def to_coo(self, row_levels=(<NUM_LIT:0>, ), column_levels=(<NUM_LIT:1>, ), sort_labels=False):
from pandas.core.sparse.scipy_sparse import _sparse_series_to_coo<EOL>A, rows, columns = _sparse_series_to_coo(self._parent,<EOL>row_levels,<EOL>column_levels,<EOL>sort_labels=sort_labels)<EOL>return A, rows, columns<EOL>
Create a scipy.sparse.coo_matrix from a SparseSeries with MultiIndex.

Use row_levels and column_levels to determine the row and column
coordinates respectively. row_levels and column_levels are the names
(labels) or numbers of the levels. {row_levels, column_levels} must be
a partition of the MultiIndex level names (or numbers).

Parameters
----------
row_levels : tuple/list
column_levels : tuple/list
sort_labels : bool, default False
    Sort the row and column labels before forming the sparse matrix.

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'])
>>> ss = s.to_sparse()
>>> A, rows, columns = ss.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)]