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Learning Jupyter

You're reading from   Learning Jupyter Select, Share, Interact and Integrate with Jupyter Notebook

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Product type Paperback
Published in Nov 2016
Publisher Packt
ISBN-13 9781785884870
Length 238 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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 Toomey Toomey
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Toomey
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Toc

Table of Contents (16) Chapters Close

Learning Jupyter
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. Introduction to Jupyter FREE CHAPTER 2. Jupyter Python Scripting 3. Jupyter R Scripting 4. Jupyter Julia Scripting 5. Jupyter JavaScript Coding 6. Interactive Widgets 7. Sharing and Converting Jupyter Notebooks 8. Multiuser Jupyter Notebooks 9. Jupyter Scala 10. Jupyter and Big Data

Scala data access in Jupyter


There is a copy of the iris dataset on the University of California, Irvine website at https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data . We will access this data and perform some simpler statistics 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"

//DEBUGGING Uncomment this line to display more information -
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 = 0.0;
var total:Double = 0.0;
for ( x <- array ) {
    if (x...
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