Also, and this is just on a more minor, pedantic note, list comprehensions basically are regular for loops, except with the use of the list-append opcode instead of a getattr-call pair. This produces a minor speedup, but it's nothing to write home about, honest. (using a different pastebin because it has a feature I needed): http://ideone.com/dT1LG . The story is a bit different on 3.x, where list comps are implemented very differently: http://ideone.com/58FGZ
Having said that, I was pretty surprised that the list comprehensions ran faster than map, exactly because of point 2 - I had assumed that map used a c-loop, whereas list comprehensions did not. Unfortunately, I don't have that code sitting around anywhere so I can't redo the tests - I just remember being very surprised by those results.
For eg. using python 2.5.2
In [9]: l = ['10'] * 5000000
In [10]: timeit -n5 map(int, l)
5 loops, best of 3: 1.29 s per loop
In [11]: timeit -n5 [int(i) for i in l]
5 loops, best of 3: 1.64 s per loop
The program initially ran for 20 minutes on each hand. I worked a day on optimizing it, and got it down to around 1-2 minutes per hand, so I learned a few tricks to make things quicker. Lessons learned:
1. Local lookups are much faster.
2. Anything that you can somehow offload to a c-loop, makes things much faster.
3. Surprisingly, the fastest construct for building a new list was always list comprehension (e.g. [x for x in calc(l) or whatever.) This was far faster than a regular ol' for-loop, of course, but was also faster than using map's and reduce's.