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Python Object-Oriented Programming

Python Object-Oriented Programming - Fourth Edition

By : Steven F. Lott, Dusty Phillips
3.9 (34)
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Python Object-Oriented Programming

Python Object-Oriented Programming

3.9 (34)
By: Steven F. Lott, Dusty Phillips

Overview of this book

Python Object-Oriented Programming, Fourth Edition is a practical guide to advancing your OOP skills with modern Python. Going beyond the fundamentals, it helps you work with Python as an OOP language, explore both common and advanced design patterns, and apply these concepts to data manipulation and testing of complex OOP systems. Each chapter features newly written open-ended exercises as well as a real-world case study, aligned with the improvements in Python 3.11—bringing faster execution and memory efficiency to your applications. Authors Steven F. Lott and Dusty Phillips provide a comprehensive, illustrative tour of important OOP concepts, such as inheritance, composition, and polymorphism, showing how they integrate with Python’s classes and data structures to facilitate good design. The book also introduces two powerful automated testing systems, unittest and pytest, and explores Python's concurrent programming ecosystem in depth. By the end of the book, you’ll have a thorough understanding of how to think about and apply object-oriented principles using Python syntax to create robust and reliable programs.
Table of Contents (17 chapters)
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15
Other Books You May Enjoy
16
Index

Summary

This chapter discussed several common design patterns in detail, with examples, UML diagrams, and a discussion of the differences between Python and statically typed object-oriented languages. The Decorator pattern is often implemented using Python's more generic decorator syntax. The Observer pattern is a useful way to decouple events from actions taken on those events. The Strategy pattern allows different algorithms to be chosen to accomplish the same task. The Command pattern helps us design active classes that share a common interface but carry out distinct actions. The State pattern looks similar to the Strategy pattern but is used instead to represent systems that can move between different states using well-defined actions. The Singleton pattern, popular in some statically typed languages, is almost always an anti-pattern in Python.

In the next chapter, we'll wrap up our discussion of design patterns.

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