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)
15
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16
Index

Case study

In this chapter's case study, we'll revisit our design, leveraging Python's @dataclass definitions. This holds some potential for streamlining our design. We'll be looking at some choices and limitations; this will lead us to explore some difficult engineering trade-offs, where there isn't one obvious best approach.

We'll also look at immutable NamedTuple class definitions. These objects have no internal state changes, leading to the possibility of some design simplifications. This will also change our design to make less use of inheritance and more use of composition.

Logical model

Let's review the design we have so far for our model.py module. This shows the hierarchy of Sample class definitions, used to reflect the various ways samples are used:

Figure 7.2: Class diagram so far

The various Sample classes are a very good fit with the dataclass definition. These objects have a number...