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

Summary

When it comes to performance, there is no holy grail, no single thing you can do to ensure peak performance in all cases. This shouldn’t worry you, however, as in most cases, you will never need to tune the performance and, if you do, a single tweak could probably fix your problem. You should be able to find performance problems and memory leaks in your code now, which is what matters most, so just try to contain yourself and only tweak when it’s actually needed.

Here is a quick recap of the tools in this chapter:

  • Measuring CPU performance: timeit, profile/cProfile, and line_profiler
  • Analyzing profiling results: SnakeViz, pyprof2calltree, and QCacheGrind
  • Measuring memory usage: tracemalloc, memory_profiler
  • Reducing memory usage and leaks: weakref and gc (garbage collector)

If you know how to use these tools, you should be able to track down and fix most performance issues in your code.

The most important takeaways...