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

    Pandas API: pandas.Series.idxmin

    Limitations
    -----------
    - Parameter ``axis`` is supported only with default value ``None``.
    - Parameter ``skipna`` cannot be ``False`` with data of string type.

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_idxmin.py
       :language: python
       :lines: 27-
       :caption: Getting the row label of the minimum value.
       :name: ex_series_idxmin

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

    .. seealso::

        :ref:`Series.idxmax <pandas.Series.idxmax>`
            Return index label of the first occurrence of maximum of values.

        `numpy.argmin <https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmin.html#numpy.argmin>`_
            Return indices of the minimum values along the given axis.

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

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

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

    Pandas API: pandas.Series.idxmin

    Limitations
    -----------
    - Parameter ``axis`` is supported only with default value ``None``.
    - Parameter ``skipna`` cannot be ``False`` with data of string type.

    Examples
    --------
    .. literalinclude:: ../../../examples/series/series_idxmin.py
       :language: python
       :lines: 27-
       :caption: Getting the row label of the minimum value.
       :name: ex_series_idxmin

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

    .. seealso::

        :ref:`Series.idxmax <pandas.Series.idxmax>`
            Return index label of the first occurrence of maximum of values.

        `numpy.argmin <https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmin.html#numpy.argmin>`_
            Return indices of the minimum values along the given axis.

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

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

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

    _func_name = 'Method idxmin().'

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

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

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

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

    positional_index = isinstance(self.index, PositionalIndexType)
    if isinstance(self.data, StringArrayType):
        def hpat_pandas_series_idxmin_str_impl(self, axis=None, skipna=None):
            if skipna is None:
                _skipna = True
            else:
                raise ValueError("Method idxmin(). Unsupported parameter 'skipna'=False with str data")

            result = numpy.argmin(self._data)
            if positional_index == True:  # noqa
                return result
            else:
                return self._index[int(result)]

        return hpat_pandas_series_idxmin_str_impl

    def hpat_pandas_series_idxmin_impl(self, axis=None, skipna=None):
        # return numpy.argmin(self._data)
        if skipna is None:
            _skipna = True
        else:
            _skipna = skipna

        if _skipna:
            result = numpy_like.nanargmin(self._data)
        else:
            result = numpy_like.argmin(self._data)

        if positional_index == True:  # noqa
            return result
        else:
            return self._index[int(result)]

        return numpy_like.argmin(self._data)

    return hpat_pandas_series_idxmin_impl
def hpat_pandas_series_idxmin ( self , axis = None , skipna = None ) : _func_name = 'Method idxmin().' ty_checker = TypeChecker ( _func_name ) ty_checker . check ( self , SeriesType ) if not isinstance ( self . data . dtype , types . Number ) : ty_checker . raise_exc ( self . data . dtype , 'int, float' , 'self.data.dtype' ) if not ( isinstance ( skipna , ( types . Omitted , types . Boolean , bool ) ) or skipna is None ) : ty_checker . raise_exc ( skipna , 'bool' , 'skipna' ) if not ( isinstance ( axis , types . Omitted ) or axis is None ) : ty_checker . raise_exc ( axis , 'None' , 'axis' ) positional_index = isinstance ( self . index , PositionalIndexType ) if isinstance ( self . data , StringArrayType ) : def hpat_pandas_series_idxmin_str_impl ( self , axis = None , skipna = None ) : if skipna is None : _skipna = True else : raise ValueError ( "Method idxmin(). Unsupported parameter 'skipna'=False with str data" ) result = numpy . argmin ( self . _data ) if positional_index == True : # noqa return result else : return self . _index [ int ( result ) ] return hpat_pandas_series_idxmin_str_impl def hpat_pandas_series_idxmin_impl ( self , axis = None , skipna = None ) : # return numpy.argmin(self._data) if skipna is None : _skipna = True else : _skipna = skipna if _skipna : result = numpy_like . nanargmin ( self . _data ) else : result = numpy_like . argmin ( self . _data ) if positional_index == True : # noqa return result else : return self . _index [ int ( result ) ] return numpy_like . argmin ( self . _data ) return hpat_pandas_series_idxmin_impl