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)" 4 4 [...] 4 1000 loops, best of 3: [...] Doesn't seem like copying is really neccessary. -- http://mail.python.org/mailman/listinfo/python-list
