Book Image

Jupyter for Data Science

By : Dan Toomey
Book Image

Jupyter for Data Science

By: Dan Toomey

Overview of this book

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks. By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.
Table of Contents (17 chapters)
Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

Combining datasets


So, we have seen moving a data frame into Spark for analysis. This appears to be very close to SQL tables. Under SQL it is standard practice not to reproduce items in different tables. For example, a product table might have the price and an order table would just reference the product table by product identifier, so as not to duplicate data. So, then another SQL practice is to join or combine the tables to come up with the full set of information needed. Keeping with the order analogy, we combine all of the tables involved as each table has pieces of data that are needed for the order to be complete.

How difficult would it be to create a set of tables and join them using Spark? We will use example tables of Product, Order, and ProductOrder:

Table

Columns

Product

Product ID,

Description,

Price

Order

Order ID,

Order Date

ProductOrder

Order ID,

Product ID,

Quantity

 

So, an Order has a list of Product/Quantity values associated.

We can populate the data frames and move them into Spark:

from...