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Mastering Python Design Patterns

Mastering Python Design Patterns

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

Mastering Python Design Patterns

4.3 (8)
By: Kamon Ayeva, Kasampalis, 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

Summary

In this chapter, we’ve explored two pivotal patterns essential for writing clean code and enhancing our testing strategies: the Mock Object pattern and the Dependency Injection pattern.

The Mock Object pattern is crucial for ensuring test isolation, which helps avoid unwanted side effects. It also facilitates behavior verification and simplifies test setup. We discussed how mocking, particularly through the unittest.mock module, allows us to simulate components within a unit test, demonstrating this with a practical example.

The Dependency Injection pattern, on the other hand, offers a robust framework for managing dependencies in a way that enhances flexibility, testability, and maintainability. It’s applicable not only in testing scenarios but also in general software design. We illustrated this pattern with an initial example that integrates mocking for either unit or integration tests. Subsequently, we explored a more advanced implementation using a...

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