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Mastering Python

Mastering Python

By : Rick Hattem
4 (11)
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Mastering Python

Mastering Python

4 (11)
By: Rick Hattem

Overview of this book

Python is a dynamic programming language. It is known for its high readability and hence it is often the first language learned by new programmers. Python being multi-paradigm, it can be used to achieve the same thing in different ways and it is compatible across different platforms. Even if you find writing Python code easy, writing code that is efficient, easy to maintain, and reuse is not so straightforward. This book is an authoritative guide that will help you learn new advanced methods in a clear and contextualised way. It starts off by creating a project-specific environment using venv, introducing you to different Pythonic syntax and common pitfalls before moving on to cover the functional features in Python. It covers how to create different decorators, generators, and metaclasses. It also introduces you to functools.wraps and coroutines and how they work. Later on you will learn to use asyncio module for asynchronous clients and servers. You will also get familiar with different testing systems such as py.test, doctest, and unittest, and debugging tools such as Python debugger and faulthandler. You will learn to optimize application performance so that it works efficiently across multiple machines and Python versions. Finally, it will teach you how to access C functions with a simple Python call. By the end of the book, you will be able to write more advanced scripts and take on bigger challenges.
Table of Contents (17 chapters)
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6
6. Generators and Coroutines – Infinity, One Step at a Time
16
Index

Chapter 14. Extensions in C/C++, System Calls, and C/C++ Libraries

Now that we know a bit more about performance and multiprocessing, we will explain another subject that is at least somewhat performance-related—the usage of C and/or C++ extensions.

There are multiple reasons to consider C/C++ extensions. Having existing libraries available is an important one, but truthfully, the most important reason is performance. In Chapter 12, Performance – Tracking and Reducing Your Memory and CPU Usage, we saw that the cProfile module is about 10 times faster than the profile module, which indicates that at least some C extensions are faster than their pure Python equivalents. This chapter will not focus on performance that much, however. The goal here is interaction with non-Python libraries. Any performance improvement will just be a completely unintentional side effect.

We will discuss the following options in this chapter:

  • Ctypes for handling foreign (C/C++) functions and...
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Mastering Python
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