SparseSeries.to_coo(self, 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: |
|
---|---|
Returns: |
|
>>> 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)]
© 2008–2012, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
Licensed under the 3-clause BSD License.
https://pandas.pydata.org/pandas-docs/version/0.25.0/reference/api/pandas.SparseSeries.to_coo.html