pandas.Series.sparse.to_coo#

Series.sparse.to_coo(row_levels=(0,), column_levels=(1,), sort_labels=False)[source]#

Create a scipy.sparse.coo_matrix from a Series 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_levelstuple/list: MultiIndex levels to use for row coordinates, specified by name or index.
column_levelstuple/list: MultiIndex levels to use for column coordinates, specified by name or index.
sort_labelsbool, default False: Sort the row and column labels before forming the sparse matrix. When row_levels and/or column_levels refer to a single level, set to True for a faster execution.

Returns:

yscipy.sparse.coo_matrix: The sparse matrix in coordinate format.
rowslist (row labels): Labels corresponding to the row coordinates.
columnslist (column labels): Labels corresponding to the column coordinates.

See also

Series.sparse.from_coo: Create a Series with sparse values from a scipy.sparse.coo_matrix.

Examples

>>> 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"],
... )
>>> 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")
>>> 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(
...     row_levels=["A", "B"], column_levels=["C", "D"], sort_labels=True
... )
>>> A
<COOrdinate sparse matrix of dtype 'float64'
    with 3 stored elements and shape (3, 4)>
>>> 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)]

pandas.Series.sparse.to_coo#

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