/list
        column_levels : tuple/list
        sort_labels : bool, default False
            Sort the row and column labels before forming the sparse matrix.
            When `row_levels` and/or `column_levels` refer to a single level,
            set to `True` for a faster execution.

        Returns
        -------
        y : scipy.sparse.coo_matrix
        rows : list (row labels)
        columns : list (column labels)

        Examples
        --------
        >>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
        >>> s.index = pd.MultiIndex.from_tuples(
        ...     [
        ...         (1, 2, "a", 0),
        ...         (1, 2, "a", 1),
        ...         (1, 1, "b", 0),
        ...         (1, 1, "b", 1),
        ...         (2, 1, "b", 0),
        ...         (2, 1, "b", 1)
        ...     ],
        ...     names=["A", "B", "C", "D"],
        ... )
        >>> s
        A  B  C  D
        1  2  a  0    3.0
                 1    NaN
           1  b  0    1.0
                 1    3.0
        2  1  b  0    NaN
                 1    NaN
        dtype: float64

        >>> ss = s.astype("Sparse")
        >>> ss
        A  B  C  D
        1  2  a  0    3.0
                 1    NaN
           1  b  0    1.0
                 1    3.0
        2  1  b  0    NaN
                 1    NaN
        dtype: Sparse[float64, nan]

        >>> A, rows, columns = ss.sparse.to_coo(
        ...     row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
        ... )
        >>> A
        <3x4 sparse matrix of type '<class 'numpy.float64'>'
        with 3 stored elements in COOrdinate format>
        >>> A.todense()
        matrix([[0., 0., 1., 3.],
        [3., 0., 0., 0.],
        [0., 0., 0., 0.]])

        >>> rows
        [(1, 1), (1, 2), (2, 1)]
        >>> columns
        [('a', 0), ('a', 1), ('b', 0), ('b', 1)]
        r