4. Jupyter Julia Scripting | Learning Jupyter

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Learning Jupyter

By : Toomey

1 (3)

Learning Jupyter

1 (3)

By: Toomey

Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we’ll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Introduction to Jupyter

1. Introduction to Jupyter

First look at Jupyter

Installing Jupyter on Windows

Installing Jupyter on Mac

Notebook structure

Notebook workflow

Basic notebook operations

Security in Jupyter

Configuration options for Jupyter

Summary

2. Jupyter Python Scripting

2. Jupyter Python Scripting

Basic Python in Jupyter

Python data access in Jupyter

Python pandas in Jupyter

Python graphics in Jupyter

Python random numbers in Jupyter

Summary

3. Jupyter R Scripting

3. Jupyter R Scripting

Adding R scripting to your installation

Basic R in Jupyter

R dataset access

R visualizations in Jupyter

R cluster analysis

R forecasting

Summary

4. Jupyter Julia Scripting

4. Jupyter Julia Scripting

Adding Julia scripting to your installation

Basic Julia in Jupyter

Julia limitations in Jupyter

Standard Julia capabilities

Julia visualizations in Jupyter

Julia Vega plotting

Julia parallel processing

Julia control flow

Julia regular expressions

Julia unit testing

Summary

5. Jupyter JavaScript Coding

5. Jupyter JavaScript Coding

Adding JavaScript scripting to your installation

JavaScript Hello World Jupyter Notebook

Basic JavaScript in Jupyter

JavaScript limitations in Jupyter

Node.js d3 package

Node.js stats-analysis package

Node.js JSON handling

Node.js canvas package

Node.js plotly package

Node.js asynchronous threads

Node.js decision-tree package

Summary

6. Interactive Widgets

6. Interactive Widgets

Installing widgets

Widget basics

Interact widget

Interactive widget

Widgets

Summary

7. Sharing and Converting Jupyter Notebooks

7. Sharing and Converting Jupyter Notebooks

Sharing notebooks

Converting notebooks

Summary

8. Multiuser Jupyter Notebooks

8. Multiuser Jupyter Notebooks

Sample interactive notebook

JupyterHub

Docker

Summary

9. Jupyter Scala

9. Jupyter Scala

Installing the Scala kernel

Scala data access in Jupyter

Scala array operations

Scala random numbers in Jupyter

Scala closures

Scala higher-order functions

Scala pattern matching

Scala case classes

Scala immutability

Scala collections

Named arguments

Scala traits

Summary

10. Jupyter and Big Data

10. Jupyter and Big Data

Apache Spark

Our first Spark script

Spark word count

Sorted word count

Estimate Pi

Log file examination

Spark primes

Spark text file analysis

Spark - evaluating history data

Summary

Julia visualizations in Jupyter

The most popular tool for visualizations in Julia is the Gadfly package. We can add the Gadfly package (as described at the beginning of this chapter) using the add function:

Pkg.add("Gadfly")

From then on, we can make reference to the Gadfly package in any script using the following command:

using Gadfly

Julia Gadfly scatterplot

We can use the plot() function with standard defaults (no type arguments) to generate a scatterplot. For example, with the simple script:

using Gadfly
srand(111)
plot(x=rand(7), y=rand(7))

Note

We use the srand() function in all examples that use random results. The srand() function sets the random number seed value, so all results in this chapter are reproducible.

We generate a nice, clean scatterplot, as shown in the following screenshot:

Julia Gadfly histogram

We can produce other graph types as well, for example, a histogram using this script:

using Gadfly
srand(111)
plot(x=randn(113), Geom.histogram(bincount=10))

This script generates...

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