################################################################################
################### Docstring (pandas.Index.get_duplicates)  ###################
################################################################################

Extract duplicated index elements.

Returns a sorted list of index elements which appear more than once in
the index.

Returns
-------
array-like
    List of duplicated indexes.

See Also
--------
Index.duplicated : Return boolean array denoting duplicates.
Index.drop_duplicates : Return Index with duplicates removed.

Examples
--------

Works on different Index of types.

>>> pd.Index([1, 2, 2, 3, 3, 3, 4]).get_duplicates()
[2, 3]
>>> pd.Index([1., 2., 2., 3., 3., 3., 4.]).get_duplicates()
[2.0, 3.0]
>>> pd.Index(['a', 'b', 'b', 'c', 'c', 'c', 'd']).get_duplicates()
['b', 'c']
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-02', '2018-01-03',
...                         '2018-01-03', '2018-01-04', '2018-01-04'],
...                        format='%Y-%m-%d')
>>> pd.Index(dates).get_duplicates()
DatetimeIndex(['2018-01-03', '2018-01-04'],
              dtype='datetime64[ns]', freq=None)

Sorts duplicated elements even when indexes are unordered.

>>> pd.Index([1, 2, 3, 2, 3, 4, 3]).get_duplicates()
[2, 3]

Return empty array-like structure when all elements are unique.

>>> pd.Index([1, 2, 3, 4]).get_duplicates()
[]
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-02', '2018-01-03'],
...                        format='%Y-%m-%d')
>>> pd.Index(dates).get_duplicates()
DatetimeIndex([], dtype='datetime64[ns]', freq=None)

################################################################################
################################## Validation ##################################
################################################################################

Docstring for "pandas.Index.get_duplicates" correct. :)