Book Image

Python Object-Oriented Programming - Fourth Edition

By : Steven F. Lott, Dusty Phillips
2 (1)
Book Image

Python Object-Oriented Programming - Fourth Edition

2 (1)
By: Steven F. Lott, Dusty Phillips

Overview of this book

Object-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.
Table of Contents (17 chapters)
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Since Python 3.7, dataclasses let us define ordinary objects with a clean syntax for specifying attributes. They look – superficially – very similar to named tuples. This is a pleasant approach that makes it easy to understand how they work.

Here's a dataclass version of our Stock example:

>>> from dataclasses import dataclass
>>> @dataclass
... class Stock:
...     symbol: str
...     current: float
...     high: float
...     low: float

For this case, the definition is nearly identical to the NamedTuple definition.

The dataclass function is applied as a class decorator, using the @ operator. We encountered decorators in Chapter 6, Abstract Base Classes and Operator Overloading. We'll dig into them deeply in Chapter 11, Common Design Patterns. This class definition syntax isn't much less verbose than an ordinary class with __init__(), but it gives us access to several additional dataclass...