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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 Book Description 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 Python's 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 book's focus on OOP, it features an in-depth look at Python's 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 Python's 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 Who this book is for 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 Cover Copyright Contributors Table of Contents Preface Chapter 1: Object-Oriented Design Introducing object-oriented Objects and classes Specifying attributes and behaviors Data describes object state Behaviors are actions Hiding details and creating the public interface Composition Inheritance Inheritance provides abstraction Multiple inheritance Case study Introduction and problem overview Context view Logical view Process view Development view Physical view Conclusion Recall Exercises Summary Chapter 2: Objects in Python Introducing type hints Type checking Creating Python classes Adding attributes Making it do something Talking to yourself More arguments Initializing the object Type hints and defaults Explaining yourself with docstrings Modules and packages Organizing modules Absolute imports Relative imports Packages as a whole Organizing our code in modules Who can access my data? Third-party libraries Case study Logical view Samples and their states Sample state transitions Class responsibilities The TrainingData class Recall Exercises Summary Chapter 3: When Objects Are Alike Basic inheritance Extending built-ins Overriding and super Multiple inheritance The diamond problem Different sets of arguments Polymorphism Case study Logical view Another distance Recall Exercises Summary Chapter 4: Expecting the Unexpected Raising exceptions Raising an exception The effects of an exception Handling exceptions The exception hierarchy Defining our own exceptions Exceptions aren't exceptional Case study Context view Processing view What can go wrong? Bad behavior Creating samples from CSV files Validating enumerated values Reading CSV files Don't repeat yourself Recall Exercises Summary Chapter 5: When to Use Object-Oriented Programming Treat objects as objects Adding behaviors to class data with properties Properties in detail Decorators – another way to create properties Deciding when to use properties Manager objects Removing duplicate code In practice Case study Input validation Input partitioning The sample class hierarchy The purpose enumeration Property setters Repeated if statements Recall Exercises Summary Chapter 6: Abstract Base Classes and Operator Overloading Creating an abstract base class The ABCs of collections Abstract base classes and type hints The collections.abc module Creating your own abstract base class Demystifying the magic Operator overloading Extending built-ins Metaclasses Case study Extending the list class with two sublists A shuffling strategy for partitioning An incremental strategy for partitioning Recall Exercises Summary Chapter 7: Python Data Structures Empty objects Tuples and named tuples Named tuples via typing.NamedTuple Dataclasses Dictionaries Dictionary use cases Using defaultdict Counter Lists Sorting lists Sets Three types of queues Case study Logical model Frozen dataclasses NamedTuple classes Conclusion Recall Exercises Summary Chapter 8: The Intersection of Object-Oriented and Functional Programming Python built-in functions The len() function The reversed() function The enumerate() function An alternative to method overloading Default values for parameters Additional details on defaults Variable argument lists Unpacking arguments Functions are objects, too Function objects and callbacks Using functions to patch a class Callable objects File I/O Placing it in context Case study Processing overview Splitting the data Rethinking classification The partition() function One-pass partitioning Recall Exercises Summary Chapter 9: Strings, Serialization, and File Paths Strings String manipulation String formatting Escaping braces f-strings can contain Python code Making it look right Custom formatters The format() method Strings are Unicode Decoding bytes to text Encoding text to bytes Mutable byte strings Regular expressions Matching patterns Matching a selection of characters Escaping characters Repeating patterns of characters Grouping patterns together Parsing information with regular expressions Other features of the re module Making regular expressions efficient Filesystem paths Serializing objects Customizing pickles Serializing objects using JSON Case study CSV format designs CSV dictionary reader CSV list reader JSON serialization Newline-delimited JSON JSON validation Recall Exercises Summary Chapter 10: The Iterator Pattern Design patterns in brief Iterators The iterator protocol Comprehensions List comprehensions Set and dictionary comprehensions Generator expressions Generator functions Yield items from another iterable Generator stacks Case study The Set Builder