Classe « DataFrame »

Méthode pandas.DataFrame.drop_duplicates

Signature de la méthode drop_duplicates

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

Description

drop_duplicates.__doc__

        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 drop duplicates in place or to return a copy.
        ignore_index : bool, default False
            If True, the resulting axis will be labeled 0, 1, ..., n - 1.

            .. versionadded:: 1.0.0

        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