(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None)
return hpat_pandas_series_var_impl
Intel Scalable Dataframe Compiler User Guide
    ********************************************

    Pandas API: pandas.Series.var

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

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_var.py
       :language: python
       :lines: 27-
       :caption: Returns unbiased variance over Series.
       :name: ex_series_var

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

    .. seealso::

        :ref:`Series.std <pandas.Series.std>`
            Returns sample standard deviation over Series.

    Intel Scalable Dataframe Compiler Developer Guide
    *************************************************

    Pandas Series method :meth:`pandas.Series.var` implementation.

    .. only:: developer
        Test: python -m sdc.runtests -k sdc.tests.test_series.TestSeries.test_series_var
Intel Scalable Dataframe Compiler User Guide
    ********************************************
Intel Scalable Dataframe Compiler User Guide ********************************************
def hpat_pandas_series_var(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None):
    """
    Intel Scalable Dataframe Compiler User Guide
    ********************************************

    Pandas API: pandas.Series.var

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

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_var.py
       :language: python
       :lines: 27-
       :caption: Returns unbiased variance over Series.
       :name: ex_series_var

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

    .. seealso::

        :ref:`Series.std <pandas.Series.std>`
            Returns sample standard deviation over Series.

    Intel Scalable Dataframe Compiler Developer Guide
    *************************************************

    Pandas Series method :meth:`pandas.Series.var` implementation.

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

    _func_name = 'Method var().'

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

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

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

    if not isinstance(ddof, (types.Omitted, int, types.Integer)):
        ty_checker.raise_exc(ddof, 'int', 'ddof')

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

    def hpat_pandas_series_var_impl(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None):
        if skipna is None:
            skipna = True

        self_data = self._data  # FIXME_Numba#6960
        if skipna:
            valuable_length = len(self_data) - numpy.sum(numpy.isnan(self_data))
            if valuable_length <= ddof:
                return numpy.nan

            return numpy_like.nanvar(self_data) * valuable_length / (valuable_length - ddof)

        if len(self_data) <= ddof:
            return numpy.nan

        return self_data.var() * len(self_data) / (len(self_data) - ddof)

    return hpat_pandas_series_var_impl
def hpat_pandas_series_var ( self , axis = None , skipna = None , level = None , ddof = 1 , numeric_only = None ) : _func_name = 'Method var().' ty_checker = TypeChecker ( _func_name ) ty_checker . check ( self , SeriesType ) if not isinstance ( self . data . dtype , types . Number ) : ty_checker . raise_exc ( self . data , 'number' , 'self.data' ) if not isinstance ( skipna , ( types . Omitted , types . Boolean , types . NoneType ) ) and skipna is not None : ty_checker . raise_exc ( skipna , 'bool' , 'skipna' ) if not isinstance ( ddof , ( types . Omitted , int , types . Integer ) ) : ty_checker . raise_exc ( ddof , 'int' , 'ddof' ) if not isinstance ( axis , ( types . Omitted , types . NoneType ) ) 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 , types . NoneType ) ) and numeric_only is not None : ty_checker . raise_exc ( numeric_only , 'None' , 'numeric_only' ) def hpat_pandas_series_var_impl ( self , axis = None , skipna = None , level = None , ddof = 1 , numeric_only = None ) : if skipna is None : skipna = True self_data = self . _data # FIXME_Numba#6960 if skipna : valuable_length = len ( self_data ) - numpy . sum ( numpy . isnan ( self_data ) ) if valuable_length <= ddof : return numpy . nan return numpy_like . nanvar ( self_data ) * valuable_length / ( valuable_length - ddof ) if len ( self_data ) <= ddof : return numpy . nan return self_data . var ( ) * len ( self_data ) / ( len ( self_data ) - ddof ) return hpat_pandas_series_var_impl