def max(self, axis=None, skipna=True):
nv.validate_minmax_axis(axis)<EOL>return nanops.nanmax(self._values, skipna=skipna)<EOL>
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

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)