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Mon Oct 20 18:15:23 EDT 2003 · https://mail.python.org/pipermail/python-dev/2003-October/039112.html

On Monday 20 October 2003 07:21 pm, Guido van Rossum wrote:
   ...
> 'average' or 'sum'.  Whether there is an actual gain in speed depends
> on how large the list is.  You should be able to time examples like
>
>    sum([x*x for x in R])
>
> vs.
>
>    def gen(R):
>        for x in R:
>            yield x*x
>    sum(gen(R))
>
> for various lengths of R.  (The latter would be a good indication of
> how fast an iterator generator could run.)

with a.py having:
def asum(R):
    sum([ x*x for x in R ])

def gen(R):
    for x in R: yield x*x
def gsum(R, gen=gen):
    sum(gen(R))

I measure:

[alex at lancelot auto]$ timeit.py -c -s'import a' -s'R=range(100)' 'a.asum(R)'
10000 loops, best of 3: 96 usec per loop
[alex at lancelot auto]$ timeit.py -c -s'import a' -s'R=range(100)' 'a.gsum(R)'
10000 loops, best of 3: 60 usec per loop
[alex at lancelot auto]$ timeit.py -c -s'import a' -s'R=range(1000)' 'a.asum(R)'
1000 loops, best of 3: 930 usec per loop
[alex at lancelot auto]$ timeit.py -c -s'import a' -s'R=range(1000)' 'a.gsum(R)'
1000 loops, best of 3: 590 usec per loop
[alex at lancelot auto]$ timeit.py -c -s'import a' -s'R=range(10000)' 'a.asum(R)'
100 loops, best of 3: 1.28e+04 usec per loop
[alex at lancelot auto]$ timeit.py -c -s'import a' -s'R=range(10000)' 'a.gsum(R)'
100 loops, best of 3: 8.4e+03 usec per loop

not sure why gsum's advantage ratio over asum seems to be roughly
constant, but, this IS what I measure!-)

Alex

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[Python-Dev] accumulator display syntax