Book Image

Mastering Python 2E - Second Edition

By : Rick van Hattem
5 (1)
Book Image

Mastering Python 2E - Second Edition

5 (1)
By: Rick van Hattem

Overview of this book

Even if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python’s capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code’s performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challenges
Table of Contents (21 chapters)
19
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20
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.

We have also seen how we can use tox to run tests in multiple sandboxed environments. If you ever have a project that also has to run on different computers or even on different Python versions, I would highly encourage you to use it.

The logging module is extremely useful when configured correctly and if your project becomes somewhat larger, it quickly becomes useful to do so. The usage of the logging system should be clear enough for most of the common use cases now, and as long as you...