ).T

            

Reported by Pylint.

              class TestMatmul:
    def test_matmul(self):
        # matmul test is for GH#10259
        a = Series(np.random.randn(4), index=["p", "q", "r", "s"])
        b = DataFrame(
            np.random.randn(3, 4), index=["1", "2", "3"], columns=["p", "q", "r", "s"]
        ).T

        # Series @ DataFrame -> Series

            

Reported by Pylint.

                  def test_matmul(self):
        # matmul test is for GH#10259
        a = Series(np.random.randn(4), index=["p", "q", "r", "s"])
        b = DataFrame(
            np.random.randn(3, 4), index=["1", "2", "3"], columns=["p", "q", "r", "s"]
        ).T

        # Series @ DataFrame -> Series
        result = operator.matmul(a, b)

            

Reported by Pylint.

                      tm.assert_series_equal(result, expected)

        # different dtypes DataFrame @ Series
        a = a.astype(int)
        result = operator.matmul(b.T, a)
        expected = Series(np.dot(b.T.values, a.T.values), index=["1", "2", "3"])
        tm.assert_series_equal(result, expected)

        msg = r"Dot product shape mismatch, \(4,\) vs \(3,\)"

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas import (
    Index,
    Series,
    array,
    date_range,
)

            

Reported by Pylint.

              import numpy as np
import pytest

from pandas import (
    Index,
    Series,
    array,
    date_range,
)

            

Reported by Pylint.

              import pandas._testing as tm


class TestView:
    def test_view_tz(self):
        # GH#24024
        ser = Series(date_range("2000", periods=4, tz="US/Central"))
        result = ser.view("i8")
        expected = Series(

            

Reported by Pylint.

              

class TestView:
    def test_view_tz(self):
        # GH#24024
        ser = Series(date_range("2000", periods=4, tz="US/Central"))
        result = ser.view("i8")
        expected = Series(
            [

            

Reported by Pylint.

              

class TestView:
    def test_view_tz(self):
        # GH#24024
        ser = Series(date_range("2000", periods=4, tz="US/Central"))
        result = ser.view("i8")
        expected = Series(
            [

            

Reported by Pylint.

                  )
    @pytest.mark.parametrize(
        "second", ["m8[ns]", "M8[ns]", "M8[ns, US/Central]", "period[D]"]
    )
    @pytest.mark.parametrize("box", [Series, Index, array])
    def test_view_between_datetimelike(self, first, second, box):

        dti = date_range("2016-01-01", periods=3)


            

Reported by Pylint.

                  )
    @pytest.mark.parametrize(
        "second", ["m8[ns]", "M8[ns]", "M8[ns, US/Central]", "period[D]"]
    )
    @pytest.mark.parametrize("box", [Series, Index, array])
    def test_view_between_datetimelike(self, first, second, box):

        dti = date_range("2016-01-01", periods=3)


            

Reported by Pylint.

              
        orig = box(dti)
        obj = orig.view(first)
        assert obj.dtype == first
        tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8)

        res = obj.view(second)
        assert res.dtype == second
        tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8)

            

Reported by Bandit.

                      tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8)

        res = obj.view(second)
        assert res.dtype == second
        tm.assert_numpy_array_equal(np.asarray(obj.view("i8")), dti.asi8)

            

Reported by Bandit.

              
        # get the numeric data
        obj = Series([1, 2, 3])
        result = obj._get_numeric_data()
        tm.assert_series_equal(result, obj)

        obj = Series([1, "2", 3.0])
        result = obj._get_numeric_data()
        expected = Series([], dtype=object, index=Index([], dtype=object))

            

Reported by Pylint.

                      tm.assert_series_equal(result, obj)

        obj = Series([1, "2", 3.0])
        result = obj._get_numeric_data()
        expected = Series([], dtype=object, index=Index([], dtype=object))
        tm.assert_series_equal(result, expected)

        obj = Series([True, False, True])
        result = obj._get_numeric_data()

            

Reported by Pylint.

                      tm.assert_series_equal(result, expected)

        obj = Series([True, False, True])
        result = obj._get_numeric_data()
        tm.assert_series_equal(result, obj)

        obj = Series(date_range("20130101", periods=3))
        result = obj._get_numeric_data()
        expected = Series([], dtype="M8[ns]", index=Index([], dtype=object))

            

Reported by Pylint.

                      tm.assert_series_equal(result, obj)

        obj = Series(date_range("20130101", periods=3))
        result = obj._get_numeric_data()
        expected = Series([], dtype="M8[ns]", index=Index([], dtype=object))
        tm.assert_series_equal(result, expected)

            

Reported by Pylint.

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


class TestGetNumericData:

            

Reported by Pylint.

              import pandas._testing as tm


class TestGetNumericData:
    def test_get_numeric_data_preserve_dtype(self):

        # get the numeric data
        obj = Series([1, 2, 3])
        result = obj._get_numeric_data()

            

Reported by Pylint.

              import pandas._testing as tm


class TestGetNumericData:
    def test_get_numeric_data_preserve_dtype(self):

        # get the numeric data
        obj = Series([1, 2, 3])
        result = obj._get_numeric_data()

            

Reported by Pylint.

              

class TestGetNumericData:
    def test_get_numeric_data_preserve_dtype(self):

        # get the numeric data
        obj = Series([1, 2, 3])
        result = obj._get_numeric_data()
        tm.assert_series_equal(result, obj)

            

Reported by Pylint.

              

class TestGetNumericData:
    def test_get_numeric_data_preserve_dtype(self):

        # get the numeric data
        obj = Series([1, 2, 3])
        result = obj._get_numeric_data()
        tm.assert_series_equal(result, obj)

            

Reported by Pylint.