[Python] Csr matrix: How to replace missing value with np.nan instead of 0?

It seems that csr_matrix fill missing value with 0 in default. So how to fill the missing value with np.nan?

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()


Output:

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


Expected:

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

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