Scipy Sparse Cumsum

Suppose I have a scipy.sparse.csr_matrix representing the values below

[[0 0 1 2 0 3 0 4]
 [1 0 0 2 0 3 4 0]]

I want to calculate the cumulative sum of non-zero values in-place, which would change the array to:

[[0 0 1 3 0 6 0 10]
 [1 0 0 3 0 6 10 0]]

The actual values are not 1, 2, 3, ...

The number of non-zero values in each row are unlikely to be the same.

How to do this fast?

Current program:

import scipy.sparse
import numpy as np

# sparse data
a = scipy.sparse.csr_matrix(
    [[0,0,1,2,0,3,0,4],
     [1,0,0,2,0,3,4,0]], 
    dtype=int)

# method
indptr = a.indptr
data = a.data
for i in range(a.shape[0]):
    st = indptr[i]
    en = indptr[i + 1]
    np.cumsum(data[st:en], out=data[st:en])

# print result
print(a.todense())

Result:

[[ 0  0  1  3  0  6  0 10]
 [ 1  0  0  3  0  6 10  0]]