from pandas import (
    Index,
    NaT,
    Period,
    PeriodIndex,
)
import pandas._testing as tm



            

Reported by Pylint.

              import pandas._testing as tm


class TestFillNA:
    def test_fillna_period(self):
        # GH#11343
        idx = PeriodIndex(["2011-01-01 09:00", NaT, "2011-01-01 11:00"], freq="H")

        exp = PeriodIndex(

            

Reported by Pylint.

              import pandas._testing as tm


class TestFillNA:
    def test_fillna_period(self):
        # GH#11343
        idx = PeriodIndex(["2011-01-01 09:00", NaT, "2011-01-01 11:00"], freq="H")

        exp = PeriodIndex(

            

Reported by Pylint.

              

class TestFillNA:
    def test_fillna_period(self):
        # GH#11343
        idx = PeriodIndex(["2011-01-01 09:00", NaT, "2011-01-01 11:00"], freq="H")

        exp = PeriodIndex(
            ["2011-01-01 09:00", "2011-01-01 10:00", "2011-01-01 11:00"], freq="H"

            

Reported by Pylint.

              

class TestFillNA:
    def test_fillna_period(self):
        # GH#11343
        idx = PeriodIndex(["2011-01-01 09:00", NaT, "2011-01-01 11:00"], freq="H")

        exp = PeriodIndex(
            ["2011-01-01 09:00", "2011-01-01 10:00", "2011-01-01 11:00"], freq="H"

            

Reported by Pylint.

              import numpy as np

from pandas import PeriodIndex
import pandas._testing as tm


class TestFactorize:
    def test_factorize(self):
        idx1 = PeriodIndex(

            

Reported by Pylint.

              import pandas._testing as tm


class TestFactorize:
    def test_factorize(self):
        idx1 = PeriodIndex(
            ["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"], freq="M"
        )


            

Reported by Pylint.

              import pandas._testing as tm


class TestFactorize:
    def test_factorize(self):
        idx1 = PeriodIndex(
            ["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"], freq="M"
        )


            

Reported by Pylint.

              

class TestFactorize:
    def test_factorize(self):
        idx1 = PeriodIndex(
            ["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"], freq="M"
        )

        exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)

            

Reported by Pylint.

              

class TestFactorize:
    def test_factorize(self):
        idx1 = PeriodIndex(
            ["2014-01", "2014-01", "2014-02", "2014-02", "2014-03", "2014-03"], freq="M"
        )

        exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)

            

Reported by Pylint.

              from datetime import datetime

import numpy as np
import pytest

from pandas import (
    DataFrame,
    Series,
    bdate_range,

            

Reported by Pylint.

                  arr = np.random.randn(100)
    locs = np.arange(20, 40)
    arr[locs] = np.NaN
    series = Series(arr, index=bdate_range(datetime(2009, 1, 1), periods=100))
    return series


@pytest.fixture
def frame():

            

Reported by Pylint.

              from datetime import datetime

import numpy as np
import pytest

from pandas import (
    DataFrame,
    Series,
    bdate_range,

            

Reported by Pylint.

                          DataFrame(np.arange(25).reshape((5, 5)), columns=["a", "b", 99, "d", "d"]),
        ] + [DataFrame(s) for s in create_series()]

    def is_constant(x):
        values = x.values.ravel("K")
        return len(set(values[notna(values)])) == 1

    def no_nans(x):
        return x.notna().all().all()

            

Reported by Pylint.

                      values = x.values.ravel("K")
        return len(set(values[notna(values)])) == 1

    def no_nans(x):
        return x.notna().all().all()

    # data is a tuple(object, is_constant, no_nans)
    data = create_series() + create_dataframes()


            

Reported by Pylint.

              import pytest

from scripts.use_pd_array_in_core import use_pd_array

BAD_FILE_0 = "import pandas as pd\npd.array"
BAD_FILE_1 = "\nfrom pandas import array"
GOOD_FILE_0 = "from pandas import array as pd_array"
GOOD_FILE_1 = "from pandas.core.construction import array as pd_array"
PATH = "t.py"

            

Reported by Pylint.

              import pytest

from scripts.use_pd_array_in_core import use_pd_array

BAD_FILE_0 = "import pandas as pd\npd.array"
BAD_FILE_1 = "\nfrom pandas import array"
GOOD_FILE_0 = "from pandas import array as pd_array"
GOOD_FILE_1 = "from pandas.core.construction import array as pd_array"
PATH = "t.py"

            

Reported by Pylint.

