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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 Memoization pattern

The Memoization pattern is a crucial optimization technique in software development that improves the efficiency of programs by caching the results of expensive function calls. This approach ensures that if a function is called with the same inputs more than once, the cached result is returned, eliminating the need for repetitive and costly computations.

Real-world examples

We can think of calculating Fibonacci numbers as a classic example of the memoization pattern. By storing previously computed values of the sequence, the algorithm avoids recalculating them, which drastically speeds up the computation of higher numbers in the sequence.

Another example is a text search algorithm. In applications dealing with large volumes of text, such as search engines or document analysis tools, caching the results of previous searches means that identical queries can return instant results, significantly improving user experience.

Use cases for the memoization...

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