If you're using Python for performance-critical applications, simple use of the integrated ctypes module (uses libffi in background) can make a world of difference. There's a modest performance overhead, but basically you're getting near C performance with proper use of ctypes.

Coincidentally, one of the usual examples given in the ctypes howto is a Point structure, just like in this post. It's simple:

  from ctypes import *

  class Point(Structure):
      _fields_ = [("x", c_int), ("y", c_int)]

Then you can use the Point class the same way you'd use a regular Python class:

  p = Point(3, 4)
  p.x == 3
  p.y == 4

Really, taking a half-day to learn how to use ctypes effectively can make a world of difference to your performance-critical Python code, when you need to stop and think about data structures. Actually, if you already know C, it's less than a half-day to learn... just an hour or so to read the basic documentation:

http://docs.python.org/2/library/ctypes.html

If you plan to write an entire application using ctypes, it'd be worth looking at Cython, which is another incredible project. But for just one or two data structures in a program, ctypes is perfect.

And best of all, ctypes is included in any regular Python distribution -- no need to install Cython or any additional software. Just run your Pythons script like you normally do.

EDIT: I was just demonstrating how to get an easy-speed up using ctypes, which is included in the Python standard library. To be clear, you would usually use this type of data structure in ctypes along with a function from a C shared library.

Furthermore, if you're serious about optimizations, and you can permit additional dependencies, you should absolutely look at cython and/or numpy, both of which are much faster than ctypes, although they do bring along additional complexity. Other commenters are also pointing out cffi, which I've never used but also bears consideration.