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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

The prototype pattern

The prototype pattern allows you to create new objects by copying existing ones, rather than creating them from scratch. This pattern is particularly useful when the cost of initializing an object is more expensive or complex than copying an existing one. In essence, the prototype pattern enables you to create a new instance of a class by duplicating an existing instance, thereby avoiding the overhead of initializing a new object.

In its simplest version, this pattern is just a clone() function that accepts an object as an input parameter and returns a clone of it. In Python, this can be done using the copy.deepcopy() function.

Real-world examples

Cloning a plant by taking a cutting is a real-world example of the prototype pattern. Using this approach, you don’t grow the plant from a seed; you create a new plant that’s a copy of an existing one.

Many Python applications make use of the prototype pattern, but it is rarely referred to as...

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