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The SciPy Sparse Matrix tutorial is very good -- but it actually leaves the section on slicing un(der)developed (still in outline form -- see section: "Handling Sparse Matrices"). I will try and update the tutorial, once this question is answered. I have a large sparse matrix -- currently in dok_matrix format. import numpy as np from scipy import sparse M = sparse.dok_matrix((10**6, 10**6)) For various methods I want to be able to slice columns and for others I want to slice rows. Ideally I would use advanced-indexing (i.e. a boolean vector, bool_vect ) with which to slice a sparse matrix M -- as in: bool_vect = np.arange(10**6)%2 # every even index out = M[bool_vect,:] # Want to select every even row or out = M[:,bool_vect] # Want to select every even column First off, dok_matrices do not support this -- but I think it works (slowly) if I first cast to lil_matrices, via sparse.lil_matrix(M) As far as I can gather from the tutorial -- to slice columns I want to use CSC and to slice rows I want to slice CSR. So does that mean I should cast the matrix M via: M.tocsc()[:,bool_vect] or M.tocsr()[bool_vect,:] I am kinda guessing here and my code is slow because of it. Any help from someone who understands how this works would be appreciated. Thanks in advance. If it turns out I should not be indexing my matrix with a boolean array, but rather a list of integers (indices) -- that is also something I would be happy to find out. Whichever is more efficient. Finally -- this is a big matrix, so bonus points if this can happen in place / with broadcasting.

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