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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 array operations


Scala does not have pandas, but we can emulate some of that logic with our own coding. We will use the same Titanic dataset used in Chapter 2 , Jupyter Python Scripting, from http://www.kaggle.com/c/titanic-gettingStarted/download/train.csv, which we have downloaded in our local space.

We can then use similar coding as was used in Chapter 2 , Jupyter Python Scripting, on pandas:

import scala.io.Source;
val filename = "train.csv"
//PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,
      Parch,Ticket,Fare,Cabin,Embarked
//1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
var males = 0
var females = 0
var males_survived = 0
var females_survived = 0
for (line <- Source.fromFile(filename).getLines) {
    var cols = line.split(",").map(_.trim);
    var sex = cols(5);
    if (sex == "male") { 
        males = males + 1;
        if (cols(1).toInt == 1) {
            males_survived = males_survived + 1;
        }
    }
    if (sex == "female") { 
        females...
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