Efficiently Row Standardize A Matrix

I need an efficient way to row standardize a sparse matrix. Given W = matrix([[0, 1, 0, 1, 0, 0, 0, 0, 0], [1, 0, 1, 0, 1, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0

Solution 1:

with a bit of matrix algebra

>>> cc
<9x9 sparse matrix of type '<type 'numpy.int32'>'
    with 24 stored elements in Compressed Sparse Row format>
>>> ccd = sparse.spdiags(1./cc.sum(1).T, 0, *cc.shape)
>>> ccn = ccd * cc
>>> np.round(ccn.todense(), 2)
array([[ 0.  ,  0.5 ,  0.  ,  0.5 ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ],
       [ 0.33,  0.  ,  0.33,  0.  ,  0.33,  0.  ,  0.  ,  0.  ,  0.  ],
       [ 0.  ,  0.5 ,  0.  ,  0.  ,  0.  ,  0.5 ,  0.  ,  0.  ,  0.  ],
       [ 0.33,  0.  ,  0.  ,  0.  ,  0.33,  0.  ,  0.33,  0.  ,  0.  ],
       [ 0.  ,  0.25,  0.  ,  0.25,  0.  ,  0.25,  0.  ,  0.25,  0.  ],
       [ 0.  ,  0.  ,  0.33,  0.  ,  0.33,  0.  ,  0.  ,  0.  ,  0.33],
       [ 0.  ,  0.  ,  0.  ,  0.5 ,  0.  ,  0.  ,  0.  ,  0.5 ,  0.  ],
       [ 0.  ,  0.  ,  0.  ,  0.  ,  0.33,  0.  ,  0.33,  0.  ,  0.33],
       [ 0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.5 ,  0.  ,  0.5 ,  0.  ]])
>>> ccn
<9x9 sparse matrix of type '<type 'numpy.float64'>'
    with 24 stored elements in Compressed Sparse Row format>