Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Expert Python Programming

You're reading from   Expert Python Programming Become a master in Python by learning coding best practices and advanced programming concepts in Python 3.7

Arrow left icon
Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789808896
Length 646 pages
Edition 3rd Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Michał Jaworski Michał Jaworski
Author Profile Icon Michał Jaworski
Michał Jaworski
Tarek Ziadé Tarek Ziadé
Author Profile Icon Tarek Ziadé
Tarek Ziadé
Arrow right icon
View More author details
Toc

Table of Contents (25) Chapters Close

Preface 1. Section 1: Before You Start
2. Current Status of Python FREE CHAPTER 3. Modern Python Development Environments 4. Section 2: Python Craftsmanship
5. Modern Syntax Elements - Below the Class Level 6. Modern Syntax Elements - Above the Class Level 7. Elements of Metaprogramming 8. Choosing Good Names 9. Writing a Package 10. Deploying the Code 11. Python Extensions in Other Languages 12. Section 3: Quality over Quantity
13. Managing Code 14. Documenting Your Project 15. Test-Driven Development 16. Section 4: Need for Speed
17. Optimization - Principles and Profiling Techniques 18. Optimization - Some Powerful Techniques 19. Concurrency 20. Section 5: Technical Architecture
21. Event-Driven and Signal Programming 22. Useful Design Patterns 23. reStructuredText Primer 24. Other Books You May Enjoy

Using architectural trade-offs

When your code can no longer be improved by reducing the complexity or choosing a proper data structure, a good approach may be to consider a trade-off. If we review users' problems and define what is really important to them, we can often relax some of the application's requirements. Performance can often be improved by doing the following:

  • Replacing exact solution algorithms with heuristics and approximation algorithms
  • Deferring some work to delayed task queues
  • Using probabilistic data structures

Let's move on and take a look at these improvement methods.

Using heuristics and approximation algorithms

Some algorithmic problems simply don't have good state-of-the-art solutions...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at €18.99/month. Cancel anytime