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
- column_levelstuple/list
- 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
- 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"], ... ) >>> 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)]