python - Iterating through a scipy.sparse vector (or matrix) -

i'm wondering best way iterate nonzero entries of sparse matrices scipy.sparse. example, if following:

from scipy.sparse import lil_matrix  x = lil_matrix( (20,1) ) x[13,0] = 1 x[15,0] = 2  c = 0 in x:   print c,   c = c+1 

the output is

0  1  2  3  4  5  6  7  8  9  10  11  12  13   (0, 0) 1.0 14  15   (0, 0) 2.0 16  17  18  19   

so appears iterator touching every element, not nonzero entries. i've had @ api

http://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.lil_matrix.html

and searched around bit, can't seem find solution works.

edit: bbtrb's method (using coo_matrix) faster original suggestion, using nonzero. sven marnach's suggestion use itertools.izip improves speed. current fastest using_tocoo_izip:

import scipy.sparse import random import itertools  def using_nonzero(x):     rows,cols = x.nonzero()     row,col in zip(rows,cols):         ((row,col), x[row,col])  def using_coo(x):     cx = scipy.sparse.coo_matrix(x)         i,j,v in zip(cx.row, cx.col, cx.data):         (i,j,v)  def using_tocoo(x):     cx = x.tocoo()         i,j,v in zip(cx.row, cx.col, cx.data):         (i,j,v)  def using_tocoo_izip(x):     cx = x.tocoo()         i,j,v in itertools.izip(cx.row, cx.col, cx.data):         (i,j,v)  n=200 x = scipy.sparse.lil_matrix( (n,n) ) _ 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