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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

In this chapter, we'll continue developing elements of the case study. We want to explore some additional features of object-oriented design in Python. The first is what is sometimes called "syntactic sugar," a handy way to write something that offers a simpler way to express something fairly complex. The second is the concept of a manager for providing a context for resource management.

In Chapter 4, Expecting the Unexpected, we built an exception for identifying invalid input data. We used the exception for reporting when the inputs couldn't be used.

Here, we'll start with a class to gather data by reading the file with properly classified training and test data. In this chapter, we'll ignore some of the exception-handling details so we can focus on another aspect of the problem: partitioning samples into testing and training subsets.

Input validation

The TrainingData object is loaded from a source file of samples...

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Python Object-Oriented Programming
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