When I run the code below outside of timeit(), it appears to complete instantaneously. However when I run it within the timeit() function, it takes much longer. Why?
>>> import timeit
>>> t = timeit.Timer("3**4**5")
>>> t.timeit()
16.55522028637718
Using: Python 3.1 (x86) - AMD Athlon 64 X2 - WinXP (32 bit)
The timeit() function runs the code many times (default one million) and takes an average of the timings.
To run the code only once, do this:
t.timeit(1)
but that will give you skewed results - it repeats for good reason.
To get the per-loop time having let it repeat, divide the result by the number of loops. Use a smaller value for the number of repeats if one million is too many:
count = 1000
print t.timeit(count) / count
Because timeit defaults to running it one million times. The point is to do micro-benchmarks, and the only way to get accurate timings of short events is to repeat them many times.
According to the docs, Timer.timeit() runs your code one million times by default. Use the "number" parameter to change this default:
t.timeit(number=100)
for example.
Timeit runs for one million loops by default.
You also may have order of operations issues: (3**4)**5 != 3**4**5.
>>> 3**4**5
37339184874102004353295975418486658822540977678373400775063693172207904061726525
12299936889388039772204687650654314751581087270545921608585813513369828091873141
91748594262580938807019951956404285571818041046681288797402925517668012340617298
39657473161915238672304623512593489605859058828465479354050593620237654780744273
05821445270589887562514528177934133521419207446230275187291854328623757370639854
85319476416926263819972887006907013899256524297198527698749274196276811060702333
710356481L
whereas:
>>> (3**4)**5
3486784401L