              

@pytest.mark.parametrize("content", [BAD_FILE_0, BAD_FILE_1])
def test_inconsistent_usage(content, capsys):
    result_msg = (
        "t.py:2:0: Don't use pd.array in core, import array as pd_array instead\n"
    )
    with pytest.raises(SystemExit, match=None):
        use_pd_array(content, PATH)

            

Reported by Pylint.

                  with pytest.raises(SystemExit, match=None):
        use_pd_array(content, PATH)
    expected_msg, _ = capsys.readouterr()
    assert result_msg == expected_msg


@pytest.mark.parametrize("content", [GOOD_FILE_0, GOOD_FILE_1])
def test_consistent_usage(content):
    # should not raise

            

Reported by Bandit.

              

@pytest.mark.parametrize("content", [GOOD_FILE_0, GOOD_FILE_1])
def test_consistent_usage(content):
    # should not raise
    use_pd_array(content, PATH)

            

Reported by Pylint.

              import pytest

from pandas._libs.tslibs.offsets import MonthOffset

import pandas.tseries.offsets as offsets


@pytest.fixture(params=[getattr(offsets, o) for o in offsets.__all__])
def offset_types(request):

            

Reported by Pylint.

              import pytest

from pandas._libs.tslibs.offsets import MonthOffset

import pandas.tseries.offsets as offsets


@pytest.fixture(params=[getattr(offsets, o) for o in offsets.__all__])
def offset_types(request):

            

Reported by Pylint.

              import pytest

from pandas._libs.tslibs.offsets import MonthOffset

import pandas.tseries.offsets as offsets


@pytest.fixture(params=[getattr(offsets, o) for o in offsets.__all__])
def offset_types(request):

            

Reported by Pylint.

              import pytest

from pandas._libs.tslibs.offsets import MonthOffset

import pandas.tseries.offsets as offsets


@pytest.fixture(params=[getattr(offsets, o) for o in offsets.__all__])
def offset_types(request):

            

Reported by Pylint.

              import pytest

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


def test_take():
    ser = Series([-1, 5, 6, 2, 4])

            

Reported by Pylint.

              import pytest

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


def test_take():
    ser = Series([-1, 5, 6, 2, 4])

            

Reported by Pylint.

              import pandas._testing as tm


def test_take():
    ser = Series([-1, 5, 6, 2, 4])

    actual = ser.take([1, 3, 4])
    expected = Series([5, 2, 4], index=[1, 3, 4])
    tm.assert_series_equal(actual, expected)

            

Reported by Pylint.

                      ser.take([2, 5])


def test_take_categorical():
    # https://github.com/pandas-dev/pandas/issues/20664
    ser = Series(pd.Categorical(["a", "b", "c"]))
    result = ser.take([-2, -2, 0])
    expected = Series(
        pd.Categorical(["b", "b", "a"], categories=["a", "b", "c"]), index=[1, 1, 0]

            

Reported by Pylint.

              from pandas.io.json import read_json


def test_deprecated_kwargs():
    df = pd.DataFrame({"A": [2, 4, 6], "B": [3, 6, 9]}, index=[0, 1, 2])
    buf = df.to_json(orient="split")
    with tm.assert_produces_warning(FutureWarning):
        tm.assert_frame_equal(df, read_json(buf, "split"))
    buf = df.to_json(orient="columns")

            

Reported by Pylint.

