Book Image

Mastering Python Design Patterns - Second Edition

By : Kamon Ayeva, Sakis Kasampalis
Book Image

Mastering Python Design Patterns - Second Edition

By: Kamon Ayeva, Sakis Kasampalis

Overview of this book

Python is an object-oriented scripting language that is used in a wide range of categories. In software engineering, a design pattern is an elected solution for solving software design problems. Although they have been around for a while, design patterns remain one of the top topics in software engineering, and are a ready source for software developers to solve the problems they face on a regular basis. This book takes you through a variety of design patterns and explains them with real-world examples. You will get to grips with low-level details and concepts that show you how to write Python code, without focusing on common solutions as enabled in Java and C++. You'll also fnd sections on corrections, best practices, system architecture, and its designing aspects. This book will help you learn the core concepts of design patterns and the way they can be used to resolve software design problems. You'll focus on most of the Gang of Four (GoF) design patterns, which are used to solve everyday problems, and take your skills to the next level with reactive and functional patterns that help you build resilient, scalable, and robust applications. By the end of the book, you'll be able to effciently address commonly faced problems and develop applications, and also be comfortable working on scalable and maintainable projects of any size.
Table of Contents (17 chapters)

Strategy pattern

Most problems can be solved in more than one way. Take, for example, the sorting problem, which is related to putting the elements of a list in a specific order.

There are many sorting algorithms, and, in general, none of them is considered the best for all cases j.mp/algocomp.

There are different criteria that help us pick a sorting algorithm on a per-case basis. Some of the things that should be taken into account are listed here:

  • A number of elements that need to be sorted: This is called the input size. Almost all the sorting algorithms behave fairly well when the input size is small, but only a few of them have good performance with a large input size.
  • The best/average/worst time complexity of the algorithm: Time complexity is (roughly) the amount of time the algorithm takes to complete, excluding coefficients and lower-order terms. This is often the most...