pandas.SparseSeries.to_coo

SparseSeries.to_coo(row_levels=(0, ), column_levels=(1, ), sort_labels=False) [source]

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

>>> from numpy import nan
>>> s = Series([3.0, nan, 1.0, 3.0, nan, nan])
>>> s.index = 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)]