Performance Tuning - applying a function to a list

>>> import timeit
>>> timeit.Timer('map(f, range(10))', 'f=lambda x: str(6+x)').timeit()
15.467374484745967
>>> timeit.Timer('[f(x) for x in range(10)]', 'f=lambda x: str(6+x)').timeit()
16.062227741235269
>>> timeit.Timer('for x in range(10): f(x)', 'f=lambda x: str(6+x)').timeit()
14.686095299821623

And so, we can see that map is still faster than list comprehensions and the for loop beats them both. If you don't need the return value of that function, don't create the list: friends don't let friends create unnecessary objects. On the other hand, if performance is critical and you need the return values, you should prefer map over a list comprehension.

Picking teeny-tiny cherries

I would love to use Bazaar or Mercurial as a DVCS; just the fact that they are written in Python (and therefore eminently hackable as far as I'm concerned) is worth making the move. I just can't get past the fact that Darcs (my current favourite) provides support for hunk cherry-picking. This is a killer feature. It's great to be able to cherry-pick files into patches, but Darcs actually allows me to package up my hunks that relate to different changes into different patches. This gives me much more control over the way I decide to describe the modifications I am making to my project. When I can only operate at a higher (coarser) level of granularity (like Mercurial or, God forbid, SVN) I get stuck fiddling around removing hunks from files before committing the patches.

Another project to take up my time

I've just tossed out the first version of a small web-stack for Python called Khepri - a clever bit of word-play on the name "Apophis". There are a million others out there but I'm very picky. This one is mine and I like the way it is so far. Obviously, it is far from being really usable but if you are a developer, you can judge for yourself. Check out the darcs repo.