pandas.SparseSeries.to_coo — pandas 0.16.2 documentation (original) (raw)

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).

New in version 0.16.0.

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)]