def max(self, axis=None, skipna: bool=True, *args, **kwargs):
    "\n        Return the maximum value of the Index.\n\n        Parameters\n        ----------\n        axis : int, optional\n            For compatibility with NumPy. Only 0 or None are allowed.\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        scalar\n            Maximum value.\n\n        See Also\n        --------\n        Index.min : Return the minimum value in an Index.\n        Series.max : Return the maximum value in a Series.\n        DataFrame.max : Return the maximum values in a DataFrame.\n\n        Examples\n        --------\n        >>> idx = pd.Index([3, 2, 1])\n        >>> idx.max()\n        3\n\n        >>> idx = pd.Index(['c', 'b', 'a'])\n        >>> idx.max()\n        'c'\n\n        For a MultiIndex, the maximum is determined lexicographically.\n\n        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])\n        >>> idx.max()\n        ('b', 2)\n        "
    nv.validate_minmax_axis(axis)
    nv.validate_max(args, kwargs)
    return nanops.nanmax(self._values, skipna=skipna)
Return the maximum value of the Index.

Parameters
----------
axis : int, optional
    For compatibility with NumPy. Only 0 or None are allowed.
skipna : bool, default True
    Exclude NA/null values when showing the result.
*args, **kwargs
    Additional arguments and keywords for compatibility with NumPy.

Returns
-------
scalar
    Maximum value.

See Also
--------
Index.min : Return the minimum value in an Index.
Series.max : Return the maximum value in a Series.
DataFrame.max : Return the maximum values in a DataFrame.

Examples
--------
>>> idx = pd.Index([3, 2, 1])
>>> idx.max()
3

>>> idx = pd.Index(['c', 'b', 'a'])
>>> idx.max()
'c'

For a MultiIndex, the maximum is determined lexicographically.

>>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])
>>> idx.max()
('b', 2)
def max(self, axis=None, skipna: bool=True, *args, **kwargs):
    "\n        Return the maximum value of the Index.\n\n        Parameters\n        ----------\n        axis : int, optional\n            For compatibility with NumPy. Only 0 or None are allowed.\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        scalar\n            Maximum value.\n\n        See Also\n        --------\n        Index.min : Return the minimum value in an Index.\n        Series.max : Return the maximum value in a Series.\n        DataFrame.max : Return the maximum values in a DataFrame.\n\n        Examples\n        --------\n        >>> idx = pd.Index([3, 2, 1])\n        >>> idx.max()\n        3\n\n        >>> idx = pd.Index(['c', 'b', 'a'])\n        >>> idx.max()\n        'c'\n\n        For a MultiIndex, the maximum is determined lexicographically.\n\n        >>> idx = pd.MultiIndex.from_product([('a', 'b'), (2, 1)])\n        >>> idx.max()\n        ('b', 2)\n        "
    nv.validate_minmax_axis(axis)
    nv.validate_max(args, kwargs)
    return nanops.nanmax(self._values, skipna=skipna)