Book Image

Python Object-Oriented Programming - Fourth Edition

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

Python Object-Oriented Programming - Fourth Edition

2 (2)
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

Recall

In this chapter, we've looked at a number of topics related to testing applications written in Python. These topics include the following:

  • We described the importance of unit testing and test-driven development as a way to be sure our software does what is expected.
  • We started by using the unittest module because it's part of the standard library and readily available. It seems a little wordy, but otherwise works well for confirming that our software works.
  • The pytest tool requires a separate installation, but it seems to produce tests that are slightly simpler than those written with the unittest module. More importantly, the sophistication of the fixture concept lets us create tests for a wide variety of scenarios.
  • The mock module, part of the unittest package, lets us create mock objects to better isolate the unit of code being tested. By isolating each piece of code, we can narrow our focus on being sure...