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

    Pandas API: pandas.Series.median

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

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_median.py
       :language: python
       :lines: 27-
       :caption: Return the median of the values for the requested axis.
       :name: ex_series_median

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

    .. seealso::
        :ref:`DataFrame.median <pandas.DataFrame.median>`
            Return the median of the values for the columns.

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

    .. only:: developer
        Test: python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_median1*
Intel Scalable Dataframe Compiler User Guide
    ********************************************
Intel Scalable Dataframe Compiler User Guide ********************************************
def hpat_pandas_series_median(self, axis=None, skipna=None, level=None, numeric_only=None):
    """
    Intel Scalable Dataframe Compiler User Guide
    ********************************************

    Pandas API: pandas.Series.median

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

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_median.py
       :language: python
       :lines: 27-
       :caption: Return the median of the values for the requested axis.
       :name: ex_series_median

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

    .. seealso::
        :ref:`DataFrame.median <pandas.DataFrame.median>`
            Return the median of the values for the columns.

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

    .. only:: developer
        Test: python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_median1*
    """

    _func_name = 'Method median().'

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

    if not isinstance(self.dtype, types.Number):
        ty_checker.raise_exc(self.dtype, 'numeric', 'self.dtype')

    if not (isinstance(axis, (types.Integer, types.UnicodeType, types.Omitted)) or axis is None):
        ty_checker.raise_exc(axis, 'int or str', 'axis')

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

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

    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) and numeric_only is not None:
        ty_checker.raise_exc(numeric_only, 'None', 'numeric_only')

    def hpat_pandas_series_median_impl(self, axis=None, skipna=None, level=None, numeric_only=None):
        if skipna is None:
            _skipna = True
        else:
            _skipna = skipna

        if _skipna:
            return numpy.nanmedian(self._data)

        return numpy.median(self._data)

    return hpat_pandas_series_median_impl
def hpat_pandas_series_median ( self , axis = None , skipna = None , level = None , numeric_only = None ) : _func_name = 'Method median().' ty_checker = TypeChecker ( _func_name ) ty_checker . check ( self , SeriesType ) if not isinstance ( self . dtype , types . Number ) : ty_checker . raise_exc ( self . dtype , 'numeric' , 'self.dtype' ) if not ( isinstance ( axis , ( types . Integer , types . UnicodeType , types . Omitted ) ) or axis is None ) : ty_checker . raise_exc ( axis , 'int or str' , 'axis' ) if not ( isinstance ( skipna , ( types . Boolean , types . Omitted , types . NoneType ) ) or skipna or skipna is None ) : ty_checker . raise_exc ( skipna , 'bool' , 'skipna' ) if not isinstance ( axis , types . Omitted ) and axis is not None : ty_checker . raise_exc ( axis , 'None' , 'axis' ) 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 ) and numeric_only is not None : ty_checker . raise_exc ( numeric_only , 'None' , 'numeric_only' ) def hpat_pandas_series_median_impl ( self , axis = None , skipna = None , level = None , numeric_only = None ) : if skipna is None : _skipna = True else : _skipna = skipna if _skipna : return numpy . nanmedian ( self . _data ) return numpy . median ( self . _data ) return hpat_pandas_series_median_impl