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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

Window Functions

Aggregate functions allow us to take many rows and convert those rows into one number. For example, the COUNT function takes in the rows of a table and returns the number of rows there are. However, we sometimes want to be able to calculate multiple rows but still keep all the rows following the calculation. For example, let's say you wanted to rank every user in order according to the time they became a customer, with the earliest customer being ranked 1, the second-earliest customer being ranked 2, and so on. You can get all the customers using the following query:

SELECT *
FROM customers
ORDER BY date_added;

You can order customers from the earliest to the most recent, but you can't assign them a number. You can use an aggregate function to get the dates and order them that way:

SELECT date_added, COUNT(*)
FROM customers
GROUP BY date_added
ORDER BY date_added

The following is the output of the preceding code:

Figure 5...
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