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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 FREE CHAPTER 2. Introducing Essential Functional Concepts 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

Generating all combinations


The itertools module also supports computing all combinations of a set of values. When looking at combinations, the order doesn't matter, so there are far fewer combinations than permutations. The number of combinations is often stated as

. This is the number of ways that we can take combinations of r things at a time from a universe of p items overall.

For example, there are 2,598,960 five-card poker hands. We can actually enumerate all 2 million hands by executing the following command:

hands = list(
    combinations(tuple(product(range(13), '♠♥♦♣')), 5))

More practically, assume we have a dataset with a number of variables. A common exploratory technique is to determine the correlation among all pairs of variables in a set of data. If there are v variables, then we will enumerate all variables that must be compared by executing the following command:

combinations(range(v), 2)

Let's get some sample data from http://www.tylervigen.com to show how this will work. We...

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