Book Image

Expert Python Programming - Fourth Edition

By : Michał Jaworski, Tarek Ziadé
Book Image

Expert Python Programming - Fourth Edition

By: Michał Jaworski, Tarek Ziadé

Overview of this book

This new edition of Expert Python Programming provides you with a thorough understanding of the process of building and maintaining Python apps. Complete with best practices, useful tools, and standards implemented by professional Python developers, this fourth edition has been extensively updated. Throughout this book, you’ll get acquainted with the latest Python improvements, syntax elements, and interesting tools to boost your development efficiency. The initial few chapters will allow experienced programmers coming from different languages to transition to the Python ecosystem. You will explore common software design patterns and various programming methodologies, such as event-driven programming, concurrency, and metaprogramming. You will also go through complex code examples and try to solve meaningful problems by bridging Python with C and C++, writing extensions that benefit from the strengths of multiple languages. Finally, you will understand the complete lifetime of any application after it goes live, including packaging and testing automation. By the end of this book, you will have gained actionable Python programming insights that will help you effectively solve challenging problems.
Table of Contents (16 chapters)
14
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15
Index

The need to use extensions

It's not easy to say when it is a reasonable decision to write extensions in C/C++. The general rule of thumb could be "never unless you have no other choice". But this is a very subjective statement that leaves a lot of place for the interpretation of what is not doable in Python. In fact, it is hard to find a thing that cannot be done using pure Python code.

Still, there are some problems where extensions may be especially useful by adding the following benefits:

  • Bypassing GIL in the CPython threading model
  • Improving performance in critical code sections
  • Integrating source code written in different languages
  • Integrating third-party dynamic libraries
  • Creating efficient custom datatypes

Of course, for every such problem, there is usually a viable native Python solution. For example, the core CPython interpreter constraints, such as GIL, can easily be overcome with a different approach to concurrency...