Book Image

Expert Python Programming - Second Edition

By : Michał Jaworski
Book Image

Expert Python Programming - Second Edition

By: Michał Jaworski

Overview of this book

Python is a dynamic programming language, used in a wide range of domains by programmers who find it simple, yet powerful. Even if you find writing Python code easy, writing code that is efficient and easy to maintain and reuse is a challenge. The focus of the book is to familiarize you with common conventions, best practices, useful tools and standards used by python professionals on a daily basis when working with code. You will begin with knowing new features in Python 3.5 and quick tricks for improving productivity. Next, you will learn advanced and useful python syntax elements brought to this new version. Using advanced object-oriented concepts and mechanisms available in python, you will learn different approaches to implement metaprogramming. You will learn to choose good names, write packages, and create standalone executables easily. You will also be using some powerful tools such as buildout and vitualenv to release and deploy the code on remote servers for production use. Moving on, you will learn to effectively create Python extensions with C, C++, cython, and pyrex. The important factors while writing code such as code management tools, writing clear documentation, and test-driven development are also covered. You will now dive deeper to make your code efficient with general rules of optimization, strategies for finding bottlenecks, and selected tools for application optimization. By the end of the book, you will be an expert in writing efficient and maintainable code.
Table of Contents (21 chapters)
Expert Python Programming Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Summary


It was a long journey, but we successfully struggled through the most basic approaches to concurrent programming 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 are much easier to use with the multiprocessing module than base threads with threading. But just after that, we have realized that we can use the same API with threads too, thanks to multiprocessing.dummy. So the choice 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 problem fit, we finally tried asynchronous programming, which should be the best solution for I/O bound applications, only to realize...