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

Providing multiuser with Docker

Docker is another mechanism that can be used to allow multiple users of the same Notebook without collision. Docker is a system that allows you to construct sets of applications into an image that can be run in the Docker container (much like ships at a dock).

Docker runs in most environments. Docker allows many instances of an image to be run in the same machine, but each maintains a separate address space. So, each user of a Docker image has their own instance of the software and their own set of data/variables. Exactly what we are looking for with our Notebook!

Each image is a complete stack of software necessary to run, for example, a web server, web applications, APIs, and so on.It is not a large leap to think of an image of your Notebook. The image contains Jupyter server code and your Notebook. The result is a completely intact unit that does not share any space with anyone else.

Getting ready

Installing Docker involves downloading the latest version (the...