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Learning Jupyter 5

You're reading from   Learning Jupyter 5 Explore interactive computing using Python, Java, JavaScript, R, Julia, and JupyterLab

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Product type Paperback
Published in Aug 2018
Publisher
ISBN-13 9781789137408
Length 282 pages
Edition 2nd Edition
Languages
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Jupyter FREE CHAPTER 2. Jupyter Python Scripting 3. Jupyter R Scripting 4. Jupyter Julia Scripting 5. Jupyter Java Coding 6. Jupyter JavaScript Coding 7. Jupyter Scala 8. Jupyter and Big Data 9. Interactive Widgets 10. Sharing and Converting Jupyter Notebooks 11. Multiuser Jupyter Notebooks 12. What's Next? 13. Other Books You May Enjoy

Scala data access in Jupyter

There is a copy of the iris dataset on the University of California's website (Irvine) at https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data. We will access this data and perform several statistical operations on the same:

The Scala code is as follows:

import scala.io.Source;
//copied file locally https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
val filename = "iris.data"
//println("SepalLength, SepalWidth, PetalLength, PetalWidth, Class");
val array = scala.collection.mutable.ArrayBuffer.empty[Float]
for (line <- Source.fromFile(filename).getLines) {
var cols = line.split(",").map(_.trim);
//println(s"${cols(0)}|${cols(1)}|${cols(2)}|${cols(3)} |${cols(4)}");
val i = cols(0).toFloat
array += i;
}
val count = array.length;
var min:Double = 9999.0;
var max:Double...
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