modin.pandas.DataFrame.drop_duplicates

DataFrame.drop_duplicates(subset=None, *, keep='first', inplace=False, ignore_index=False) → Optional[DataFrame][source]

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Parameters:

  • subset (column label or sequence of labels, optional) – Only consider certain columns for identifying duplicates, by default use all columns.

  • keep ({'first', 'last', False}, default 'first') – Determines which duplicates (if any) to keep. ‘first’ : Drop duplicates except for the first occurrence. ‘last’ : Drop duplicates except for the last occurrence. False : Drop all duplicates.

  • inplace (bool, default False) – Whether to modify the DataFrame rather than creating a new one.

  • ignore_index (bool, default False) – If True, the resulting axis will be labeled 0, 1, …, n - 1.

Returns:

DataFrame with duplicates removed or None if inplace=True.

Return type:

DataFrame or None

Examples

Consider dataset containing ramen rating.

>>> df = pd.DataFrame({
...     'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
...     'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
...     'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
     brand style  rating
0  Yum Yum   cup     4.0
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

By default, it removes duplicate rows based on all columns.

>>> df.drop_duplicates()
     brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5
3  Indomie  pack    15.0
4  Indomie  pack     5.0

To remove duplicates on specific column(s), use subset.

>>> df.drop_duplicates(subset=['brand'])
     brand style  rating
0  Yum Yum   cup     4.0
2  Indomie   cup     3.5

To remove duplicates and keep last occurrences, use keep.

>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
     brand style  rating
1  Yum Yum   cup     4.0
2  Indomie   cup     3.5
4  Indomie  pack     5.0