(self, axis=None, skipna=None, level=None, numeric_only=None, min_count=0)
return hpat_pandas_series_prod_impl
Intel Scalable Dataframe Compiler User Guide
    ********************************************

    Pandas API: pandas.Series.prod

    Limitations
    -----------
    - Parameters ``axis``, ``level``, ``numeric_only`` and ``min_count`` \
        are currently unsupported by Intel Scalable Dataframe Compiler

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_prod.py
       :language: python
       :lines: 27-
       :caption: Return the product of the values.
       :name: ex_series_prod

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

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

    .. only:: developer
        Test: python -m sdc.runtests sdc.tests.test_series -k series_prod
Intel Scalable Dataframe Compiler User Guide
    ********************************************
Intel Scalable Dataframe Compiler User Guide ********************************************
def hpat_pandas_series_prod(self, axis=None, skipna=None, level=None, numeric_only=None, min_count=0):
    """
    Intel Scalable Dataframe Compiler User Guide
    ********************************************

    Pandas API: pandas.Series.prod

    Limitations
    -----------
    - Parameters ``axis``, ``level``, ``numeric_only`` and ``min_count`` \
        are currently unsupported by Intel Scalable Dataframe Compiler

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_prod.py
       :language: python
       :lines: 27-
       :caption: Return the product of the values.
       :name: ex_series_prod

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

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

    .. only:: developer
        Test: python -m sdc.runtests sdc.tests.test_series -k series_prod
    """

    _func_name = 'Method prod().'

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

    if not isinstance(self.data.dtype, (types.Integer, types.Float)):
        ty_checker.raise_exc(self.data.dtype, 'numeric', 'self.data.dtype')

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

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

    if not (isinstance(level, (types.Integer, types.StringLiteral, types.Omitted, types.NoneType)) or level is None):
        ty_checker.raise_exc(level, 'int or str', 'level')

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

    if not (isinstance(min_count, (types.Integer, types.Omitted)) or min_count == 0):
        ty_checker.raise_exc(min_count, 'int', 'min_count')

    def hpat_pandas_series_prod_impl(self, axis=None, skipna=None, level=None, numeric_only=None, min_count=0):
        if skipna is None:
            _skipna = True
        else:
            _skipna = skipna

        series_data = self._data  # FIXME_Numba#6960
        if _skipna:
            return numpy_like.nanprod(series_data)
        else:
            return numpy.prod(self._data)

    return hpat_pandas_series_prod_impl
def hpat_pandas_series_prod ( self , axis = None , skipna = None , level = None , numeric_only = None , min_count = 0 ) : _func_name = 'Method prod().' ty_checker = TypeChecker ( _func_name ) ty_checker . check ( self , SeriesType ) if not isinstance ( self . data . dtype , ( types . Integer , types . Float ) ) : ty_checker . raise_exc ( self . data . dtype , 'numeric' , 'self.data.dtype' ) if not ( isinstance ( axis , ( types . Integer , types . Omitted ) ) or axis is None ) : ty_checker . raise_exc ( axis , 'int' , 'axis' ) if not ( isinstance ( skipna , ( types . Omitted , types . Boolean , types . NoneType ) ) or skipna is None or skipna is True ) : ty_checker . raise_exc ( skipna , 'bool' , 'skipna' ) if not ( isinstance ( level , ( types . Integer , types . StringLiteral , types . Omitted , types . NoneType ) ) or level is None ) : ty_checker . raise_exc ( level , 'int or str' , 'level' ) if not ( isinstance ( numeric_only , ( types . Boolean , types . Omitted ) ) or numeric_only is None ) : ty_checker . raise_exc ( numeric_only , 'bool' , 'numeric_only' ) if not ( isinstance ( min_count , ( types . Integer , types . Omitted ) ) or min_count == 0 ) : ty_checker . raise_exc ( min_count , 'int' , 'min_count' ) def hpat_pandas_series_prod_impl ( self , axis = None , skipna = None , level = None , numeric_only = None , min_count = 0 ) : if skipna is None : _skipna = True else : _skipna = skipna series_data = self . _data # FIXME_Numba#6960 if _skipna : return numpy_like . nanprod ( series_data ) else : return numpy . prod ( self . _data ) return hpat_pandas_series_prod_impl