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Functional Python Programming

You're reading from   Functional Python Programming Discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788627061
Length 408 pages
Edition 2nd Edition
Languages
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Toc

Table of Contents (18) Chapters Close

Preface 1. Understanding Functional Programming 2. Introducing Essential Functional Concepts FREE CHAPTER 3. Functions, Iterators, and Generators 4. Working with Collections 5. Higher-Order Functions 6. Recursions and Reductions 7. Additional Tuple Techniques 8. The Itertools Module 9. More Itertools Techniques 10. The Functools Module 11. Decorator Design Techniques 12. The Multiprocessing and Threading Modules 13. Conditional Expressions and the Operator Module 14. The PyMonad Library 15. A Functional Approach to Web Services 16. Optimizations and Improvements 17. Other Books You May Enjoy

Avoiding stateful classes by using families of tuples


In several previous examples, we've shown the idea of Wrap-Unwrap design patterns that allow us to work with anonymous and named tuples. The point of this kind of design is to use immutable objects that wrap other immutable objects instead of mutable instance variables.

A common statistical measure of correlation between two sets of data is the Spearman rank correlation. This compares the rankings of two variables. Rather than trying to compare values, which might have different scales, we'll compare the relative orders. For more information, visit: http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient.

Computing the Spearman rank correlation requires assigning a rank value to each observation. It seems like we should be able to use enumerate(sorted()) to do this. Given two sets of possibly correlated data, we can transform each set into a sequence of rank values and compute a measure of correlation.

We'll apply the Wrap...

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