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 Create succinct and expressive implementations with functional programming in Python

Arrow left icon
Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781784396992
Length 360 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Steven F. Lott Steven F. Lott
Author Profile Icon Steven F. Lott
Steven F. Lott
Arrow right icon
View More author details
Toc

Table of Contents (18) Chapters Close

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

Group-by reductions – from many to fewer

A very common operation is a reduction that groups values by some key or indicator. In SQL, this is often called the SELECT GROUP BY operation. The raw data is grouped by some columns value and reductions (sometimes aggregate functions) are applied to other columns. The SQL aggregate functions include SUM, COUNT, MAX, and MIN.

The statistical summary called the mode is a count that's grouped by some independent variable. Python offers us several ways to group data before computing a reduction of the grouped values. We'll start by looking at two ways to get simple counts of grouped data. Then we'll look at ways to compute different summaries of grouped data.

We'll use the trip data that we computed in Chapter 4, Working with Collections. This data started as a sequence of latitude-longitude waypoints. We restructured it to create legs represented by three tuples of start, end, and distance for the leg. The data looks as follows...

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