Build software that combines Python's expressivity with the performance and control of C (and C++). It's possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. In this practical guide, you'll learn how to use Cython to improve Python's performance--up to 3000x-- and to wrap C and C++ libraries in Python with ease.
Author Kurt Smith takes you through Cython's capabilities, with sample code and in-depth practice exercises. If you're just starting with Cython, or want to go deeper, you'll learn how this language is an essential part of any performance-oriented Python programmer's arsenal.
Use Cython's static typing to speed up Python code
Gain hands-on experience using Cython features to boost your numeric-heavy Python
Create new types with Cython--and see how fast object-oriented programming in Python can be
Effectively organize Cython code into separate modules and packages without sacrificing performance
Use Cython to give Pythonic interfaces to C and C++ libraries
Optimize code with Cython's runtime and compile-time profiling tools
Use Cython's prange function to parallelize loops transparently with OpenMP