Is numpy.transpose reordering data in memory?

Question

In order to speed up the functions like np.std, np.sum etc along an axis of an n dimensional huge numpy array, it is recommended to apply along the last axis.

When I

Answer 1

Transpose just changes the strides, it doesn't touch the actual array. I think the reason why sum etc. along the final axis is recommended (I'd like to see the source for that, btw.) is that when an array is C-ordered, walking along the final axis preserves locality of reference. That won't be the case after you transpose, since the transposed array will be Fortran-ordered.

Answer 2

To elaborate on larsman's answer, here are some timings:

# normal C (row-major) order array
>>> %%timeit a = np.random.randn(500, 400)
>>> np.sum(a, axis=1)
1000 loops, best of 3: 272 us per loop

# transposing and summing along the first axis makes no real difference 
# to performance
>>> %%timeit a = np.random.randn(500, 400)
>>> np.sum(a.T, axis=0)
1000 loops, best of 3: 269 us per loop

# however, converting to Fortran (column-major) order does improve speed...
>>> %%timeit a = np.asfortranarray(np.random.randn(500,400))
>>> np.sum(a, axis=1)
10000 loops, best of 3: 114 us per loop

# ... but only if you don't count the conversion in the timed operations
>>> %%timeit a = np.random.randn(500, 400)
>>> np.sum(np.asfortranarray(a), axis=1)
1000 loops, best of 3: 599 us per loop

In summary, it might make sense to convert your arrays to Fortran order if you're going to apply a lot of operations over the columns, but the conversion itself is costly and almost certainly not worth it for a single operation.