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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Index

Improving execution time

Much can be said about performance optimization, but truthfully, if you have read the entire book up to this point, you know most of the Python-specific techniques for writing fast code. The most important factor in overall application performance will always be the choice of algorithms and, by extension, the data structures. Searching for an item within a list (O(n)) is almost always a worse idea than searching for an item in a dict or set (O(1)), as we have seen in Chapter 4.

Naturally, there are more factors and tricks that can help make your application faster. The extremely abbreviated version of all performance tips is quite simple, however: do as little as possible. No matter how fast you make your calculations and operations, doing nothing at all will always be faster. The following sections cover some of the most common performance bottlenecks in Python and test a few common assumptions about performance, such as the performance of try/except...