(self, axis=None, skipna=None, level=None, numeric_only=None)
return sdc_pandas_series_skew_impl
Intel Scalable Dataframe Compiler User Guide
        ********************************************

        Pandas API: pandas.Series.skew

        Limitations
        -----------
        - Parameters ``level`` and ``numeric_only`` are supported only with default value ``None``.

        Examples
        --------
        .. literalinclude:: ../../../examples/series/series_skew.py
           :language: python
           :lines: 27-
           :caption: Unbiased rolling skewness.
           :name: ex_series_skew

        .. command-output:: python ./series/series_skew.py
           :cwd: ../../../examples

        Intel Scalable Dataframe Compiler Developer Guide
        *************************************************
        Pandas Series method :meth:`pandas.Series.skew` implementation.

        .. only:: developer
            Test: python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_skew*
Intel Scalable Dataframe Compiler User Guide
        ********************************************
Intel Scalable Dataframe Compiler User Guide ********************************************
def sdc_pandas_series_skew(self, axis=None, skipna=None, level=None, numeric_only=None):
    """
        Intel Scalable Dataframe Compiler User Guide
        ********************************************

        Pandas API: pandas.Series.skew

        Limitations
        -----------
        - Parameters ``level`` and ``numeric_only`` are supported only with default value ``None``.

        Examples
        --------
        .. literalinclude:: ../../../examples/series/series_skew.py
           :language: python
           :lines: 27-
           :caption: Unbiased rolling skewness.
           :name: ex_series_skew

        .. command-output:: python ./series/series_skew.py
           :cwd: ../../../examples

        Intel Scalable Dataframe Compiler Developer Guide
        *************************************************
        Pandas Series method :meth:`pandas.Series.skew` implementation.

        .. only:: developer
            Test: python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_skew*
        """
    _func_name = 'Method Series.skew().'

    ty_checker = TypeChecker(_func_name)
    ty_checker.check(self, SeriesType)

    if not isinstance(axis, (types.Integer, types.NoneType, types.Omitted)) and axis is not None:
        ty_checker.raise_exc(axis, 'int64', 'axis')

    if not isinstance(skipna, (types.Boolean, types.NoneType, types.Omitted)) and skipna is not None:
        ty_checker.raise_exc(skipna, 'bool', 'skipna')

    if not isinstance(level, (types.Omitted, types.NoneType)) and level is not None:
        ty_checker.raise_exc(level, 'None', 'level')

    if not isinstance(numeric_only, (types.Omitted, types.NoneType)) and numeric_only is not None:
        ty_checker.raise_exc(numeric_only, 'None', 'numeric_only')

    def sdc_pandas_series_skew_impl(self, axis=None, skipna=None, level=None, numeric_only=None):
        if axis != 0 and axis is not None:
            raise ValueError('Parameter axis must be only 0 or None.')

        if skipna is None:
            _skipna = True
        else:
            _skipna = skipna

        if _skipna:
            return numpy_like.nanskew(self._data)

        return numpy_like.skew(self._data)

    return sdc_pandas_series_skew_impl
def sdc_pandas_series_skew ( self , axis = None , skipna = None , level = None , numeric_only = None ) : _func_name = 'Method Series.skew().' ty_checker = TypeChecker ( _func_name ) ty_checker . check ( self , SeriesType ) if not isinstance ( axis , ( types . Integer , types . NoneType , types . Omitted ) ) and axis is not None : ty_checker . raise_exc ( axis , 'int64' , 'axis' ) if not isinstance ( skipna , ( types . Boolean , types . NoneType , types . Omitted ) ) and skipna is not None : ty_checker . raise_exc ( skipna , 'bool' , 'skipna' ) if not isinstance ( level , ( types . Omitted , types . NoneType ) ) and level is not None : ty_checker . raise_exc ( level , 'None' , 'level' ) if not isinstance ( numeric_only , ( types . Omitted , types . NoneType ) ) and numeric_only is not None : ty_checker . raise_exc ( numeric_only , 'None' , 'numeric_only' ) def sdc_pandas_series_skew_impl ( self , axis = None , skipna = None , level = None , numeric_only = None ) : if axis != 0 and axis is not None : raise ValueError ( 'Parameter axis must be only 0 or None.' ) if skipna is None : _skipna = True else : _skipna = skipna if _skipna : return numpy_like . nanskew ( self . _data ) return numpy_like . skew ( self . _data ) return sdc_pandas_series_skew_impl