import numpy as np

from pandas._libs.tslibs import parsing


def parse_date_time(date_col, time_col):
    """
    Parse columns with dates and times into a single datetime column.

            

Reported by Pylint.

                  """
    warnings.warn(
        """
        Use pd.to_datetime({"year": year_col, "month": month_col, "day": day_col}) instead to get a Pandas Series.
        Use ser = pd.to_datetime({"year": year_col, "month": month_col, "day": day_col}) and
        np.array([s.to_pydatetime() for s in ser]) instead to get a Numpy array.
""",  # noqa: E501
        FutureWarning,
        stacklevel=2,

            

Reported by Pylint.

                  return parsing.try_parse_year_month_day(year_col, month_col, day_col)


def parse_all_fields(year_col, month_col, day_col, hour_col, minute_col, second_col):
    """
    Parse columns with datetime information into a single datetime column.

    .. deprecated:: 1.2
    """

            

Reported by Pylint.

                  warnings.warn(
        """
        Use pd.to_datetime({"year": year_col, "month": month_col, "day": day_col,
        "hour": hour_col, "minute": minute_col, second": second_col}) instead to get a Pandas Series.
        Use ser = pd.to_datetime({"year": year_col, "month": month_col, "day": day_col,
        "hour": hour_col, "minute": minute_col, second": second_col}) and
        np.array([s.to_pydatetime() for s in ser]) instead to get a Numpy array.
""",  # noqa: E501
        FutureWarning,

            

Reported by Pylint.

                      stacklevel=2,
    )

    N = _check_columns(cols)
    results = np.empty(N, dtype=object)

    for i in range(N):
        args = [c[i] for c in cols]
        results[i] = parse_func(*args)

            

Reported by Pylint.

              

def _check_columns(cols):
    if not len(cols):
        raise AssertionError("There must be at least 1 column")

    head, tail = cols[0], cols[1:]

    N = len(head)

            

Reported by Pylint.

              
    head, tail = cols[0], cols[1:]

    N = len(head)

    for i, n in enumerate(map(len, tail)):
        if n != N:
            raise AssertionError(
                f"All columns must have the same length: {N}; column {i} has length {n}"

            

Reported by Pylint.

              
    N = len(head)

    for i, n in enumerate(map(len, tail)):
        if n != N:
            raise AssertionError(
                f"All columns must have the same length: {N}; column {i} has length {n}"
            )


            

Reported by Pylint.

              from typing import (
    Dict,
    Optional,
)

import numpy as np

from pandas.compat._optional import import_optional_dependency


            

Reported by Pylint.

                  numba = import_optional_dependency("numba")

    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
    def online_ewma(
        values: np.ndarray,
        deltas: np.ndarray,
        minimum_periods: int,
        old_wt_factor: float,
        new_wt: float,

            

Reported by Pylint.

                      nobs = (~np.isnan(weighted_avg)).astype(np.int64)
        result[0] = np.where(nobs >= minimum_periods, weighted_avg, np.nan)

        for i in range(1, len(values)):
            cur = values[i]
            is_observations = ~np.isnan(cur)
            nobs += is_observations.astype(np.int64)
            for j in numba.prange(len(cur)):
                if not np.isnan(weighted_avg[j]):

            

Reported by Pylint.

                  return online_ewma


class EWMMeanState:
    def __init__(self, com, adjust, ignore_na, axis, shape):
        alpha = 1.0 / (1.0 + com)
        self.axis = axis
        self.shape = shape
        self.adjust = adjust

            

Reported by Pylint.

                  return online_ewma


class EWMMeanState:
    def __init__(self, com, adjust, ignore_na, axis, shape):
        alpha = 1.0 / (1.0 + com)
        self.axis = axis
        self.shape = shape
        self.adjust = adjust

            

Reported by Pylint.

