pandas.Series.sparse.to_coo#
- Series.sparse.to_coo(row_levels=(0,), column_levels=(1,), sort_labels=False)[源代码]#
从具有多索引的Series创建一个scipy.parse.coo_Matrix。
使用ROW_LEVES和COLUMN_LEVES分别确定行和列坐标。ROW_LEVELES和COLUMN_LEVES是级别的名称(标签)或编号。{ROW_LEVELES,COLUMN_LEVES}必须是多索引级别名称(或数字)的分区。
- 参数
- row_levels元组/列表
- column_levels元组/列表
- sort_labels布尔值,默认为False
在形成稀疏矩阵之前对行标签和列标签进行排序。什么时候 row_levels 和/或 column_levels 引用单个级别,设置为 True 为了更快的执行。
- 退货
- yscipy.sparse.coo_matrix
- rows列表(行标签)
- columns列表(列标签)
示例
>>> 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)]