OK, you won. I read in an (regretably old) guidline for improving
Python's performance that you should prefer map() compared to list
comprehensions. Apparently the performance of list comprehensions has
improved a lot, which is great. (Or the overhead of calling map() got
too big, but I hope this is not the case.) So, what is the purpose of
map()? Should it too be deprecated?
Andreas
Skip Montanaro wrote:
Andreas> Yeeh, I was expecting something like that. The only reason to
Andreas> use map() at all is for improving the performance. That is
Andreas> lost when using list comprehensions (as far as I know). So,
Andreas> this is *no* option for larger jobs.
Did you test your hypothesis?
% python -m timeit -s 'lst = ("abc"*10).split() ; import string'
'map(string.upper, lst)'
100000 loops, best of 3: 9.24 usec per loop
% python -m timeit -s 'lst = ("abc"*10).split()' '[s.upper() for s in lst]'
100000 loops, best of 3: 4.18 usec per loop
% python -m timeit -s 'lst = ("abc"*100).split() ; import string'
'map(string.upper, lst)'
100000 loops, best of 3: 16.1 usec per loop
% python -m timeit -s 'lst = ("abc"*100).split()' '[s.upper() for s in lst]'
100000 loops, best of 3: 10.8 usec per loop
% python -m timeit -s 'lst = ("abc"*1000).split() ; import string'
'map(string.upper, lst)'
10000 loops, best of 3: 72.7 usec per loop
% python -m timeit -s 'lst = ("abc"*1000).split()' '[s.upper() for s in lst]'
10000 loops, best of 3: 67.7 usec per loop
% python -m timeit -s 'lst = ("abc"*10000).split() ; import string'
'map(string.upper, lst)'
1000 loops, best of 3: 844 usec per loop
% python -m timeit -s 'lst = ("abc"*10000).split()' '[s.upper() for s in
lst]'
1000 loops, best of 3: 828 usec per loop
% python -m timeit -s 'lst = ["abc"*10]*10 ; import string'
'map(string.upper, lst)'
10000 loops, best of 3: 42.7 usec per loop
% python -m timeit -s 'lst = ["abc"*10]*10' '[s.upper() for s in lst]'
10000 loops, best of 3: 26.5 usec per loop
% python -m timeit -s 'lst = ["abc"*10]*100 ; import string'
'map(string.upper, lst)'
1000 loops, best of 3: 376 usec per loop
% python -m timeit -s 'lst = ["abc"*10]*100' '[s.upper() for s in lst]'
1000 loops, best of 3: 230 usec per loop
% python -m timeit -s 'lst = ["abc"*10]*1000 ; import string'
'map(string.upper, lst)'
100 loops, best of 3: 3.72 msec per loop
% python -m timeit -s 'lst = ["abc"*10]*1000' '[s.upper() for s in lst]'
100 loops, best of 3: 2.23 msec per loop
The above results are using Python CVS (aka 2.5a0).
Skip
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