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Classe « DataFrame »

Méthode pandas.DataFrame.drop_duplicates

Signature de la méthode drop_duplicates

def drop_duplicates(self, subset: 'Hashable | Sequence[Hashable] | None' = None, *, keep: 'DropKeep' = 'first', inplace: 'bool' = False, ignore_index: 'bool' = False) -> 'DataFrame | None' 

Description

help(DataFrame.drop_duplicates)

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 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.

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 or None
    DataFrame with duplicates removed or None if ``inplace=True``.

See Also
--------
DataFrame.value_counts: Count unique combinations of columns.

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

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