pandas.Series.drop_duplicates#

Series.drop_duplicates(*, keep='first', inplace=False, ignore_index=False)[source]#

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.

New 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