I found the various answers really elegant (especially Alex Martelli's) but I wanted to quantify performance first hand, so I cooked up the following script:
from itertools import repeat
N = 10000000
def payload(a):
pass
def standard(N):
for x in range(N):
payload(None)
def underscore(N):
for _ in range(N):
payload(None)
def loopiter(N):
for _ in repeat(None, N):
payload(None)
def loopiter2(N):
for _ in map(payload, repeat(None, N)):
pass
if __name__ == '__main__':
import timeit
print("standard: ",timeit.timeit("standard({})".format(N),
setup="from __main__ import standard", number=1))
print("underscore: ",timeit.timeit("underscore({})".format(N),
setup="from __main__ import underscore", number=1))
print("loopiter: ",timeit.timeit("loopiter({})".format(N),
setup="from __main__ import loopiter", number=1))
print("loopiter2: ",timeit.timeit("loopiter2({})".format(N),
setup="from __main__ import loopiter2", number=1))
I also came up with an alternative solution that builds on Martelli's one and uses map()
to call the payload function. OK, I cheated a bit in that I took the freedom of making the payload accept a parameter that gets discarded: I don't know if there is a way around this. Nevertheless, here are the results:
standard: 0.8398549720004667
underscore: 0.8413165839992871
loopiter: 0.7110594899968419
loopiter2: 0.5891903560004721
so using map yields an improvement of approximately 30% over the standard for loop and an extra 19% over Martelli's.