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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Case study

We'll return to some material from an earlier chapter and apply some careful testing to be sure we've got a good, workable implementation. Back in Chapter 3, When Objects Are Alike, we looked at the distance computations that are part of the k-nearest neighbors classifier. In that chapter, we looked at several computations that produced slightly different results:

  • Euclidean distance: This is the direct line from one sample to another.
  • Manhattan distance: This follows streets-and-avenues around a grid (like the city of Manhattan), adding up the steps required along a series of straight-line paths.
  • Chebyshev distance: This is the largest of the streets-and-avenues distances.
  • Sorensen distance: This is a variation of the Manhattan distance that weights nearby steps more heavily than distant steps. It tends to magnify small distances, making more subtle discriminations.

These algorithms all produce distinct results...