I was surprised to see that an in-place modification of a 2-d array
turns out to be slower from the respective non-mutating operation on 1-
d arrays, although the latter creates new array objects. Here is the
benchmarking code:
import timeit
for n in 10,100,1000,10000:
setup = 'from numpy.random import random;' \
'm=random((%d,2));' \
'u1=random(%d);' \
'u2=u1.reshape((u1.size,1))' % (n,n)
timers = [timeit.Timer(stmt,setup) for stmt in
# 1-d operations; create new arrays
'a0 = m[:,0]-u1; a1 = m[:,1]-u1',
# 2-d in place operation
'm -= u2'
]
print n, [min(timer.repeat(3,1000)) for timer in timers]
And some results (Python 2.5, WinXP):
10 [0.010832382327921563, 0.0045706926438974782]
100 [0.010882668048592767, 0.021704993232380093]
1000 [0.018272154701226007, 0.19477587235249172]
10000 [0.073787590322233698, 1.9234369172618306]
So the 2-d in-place modification time grows linearly with the array
size but the 1-d operations are much more efficient, despite
allocating new arrays while doing so. What gives ?
George