I believe the difference here is entirely in the cost of 1000000 additions. Testing with 64-bit Python.org 3.3.0 on Mac OS X:
In [698]: %timeit len ([None for n in range (1, 1000000) if n%3 == 1])
10 loops, best of 3: 127 ms per loop
In [699]: %timeit sum (1 for n in range (1, 1000000) if n%3 == 1)
10 loops, best of 3: 138 ms per loop
In [700]: %timeit sum ([1 for n in range (1, 1000000) if n%3 == 1])
10 loops, best of 3: 139 ms per loop
So, it's not that the comprehension is faster than the genexp; they both take about the same time. But calling len
on a list
is instant, while summing 1M numbers adds another 7% to the total time.
Throwing a few different numbers at it, this seems to hold up unless the list is very tiny (in which case it does seem to get faster), or large enough that memory allocation starts to become a significant factor (which it isn't yet, at 333K).