pyspark.pandas.Series.drop_duplicates

Series.drop_duplicates(keep: Union[bool, str] = 'first', inplace: bool = False) → Optional[pyspark.pandas.series.Series]

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.

Returns

Series

Series with duplicates dropped.

Examples

Generate a Series with duplicated entries.

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

The value False for parameter ‘keep’ discards all sets of duplicated entries. Setting the value of ‘inplace’ to True performs the operation inplace and returns None.

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

pyspark.pandas.Series.droplevel pyspark.pandas.Series.equals