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_levels:tuple/list

column_levels:tuple/list

sort_labels:bool, 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

y:scipy.sparse.coo_matrix

rows:list (row labels)

columns:list (column labels)

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
<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)]