Classe « DataFrame »

Méthode pandas.DataFrame.duplicated

Signature de la méthode duplicated

def duplicated(self, subset: 'Optional[Union[Hashable, Sequence[Hashable]]]' = None, keep: 'Union[str, bool]' = 'first') -> 'Series' 

Description

duplicated.__doc__

        Return boolean Series denoting duplicate rows.

        Considering certain columns is optional.

        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 mark.

            - ``first`` : Mark duplicates as ``True`` except for the first occurrence.
            - ``last`` : Mark duplicates as ``True`` except for the last occurrence.
            - False : Mark all duplicates as ``True``.

        Returns
        -------
        Series
            Boolean series for each duplicated rows.

        See Also
        --------
        Index.duplicated : Equivalent method on index.
        Series.duplicated : Equivalent method on Series.
        Series.drop_duplicates : Remove duplicate values from Series.
        DataFrame.drop_duplicates : Remove duplicate values from DataFrame.

        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, for each set of duplicated values, the first occurrence
        is set on False and all others on True.

        >>> df.duplicated()
        0    False
        1     True
        2    False
        3    False
        4    False
        dtype: bool

        By using 'last', the last occurrence of each set of duplicated values
        is set on False and all others on True.

        >>> df.duplicated(keep='last')
        0     True
        1    False
        2    False
        3    False
        4    False
        dtype: bool

        By setting ``keep`` on False, all duplicates are True.

        >>> df.duplicated(keep=False)
        0     True
        1     True
        2    False
        3    False
        4    False
        dtype: bool

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

        >>> df.duplicated(subset=['brand'])
        0    False
        1     True
        2    False
        3     True
        4     True
        dtype: bool