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SparseSeries.to_coo(row_levels=(0, ), column_levels=(1, ), sort_labels=False)
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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.
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)]
SparseSeries.to_coo()
2017-01-12 04:55:25
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