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 -- http://mail.python.org/mailman/listinfo/python-list
