Book Image

Secret Recipes of the Python Ninja

Book Image

Secret Recipes of the Python Ninja

Overview of this book

This book covers the unexplored secrets of Python, delve into its depths, and uncover its mysteries. You’ll unearth secrets related to the implementation of the standard library, by looking at how modules actually work. You’ll understand the implementation of collections, decimals, and fraction modules. If you haven’t used decorators, coroutines, and generator functions much before, as you make your way through the recipes, you’ll learn what you’ve been missing out on. We’ll cover internal special methods in detail, so you understand what they are and how they can be used to improve the engineering decisions you make. Next, you’ll explore the CPython interpreter, which is a treasure trove of secret hacks that not many programmers are aware of. We’ll take you through the depths of the PyPy project, where you’ll come across several exciting ways that you can improve speed and concurrency. Finally, we’ll take time to explore the PEPs of the latest versions to discover some interesting hacks.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell
Foreword
Contributors
Preface
Index

What is PyPy?


PyPy is an alternative implementation of Python. While normal Python is built using the C language (hence the alternative term: CPython), PyPy is built on the RPython (Restricted Python) language . RPython constrains the Python language; these constraints mean that PyPy can look at the RPython code, translate it into C code, and then compile it to machine code.

The main aspects of PyPy is the just-in-time (JIT) compiler. Specifically, it uses a tracing JIT, which monitors frequently executed loops and compiles them into native machine code. Since programs frequently spend much of their time in loops, compiling those loops to native code maximizes the speed at which they process data.

Using RPython, the JIT compiler receives known code, that is, the compiler doesn't have to spend time parsing the metadata of the code to determine what type an object is, how much memory space is taken up, and so on. Thus, it is able to effectively convert the CPython code into C code and then to...