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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

Aggregate Functions

With data, we are often interested in understanding the properties of an entire column or table as opposed to just seeing individual rows of data. As a simple example, let's say you were wondering how many customers ZoomZoom has. You could select all the data from the table and then see how many rows were pulled back, but it would be incredibly tedious to do so. Luckily, there are functions provided by SQL that can be used to do calculations on large groups of rows. These functions are called aggregate functions. The aggregate function takes in one or more columns with multiple rows and returns a number based on those columns. As an illustration, we can use the COUNT function to count how many rows there are in the customers table to figure out how many customers ZoomZoom has:

SELECT COUNT(customer_id) FROM customers;

The COUNT function will return the number of rows without a NULL value in the column. As the customer_id column is a primary key and cannot...

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