Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Jupyter for Data Science

You're reading from   Jupyter for Data Science Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

Arrow left icon
Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Author (1):
Arrow left icon
Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
Arrow right icon
View More author details
Toc

Table of Contents (11) Chapters Close

Preface 1. Jupyter and Data Science FREE CHAPTER 2. Working with Analytical Data on Jupyter 3. Data Visualization and Prediction 4. Data Mining and SQL Queries 5. R with Jupyter 6. Data Wrangling 7. Jupyter Dashboards 8. Statistical Modeling 9. Machine Learning Using Jupyter 10. Optimizing Jupyter Notebooks

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...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image