Stargaming wrote: > Alex Martelli schrieb: >> 7stud <[EMAIL PROTECTED]> wrote: >> ... >> >>>>.append - easy to measure, too: >>>> >>>>brain:~ alex$ python -mtimeit 'L=range(3); n=23' 'x=L[:]; x.append(n)' >>>>1000000 loops, best of 3: 1.31 usec per loop >>>> >>>>brain:~ alex$ python -mtimeit 'L=range(3); n=23' 'x=L[:]; x+=[n]' >>>>1000000 loops, best of 3: 1.52 usec per loop >>>> >>>>Alex >>> >>>Why is it necessary to copy L? >> >> >> If you don't, then L gets longer and longer -- to over a million >> elements by the end of the loop -- so we're measuring something that's >> potentially very different from the problem under study, "what's the >> best way to append one item to a 3-items list". >> >> That's important to consider for any microbenchmark of code that changes >> some existing state: make sure you're measuring a piece of code that >> _overall_ does NOT change said existing state in a cumulative way, >> otherwise you may be measuring something very different from the issue >> of interest. It's maybe the only important caveat about using "python >> -mtimeit". >> >> >> Alex >> > > Cannot reproduce that. Consider the following: > > $ python -mtimeit "L=range(3)" "L.append(1); print len(L)"
That should be $ python2.5 -m timeit -s "L = range(3)" "L.append(1); print len(L)" | tail 999995 999996 999997 999998 999999 1000000 1000001 1000002 1000003 1000000 loops, best of 3: 1.98 usec per loop Note the -s before the initialization statement that Alex meant to add but didn't. If that is missing L = range(3) is executed on every iteration. Peter -- http://mail.python.org/mailman/listinfo/python-list
