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SQL for Data Analytics

You're reading from   SQL for Data Analytics Perform fast and efficient data analysis with the power of SQL

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Product type Paperback
Published in Aug 2019
Publisher Packt
ISBN-13 9781789807356
Length 386 pages
Edition 1st Edition
Languages
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Authors (3):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Matt Goldwasser Matt Goldwasser
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Matt Goldwasser
Upom Malik Upom Malik
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Upom Malik
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Table of Contents (11) Chapters Close

Preface 1. Understanding and Describing Data FREE CHAPTER 2. The Basics of SQL for Analytics 3. SQL for Data Preparation 4. Aggregate Functions for Data Analysis 5. Window Functions for Data Analysis 6. Importing and Exporting Data 7. Analytics Using Complex Data Types 8. Performant SQL 9. Using SQL to Uncover the Truth – a Case Study Appendix

Aggregate Functions with GROUP BY

We have now used aggregate functions to calculate statistics for an entire column. However, often, we are not interested in the aggregate values for a whole table, but for smaller groups in the table. To illustrate, let's go back to the customers table. We know the total number of customers is 50,000. But we might want to know how many customers we have in each state. How would we calculate this?

We could determine how many states there are with the following query:

SELECT DISTINCT state FROM customers;

Once you have the list of states, you could then run the following query for each state:

SELECT COUNT(*) FROM customer WHERE state='{state}'

Although you can do this, it is incredibly tedious and can take an incredibly long time if there are many states. Is there a better way? There is, and it is through the use of the GROUP BY clause.

GROUP BY

GROUP BY is a clause that divides the rows of a dataset into multiple...

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