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Python Object-Oriented Programming

You're reading from   Python Object-Oriented Programming Build robust and maintainable object-oriented Python applications and libraries

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781801077262
Length 714 pages
Edition 4th Edition
Languages
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Author (1):
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Dusty Phillips Dusty Phillips
Author Profile Icon Dusty Phillips
Dusty Phillips
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Toc

Table of Contents (17) Chapters Close

Preface 1. Object-Oriented Design 2. Objects in Python FREE CHAPTER 3. When Objects Are Alike 4. Expecting the Unexpected 5. When to Use Object-Oriented Programming 6. Abstract Base Classes and Operator Overloading 7. Python Data Structures 8. The Intersection of Object-Oriented and Functional Programming 9. Strings, Serialization, and File Paths 10. The Iterator Pattern 11. Common Design Patterns 12. Advanced Design Patterns 13. Testing Object-Oriented Programs 14. Concurrency 15. Other Books You May Enjoy
16. Index

Sets

Lists are extremely versatile tools that suit many container object applications. But they are not useful when we want to ensure that objects in a list are unique. For example, a song library may contain many songs by the same artist. If we want to sort through the library and create a list of all the artists, we would have to check the list to see whether we've added the artist already, before we add them again.

This is where sets come in. Sets come from mathematics, where they represent an unordered group of unique items. We can try to add an item to a set five times, but the "is a member of a set" doesn't change after the first time we add it.

In Python, sets can hold any hashable object, not just strings or numbers. Hashable objects implement the __hash__() method; these are the same objects that can be used as keys in dictionaries; so again, mutable lists, sets, and dictionaries are out. Like mathematical sets, they can store only one copy of...

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