Closures in Python
Published . Tags: computer-science, python.
I don’t exactly make my love affair with Lisp a secret. Imperative languages are just fine, but I love me some functional programming.
I’ve recently started seriously digging into Python, though, and I think it’s
going to be the Imperative Language That Makes Me Cry The Least, at least until
I come upon something better. It’s got the holy higher-order trinity of
filter, syntactically sugary list comprehensions, lambda
expressions, and it even uses
** as the exponentiation operator, as God and
Fortran intended.1 It also has closures, which are magic if you
haven’t seen them before, so I’m going to yammer on about them for a bit.
To be fair, I’m probably not the best person in the world to tell you about lexical closures. You really want Wikipedia. In brief, though, a closure is a function that can access state in the scope in which it was defined. An example would probably be clearest:2
def make_power_fn(power): def power_fn(base): return base ** power return power_fn cube = make_power_fn(3) print map(cube, [1, 2, 3, 4, 5])
Executing the above code applies our cube function to every item in the list and
[1, 8, 27, 64, 125]. Neat, right? The magic part is that
effectively able to access
power because it was defined within the enclosing
Anyway, closures! Now you know!
Python still can’t make “real” metaprogramming trivial the way Lisp does, though. But it’s about as good as it can be without actually being its own abstract syntax tree. ↩
“Haskell and the Lisps” would be an awesome band name, you guys. ↩