pandas.DataFrame.drop_duplicates#

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

Return DataFrame with duplicate rows removed.

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

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 modify the DataFrame rather than creating a new one.

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.

See also

DataFrame.value_counts

Count unique combinations of columns.

Notes

This method requires columns specified by subset to be of hashable type. Passing unhashable columns will raise a TypeError.

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