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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 2. The Basics of SQL for Analytics FREE CHAPTER 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

The HAVING Clause

We can now perform all sorts of aggregate operations using GROUP BY. Sometimes, though, certain rows in aggregate functions may not be useful, and you may like to remove them from the query output. For example, when doing the customer counts, perhaps you are only interested in places that have at least 1,000 customers. Your first instinct may be to write something such as this:

SELECT state, COUNT(*)
FROM customers
WHERE COUNT(*)>=1,000
GROUP BY state
ORDER BY state

However, you will find that the query does not work and gives you the following error:

Figure 4.18: Error showing the query not working

In order to use filter on aggregate functions, you need to use a new clause, HAVING. The HAVING clause is similar to the WHERE clause, except it is specifically designed for GROUP BY queries. The general structure of a GROUP BY operation with a HAVING statement is:

SELECT {KEY}, {AGGFUNC(column1)}
FROM {table1}
GROUP BY {KEY}
HAVING...
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