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

    Pandas API: pandas.Series.min

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

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_min.py
       :language: python
       :lines: 27-
       :caption: Getting the minimum value of Series elements
       :name: ex_series_min

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

    .. seealso::

        :ref:`Series.sum <pandas.Series.sum>`
            Return the sum.

        :ref:`Series.min <pandas.Series.min>`
            Return the minimum.

        :ref:`Series.max <pandas.Series.max>`
            Return the maximum.

        :ref:`Series.idxmin <pandas.Series.idxmin>`
            Return the index of the minimum.

        :ref:`Series.idxmax <pandas.Series.idxmax>`
            Return the index of the maximum.

        :ref:`DataFrame.sum <pandas.DataFrame.sum>`
            Return the sum over the requested axis.

        :ref:`DataFrame.min <pandas.DataFrame.min>`
            Return the minimum over the requested axis.

        :ref:`DataFrame.max <pandas.DataFrame.max>`
            Return the maximum over the requested axis.

        :ref:`DataFrame.idxmin <pandas.DataFrame.idxmin>`
            Return the index of the minimum over the requested axis.

        :ref:`DataFrame.idxmax <pandas.DataFrame.idxmax>`
            Return the index of the maximum over the requested axis.

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

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

    Pandas API: pandas.Series.min

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

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_min.py
       :language: python
       :lines: 27-
       :caption: Getting the minimum value of Series elements
       :name: ex_series_min

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

    .. seealso::

        :ref:`Series.sum <pandas.Series.sum>`
            Return the sum.

        :ref:`Series.min <pandas.Series.min>`
            Return the minimum.

        :ref:`Series.max <pandas.Series.max>`
            Return the maximum.

        :ref:`Series.idxmin <pandas.Series.idxmin>`
            Return the index of the minimum.

        :ref:`Series.idxmax <pandas.Series.idxmax>`
            Return the index of the maximum.

        :ref:`DataFrame.sum <pandas.DataFrame.sum>`
            Return the sum over the requested axis.

        :ref:`DataFrame.min <pandas.DataFrame.min>`
            Return the minimum over the requested axis.

        :ref:`DataFrame.max <pandas.DataFrame.max>`
            Return the maximum over the requested axis.

        :ref:`DataFrame.idxmin <pandas.DataFrame.idxmin>`
            Return the index of the minimum over the requested axis.

        :ref:`DataFrame.idxmax <pandas.DataFrame.idxmax>`
            Return the index of the maximum over the requested axis.

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

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

    _func_name = 'Method min().'

    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, 'int, float', 'self.data.dtype')

    if not (isinstance(skipna, (types.Omitted, types.Boolean, types.NoneType)) or skipna is True 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_min_impl(self, axis=None, skipna=None, level=None, numeric_only=None):
        if skipna is None:
            _skipna = True
        else:
            _skipna = skipna

        series_data = self._data  # FIXME_Numba#6960
        if _skipna:
            return numpy_like.nanmin(series_data)

        return self._data.min()

    return hpat_pandas_series_min_impl
def hpat_pandas_series_min ( self , axis = None , skipna = None , level = None , numeric_only = None ) : _func_name = 'Method min().' 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 , 'int, float' , 'self.data.dtype' ) if not ( isinstance ( skipna , ( types . Omitted , types . Boolean , types . NoneType ) ) or skipna is True 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_min_impl ( self , axis = None , skipna = None , level = None , numeric_only = None ) : if skipna is None : _skipna = True else : _skipna = skipna series_data = self . _data # FIXME_Numba#6960 if _skipna : return numpy_like . nanmin ( series_data ) return self . _data . min ( ) return hpat_pandas_series_min_impl