Bruno Desthuilliers wrote:
> [EMAIL PROTECTED] a écrit :
> > Problem:
> >
> > You have a list of unknown length,
>
> This doesn't exist in Python:
> len(alist)
>
> > such as this: list > [X,X,X,O,O,O,O]. You want to extract all and only the
> > X's.
>
> braindead solution - relying on zeros being zeros or any other False value:
> all_xxx
> > You know
> > the X's are all up front and you know that the item after the last X is
> > an O, or that the list ends with an X. There are never O's between
> > X's.
> >
>
> Then it *may* be possible to optimize - but code will break as soon as
> this rule change. So is there a real need for optimisation ? (hint: dont
> guess, profile)
>
>
> FWIW, I've collected all suggestions here (and perhaps some others) and
> hacked a Q&D benchmark. Usage is:
>
> python test_lists.py [repeat [list_size [xcount]]
>
> where:
> * repeat is the number of iteration, default to 1000
> * list_size is the size of the list, default to 100
> * xcount is the number or non-zero values in the list, default is random
>
> I've run it a few times, and it seems that in most cases,
> * the bisect-like approach (Alex Martelli / Karl Friedrich Bolz) is
> the winner
> * the while/pop approach of the OP (slightly rewritten...) is really wrong
>
> FWIW, one of the dummiest possible approach IMHO
> (ie filter(None, alist)) behaves quite correctly wrt some other
> 'optimized' approachs - and is still safe if the rules about the
> composition of the list changes... Could it be that no optimization is
> sometime the best optimisation ?-)
>
>
> # test_lists.py
> from itertools import takewhile
> from timeit import Timer
> from random import randint
>
> def get_list(size, xcount=None):
> if xcount is None:
> xcount else:
> assert xcount < zcount return [1] * xcount + [0] * zcount
>
> def with_while(alist):
> res while alist and alist[0]:
> res.append(alist.pop(0))
> return res
>
> def with_for(alist):
> res for x in alist:
> if not x: break
> res.append(x)
> return res
>
> def with_filter(alist):
> return filter(None, alist)
>
> def with_comprehension(alist):
> return [x for x in alist if x]
>
> def with_takewhile(alist):
> return list(takewhile(lambda x: x!=0, alist))
>
> def with_slice_try(alist):
> try:
> return alist[:alist.index(0)]
> except ValueError:
> return alist[:]
>
> def with_slice_safe(alist):
> alist.append(0)
> return alist[:alist.index(0)]
>
> def with_delslice_safe(alist):
> alist.append(0)
> del alist[alist.index(0):]
> return alist
>
> def with_sect(alist):
> low high while low < high:
> mid if alist[mid] = 0:
> high else:
> low return alist[:low]
>
> _candidates if n.startswith('with_') and callable(o)]
>
> def run_test(repeat00, list_size='100', xcount='None'):
> global _candidate
>
> print """
> params :
> * repeat : %s
> * list_size : %s
> * xcounts : %s
> """ % (repeat, list_size, xcount)
> results for n in _candidates:
> stm, stp 'from __main__ import %s, get_list' %
> n)
> results[n]
> sorted_results for n, time in
> results.items()])
> for _, result in sorted_results:
> print "%s : %s" % result
>
>
> def main(args):
> try:
> repeat except:
> repeat try:
> list_size except:
> list_size try:
> xcount except:
> xcount
> run_test(repeat, list_size, xcount)
>
> if __name__ = '__main__':
> import sys
> main(sys.argv[1:])
>
>
> HTH
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