              

def test_deprecated_kwargs():
    df = pd.DataFrame({"A": [2, 4, 6], "B": [3, 6, 9]}, index=[0, 1, 2])
    buf = df.to_json(orient="split")
    with tm.assert_produces_warning(FutureWarning):
        tm.assert_frame_equal(df, read_json(buf, "split"))
    buf = df.to_json(orient="columns")
    with tm.assert_produces_warning(FutureWarning):

            

Reported by Pylint.

                      tm.assert_frame_equal(df, read_json(buf, "index"))


def test_good_kwargs():
    df = pd.DataFrame({"A": [2, 4, 6], "B": [3, 6, 9]}, index=[0, 1, 2])
    with tm.assert_produces_warning(None):
        tm.assert_frame_equal(df, read_json(df.to_json(orient="split"), orient="split"))
        tm.assert_frame_equal(
            df, read_json(df.to_json(orient="columns"), orient="columns")

            

Reported by Pylint.

              

def test_good_kwargs():
    df = pd.DataFrame({"A": [2, 4, 6], "B": [3, 6, 9]}, index=[0, 1, 2])
    with tm.assert_produces_warning(None):
        tm.assert_frame_equal(df, read_json(df.to_json(orient="split"), orient="split"))
        tm.assert_frame_equal(
            df, read_json(df.to_json(orient="columns"), orient="columns")
        )

            

Reported by Pylint.

              from datetime import datetime

import numpy as np

import pandas._testing as tm

import pandas.io.date_converters as conv



            

Reported by Pylint.

              import pandas.io.date_converters as conv


def test_parse_date_time():

    dates = np.array(["2007/1/3", "2008/2/4"], dtype=object)
    times = np.array(["05:07:09", "06:08:00"], dtype=object)
    expected = np.array([datetime(2007, 1, 3, 5, 7, 9), datetime(2008, 2, 4, 6, 8, 0)])
    with tm.assert_produces_warning(FutureWarning):

            

Reported by Pylint.

                  tm.assert_numpy_array_equal(result, expected)


def test_parse_date_fields():
    days = np.array([3, 4])
    months = np.array([1, 2])
    years = np.array([2007, 2008])
    expected = np.array([datetime(2007, 1, 3), datetime(2008, 2, 4)])


            

Reported by Pylint.

                  tm.assert_numpy_array_equal(result, expected)


def test_parse_all_fields():
    hours = np.array([5, 6])
    minutes = np.array([7, 8])
    seconds = np.array([9, 0])

    days = np.array([3, 4])

            

Reported by Pylint.

              from pandas.io.parsers import TextParser


def test_read_data_list(all_parsers):
    parser = all_parsers
    kwargs = {"index_col": 0}
    data = "A,B,C\nfoo,1,2,3\nbar,4,5,6"

    data_list = [["A", "B", "C"], ["foo", "1", "2", "3"], ["bar", "4", "5", "6"]]

            

Reported by Pylint.

                  tm.assert_frame_equal(result, expected)


def test_reader_list(all_parsers):
    data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15

            

Reported by Pylint.

                  tm.assert_frame_equal(chunks[2], expected[4:])


def test_reader_list_skiprows(all_parsers):
    data = """index,A,B,C,D
foo,2,3,4,5
bar,7,8,9,10
baz,12,13,14,15
qux,12,13,14,15

            

Reported by Pylint.

                  tm.assert_frame_equal(chunks[0], expected[1:3])


def test_read_csv_parse_simple_list(all_parsers):
    parser = all_parsers
    data = """foo
bar baz
qux foo
foo

            

Reported by Pylint.

              from io import StringIO

import numpy as np
import pytest

from pandas import (
    DataFrame,
    option_context,
)

            

Reported by Pylint.

              

@pytest.mark.parametrize("na_filter", [True, False])
def test_inf_parsing(all_parsers, na_filter):
    parser = all_parsers
    data = """\
,A
a,inf
b,-inf

            

Reported by Pylint.

              

@pytest.mark.parametrize("na_filter", [True, False])
def test_infinity_parsing(all_parsers, na_filter):
    parser = all_parsers
    data = """\
,A
a,Infinity
b,-Infinity

            

Reported by Pylint.

                  tm.assert_frame_equal(result, expected)


def test_read_csv_with_use_inf_as_na(all_parsers):
    # https://github.com/pandas-dev/pandas/issues/35493
    parser = all_parsers
    data = "1.0\nNaN\n3.0"
    with option_context("use_inf_as_na", True):
        result = parser.read_csv(StringIO(data), header=None)

            

Reported by Pylint.