python - Csr 矩阵 : How to replace missing value with np. nan 而不是 0？

Question

似乎 csr_matrix 默认用 0 填充缺失值。那么如何用np.nan来填充缺失值呢？

from scipy.sparse import csr_matrix
row = np.array([0, 0, 1, 2, 2, 2])
col = np.array([0, 2, 2, 0, 1, 2])
data = np.array([0, 2, 3, 4, 5, 6])
csr_matrix((data, (row, col)), shape=(3, 3)).toarray()

输出:

array([[0, 0, 2],
       [0, 0, 3],
       [4, 5, 6]])

预期:

array([[0, np.nan, 2],
       [np.nan, np.nan, 3],
       [4, 5, 6]])

Answer 1

解决方法如下:

from scipy.sparse import csr_matrix
row = np.array([0, 0, 1, 2, 2, 2])
col = np.array([0, 2, 2, 0, 1, 2])
data = np.array([0, 2, 3, 4, 5, 6])

mask = csr_matrix(([1]*len(data), (row, col)), shape=(3, 3)).toarray()
mask[mask==0] = np.nan

csr_matrix((data, (row, col)), shape=(3, 3)).toarray() * mask