background Multiple partitions Testing The essential k-NN algorithm k-NN using the bisect module k-NN using the heapq module Conclusion Recall Exercises Summary Chapter 11: Common Design Patterns The Decorator pattern A Decorator example Decorators in Python The Observer pattern An Observer example The Strategy pattern A Strategy example Strategy in Python The Command pattern A Command example The State pattern A State example State versus Strategy The Singleton pattern Singleton implementation Case study Recall Exercises Summary Chapter 12: Advanced Design Patterns The Adapter pattern An Adapter example The Façade pattern A Façade example The Flyweight pattern A Flyweight example in Python Multiple messages in a buffer Memory optimization via Python's __slots__ The Abstract Factory pattern An Abstract Factory example Abstract Factories in Python The Composite pattern A Composite example The Template pattern A Template example Case study Recall Exercises Summary Chapter 13: Testing Object-Oriented Programs Why test? Test-driven development Testing objectives Testing patterns Unit testing with unittest Unit testing with pytest pytest's setup and teardown functions pytest fixtures for setup and teardown More sophisticated fixtures Skipping tests with pytest Imitating objects using Mocks Additional patching techniques The sentinel object How much testing is enough? Testing and development Case study Unit testing the distance classes Unit testing the Hyperparameter class Recall Exercises Summary Chapter 14: Concurrency Background on concurrent processing Threads The many problems with threads Shared memory The global interpreter lock Thread overhead Multiprocessing Multiprocessing pools Queues The problems with multiprocessing Futures AsyncIO AsyncIO in action Reading an AsyncIO future AsyncIO for networking Design considerations A log writing demonstration AsyncIO clients The dining philosophers benchmark Case study Recall Exercises Summary Packt Page Other Books You May Enjoy Index **A comprehensive guide to exploring modern Python through data structures, design patterns, and effective object-oriented techniques** * 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 Python's 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 book's focus on OOP, it features an in-depth look at Python's 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 Python's 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. * 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. 1. Object-Oriented Design 2. Objects in Python 3. When Objects Are Alike 4. Expecting the Unexpected 5. When to Use Object-Oriented Programming 6. Abstract Base Classes and Operator Overloading 7. Python Data Structures 8. The Intersection of Object-Oriented and Functional Programming 9. Strings, Serialization, and File Paths 10. The Iterator Pattern 11. Common Design Patterns 12. Advanced Design Patterns 13. Testing Object-Oriented Programs 14. Concurrency A comprehensive guide to exploring modern Python through data structures, design patterns, and effective object-oriented techniquesKey FeaturesBuild an intuitive understanding of object-oriented design, from introductory to mature programsLearn the ins and outs of Python syntax, libraries, and best practicesExamine a machine-learning case study at the end of each chapterBook DescriptionObject-oriented programming (OOP) is a popular design paradigm in which data and behaviors are encapsulated in such a way that they can be manipulated together. 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 comprehensive, illustrative tour of important OOP concepts, such as inheritance, composition, and polymorphism, and explain how they work together with Python's classes and data structures to facilitate good design. In addition, the book also features an in-depth look at Python's exception handling and how functional programming intersects with OOP. Two very powerful automated testing systems, unittest and pytest, are introduced. The final chapter provides a detailed discussion of Python's 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 learnImplement objects in Python by creating classes and defining methodsExtend class functionality using inheritanceUse exceptions to handle unusual situations cleanlyUnderstand when to use object-oriented features, and more importantly, when not to use themDiscover several widely used design patterns and how they are implemented in PythonUncover the simplicity of unit and integration testing and understand why they are so importantLearn to statically type check your dynamic codeUnderstand concurrency with asyncio and how it speeds up programsWho this book is forIf 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. Being familiar with object-oriented design is an essential part of programming in Python. This new edition includes all the topics that made Python Object-Oriented Programming an instant Packt classic. Moreover, it's packed with updated content to reflect more recent changes in the core Python libraries and cover modern third-party packages.