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

Reading JSON files

The modern approach to file formats is to use JavaScript Object Notation (JSON). It was specifically developed for interpretation by JavaScript coding, typically in a website. For example, a web page needs a list of products and asks for that list from the web server coding. The web server coding responds with the information encoded in JSON format, knowing that JavaScript resides in the web application and can easily interpret the information.

As it became more popular to use JSON on web applications, many realized it was a robust data format supporting hierarchical structures without having to resort to the flattening required by CSV. People then started using JSON in a variety of applications, not just website intercommunication.

Getting ready

We need to locate a JSON file of reasonable complexity. There are many. You will see in most of the standard repositories for datasets that a good sprinkling of JSON format files is now available. In this example, I am referencing...