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Jupyter for Data Science

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

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781785880070
Length 242 pages
Edition 1st Edition
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Author (1):
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Dan Toomey Dan Toomey
Author Profile Icon Dan Toomey
Dan Toomey
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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...
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