Book Image

Jupyter Cookbook

By : Dan Toomey
Book Image

Jupyter Cookbook

By: Dan Toomey

Overview of this book

Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Producing a Scatter plot matrix using R

A Scatter plot matrix is a useful device to display a miniature Scatter plot of every variable in your dataset against every other variable. The resulting display gives you a quick scan to determine variables that may be related.

How to do it...

Use this script:

# load the iris dataset
data <- read.csv("")

#Let us also clean up the data so as to be more readable
colnames(data) <- c("sepal_length", "sepal_width", "petal_length", "petal_width", "species")


This produces this graphic:

The pairs graphic shows petal width and petal length as related (fairly good straight lines of the plot points), and little relationship between sepal length and sepal width.

How it works...

The pairs function draws upon the underlying plot to walk through all pairs of data points in the dataset and produce a Scatter plot. I have used this many times to get a quick handle on which variables...