Classe « DataFrame »

Méthode pandas.DataFrame.drop

Signature de la méthode drop

def drop(self, labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise') 

Description

drop.__doc__

        Drop specified labels from rows or columns.

        Remove rows or columns by specifying label names and corresponding
        axis, or by specifying directly index or column names. When using a
        multi-index, labels on different levels can be removed by specifying
        the level.

        Parameters
        ----------
        labels : single label or list-like
            Index or column labels to drop.
        axis : {0 or 'index', 1 or 'columns'}, default 0
            Whether to drop labels from the index (0 or 'index') or
            columns (1 or 'columns').
        index : single label or list-like
            Alternative to specifying axis (``labels, axis=0``
            is equivalent to ``index=labels``).
        columns : single label or list-like
            Alternative to specifying axis (``labels, axis=1``
            is equivalent to ``columns=labels``).
        level : int or level name, optional
            For MultiIndex, level from which the labels will be removed.
        inplace : bool, default False
            If False, return a copy. Otherwise, do operation
            inplace and return None.
        errors : {'ignore', 'raise'}, default 'raise'
            If 'ignore', suppress error and only existing labels are
            dropped.

        Returns
        -------
        DataFrame or None
            DataFrame without the removed index or column labels or
            None if ``inplace=True``.

        Raises
        ------
        KeyError
            If any of the labels is not found in the selected axis.

        See Also
        --------
        DataFrame.loc : Label-location based indexer for selection by label.
        DataFrame.dropna : Return DataFrame with labels on given axis omitted
            where (all or any) data are missing.
        DataFrame.drop_duplicates : Return DataFrame with duplicate rows
            removed, optionally only considering certain columns.
        Series.drop : Return Series with specified index labels removed.

        Examples
        --------
        >>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
        ...                   columns=['A', 'B', 'C', 'D'])
        >>> df
           A  B   C   D
        0  0  1   2   3
        1  4  5   6   7
        2  8  9  10  11

        Drop columns

        >>> df.drop(['B', 'C'], axis=1)
           A   D
        0  0   3
        1  4   7
        2  8  11

        >>> df.drop(columns=['B', 'C'])
           A   D
        0  0   3
        1  4   7
        2  8  11

        Drop a row by index

        >>> df.drop([0, 1])
           A  B   C   D
        2  8  9  10  11

        Drop columns and/or rows of MultiIndex DataFrame

        >>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
        ...                              ['speed', 'weight', 'length']],
        ...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
        ...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
        >>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
        ...                   data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
        ...                         [250, 150], [1.5, 0.8], [320, 250],
        ...                         [1, 0.8], [0.3, 0.2]])
        >>> df
                        big     small
        lama    speed   45.0    30.0
                weight  200.0   100.0
                length  1.5     1.0
        cow     speed   30.0    20.0
                weight  250.0   150.0
                length  1.5     0.8
        falcon  speed   320.0   250.0
                weight  1.0     0.8
                length  0.3     0.2

        >>> df.drop(index='cow', columns='small')
                        big
        lama    speed   45.0
                weight  200.0
                length  1.5
        falcon  speed   320.0
                weight  1.0
                length  0.3

        >>> df.drop(index='length', level=1)
                        big     small
        lama    speed   45.0    30.0
                weight  200.0   100.0
        cow     speed   30.0    20.0
                weight  250.0   150.0
        falcon  speed   320.0   250.0
                weight  1.0     0.8