code: def max(self, axis=None, skipna=True):
          
          nv.validate_minmax_axis(axis)
          return nanops.nanmax(self._values, skipna=skipna)
docs: 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)