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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
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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 (11) Chapters Close

Preface 1. Introduction to Jupyter 2. Jupyter Python Scripting FREE CHAPTER 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 random numbers in Jupyter


In this example, we simulate a rolling dice and count how many times each combination appears. We then present a simple histogram for illustrative purposes.

The script is as follows:

val r = new scala.util.Random
r.setSeed(113L)
val samples = 1000
var dice = new Array[Int](12)
for( i <- 1 to samples){
    var total = r.nextInt(6) + r.nextInt(6)
    dice(total) = dice(total) + 1
}
val max = dice.reduceLeft(_ max _)
for( i <- 0 to 11) {
    var str = ""
    for( j <- 1 to dice(i)/3) {
        str = str + "X"
    }
    print(i+1, str, "\n")
}

We first pull in the Scala random library. We set the seed (in order to have repeatable results). We are drawing 1,000 rolls. For each roll, we increment a counter of how many times the total of pips on die 1 and die 2 appear. Then we present an abbreviated histogram of the results.

Scala has a number of shortcut methods for quick scanning through a list/collection, as seen in the reduceLeft(_ max _) statement. We...

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