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

Memory usage

So far, we have simply looked at the execution times and largely ignored the memory usage of the scripts. In many cases, the execution times are the most important, but memory usage should not be ignored. In almost all cases, CPU and memory are traded; an algorithm either uses a lot of CPU time or a lot of memory, which means that both do matter a lot.

Within this section, we are going to look at:

  • Analyzing memory usage
  • When Python leaks memory and how to avoid these scenarios
  • How to reduce memory usage

tracemalloc

Monitoring memory usage used to be something that was only possible through external Python modules such as Dowser or Heapy. While those modules still work, they are partially obsolete now because of the tracemalloc module. Let’s give the tracemalloc module a try to see how easy memory usage monitoring is nowadays:

import tracemalloc

if __name__ == '__main__':
    tracemalloc.start()

    # Reserve...