pythonnumpy遍历_遍历numpy.array的任意维度
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.
解决方案
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