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Mastering Python Design Patterns

Mastering Python Design Patterns - Third Edition

By : Kamon Ayeva, Sakis Kasampalis
4.3 (8)
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Mastering Python Design Patterns

Mastering Python Design Patterns

4.3 (8)
By: Kamon Ayeva, Sakis Kasampalis

Overview of this book

As software systems become increasingly complex, maintaining code quality, scalability, and efficiency can be a daunting challenge. Mastering Python Design Patterns is an essential resource that equips you with the tools you need to overcome these hurdles and create robust, scalable applications. The book delves into design principles and patterns in Python, covering both classic and modern patterns, and apply them to solve daily challenges as a Python developer or architect. Co-authored by two Python experts with a combined experience of three decades, this new edition covers creational, structural, behavioral, and architectural patterns, including concurrency, asynchronous, and performance patterns. You'll find out how these patterns are relevant to various domains, such as event handling, concurrency, distributed systems, and testing. Whether you're working on user interfaces (UIs), web apps, APIs, data pipelines, or AI models, this book equips you with the knowledge to build robust and maintainable software. The book also presents Python anti-patterns, helping you avoid common pitfalls and ensuring your code remains clean and efficient. By the end of this book, you'll be able to confidently apply classic and modern Python design patterns to build robust, scalable applications.
Table of Contents (17 chapters)
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1
Part 1: Start with Principles
4
Part 2: From the Gang of Four
8
Part 3: Beyond the Gang of Four

The Template pattern

A key ingredient in writing good code is avoiding redundancy. In OOP, methods and functions are important tools that we can use to avoid writing redundant code.

Remember the sorted() example we saw when discussing the Strategy pattern. That function is generic enough that it can be used to sort more than one data structure (lists, tuples, and named tuples) using arbitrary keys. That’s the definition of a good function.

Functions such as sorted() demonstrate the ideal case. However, we cannot always write 100% generic code.

In the process of writing code that handles algorithms in the real world, we often end up writing redundant code. That’s the problem solved by the Template design pattern. This pattern focuses on eliminating code redundancy. The idea is that we should be able to redefine certain parts of an algorithm without changing its structure.

Real-world examples

The daily routine of a worker, especially for workers of the same...

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