Asked By – Ram Rachum
Is there a less verbose alternative to this:
for x in xrange(array.shape): for y in xrange(array.shape): do_stuff(x, y)
I came up with this:
for x, y in itertools.product(map(xrange, array.shape)): do_stuff(x, y)
Which saves one indentation, but is still pretty ugly.
I’m hoping for something that looks like this pseudocode:
for x, y in array.indices: do_stuff(x, y)
Does anything like that exist?
Now we will see solution for issue: Iterating over a numpy array
I think you’re looking for the ndenumerate.
>>> a =numpy.array([[1,2],[3,4],[5,6]]) >>> for (x,y), value in numpy.ndenumerate(a): ... print x,y ... 0 0 0 1 1 0 1 1 2 0 2 1
Regarding the performance. It is a bit slower than a list comprehension.
X = np.zeros((100, 100, 100)) %timeit list([((i,j,k), X[i,j,k]) for i in range(X.shape) for j in range(X.shape) for k in range(X.shape)]) 1 loop, best of 3: 376 ms per loop %timeit list(np.ndenumerate(X)) 1 loop, best of 3: 570 ms per loop
If you are worried about the performance you could optimise a bit further by looking at the implementation of
ndenumerate, which does 2 things, converting to an array and looping. If you know you have an array, you can call the
.coords attribute of the flat iterator.
a = X.flat %timeit list([(a.coords, x) for x in a.flat]) 1 loop, best of 3: 305 ms per loop
This question is answered By – SiggyF
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