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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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Free Chapter
1
Part 1: Start with Principles
4
Part 2: From the Gang of Four
8
Part 3: Beyond the Gang of Four

Summary

Throughout this chapter, we have explored patterns that developers can use to enhance the efficiency and scalability of applications.

The cache-aside pattern teaches us how to manage cache effectively, ensuring data is fetched and stored in a manner that optimizes performance and consistency, particularly in environments with dynamic data sources.

The memoization pattern demonstrates the power of caching function results to speed up applications by avoiding redundant computations. This pattern is beneficial for expensive, repeatable operations and can dramatically improve the performance of recursive algorithms and complex calculations.

Finally, the lazy loading pattern emphasizes delaying the initialization of resources until they are needed. This approach not only improves the startup time of applications but also reduces memory overhead, making it ideal for resource-intensive operations that may not always be necessary for the user’s interactions.

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