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

Sharing your Notebook using nbviewer

Built into Jupyter is a tool called nbviewer, responsible for exposing your Notebook as a web page. nbviewer is used through a public Notebook sharing service, the Notebook Viewer, at

nbviewer is fully supported by the Jupyter project. So, if you encounter any issues they will help.

You can use nbviewer in conjunction with Docker or standalone.

How to do it...

To use nbviewer with Docker, you can use Docker commands directly to load your Notebook:

$ docker pull jupyter/nbviewer
$ docker run -p 8080:8080 jupyter/nbviewer

These commands are as follows:

  • The docker pull command downloads nbviewer from the code repository where all Jupyter products are maintained onto your machine
  • The docker run command executes nbviewer (just downloaded) and exposes Jupyter at port 8080 (which is a standard HTTP port address)

Once executed, if you open a browser to the local machine and port 8080, you see the standard Jupyter home page. You can then add...