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Mastering Object-Oriented Python

Mastering Object-Oriented Python

By : Steven F. Lott
3.8 (4)
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Mastering Object-Oriented Python

Mastering Object-Oriented Python

3.8 (4)
By: Steven F. Lott

Overview of this book

Object-oriented programming (OOP) is a relatively complex discipline to master, and it can be difficult to see how general principles apply to each language's unique features. With the help of the latest edition of Mastering Objected-Oriented Python, you'll be shown how to effectively implement OOP in Python, and even explore Python 3.x. Complete with practical examples, the book guides you through the advanced concepts of OOP in Python, and demonstrates how you can apply them to solve complex problems in OOP. You will learn how to create high-quality Python programs by exploring design alternatives and determining which design offers the best performance. Next, you'll work through special methods for handling simple object conversions and also learn about hashing and comparison of objects. As you cover later chapters, you'll discover how essential it is to locate the best algorithms and optimal data structures for developing robust solutions to programming problems with minimal computer processing. Finally, the book will assist you in leveraging various Python features by implementing object-oriented designs in your programs. By the end of this book, you will have learned a number of alternate approaches with different attributes to confidently solve programming problems in Python.
Table of Contents (25 chapters)
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Section 1: Tighter Integration Via Special Methods
11
Section 2: Object Serialization and Persistence
17
Section 3: Object-Oriented Testing and Debugging

Consistency and style

All of the examples in the book were prepared using the black tool to provide consistent formatting. Some additional manual adjustments were made to keep code within the narrow sizes of printed material.

A common alternative to using black is to use pylint to identify formatting problems. These can then be corrected. In addition to detailed analysis of code quality, the pylint tool offers a numeric quality score. For this book, some pylint rules needed to be disabled. For example, the modules often have imports that are not in the preferred order; some modules also have imports that are relevant to doctest examples, and appear to be unused; some examples use global variables; and some class definitions are mere skeletons without appropriate method definitions.

Using pylint to locate potential problems is essential. It's often helpful to silence pylint...

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