def drop_duplicates(self, keep='<STR_LIT>'):
return super().drop_duplicates(keep=keep)<EOL>
Return Index with duplicate values removed.

Parameters
----------
keep : {'first', 'last', ``False``}, default 'first'
    - 'first' : Drop duplicates except for the first occurrence.
    - 'last' : Drop duplicates except for the last occurrence.
    - ``False`` : Drop all duplicates.

Returns
-------
deduplicated : Index

See Also
--------
Series.drop_duplicates : Equivalent method on Series.
DataFrame.drop_duplicates : Equivalent method on DataFrame.
Index.duplicated : Related method on Index, indicating duplicate
    Index values.

Examples
--------
Generate an pandas.Index with duplicate values.

>>> idx = pd.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])

The `keep` parameter controls  which duplicate values are removed.
The value 'first' keeps the first occurrence for each
set of duplicated entries. The default value of keep is 'first'.

>>> idx.drop_duplicates(keep='first')
Index(['lama', 'cow', 'beetle', 'hippo'], dtype='object')

The value 'last' keeps the last occurrence for each set of duplicated
entries.

>>> idx.drop_duplicates(keep='last')
Index(['cow', 'beetle', 'lama', 'hippo'], dtype='object')

The value ``False`` discards all sets of duplicated entries.

>>> idx.drop_duplicates(keep=False)
Index(['cow', 'beetle', 'hippo'], dtype='object')