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Functional Python Programming

You're reading from   Functional Python Programming Create succinct and expressive implementations with functional programming in Python

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Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781784396992
Length 360 pages
Edition 1st Edition
Languages
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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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Table of Contents (18) Chapters Close

Preface 1. Introducing Functional Programming FREE CHAPTER 2. Introducing Some Functional Features 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 Index

Functional composition and the PyMonad multiplication operator

One of the significant values of curried functions is the ability to combine them via functional composition. We looked at functional composition in Chapter 5, Higher-order Functions, and Chapter 11, Decorator Design Techniques.

When we've created a curried function, we can easily perform function composition to create a new, more complex curried function. In this case, the PyMonad package defines the * operator for composing two functions. To show how this works, we'll define two curried functions that we can compose. First, we'll define a function that computes the product, and then we'll define a function that computes a specialized range of values.

Here's our first function that computes the product:

import  operator
prod = myreduce(operator.mul)

This is based on our curried myreduce() function that was defined previously. It uses the operator.mul() function to compute a "times-reduction"...

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