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

Testing with py.test


The py.test tool makes it very easy to write tests and run them. There are a few other options such as nose and the bundled unittest module available, but the py.test library offers a very good combination of usability and active development. In the past, I was an avid nose user but have since switched to py.test as it tends to be easier to use and has better community support, in my experience at least. Regardless, nose is still a good choice, and if you're already using it, there is little reason to switch and rewrite all of your tests. When writing tests for a new project, however, py.test can be much more convenient.

Now, we will run the doctests from the previously discussed square.py file using py.test.

First, start by installing py.test, of course:

pip install pytest

Now you can do a test run, so let's give the doctests we have in square.py a try:

# py.test --doctest-modules -v square.py
======================== test session starts ========================
platform...