Iterating over arbitrary dimension of numpy.array in Python Programming Language?

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

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``````for i in range(c.shape[-1]):"
print c[:,:,i]"
``````
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or, better (faster, more general and more explicit):

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``````for i in range(c.shape[-1]):"
print c[...,i]"
``````
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However, the first approach above appears to be about twice as slow as the `swapaxes()` approach:

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``````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"
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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"
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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"
``````
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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).

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

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``````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"
``````
"

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