              

class EWMMeanState:
    def __init__(self, com, adjust, ignore_na, axis, shape):
        alpha = 1.0 / (1.0 + com)
        self.axis = axis
        self.shape = shape
        self.adjust = adjust
        self.ignore_na = ignore_na

            

Reported by Pylint.

                      self.old_wt = np.ones(self.shape[self.axis - 1])
        self.last_ewm = None

    def run_ewm(self, weighted_avg, deltas, min_periods, ewm_func):
        result, old_wt = ewm_func(
            weighted_avg,
            deltas,
            min_periods,
            self.old_wt_factor,

            

Reported by Pylint.

                      self.last_ewm = result[-1]
        return result

    def reset(self):
        self.old_wt = np.ones(self.shape[self.axis - 1])
        self.last_ewm = None

            

Reported by Pylint.

              import pytest

from pandas.core.frame import DataFrame


@pytest.fixture
def dataframe():
    return DataFrame({"a": [1, 2], "b": [3, 4]})


            

Reported by Pylint.

                      ],
    )
    @pytest.mark.parametrize("inplace", [1, "True", [1, 2, 3], 5.0])
    def test_validate_bool_args(self, dataframe, func, inplace):
        msg = 'For argument "inplace" expected type bool'
        kwargs = {"inplace": inplace}

        if func == "query":
            kwargs["expr"] = "a > b"

            

Reported by Pylint.

              import pytest

from pandas.core.frame import DataFrame


@pytest.fixture
def dataframe():
    return DataFrame({"a": [1, 2], "b": [3, 4]})


            

Reported by Pylint.

              

@pytest.fixture
def dataframe():
    return DataFrame({"a": [1, 2], "b": [3, 4]})


class TestDataFrameValidate:
    """Tests for error handling related to data types of method arguments."""

            

Reported by Pylint.

                  return DataFrame({"a": [1, 2], "b": [3, 4]})


class TestDataFrameValidate:
    """Tests for error handling related to data types of method arguments."""

    @pytest.mark.parametrize(
        "func",
        [

            

Reported by Pylint.

                          "drop_duplicates",
            "sort_values",
        ],
    )
    @pytest.mark.parametrize("inplace", [1, "True", [1, 2, 3], 5.0])
    def test_validate_bool_args(self, dataframe, func, inplace):
        msg = 'For argument "inplace" expected type bool'
        kwargs = {"inplace": inplace}


            

Reported by Pylint.

                          "drop_duplicates",
            "sort_values",
        ],
    )
    @pytest.mark.parametrize("inplace", [1, "True", [1, 2, 3], 5.0])
    def test_validate_bool_args(self, dataframe, func, inplace):
        msg = 'For argument "inplace" expected type bool'
        kwargs = {"inplace": inplace}


            

Reported by Pylint.

                      .. versionadded:: 1.1.0
    """
    import_optional_dependency("pyarrow")
    from pyarrow import feather

    if not isinstance(df, DataFrame):
        raise ValueError("feather only support IO with DataFrames")

    valid_types = {"string", "unicode"}

            

Reported by Pylint.

                  type of object stored in file
    """
    import_optional_dependency("pyarrow")
    from pyarrow import feather

    with get_handle(
        path, "rb", storage_options=storage_options, is_text=False
    ) as handles:


            

Reported by Pylint.

              from pandas.io.common import get_handle


@doc(storage_options=generic._shared_docs["storage_options"])
def to_feather(
    df: DataFrame,
    path: FilePathOrBuffer[AnyStr],
    storage_options: StorageOptions = None,
    **kwargs,

            

Reported by Pylint.

                      feather.write_feather(df, handles.handle, **kwargs)


@doc(storage_options=generic._shared_docs["storage_options"])
def read_feather(
    path, columns=None, use_threads: bool = True, storage_options: StorageOptions = None
):
    """
    Load a feather-format object from the file path.

            

Reported by Pylint.

              

@doc(storage_options=generic._shared_docs["storage_options"])
def to_feather(
    df: DataFrame,
    path: FilePathOrBuffer[AnyStr],
    storage_options: StorageOptions = None,
    **kwargs,
):

            

Reported by Pylint.

                      .. versionadded:: 1.1.0
    """
    import_optional_dependency("pyarrow")
    from pyarrow import feather

    if not isinstance(df, DataFrame):
        raise ValueError("feather only support IO with DataFrames")

    valid_types = {"string", "unicode"}

            

Reported by Pylint.

                  type of object stored in file
    """
    import_optional_dependency("pyarrow")
    from pyarrow import feather

    with get_handle(
        path, "rb", storage_options=storage_options, is_text=False
    ) as handles:


            

Reported by Pylint.

