.

        Notes
        -----
        This routine will explode list-likes including lists, tuples, sets,
        Series, and np.ndarray. The result dtype of the subset rows will
        be object. Scalars will be returned unchanged, and empty list-likes will
        result in a np.nan for that row. In addition, the ordering of rows in the
        output will be non-deterministic when exploding sets.

        Reference :ref:`the user guide <reshaping.explode>` for more examples.

        Examples
        --------
        >>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],
        ...                    'B': 1,
        ...                    'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
        >>> df
                   A  B          C
        0  [0, 1, 2]  1  [a, b, c]
        1        foo  1        NaN
        2         []  1         []
        3     [3, 4]  1     [d, e]

        Single-column explode.

        >>> df.explode('A')
             A  B          C
        0    0  1  [a, b, c]
        0    1  1  [a, b, c]
        0    2  1  [a, b, c]
        1  foo  1        NaN
        2  NaN  1         []
        3    3  1     [d, e]
        3    4  1     [d, e]

        Multi-column explode.

        >>> df.explode(list('AC'))
             A  B    C
        0    0  1    a
        0    1  1    b
        0    2  1    c
        1  foo  1  NaN
        2  NaN  1  NaN
        3    3  1    d
        3    4  1    e
        z5DataFrame columns must be unique. Duplicate columns: c