Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
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

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

Summary


In this chapter, we've looked at a number of functions in the functools module. This library module provides a number of functions that help us create sophisticated functions and classes.

We've looked at the @lru_cache function as a way to boost certain types of applications with frequent re-calculations of the same values. This decorator is of tremendous value for certain kinds of functions that take the integer or the string argument values. It can reduce processing by simply implementing memoization.

We looked at the @total_ordering function as a decorator to help us build objects that support rich ordering comparisons. This is at the fringe of functional programming, but is very helpful when creating new kinds of numbers.

The partial() function creates a new function with the partial application of argument values. As an alternative, we can build a lambda with similar features. The use case for this is ambiguous.

We also looked at the reduce() function as a higher-order function...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image