              #else
        snprintf(precision_str + 2, sizeof(precision_str) - 2, "%ug",
                 enc->doublePrecision);
        enc->offset += snprintf(str, enc->end - enc->offset, precision_str,
                                neg ? -value : value);
#endif
        return TRUE;
    }


            

Reported by FlawFinder.

                          SetError(NULL, enc, "Could not reserve memory block");
            return;
        }
        memcpy(enc->start, oldStart, offset);
    }
    enc->offset = enc->start + offset;
    enc->end = enc->start + newSize;
}


            

Reported by FlawFinder.

                                  return FALSE;
                }

                memcpy(&in16, io, sizeof(JSUTF16));
                in = (JSUTF32)in16;

#ifdef __LITTLE_ENDIAN__
                ucs = ((in & 0x1f) << 6) | ((in >> 8) & 0x3f);
#else

            

Reported by FlawFinder.

                                  return FALSE;
                }

                memcpy(&in16, io, sizeof(JSUTF16));
                memcpy(&in8, io + 2, sizeof(JSUINT8));
#ifdef __LITTLE_ENDIAN__
                in = (JSUTF32)in16;
                in |= in8 << 16;
                ucs = ((in & 0x0f) << 12) | ((in & 0x3f00) >> 2) |

            

Reported by FlawFinder.

                              }

                memcpy(&in16, io, sizeof(JSUTF16));
                memcpy(&in8, io + 2, sizeof(JSUINT8));
#ifdef __LITTLE_ENDIAN__
                in = (JSUTF32)in16;
                in |= in8 << 16;
                ucs = ((in & 0x0f) << 12) | ((in & 0x3f00) >> 2) |
                      ((in & 0x3f0000) >> 16);

            

Reported by FlawFinder.

                                  return FALSE;
                }

                memcpy(&in, io, sizeof(JSUTF32));
#ifdef __LITTLE_ENDIAN__
                ucs = ((in & 0x07) << 18) | ((in & 0x3f00) << 4) |
                      ((in & 0x3f0000) >> 10) | ((in & 0x3f000000) >> 24);
#else
                ucs = ((in & 0x07000000) >> 6) | ((in & 0x3f0000) >> 4) |

            

Reported by FlawFinder.

                  /* if input is beyond the thresholds, revert to exponential */
    const double thres_max = (double)1e16 - 1;
    const double thres_min = (double)1e-15;
    char precision_str[20];
    int count;
    double diff = 0.0;
    char *str = enc->offset;
    char *wstr = str;
    unsigned long long whole;

            

Reported by FlawFinder.

                  if not os.path.isfile(filename):
        raise RuntimeError(f"Could not find junit file {repr(filename)}")

    tree = et.parse(filename)
    root = tree.getroot()
    current_class = ""
    for el in root.iter("testcase"):
        cn = el.attrib["classname"]
        for sk in el.findall("skipped"):

            

Reported by Bandit.

              #!/usr/bin/env python3
import os
import xml.etree.ElementTree as et


def main(filename):
    if not os.path.isfile(filename):
        raise RuntimeError(f"Could not find junit file {repr(filename)}")


            

Reported by Pylint.

              #!/usr/bin/env python3
import os
import xml.etree.ElementTree as et


def main(filename):
    if not os.path.isfile(filename):
        raise RuntimeError(f"Could not find junit file {repr(filename)}")


            

Reported by Bandit.

              import xml.etree.ElementTree as et


def main(filename):
    if not os.path.isfile(filename):
        raise RuntimeError(f"Could not find junit file {repr(filename)}")

    tree = et.parse(filename)
    root = tree.getroot()

            

Reported by Pylint.

                  tree = et.parse(filename)
    root = tree.getroot()
    current_class = ""
    for el in root.iter("testcase"):
        cn = el.attrib["classname"]
        for sk in el.findall("skipped"):
            old_class = current_class
            current_class = cn
            if old_class != current_class:

            

Reported by Pylint.

