Hi,
Is there a good reason for ndenumerate in numpy being slower than
standard indexing?
For example:
---
import numpy as np
def fast_itt(a):
for index, value in np.ndenumerate(a):
a[index] += 1
def slow_itt(a):
for r in range(0, a.shape[0]):
for c in range(0, a.shape[1]):
a[r,c] += 1
a = np.zeros((100,100))
%timeit fast_itt(a)
10 loops, best of 3: 25.7 ms per loop
%timeit slow_itt(a)
100 loops, best of 3: 13 ms per loop
---
I appreciate that there are better ways of operating on arrays but
there are many good reasons for permuting through indices and
ndenumerate is a nice way of this...
I am left wondering why it performs badly in this case.
Cheers,
Nathan.
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