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

Question

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].

Asked By: zell

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Source

Answer 1

I think you can use:

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

output: [0, 3]

Answered By: mozway

Answer 2

from scipy.sparse import csr_matrix                                                                     
                                                                                                        
A = csr_matrix([[1,2,0],[0,0,3],[4,0,5]])                                                               
                                                                                                        
nonzero_indices = A.nonzero()

nonzero() will return a tuple of two lists containing the indices you’re looking for. For example:

for i,_ in enumerate(nonzero_indices[0]):                                                               
    print(nonzero_indices[0][i], nonzero_indices[1][i])

will give

Answered By: JustLearning

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

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