pandas.Series.sparse.to_coo¶

Series.sparse.to_coo(self, 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
column_levelstuple/list
sort_labelsbool, default False: Sort the row and column labels before forming the sparse matrix.

Returns

yscipy.sparse.coo_matrix
rowslist (row labels)
columnslist (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'])
>>> ss = s.astype("Sparse")
>>> 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)]

pandas.Series.sparse.from_coo pandas.Series.plot.area