to_coo(self, row_levels=(0,), column_levels=(1,), sort_labels=False)
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).
Sort the row and column labels before forming the sparse matrix. When :None:None:`row_levels` and/or :None:None:`column_levels` refer to a single level, set to :None:None:`True` for a faster execution.
Create a scipy.sparse.coo_matrix from a Series with MultiIndex.
>>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])This example is valid syntax, but we were not able to check execution
... 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"],
... )
... s A B C D 1 2 a 0 3.0 1 NaN 1 b 0 1.0 1 3.0 2 1 b 0 NaN 1 NaN dtype: float64
>>> ss = s.astype("Sparse")This example is valid syntax, but we were not able to check execution
... ss A B C D 1 2 a 0 3.0 1 NaN 1 b 0 1.0 1 3.0 2 1 b 0 NaN 1 NaN dtype: Sparse[float64, nan]
>>> A, rows, columns = ss.sparse.to_coo(This example is valid syntax, but we were not able to check execution
... 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.]])This example is valid syntax, but we were not able to check execution
>>> rows [(1, 1), (1, 2), (2, 1)]This example is valid syntax, but we were not able to check execution
>>> columns [('a', 0), ('a', 1), ('b', 0), ('b', 1)]See :
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