Why is numpy.cumsum (along axis=0) so much slower than a simple loop? The
same goes for numpy.add.accumulate
# cumsumtest.py
import numpy as np
def loopcumsum(a):
csum = np.empty_like(a)
s = 0.0
for i in range(len(a)):
csum[i] = s = s + a[i]
return csum
npcumsum = lambda a: np.cumsum(a, axis=0)
addaccum = lambda a: np.add.accumulate(a)
shape = (100, 8, 512)
a = np.arange(np.prod(shape), dtype='f').reshape(shape)
# check that we get the same results
print (npcumsum(a)==loopcumsum(a)).all()
print (addaccum(a)==loopcumsum(a)).all()
ipython session:
In [1]: from cumsumtest import *
True
True
In [2]: timeit npcumsum(a)
100 loops, best of 3: 14.7 ms per loop
In [3]: timeit addaccum(a)
100 loops, best of 3: 15.4 ms per loop
In [4]: timeit loopcumsum(a)
100 loops, best of 3: 2.16 ms per loop
Dave Cook
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