Book Image

Learning Jupyter 5 - Second Edition

Book Image

Learning Jupyter 5 - Second Edition

Overview of this book

The Jupyter Notebook 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, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization.
Table of Contents (18 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

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) by using the add function:

Pkg.add("Gadfly") 

 

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

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 following 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 that all of the results in this chapter are reproducible.

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

 

I did notice that if you click on the ? symbol that appears in the top-right of the graphic if you click on the graphic, a message box is displayed that enables finer control over...