Book Image

Clojure Data Analysis Cookbook

By : Eric Rochester
Book Image

Clojure Data Analysis Cookbook

By: Eric Rochester

Overview of this book

<p>Data is everywhere and it's increasingly important to be able to gain insights that we can act on. Using Clojure for data analysis and collection, this book will show you how to gain fresh insights and perspectives from your data with an essential collection of practical, structured recipes.<br /><br />"The Clojure Data Analysis Cookbook" presents recipes for every stage of the data analysis process. Whether scraping data off a web page, performing data mining, or creating graphs for the web, this book has something for the task at hand.<br /><br />You'll learn how to acquire data, clean it up, and transform it into useful graphs which can then be analyzed and published to the Internet. Coverage includes advanced topics like processing data concurrently, applying powerful statistical techniques like Bayesian modelling, and even data mining algorithms such as K-means clustering, neural networks, and association rules.</p>
Table of Contents (18 chapters)
Clojure Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Normalizing numbers


If we need to read in numbers as strings, we have to worry about how they're formatted. But we'll probably want the computer to deal with them as numbers, not as strings, and that can't happen if the string contains a comma or a period to separate the thousands place.

In this recipe, we'll write a short function that takes a number string and returns the number. The function will strip out all the extra punctuation inside the number, and only leave the last separator: hopefully the one marking the decimal place.

Getting ready

To write this function, we just need to have access to the clojure.string library. We get this access using the following instruction:

(require '[clojure.string :as string])

How to do it…

The function itself is pretty short:

(defn normalize-number
  [n]
  (let [v (string/split n #"[,.]")
        [pre post] (split-at (dec (count v)) v)]
    (Double/parseDouble (apply str (concat pre [\.] post)))))

And using it is also straightforward:

user=> (normalize...