Book Image

Expert Python Programming - Third Edition

By : Michał Jaworski, Tarek Ziadé
Book Image

Expert Python Programming - Third Edition

By: Michał Jaworski, Tarek Ziadé

Overview of this book

Python is a dynamic programming language that's used in a wide range of domains thanks to its simple yet powerful nature. Although writing Python code is easy, making it readable, reusable, and easy to maintain is challenging. Complete with best practices, useful tools, and standards implemented by professional Python developers, the third edition of Expert Python Programming will help you overcome this challenge. The book will start by taking you through the new features in Python 3.7. You'll then learn the advanced components of Python syntax, in addition to understanding how to apply concepts of various programming paradigms, including object-oriented programming, functional programming, and event-driven programming. This book will also guide you through learning the naming best practices, writing your own distributable Python packages, and getting up to speed with automated ways to deploy your software on remote servers. You’ll discover how to create useful Python extensions with C, C++, Cython, and CFFI. Furthermore, studying about code management tools, writing clear documentation, and exploring test-driven development will help you write clean code. By the end of the book, you will have become an expert in writing efficient and maintainable Python code.
Table of Contents (25 chapters)
Free Chapter
1
Section 1: Before You Start
4
Section 2: Python Craftsmanship
12
Section 3: Quality over Quantity
16
Section 4: Need for Speed
20
Section 5: Technical Architecture
23
reStructuredText Primer

Summary

It was a long journey, but we successfully struggled through most of the basic approaches to concurrent programming that are available for Python programmers.

After explaining what concurrency really is, we jumped into action and dissected one of the typical concurrent problems with the help of multithreading. After identifying the basic deficiencies of our code and fixing them, we turned to multiprocessing to see how it would work in our case.

We found that multiple processes with the multiprocessing module are a lot easier to use than base threads with threading. But just after that, we realized that we can use the same API for threads too, thanks to the multiprocessing.dummy module. So, the decision between multiprocessing and multithreading is now only a matter of which solution better suits the problem and not which solution has a better interface.

And speaking about...