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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

Reducing with operator module functions


We'll look at one more way that we can use the operator definitions. We can use them with the built-in functools.reduce() function. The sum() function, for example, can be defined as follows:

sum = functools.partial(functools.reduce, operator.add)

This creates a partially evaluated version of the reduce() function with the first argument supplied. In this case, it's the + operator, implemented via the operator.add() function.

If we have a requirement for a similar function that computes a product, we can define it like this:

prod = functools.partial(functools.reduce, operator.mul)

This follows the pattern shown in the preceding example. We have a partially evaluated reduce() function with the first argument of the * operator, as implemented by the operator.mul() function.

It's not clear whether we can do similar things with too many of the other operators. We might be able to find a use for the operator.concat() function, as well as the operator.and() and...

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