@doc(op='max', oppose='min', value='largest')
def argmax(self, axis=None, skipna: bool=True, *args, **kwargs) -> int:
    "\n        Return int position of the {value} value in the Series.\n\n        If the {op}imum is achieved in multiple locations,\n        the first row position is returned.\n\n        Parameters\n        ----------\n        axis : {{None}}\n            Dummy argument for consistency with Series.\n        skipna : bool, default True\n            Exclude NA/null values when showing the result.\n        *args, **kwargs\n            Additional arguments and keywords for compatibility with NumPy.\n\n        Returns\n        -------\n        int\n            Row position of the {op}imum value.\n\n        See Also\n        --------\n        Series.arg{op} : Return position of the {op}imum value.\n        Series.arg{oppose} : Return position of the {oppose}imum value.\n        numpy.ndarray.arg{op} : Equivalent method for numpy arrays.\n        Series.idxmax : Return index label of the maximum values.\n        Series.idxmin : Return index label of the minimum values.\n\n        Examples\n        --------\n        Consider dataset containing cereal calories\n\n        >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,\n        ...                'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})\n        >>> s\n        Corn Flakes              100.0\n        Almond Delight           110.0\n        Cinnamon Toast Crunch    120.0\n        Cocoa Puff               110.0\n        dtype: float64\n\n        >>> s.argmax()\n        2\n        >>> s.argmin()\n        0\n\n        The maximum cereal calories is the third element and\n        the minimum cereal calories is the first element,\n        since series is zero-indexed.\n        "
    delegate = self._values
    nv.validate_minmax_axis(axis)
    skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
    if isinstance(delegate, ExtensionArray):
        if ((not skipna) and delegate.isna().any()):
            return (- 1)
        else:
            return delegate.argmax()
    else:
        return nanops.nanargmax(delegate, skipna=skipna)
Return int position of the {value} value in the Series.

If the {op}imum is achieved in multiple locations,
the first row position is returned.

Parameters
----------
axis : {{None}}
    Dummy argument for consistency with Series.
skipna : bool, default True
    Exclude NA/null values when showing the result.
*args, **kwargs
    Additional arguments and keywords for compatibility with NumPy.

Returns
-------
int
    Row position of the {op}imum value.

See Also
--------
Series.arg{op} : Return position of the {op}imum value.
Series.arg{oppose} : Return position of the {oppose}imum value.
numpy.ndarray.arg{op} : Equivalent method for numpy arrays.
Series.idxmax : Return index label of the maximum values.
Series.idxmin : Return index label of the minimum values.

Examples
--------
Consider dataset containing cereal calories

>>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,
...                'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})
>>> s
Corn Flakes              100.0
Almond Delight           110.0
Cinnamon Toast Crunch    120.0
Cocoa Puff               110.0
dtype: float64

>>> s.argmax()
2
>>> s.argmin()
0

The maximum cereal calories is the third element and
the minimum cereal calories is the first element,
since series is zero-indexed.
@doc(op='max', oppose='min', value='largest')
def argmax(self, axis=None, skipna: bool=True, *args, **kwargs) -> int:
    "\n        Return int position of the {value} value in the Series.\n\n        If the {op}imum is achieved in multiple locations,\n        the first row position is returned.\n\n        Parameters\n        ----------\n        axis : {{None}}\n            Dummy argument for consistency with Series.\n        skipna : bool, default True\n            Exclude NA/null values when showing the result.\n        *args, **kwargs\n            Additional arguments and keywords for compatibility with NumPy.\n\n        Returns\n        -------\n        int\n            Row position of the {op}imum value.\n\n        See Also\n        --------\n        Series.arg{op} : Return position of the {op}imum value.\n        Series.arg{oppose} : Return position of the {oppose}imum value.\n        numpy.ndarray.arg{op} : Equivalent method for numpy arrays.\n        Series.idxmax : Return index label of the maximum values.\n        Series.idxmin : Return index label of the minimum values.\n\n        Examples\n        --------\n        Consider dataset containing cereal calories\n\n        >>> s = pd.Series({{'Corn Flakes': 100.0, 'Almond Delight': 110.0,\n        ...                'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}})\n        >>> s\n        Corn Flakes              100.0\n        Almond Delight           110.0\n        Cinnamon Toast Crunch    120.0\n        Cocoa Puff               110.0\n        dtype: float64\n\n        >>> s.argmax()\n        2\n        >>> s.argmin()\n        0\n\n        The maximum cereal calories is the third element and\n        the minimum cereal calories is the first element,\n        since series is zero-indexed.\n        "
    delegate = self._values
    nv.validate_minmax_axis(axis)
    skipna = nv.validate_argmax_with_skipna(skipna, args, kwargs)
    if isinstance(delegate, ExtensionArray):
        if ((not skipna) and delegate.isna().any()):
            return (- 1)
        else:
            return delegate.argmax()
    else:
        return nanops.nanargmax(delegate, skipna=skipna)