I benchmarked these two functions (they unzip pairs back into source lists, came from here):

n = 10**7
a = list(range(n))
b = list(range(n))
pairs = list(zip(a, b))

def f1(a, b, pairs):
    a[:], b[:] = zip(*pairs)

def f2(a, b, pairs):
    for i, (a[i], b[i]) in enumerate(pairs):
        pass

Results with timeit.timeit (five rounds, numbers are seconds):

f1 1.06   f2 1.57   
f1 0.96   f2 1.69   
f1 1.00   f2 1.85   
f1 1.11   f2 1.64   
f1 0.95   f2 1.63   

So clearly f1 is a lot faster than f2, right?

But then I also measured with timeit.default_timer and got a completely different picture:

f1 7.28   f2 1.92   
f1 5.34   f2 1.66   
f1 6.46   f2 1.70   
f1 6.82   f2 1.59   
f1 5.88   f2 1.63   

So clearly f2 is a lot faster, right?

Sigh. Why do the timings totally differ like that, and which timing method should I believe?

Full benchmark code:

from timeit import timeit, default_timer

n = 10**7
a = list(range(n))
b = list(range(n))
pairs = list(zip(a, b))

def f1(a, b, pairs):
    a[:], b[:] = zip(*pairs)

def f2(a, b, pairs):
    for i, (a[i], b[i]) in enumerate(pairs):
        pass

print('timeit')
for _ in range(5):
    for f in f1, f2:
        t = timeit(lambda: f(a, b, pairs), number=1)
        print(f.__name__, '%.2f' % t, end='   ')
    print()

print('default_timer')
for _ in range(5):
    for f in f1, f2:
        t0 = default_timer()
        f(a, b, pairs)
        t = default_timer() - t0
        print(f.__name__, '%.2f' % t, end='   ')
    print()