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

Sharing data between threads and processes

Data sharing is really the most difficult part about multiprocessing, multithreading, and distributed programming in general: which data to pass along, which data to share, and which data to skip. The theory is really simple, however: whenever possible, don’t transfer any data, don’t share any data, and keep everything local. This is essentially the functional programming paradigm, which is why functional programming mixes really well with multiprocessing. In practice, regrettably, this is simply not always possible. The multiprocessing library has several options to share data, but internally they break down to two different options:

  • Shared memory: This is by far the fastest solution since it has very little overhead, but it can only be used for immutable types and is restricted to a select few types and custom objects that are created through multiprocessing.sharedctypes. This is a fantastic solution if you only...