Python in Practice: Create Better Programs Using Concurrency, Libraries, and Patterns

5. Extending Python

§5.1. Accessing C Libraries with ctypes

§5.2. Using Cython

§5.2.1. Accessing C Libraries with Cython

§5.2.2. Writing Cython Modules for Greater Speed

§5.3. Case Study: An Accelerated Image Package

Python is fast enough for the vast majority of programs. And in those cases where it isn’t, we can often achieve sufficient speedups by using concurrency, as we saw in the previous chapter. Sometimes, though, we really do need to do faster processing. There are three key ways we can make our Python programs run faster: we can use PyPy (pypy.org), which has a built-in JIT (Just in Time compiler); we can use C or C++ code for time-critical processing; or we can compile our Python (or Cython) code into C using Cython.^*

^*New Python ...