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


In this chapter, we cover the methods for accessing big data from Jupyter. Big data is meant to be large data files, often in the many millions of rows. Big data is a topic of discussion in many firms. Most firms have it in one form or another, and they are trying hard to draw some value from all of the data they have stored.

An up-and-coming language for dealing with large datasets is Spark. Spark is an open source toolset specifically made for dealing with large datasets. We can use Spark coding in Jupyter much like the other languages we have seen.

In Chapter 2,Adding an Engine, we dealt with installing Spark for use in Jupyter. For this chapter, we will be using the Python 3 engine for further work. As a reminder, we start a Notebook using the Python 3 engine and then import the Python-Spark library to invoke Spark functionality.

Most importantly, we will be using Spark to access big data.