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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
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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 random numbers in Jupyter


In this example, we simulate rolling dice and counting 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 1000 rolls. For each roll, we increment a counter of how many times the total number of pips on die one and die two appear. We then present an abbreviated histogram of the results.

Scala has a number of shortcut methods for quickly scanning through a list/collection, as seen in the reduceLeft...

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