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# pandas.Series.drop_duplicates

Series.drop_duplicates(*, keep='first', inplace=False, ignore_index=False)

  Return Series with duplicate values removed.

  Parameters:
    keep{‘first’, ‘last’, `False`}, default ‘first’

      Method to handle dropping duplicates:

      - ‘first’ : Drop duplicates except for the first occurrence.

      - ‘last’ : Drop duplicates except for the last occurrence.

      - `False` : Drop all duplicates.

    inplacebool, default `False`

      If `True`, performs operation inplace and returns None.

    ignore_indexbool, default `False`

      If `True`, the resulting axis will be labeled 0, 1, …, n - 1.

      Added in version 2.0.0.

  Returns:
    Series or None

      Series with duplicates dropped or None if `inplace=True`.

  See also

  `Index.drop_duplicates`

    Equivalent method on Index.

  `DataFrame.drop_duplicates`

    Equivalent method on DataFrame.

  `Series.duplicated`

    Related method on Series, indicating duplicate Series values.

  `Series.unique`

    Return unique values as an array.

  Examples

  Generate a Series with duplicated entries.

      >>> s = pd.Series(['llama', 'cow', 'llama', 'beetle', 'llama', 'hippo'],
      ...               name='animal')
      >>> s
      0     llama
      1       cow
      2     llama
      3    beetle
      4     llama
      5     hippo
      Name: animal, dtype: object

  With the ‘keep’ parameter, the selection behaviour of duplicated values can be changed. The value ‘first’ keeps the first occurrence for each set of duplicated entries. The default value of keep is ‘first’.

      >>> s.drop_duplicates()
      0     llama
      1       cow
      3    beetle
      5     hippo
      Name: animal, dtype: object

  The value ‘last’ for parameter ‘keep’ keeps the last occurrence for each set of duplicated entries.

      >>> s.drop_duplicates(keep='last')
      1       cow
      3    beetle
      4     llama
      5     hippo
      Name: animal, dtype: object

  The value `False` for parameter ‘keep’ discards all sets of duplicated entries.

      >>> s.drop_duplicates(keep=False)
      1       cow
      3    beetle
      5     hippo
      Name: animal, dtype: object