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

Python makes extensive use of iterators and iterable collections. This underlying aspect appears in many places. Each for statement makes implicit use of this. When we use functional programming techniques, such as generator expressions, and the map()filter(), and reduce() functions, we're exploiting iterators.

Python has an itertools module full of additional iterator-based design patterns. This is worthy of study because it provides many examples of common operations that are readily available using built-in constructs.

We can apply these concepts in a number of places in our case study:

  • Partitioning all the original samples into testing and training subsets.
  • Testing a particular k and distance hyperparameter set by classifying all the test cases.
  • The k-nearest neighbors (k-NN) algorithm itself and how it locates the k nearest neighbors from all the training samples.
  • ...