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