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.

inplace:bool, default False

If True, performs operation inplace and returns None.

ignore_index:bool, 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