                  root = tree.getroot()
    current_class = ""
    for el in root.iter("testcase"):
        cn = el.attrib["classname"]
        for sk in el.findall("skipped"):
            old_class = current_class
            current_class = cn
            if old_class != current_class:
                yield None

            

Reported by Pylint.

                  current_class = ""
    for el in root.iter("testcase"):
        cn = el.attrib["classname"]
        for sk in el.findall("skipped"):
            old_class = current_class
            current_class = cn
            if old_class != current_class:
                yield None
            yield {

            

Reported by Pylint.

              from io import StringIO

import numpy as np
import pytest

import pandas as pd
from pandas.tests.extension.base.base import BaseExtensionTests



            

Reported by Pylint.

              from io import StringIO

import numpy as np
import pytest

import pandas as pd
from pandas.tests.extension.base.base import BaseExtensionTests



            

Reported by Pylint.

              from io import StringIO

import numpy as np
import pytest

import pandas as pd
from pandas.tests.extension.base.base import BaseExtensionTests



            

Reported by Pylint.

              from pandas.tests.extension.base.base import BaseExtensionTests


class BaseParsingTests(BaseExtensionTests):
    @pytest.mark.parametrize("engine", ["c", "python"])
    def test_EA_types(self, engine, data):
        df = pd.DataFrame({"with_dtype": pd.Series(data, dtype=str(data.dtype))})
        csv_output = df.to_csv(index=False, na_rep=np.nan)
        result = pd.read_csv(

            

Reported by Pylint.

              
class BaseParsingTests(BaseExtensionTests):
    @pytest.mark.parametrize("engine", ["c", "python"])
    def test_EA_types(self, engine, data):
        df = pd.DataFrame({"with_dtype": pd.Series(data, dtype=str(data.dtype))})
        csv_output = df.to_csv(index=False, na_rep=np.nan)
        result = pd.read_csv(
            StringIO(csv_output), dtype={"with_dtype": str(data.dtype)}, engine=engine
        )

            

Reported by Pylint.

              
class BaseParsingTests(BaseExtensionTests):
    @pytest.mark.parametrize("engine", ["c", "python"])
    def test_EA_types(self, engine, data):
        df = pd.DataFrame({"with_dtype": pd.Series(data, dtype=str(data.dtype))})
        csv_output = df.to_csv(index=False, na_rep=np.nan)
        result = pd.read_csv(
            StringIO(csv_output), dtype={"with_dtype": str(data.dtype)}, engine=engine
        )

            

Reported by Pylint.

              class BaseParsingTests(BaseExtensionTests):
    @pytest.mark.parametrize("engine", ["c", "python"])
    def test_EA_types(self, engine, data):
        df = pd.DataFrame({"with_dtype": pd.Series(data, dtype=str(data.dtype))})
        csv_output = df.to_csv(index=False, na_rep=np.nan)
        result = pd.read_csv(
            StringIO(csv_output), dtype={"with_dtype": str(data.dtype)}, engine=engine
        )
        expected = df

            

Reported by Pylint.

              from pandas import (
    Index,
    Timestamp,
    date_range,
)


class TestAsOf:
    def test_asof_partial(self):

            

Reported by Pylint.

              )


class TestAsOf:
    def test_asof_partial(self):
        index = date_range("2010-01-01", periods=2, freq="m")
        expected = Timestamp("2010-02-28")
        result = index.asof("2010-02")
        assert result == expected

            

Reported by Pylint.

              )


class TestAsOf:
    def test_asof_partial(self):
        index = date_range("2010-01-01", periods=2, freq="m")
        expected = Timestamp("2010-02-28")
        result = index.asof("2010-02")
        assert result == expected

            

Reported by Pylint.

              

class TestAsOf:
    def test_asof_partial(self):
        index = date_range("2010-01-01", periods=2, freq="m")
        expected = Timestamp("2010-02-28")
        result = index.asof("2010-02")
        assert result == expected
        assert not isinstance(result, Index)

            

Reported by Pylint.

