# pandas.Index.get_duplicates

`Index.``get_duplicates`()

  Extract duplicated index elements.

  Deprecated since version 0.23.0: Use idx[idx.duplicated()].unique() instead

  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()  # doctest: +SKIP
      [2, 3]

  Note that for a DatetimeIndex, it does not return a list but a new DatetimeIndex:

      >>> 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()  # doctest: +SKIP
      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()  # doctest: +SKIP
      [2, 3]

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

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

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