Book Image

Expert Python Programming - Fourth Edition

By : Michał Jaworski, Tarek Ziadé
Book Image

Expert Python Programming - Fourth Edition

By: Michał Jaworski, Tarek Ziadé

Overview of this book

This new edition of Expert Python Programming provides you with a thorough understanding of the process of building and maintaining Python apps. Complete with best practices, useful tools, and standards implemented by professional Python developers, this fourth edition has been extensively updated. Throughout this book, you’ll get acquainted with the latest Python improvements, syntax elements, and interesting tools to boost your development efficiency. The initial few chapters will allow experienced programmers coming from different languages to transition to the Python ecosystem. You will explore common software design patterns and various programming methodologies, such as event-driven programming, concurrency, and metaprogramming. You will also go through complex code examples and try to solve meaningful problems by bridging Python with C and C++, writing extensions that benefit from the strengths of multiple languages. Finally, you will understand the complete lifetime of any application after it goes live, including packaging and testing automation. By the end of this book, you will have gained actionable Python programming insights that will help you effectively solve challenging problems.
Table of Contents (16 chapters)
14
Other Books You May Enjoy
15
Index

Leveraging 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 that could run within a time span that would be acceptable to the user.

For example, consider a program that deals with complex optimization problems, such as the Traveling Salesman Problem (TSP) or the Vehicle...