              

class TestAsOf:
    def test_asof_partial(self):
        index = date_range("2010-01-01", periods=2, freq="m")
        expected = Timestamp("2010-02-28")
        result = index.asof("2010-02")
        assert result == expected
        assert not isinstance(result, Index)

            

Reported by Pylint.

                      index = date_range("2010-01-01", periods=2, freq="m")
        expected = Timestamp("2010-02-28")
        result = index.asof("2010-02")
        assert result == expected
        assert not isinstance(result, Index)

            

Reported by Bandit.

                      expected = Timestamp("2010-02-28")
        result = index.asof("2010-02")
        assert result == expected
        assert not isinstance(result, Index)

            

Reported by Bandit.

              import operator

import pytest

from pandas import Series


@pytest.fixture
def dtype():

            

Reported by Pylint.

              

@pytest.fixture(params=["data", "data_missing"])
def all_data(request, data, data_missing):
    """Parametrized fixture giving 'data' and 'data_missing'"""
    if request.param == "data":
        return data
    elif request.param == "data_missing":
        return data_missing

            

Reported by Pylint.

              

@pytest.fixture(params=["data", "data_missing"])
def all_data(request, data, data_missing):
    """Parametrized fixture giving 'data' and 'data_missing'"""
    if request.param == "data":
        return data
    elif request.param == "data_missing":
        return data_missing

            

Reported by Pylint.

              

@pytest.fixture
def data_repeated(data):
    """
    Generate many datasets.

    Parameters
    ----------

            

Reported by Pylint.

              import operator

import pytest

from pandas import Series


@pytest.fixture
def dtype():

            

Reported by Pylint.

              

@pytest.fixture(params=["data", "data_missing"])
def all_data(request, data, data_missing):
    """Parametrized fixture giving 'data' and 'data_missing'"""
    if request.param == "data":
        return data
    elif request.param == "data_missing":
        return data_missing

            

Reported by Pylint.

              @pytest.fixture(params=["data", "data_missing"])
def all_data(request, data, data_missing):
    """Parametrized fixture giving 'data' and 'data_missing'"""
    if request.param == "data":
        return data
    elif request.param == "data_missing":
        return data_missing



            

Reported by Pylint.

                  @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
    def ewma_table(
        values: np.ndarray,
        begin: np.ndarray,
        end: np.ndarray,
        minimum_periods: int,
    ) -> np.ndarray:
        alpha = 1.0 / (1.0 + com)
        old_wt_factor = 1.0 - alpha

            

Reported by Pylint.

                  def ewma_table(
        values: np.ndarray,
        begin: np.ndarray,
        end: np.ndarray,
        minimum_periods: int,
    ) -> np.ndarray:
        alpha = 1.0 / (1.0 + com)
        old_wt_factor = 1.0 - alpha
        new_wt = 1.0 if adjust else alpha

            

Reported by Pylint.

              from __future__ import annotations

import functools
from typing import (
    Any,
    Callable,
)

import numpy as np

            

Reported by Pylint.

                  numba = import_optional_dependency("numba")

    @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
    def ewma(
        values: np.ndarray,
        begin: np.ndarray,
        end: np.ndarray,
        minimum_periods: int,
    ) -> np.ndarray:

            

Reported by Pylint.

                      old_wt_factor = 1.0 - alpha
        new_wt = 1.0 if adjust else alpha

        for i in numba.prange(len(begin)):
            start = begin[i]
            stop = end[i]
            window = values[start:stop]
            sub_result = np.empty(len(window))


            

Reported by Pylint.

              # axis for all np.nan* agg functions
# https://github.com/numba/numba/issues/1269
@functools.lru_cache(maxsize=None)
def generate_manual_numpy_nan_agg_with_axis(nan_func):
    numba = import_optional_dependency("numba")

    @numba.jit(nopython=True, nogil=True, parallel=True)
    def nan_agg_with_axis(table):
        result = np.empty(table.shape[1])

            

Reported by Pylint.

                      weighted_avg = values[0].copy()
        nobs = (~np.isnan(weighted_avg)).astype(np.int64)
        result[0] = np.where(nobs >= minimum_periods, weighted_avg, np.nan)
        for i in range(1, len(values)):
            cur = values[i]
            is_observations = ~np.isnan(cur)
            nobs += is_observations.astype(np.int64)
            for j in numba.prange(len(cur)):
                if not np.isnan(weighted_avg[j]):

            

Reported by Pylint.