in place and return None.
        errors : {'ignore', 'raise'}, default 'raise'
            If 'ignore', suppress error and only existing labels are
            dropped.

        Returns
        -------
        DataFrame or None
            Returns DataFrame or None DataFrame with the specified
            index or column labels removed 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=[['llama', '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
        llama   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

        Drop a specific index combination from the MultiIndex
        DataFrame, i.e., drop the combination ``'falcon'`` and
        ``'weight'``, which deletes only the corresponding row

        >>> df.drop(index=('falcon', 'weight'))
                        big     small
        llama   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
                length  0.3     0.2

        >>> df.drop(index='cow', columns='small')
                        big
        llama   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
        llama   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
        )