Iterating through a scipy.sparse vector (or matrix)

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 import lil_matrix

x = lil_matrix( (20,1) )
x[13,0] = 1
x[15,0] = 2

c = 0
for i in x:
print c, i
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 it appears the iterator is touching every element, not just the nonzero entries. I've had a look at the API

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

and searched around a bit, but I can't seem to find a solution that works.

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
```