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Mastering Object-Oriented Python

You're reading from   Mastering Object-Oriented Python Build powerful applications with reusable code using OOP design patterns and Python 3.7

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Product type Paperback
Published in Jun 2019
Publisher Packt
ISBN-13 9781789531367
Length 770 pages
Edition 2nd Edition
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Author (1):
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Steven F. Lott Steven F. Lott
Author Profile Icon Steven F. Lott
Steven F. Lott
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Toc

Table of Contents (25) Chapters Close

Preface 1. Section 1: Tighter Integration Via Special Methods FREE CHAPTER
2. Preliminaries, Tools, and Techniques 3. The __init__() Method 4. Integrating Seamlessly - Basic Special Methods 5. Attribute Access, Properties, and Descriptors 6. The ABCs of Consistent Design 7. Using Callables and Contexts 8. Creating Containers and Collections 9. Creating Numbers 10. Decorators and Mixins - Cross-Cutting Aspects 11. Section 2: Object Serialization and Persistence
12. Serializing and Saving - JSON, YAML, Pickle, CSV, and XML 13. Storing and Retrieving Objects via Shelve 14. Storing and Retrieving Objects via SQLite 15. Transmitting and Sharing Objects 16. Configuration Files and Persistence 17. Section 3: Object-Oriented Testing and Debugging
18. Design Principles and Patterns 19. The Logging and Warning Modules 20. Designing for Testability 21. Coping with the Command Line 22. Module and Package Design 23. Quality and Documentation 24. Other Books You May Enjoy

Defining a new kind of sequence

A common requirement that we have when performing statistical analysis is to compute basic means, modes, and standard deviations on a collection of data. Our blackjack simulation will produce outcomes that must be analyzed statistically to see if we have actually invented a better strategy.

When we simulate the playing strategy for a game, we will develop some outcome data that will be a sequence of numbers that show the final result of playing the game several times with a given strategy.

We could accumulate the outcomes into a built-in list class. We can compute the mean via , where is the number of elements in :

def mean(outcomes: List[float]) -> float:
return sum(outcomes) / len(outcomes)

Standard deviation can be computed via :

def stdev(outcomes: List[float]) -> float:
n = float(len(outcomes))
return math.sqrt(
n ...
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