Iterating through a scipy.sparse vector (or matrix)

Question

I\'m wondering what the best way is to iterate nonzero entries of sparse matrices with scipy.sparse. For example, if I do the following:

from scipy.sparse im         


        

Answer 1

Edit: bbtrb's method (using coo_matrix) is much faster than my original suggestion, using nonzero. Sven Marnach's suggestion to use itertools.izip also improves the speed. Current fastest is using_tocoo_izip:

import scipy.sparse
import random
import itertools

def using_nonzero(x):
    rows,cols = x.nonzero()
    for row,col in zip(rows,cols):
        ((row,col), x[row,col])

def using_coo(x):
    cx = scipy.sparse.coo_matrix(x)    
    for i,j,v in zip(cx.row, cx.col, cx.data):
        (i,j,v)

def using_tocoo(x):
    cx = x.tocoo()    
    for i,j,v in zip(cx.row, cx.col, cx.data):
        (i,j,v)

def using_tocoo_izip(x):
    cx = x.tocoo()    
    for i,j,v in itertools.izip(cx.row, cx.col, cx.data):
        (i,j,v)

N=200
x = scipy.sparse.lil_matrix( (N,N) )
for _ in xrange(N):
    x[random.randint(0,N-1),random.randint(0,N-1)]=random.randint(1,100)

yields these timeit results:

% python -mtimeit -s'import test' 'test.using_tocoo_izip(test.x)'
1000 loops, best of 3: 670 usec per loop
% python -mtimeit -s'import test' 'test.using_tocoo(test.x)'
1000 loops, best of 3: 706 usec per loop
% python -mtimeit -s'import test' 'test.using_coo(test.x)'
1000 loops, best of 3: 802 usec per loop
% python -mtimeit -s'import test' 'test.using_nonzero(test.x)'
100 loops, best of 3: 5.25 msec per loop