Steven F. Lott - Python Object-Oriented Programming: Build robust and maintainable object-oriented Python applications and libraries
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A comprehensive guide to exploring modern Python through data structures, design patterns, and effective object-oriented techniques
Key Features- Build an intuitive understanding of object-oriented design, from introductory to mature programs
- Learn the ins and outs of Python syntax, libraries, and best practices
- Examine a machine-learning case study at the end of each chapter
Python Object-Oriented Programming, Fourth Edition dives deep into the various aspects of OOP, Python as an OOP language, common and advanced design patterns, and hands-on data manipulation and testing of more complex OOP systems. These concepts are consolidated by open-ended exercises, as well as a real-world case study at the end of every chapter, newly written for this edition. All example code is now compatible with Python 3.9+ syntax and has been updated with type hints for ease of learning.
Steven and Dusty provide a friendly, comprehensive tour of important OOP concepts, such as inheritance, composition, and polymorphism, and explain how they work together with Pythons classes and data structures to facilitate good design. UML class diagrams are generously used throughout the text for you to understand class relationships. Beyond the books focus on OOP, it features an in-depth look at Pythons exception handling and how functional programming intersects with OOP. Not one, but two very powerful automated testing systems, unittest and pytest, are introduced in this book. The final chapter provides a detailed discussion of Pythons concurrent programming ecosystem.
By the end of the book, you will have a thorough understanding of how to think about and apply object-oriented principles using Python syntax and be able to confidently create robust and reliable programs.
What you will learn- Implement objects in Python by creating classes and defining methods
- Extend class functionality using inheritance
- Use exceptions to handle unusual situations cleanly
- Understand when to use object-oriented features, and more importantly, when not to use them
- Discover several widely used design patterns and how they are implemented in Python
- Uncover the simplicity of unit and integration testing and understand why they are so important
- Learn to statically type check your dynamic code
- Understand concurrency with asyncio and how it speeds up programs
If you are new to object-oriented programming techniques, or if you have basic Python skills and wish to learn how and when to correctly apply OOP principles in Python, this is the book for you. Moreover, if you are an object-oriented programmer coming from other languages or seeking a leg up in the new world of Python, you will find this book a useful introduction to Python. Minimal previous experience with Python is necessary.
Table of Contents- Object-Oriented Design
- Objects in Python
- When Objects Are Alike
- Expecting the Unexpected
- When to Use Object-Oriented Programming
- Abstract Base Classes and Operator Overloading
- Python Data Structures
- The Intersection of Object-Oriented and Functional Programming
- Strings, Serialization, and File Paths
- The Iterator Pattern
- Common Design Patterns
- Advanced Design Patterns
- Testing Object-Oriented Programs
- Concurrency
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Python Object-Oriented Programming
Fourth Edition
Build robust and maintainable object-oriented Python applications and libraries
Steven F. Lott
Dusty Phillips
BIRMINGHAMMUMBAI
Python Object-Oriented Programming
Fourth Edition
Copyright 2021 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Producer: Dr. Shailesh Jain
Acquisition Editor Peer Reviews: Saby D'silva
Project Editor: Parvathy Nair
Content Development Editor: Lucy Wan
Copy Editor: Safis Editor
Technical Editor: Aditya Sawant
Proofreader: Safis Editor
Indexer: Tejal Daruwale Soni
Presentation Designer: Pranit Padwal
First published: July 2010
Second edition: August 2015
Third edition: October 2018
Fourth edition: June 2021
Production reference: 1250621
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-80107-726-2
www.packt.com
Steven Lott has been programming since computers were large, expensive, and rare. Working for decades in high tech has given him exposure to a lot of ideas and techniquessome bad, but most are useful and helpful to others.
Steven has been working with Python since the '90s, building a variety of tools and applications. He's written a number of titles for Packt Publishing, including Mastering Object-Oriented, Modern Python Cookbook, and Functional Python Programming.
He's a technomad, and lives on a boat that's usually located on the east coast of the US. He tries to live by the words "Don't come home until you have a story."
Dusty Phillips is a Canadian author and software developer. His storied career has included roles with the world's biggest government, the world's biggest social network, a two person startup, and everything in between. In addition to Python Object-Oriented Programming, Dusty wrote Creating Apps In Kivy (O'Reilly) and is now focused on writing fiction.
Thank you to Steven Lott, for finishing what I started, to all my readers for appreciating what I write, and to my wife, Jen Phillips, for everything else.
Bernt Gbor, originally from Transylvania, works as a senior software engineer at Bloomberg in London, UK. During his day job, he primarily focuses on improving the quality of the data ingestion pipeline at Bloomberg with predominant use of the Python programming language and paradigms. He's been working with Python for over ten years and is a major open-source contributor in this domain, with a particular focus on the packaging area. He's also the author and maintainer of high-profile projects such as virtualenv, build, andtox. For more information, see https://bernat.tech/about .
I would like to express my very great appreciation to Lisa, my fiance, for her invaluable support on a daily basis. Love you!
The Python programming language is extremely popular and used for a variety of applications. The Python language is designed to make it relatively easy to create small programs. To create more sophisticated software, we need to acquire a number of important programming and software design skills.
This book describes the object-oriented approach to creating programs in Python. It introduces the terminology of object-oriented programming, demonstrating software design and Python programming through step-by-step examples. It describes how to make use of inheritance and composition to build software from individual elements. It shows how to use Python's built-in exceptions and data structures, as well as elements of the Python standard library. A number of common design patterns are described with detailed examples.
This book covers how to write automated tests to confirm that our software works. It also shows how to use the various concurrency libraries available as part of Python; this lets us write software that can make use of multiple cores and multiple processors in a modern computer. An extended case study covers a simple machine learning example, showing a number of alternative solutions to a moderately complicated problem.
This book targets people who are new to object-oriented programming in Python. It assumes basic Python skills. For readers with a background in another object-oriented programming language, this book will expose many distinctive features of Python's approach.
Because of Python's use for data science and data analytics, this book touches on the related math and statistics concepts. Some knowledge in these areas can help to make the applications of the concepts more concrete.
This book is divided into four overall sections. The first six chapters provide the core principles and concepts of object-oriented programming and how these are implemented in Python. The next three chapters take a close look at Python built-in features through the lens of object-oriented programming. Chapters 10, 11, and 12 look at a number of common design patterns and how these can be handled in Python. The final section covers two additional topics: testing and concurrency.
Chapter 1, Object-Oriented Design, introduces the core concepts underlying object-oriented design. This provides a road map through the ideas of state and behavior, attributes and methods, and how objects are grouped into classes. This chapter also looks an encapsulation, inheritance, and composition. The case study for this chapter introduces the machine learning problem, which is an implementation of a k-nearest neighbors (k-NN) classifier.
Chapter 2, Objects in Python, shows how class definitions work in Python. This will include the type annotations, called type hints, class definitions, modules, and packages. We'll talk about practical considerations for class definition and encapsulation. The case study will begin to implement some of the classes for the k-NN classifier.
Chapter 3, When Objects Are Alike, addresses how classes are related to each other. This will include how to make use of inheritance and multiple inheritance. We'll look at the concept of polymorphism among the classes in a class hierarchy. The case study will look at alternative designs for the distance computations used to find the nearest neighbors.
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