cudf.DataFrame.drop_duplicates

DataFrame.drop_duplicates(subset=None, keep='first', inplace=False, ignore_index=False)

Return DataFrame with duplicate rows removed, optionally only considering certain subset of columns.

Parameters:

subsetcolumn label or sequence of labels, optional

Only consider certain columns for identifying duplicates, by default use all of the 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.

inplacebool, default False

Whether to drop duplicates in place or to return a copy.

ignore_indexbool, default False

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

Returns:

DataFrame or None

DataFrame with duplicates removed or None if inplace=True.

Examples

>>> import cudf
>>> df = cudf.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. Note that order of the rows being returned is not guaranteed to be sorted.

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

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

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

To remove duplicates and keep last occurrences, use keep.

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