Book Image

Expert Python Programming - Second Edition

By : Michał Jaworski
Book Image

Expert Python Programming - Second Edition

By: Michał Jaworski

Overview of this book

Python is a dynamic programming language, used in a wide range of domains by programmers who find it simple, yet powerful. Even if you find writing Python code easy, writing code that is efficient and easy to maintain and reuse is a challenge. The focus of the book is to familiarize you with common conventions, best practices, useful tools and standards used by python professionals on a daily basis when working with code. You will begin with knowing new features in Python 3.5 and quick tricks for improving productivity. Next, you will learn advanced and useful python syntax elements brought to this new version. Using advanced object-oriented concepts and mechanisms available in python, you will learn different approaches to implement metaprogramming. You will learn to choose good names, write packages, and create standalone executables easily. You will also be using some powerful tools such as buildout and vitualenv to release and deploy the code on remote servers for production use. Moving on, you will learn to effectively create Python extensions with C, C++, cython, and pyrex. The important factors while writing code such as code management tools, writing clear documentation, and test-driven development are also covered. You will now dive deeper to make your code efficient with general rules of optimization, strategies for finding bottlenecks, and selected tools for application optimization. By the end of the book, you will be an expert in writing efficient and maintainable code.
Table of Contents (21 chapters)
Expert Python Programming Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Using architectural trade-offs


When your code cannot be improved any further by reducing the complexity or choosing the proper data structure, a good approach may be to consider doing some trade-offs. If we review user problems and define what is really important for them, we can relax some of the application requirements. The performance can often be improved by:

  • Replacing exact solution algorithms with heuristics and approximation algorithms

  • Deferring some work to delayed task queues

  • Using probabilistic data structures

Using heuristics and approximation algorithms

Some algorithmic problems simply don't have good state of the art solutions that could run in time acceptable to the user. For example, consider a program that deals with some complex optimization problems such as Traveling Salesman Problem (TSP) or Vehicle Routing Problem (VRP). Both problems are NP-hard problems in combinatorial optimization. The exact algorithms for such problems that have low complexity are not known. This means...