How to find the indices of columns that are not entirely zeros of a sparse matrix

I have a large sparse array (Python csr). How can I find the indices of columns that are not entirely zeros?

For example, if the matrix looks like s constructed below

In [13]: import  scipy.sparse as sparse

In [14]: s=sparse.dok_matrix((2,4))

In [15]: s[0,0]=8; s[0,3]=9

In [16]:  print (s.toarray())
[[8. 0. 0. 9.]
 [0. 0. 0. 0.]]

The nonzero indices for the matrix s will be [0,3].

>Solution :

I think you can use:

import numpy as np
np.nonzero((s!=0).sum(0))[1]

output: [0, 3]