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

Unit testing with pytest

We can create unit tests using a library that provides a common framework for the test scenarios, along with a test runner to execute the tests and log results. Unit tests focus on testing the least amount of code possible in any one test. The standard library includes the unittest package. While widely used, this package tends to force us to create a fair amount of boilerplate code for each test case.

One of the more popular alternatives to the standard library unittest is pytest. This has the advantage of letting us write smaller, and more clear, test cases. The lack of overheads makes this a desirable alternative.

Since pytest is not part of the standard library, you'll need to download and install it yourself. You can get it from the pytest home page at https://docs.pytest.org/en/stable/. You can install it with any of the installers.

In a Terminal window, activate the virtual environment you're working in...