Software timing benchmarks are notoriously noisy because your computer isn't a very good controlled experimental platform.
- Some algorithms are nondeterministic. For instance, some quicksort implementations choose a random pivot element.
- While you're running your benchmarks, your python process may get preempted by other running applications, operating system threads, etc.
- Even if your process got the same amount of CPU during every run, the timing may be different because a different mix of applications is running so your processor's memory caches have different hit/miss patterns.
- Some runs may get better branch prediction than others (again, due to the mix of other processes running). This will cause more pipeline stalls.
I would suspect that the effects I've given are given in order of decreasing strength (except that I believe Python's sort algorithm is deterministic, so the first effect is not a factor here). I also suspect that I've just scratched the surface.
EDIT:
OK, for reversing a number, suppose that your typical input will be 5 digits then you can use this.
if __name__ == "__main__":
from timeit import Timer
import random
def test(f):
f(random.randint(10000,99999))
print Timer(lambda: test(reverse_num)).timeit(number = 10000000)
print Timer(lambda: test(reverse_num2)).timeit(number = 10000000)
