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Python Object-Oriented Programming

Python Object-Oriented Programming - Fourth Edition

By : Steven F. Lott, Dusty Phillips
3.9 (34)
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Python Object-Oriented Programming

Python Object-Oriented Programming

3.9 (34)
By: Steven F. Lott, Dusty Phillips

Overview of this book

Python Object-Oriented Programming, Fourth Edition is a practical guide to advancing your OOP skills with modern Python. Going beyond the fundamentals, it helps you work with Python as an OOP language, explore both common and advanced design patterns, and apply these concepts to data manipulation and testing of complex OOP systems. Each chapter features newly written open-ended exercises as well as a real-world case study, aligned with the improvements in Python 3.11—bringing faster execution and memory efficiency to your applications. Authors Steven F. Lott and Dusty Phillips provide a comprehensive, illustrative tour of important OOP concepts, such as inheritance, composition, and polymorphism, showing how they integrate with Python’s classes and data structures to facilitate good design. The book also introduces two powerful automated testing systems, unittest and pytest, and explores Python's concurrent programming ecosystem in depth. By the end of the book, you’ll have a thorough understanding of how to think about and apply object-oriented principles using Python syntax to create robust and reliable programs.
Table of Contents (17 chapters)
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15
Other Books You May Enjoy
16
Index

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.
  • ...
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Python Object-Oriented Programming
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