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Mastering Python

You're reading from   Mastering Python Master the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers

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Product type Paperback
Published in Apr 2016
Publisher Packt
ISBN-13 9781785289729
Length 486 pages
Edition 1st Edition
Languages
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Author (1):
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Rick Hattem Rick Hattem
Author Profile Icon Rick Hattem
Rick Hattem
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Table of Contents (17) Chapters Close

Preface 1. Getting Started – One Environment per Project FREE CHAPTER 2. Pythonic Syntax, Common Pitfalls, and Style Guide 3. Containers and Collections – Storing Data the Right Way 4. Functional Programming – Readability Versus Brevity 5. Decorators – Enabling Code Reuse by Decorating 6. Generators and Coroutines – Infinity, One Step at a Time 7. Async IO – Multithreading without Threads 8. Metaclasses – Making Classes (Not Instances) Smarter 9. Documentation – How to Use Sphinx and reStructuredText 10. Testing and Logging – Preparing for Bugs 11. Debugging – Solving the Bugs 12. Performance – Tracking and Reducing Your Memory and CPU Usage 13. Multiprocessing – When a Single CPU Core Is Not Enough 14. Extensions in C/C++, System Calls, and C/C++ Libraries 15. Packaging – Creating Your Own Libraries or Applications Index

Summary

This chapter showed us what the Pythonic philosophy is all about and explained to us what the Zen of Python is all about. While code style is highly personal, Python has a few, very helpful guidelines that at least keep people mostly on the same page and style. In the end, we are all consenting adults; everyone has the right to write code as he/she sees fit. But I do request you. Please read through the style guides and try to adhere to them unless you have a really good reason not to.

With all that power comes great responsibility, and so do a few pitfalls, though there aren't too many. Some are tricky enough to fool me regularly and I've been writing Python for a long time! Python improves all the time though. Many pitfalls have been taken care of since Python 2, but some will always remain. For example, recursive imports and definitions can easily bite you in most languages that support them, but that doesn't mean we'll stop trying to improve Python.

A good...

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