![]() Say you want to time x += 1, to find out how long addition takes: > python -m timeit -s "x = 0" "x += 1"ġ0000000 loops, best of 3: 0.0476 usec per loop There are a few caveats with timeit that hold everywhere. I personally far prefer using Timer(.).repeat(.) as I've shown above. It's worth noting that this is a convenience wrapper for Timer(.).timeit(.) and so isn't particularly good at timing. " lets you use code from your main module inside the artificial environment created by timeit. Which would give you something closer to the interface from the command-line, but in a much less cool manner. You can also do: timeit.timeit(".", setup="from _main_ import. Times = timeit.Timer(partial(to_time, test_items)).repeat(3, 1000) Thus you could have something like: from functools import partial You should normally combine this with functools.partial instead of lambda. repeat and take the minimum: min(timeit.Timer(function).repeat(repeat=REPEATS, number=NUMBER)) Timeit.Timer(function).timeit(number=NUMBER)Īnd that gives you cumulative ( not minimum!) time to run that number of times. Well, the simple way is to do: def function(.): This can take a bit longer due to the multiple initialisations, but normally that's not a big deal.īut what if you want to use timeit inside your module? Python -m timeit -s "$SETUP" "better_minmod(arr1)" Python -m timeit -s "$SETUP" "pure_minmod(arr1)" Python -m timeit -s "$SETUP" "Minmod(arr1)" Instead, I tend to make shell scripts: SETUP=" I suggest avoiding that because the analysis and timing is simply better on the command line. If you want to have longer scripts you might be tempted to move to timeit inside a Python script. If you want multiple lines, you can either use the shell's automatic continuation or use separate arguments: %~> python -m timeit -s "x = range(10000)" -s "y = range(100)" "sum(x)" "min(y)" You can set stuff up: %~> python -m timeit -s "x = range(10000)" "sum(x)" So, the command-line interface: %~> python -m timeit "1 + 2"ġ0000000 loops, best of 3: 0.0468 usec per loop There's no way to get negative error because a computer can't ever compute faster than it can compute! So the shortest time has the least error in it. This is good because all error in timing is positive. On the command line, timeit does proper statistical analysis: it tells you how long the shortest run took. ![]() I'll let you in on a secret: the best way to use timeit is on the command line. ![]() Those are my tips for using timeit correctly. Note that the series of statements makes a fresh copy of the unsorted data on every pass.Īlso, note the timing technique of running the measurement suite seven times and keeping only the best time - this can really help reduce measurement distortions due to other processes running on your system. ![]() > print min(timeit.Timer('a=s timsort(a)', setup=setup).repeat(7, 1000)) Here is an example of how to set up a test for sorting: > import timeit So, if you want to test sorting, some care is required so that one pass at an in-place sort doesn't affect the next pass with already sorted data (that, of course, would make the Timsort really shine because it performs best when the data already partially ordered). The way timeit works is to run setup code once and then make repeated calls to a series of statements. ![]()
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