[Tutor] List comprehension question

Steven D'Aprano steve at pearwood.info
Wed Nov 10 11:35:42 CET 2010

Richard D. Moores wrote:
> On Tue, Nov 9, 2010 at 12:54, Steven D'Aprano <steve at pearwood.info> wrote:
>> Richard D. Moores wrote:
>>> See <http://tutoree7.pastebin.com/R82876Eg> for a speed test with n =
>>> 100,000 and 100,000 loops
>> As a general rule, you shouldn't try to roll your own speed tests. There are
>> various subtleties that can throw your results right out. Timing small code
>> snippets on modern multi-tasking computers is fraught with difficulties.
> Yes, but I thought that if I ran the tests the way I did, several
> times and then reversing the order of the functions, and seeing that
> the results were very similar, I could be pretty sure of my findings.
> Could you point out where taking even that amount of care could lead
> me astray?

Well, the biggest problem is that you're re-inventing the wheel. But in 
truth, what you did was not terrible, particularly if you're using 
Python 3 where range() is quite lightweight rather than a heavy 
operation that creates a big list.

There are various traps when timing code:

* There are commonly two functions you use for getting the time: 
time.time() and time.clock(). On Windows, the best one to use is 
clock(), but on other operating systems, you should probably use time(). 
  The timeit module automatically chooses the right one for you.

* Comparing apples and oranges. There's not a lot that timeit can do 
there, since it requires you *understand* what the code is that you are 
timing, but it helps by allowing you to split initialisation away from 
the code you are timing.

* Timing too much. One trap is to do this:

t = time.time()
for _ in range(1000000):
t = time.time() - t

This is especially bad in Python 2.x!

The problem is that you are including the time it takes to create a list 
of one million integers in your timing code. That's like starting the 
stop watch at a race while the athletes are still in the changing rooms, 
getting dressed, and yet you'd be amazed how often people do it! If the 
time taken by each call to do_something() is very large, then the extra 
time will be negligible, but if do_something is a fast snippet, it might 
take you nearly as long to build the list as it does to run the code 
you're trying to time!

timeit ensures that as little as possible overhead is included in the 
execution being timed.

* Forgetting that computers are multi-tasking machines these days. At 
every instant, the operating system is giving tiny time-slices to your 
program and potentially dozens of others. If you're busy loading a dozen 
web pages, playing a DVD, compiling a large program and trying to time a 
code snippet, it will be obvious that everything is going to be running 
slow. But if you time something *once*, what's not obvious is that just 
by chance you might get a number that is much slower than normal because 
the OS *just happened* to call your anti-virus during the test. timeit 
fights against that by running your code in a loop, and then working out 
the average time per loop, *and* then repeating the loop multiple times 
(three by default) and picking the lowest figure. Higher figures are 
slower due to external factors.

* Forgetting your CPU cache effects. Modern CPUs do all sorts of amazing 
wizardry to make code run fast. Sometimes slow, but hopefully more often 
fast. The first time you run a piece of code, chances are it will be 
slower than normal, because the hardware caches will have other stuff in 
them. But the second time, the code and data will already be lined up in 
the cache and pipelines ready to go. This is why if you time something a 
dozen times, the *first* time could be two or three times slower than 
the rest.

I've probably forgotten some... but anyway, the important thing is that 
there's lots of subtleties to timing code, and Tim Peters has already 
thought of them so you don't have to :)


More information about the Tutor mailing list