Chapter 1, Cython Won't Bite, will give you an introduction to what Cython is and how it works. It covers setting up your environment and running the "Hello World" application.

Chapter 2, Understanding Cython, will start to get serious with Cython and will discuss how to describe C declarations with respect to Cython along with calling conventions and type conversion.

Chapter 3, Extending Applications, will walk you through comparing the execution of pure Python code with the Cython version of the same code. We also look at extending Tmux, a pure C project, with Cython.

Chapter 4, Debugging Cython, will cover how to use GDB to debug your code and the relative GDB commands. There is also an extensive section on caveats and things to be aware of as well as conventions.

Chapter 5, Advanced Cython, will cover the usage of C++ with Cython, which is just as easy as using C with Cython. We will also work through all the syntax necessary to wrap C++. We will then look into the caveats and more on optimizations, comparing a Python XML parser with a Cython XML parser on large XML files.

Chapter 6, Further Reading, wraps up the book with a final look at some caveats and conventions. Then, we compare Cython against other similar tools like Numba and SWIG, and we will discuss how its used in NumPy and how we can use PyPy and Python 3.

For this book, I used my MacBook and an Ubuntu virtual machine (GDB is too old on Mac OS X for debugging). You will require the following on Mac OS X:

On Ubuntu, you can install most components via the following:

$ sudo apt-get install build-essential gdb cython

Of course, I will go over this in the introduction, but as long as you have a C compiler and Python and have Python headers installed, you will have everything you need for Cython.

This book is intended for C developers who like using Python and Python users wanting to implement native C/C++ extensions to Python. As a reader, you can expect to be shown how you can develop applications with Cython, with an emphasis on extending existing systems with help on how you can approach it.

Extending legacy systems can be difficult, but the rewards can be great. Consider very low-level system daemons that we could abstract and extend them and interact with the data from Python in a nice high-level way while leaving all performance-sensitive code alone! This model of development can prove to be efficient and of great return to development time; this can be particularly expensive when it comes to C applications.

It also allows for much more rapid development of the state or logic in a system. There is no need to worry about long data conversion algorithms in C for doing small things and then needing to change it all again.

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "But note that you cannot use del on this instance else you will get an error."

A block of code is set as follows:

#ifndef __MY_HEADER_H__
#define __MY_HEADER_H__

namespace mynamespace {
  void myFunc (void);

  class myClass {
  public:
    int x;
    void printMe (void);
  };
}

#endif //__MY_HEADER_H__

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

#ifndef __MY_HEADER_H__
#define __MY_HEADER_H__

namespace mynamespace {
  void myFunc (void);

  class myClass {
  public:
    int x;
    void printMe (void);
  };
}

#endif //__MY_HEADER_H__

Any command-line input or output is written as follows:

philips-macbook:primes redbrain$ cython pyprimes.py –embed
philips-macbook:primes redbrain$ gcc -g -O2 pyprimes.c -o pyprimes `python-config --includes –libs`

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