python - Iterating over arbitrary dimension of numpy.array

Question

Is there function to get an iterator over an arbitrary dimension of a numpy array?

Iterating over the first dimension is easy...

In [63]: c = numpy.arange(24).reshape(2,3,4)

In [64]: for r in c :
   ....:     print r
   ....: 
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
[[12 13 14 15]
 [16 17 18 19]
 [20 21 22 23]]

But iterating over other dimensions is harder. For example, the last dimension:

In [73]: for r in c.swapaxes(2,0).swapaxes(1,2) :
   ....:     print r
   ....: 
[[ 0  4  8]
 [12 16 20]]
[[ 1  5  9]
 [13 17 21]]
[[ 2  6 10]
 [14 18 22]]
[[ 3  7 11]
 [15 19 23]]

I'm making a generator to do this myself, but I'm surprised there isn't a function named something like numpy.ndarray.iterdim(axis=0) to do this automatically.

Answer 1

What you propose is quite fast, but the legibility can be improved with the clearer forms:

for i in range(c.shape[-1]):
    print c[:,:,i]

or, better (faster, more general and more explicit):

for i in range(c.shape[-1]):
    print c[...,i]

However, the first approach above appears to be about twice as slow as the swapaxes() approach:

python -m timeit -s 'import numpy; c = numpy.arange(24).reshape(2,3,4)' 
    'for r in c.swapaxes(2,0).swapaxes(1,2): u = r'
100000 loops, best of 3: 3.69 usec per loop

python -m timeit -s 'import numpy; c = numpy.arange(24).reshape(2,3,4)' 
    'for i in range(c.shape[-1]): u = c[:,:,i]'
100000 loops, best of 3: 6.08 usec per loop

python -m timeit -s 'import numpy; c = numpy.arange(24).reshape(2,3,4)' 
    'for r in numpy.rollaxis(c, 2): u = r'
100000 loops, best of 3: 6.46 usec per loop

I would guess that this is because swapaxes() does not copy any data, and because the handling of c[:,:,i] might be done through general code (that handles the case where : is replaced by a more complicated slice).

Note however that the more explicit second solution c[...,i] is both quite legible and quite fast:

python -m timeit -s 'import numpy; c = numpy.arange(24).reshape(2,3,4)' 
    'for i in range(c.shape[-1]): u = c[...,i]'
100000 loops, best of 3: 4.74 usec per loop