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DOC: update the Index.get duplicates docstring #20321

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48 changes: 48 additions & 0 deletions pandas/core/indexes/base.py
Original file line number Diff line number Diff line change
Expand Up @@ -1710,6 +1710,54 @@ def _invalid_indexer(self, form, key):
kind=type(key)))

def get_duplicates(self):
"""
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)
"""
from collections import defaultdict
counter = defaultdict(lambda: 0)
for k in self.values:
Expand Down