Book Image

Mastering Python

By : Rick van Hattem
Book Image

Mastering Python

By: Rick van 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 (22 chapters)
Mastering Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
6
Generators and Coroutines – Infinity, One Step at a Time
Index

Summary


This chapter showed us how to write doctests, make use of the shortcuts provided by py.test, and use the logging module. With testing, there is never a one-size-fits-all solution. While the doctest system is very useful in many cases for providing both documentation and tests at the same time, in many functions, there are edge cases that simply don't matter for documentation, but still need to be tested. This is where regular unit tests come in and where py.test helps a lot.

Because the py.test library is always evolving, this chapter cannot fully cover everything you will need, but it should provide you with enough of a basis to be able to use it effectively and extend it where needed.

The logging module is extremely useful but it's also a pain if configured incorrectly. Unfortunately, the right configuration can be a bit obscure when multiple modules are trying to configure logging simultaneously. The usage of the logging system should be clear enough for most of the common use cases...