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The Applied SQL Data Analytics Workshop - Second Edition

You're reading from  The Applied SQL Data Analytics Workshop - Second Edition

Product type Book
Published in Feb 2020
Publisher Packt
ISBN-13 9781800203679
Pages 484 pages
Edition 2nd Edition
Languages
Authors (3):
Matt Goldwasser Matt Goldwasser
Profile icon Matt Goldwasser
Upom Malik Upom Malik
Profile icon Upom Malik
Benjamin Johnston Benjamin Johnston
Profile icon Benjamin Johnston
View More author details

Statistics with Window Functions

Now that we understand how window functions work, we can start using them to calculate useful statistics, such as ranks, percentiles, and rolling statistics.

In the following table, we have summarized a variety of statistical functions that are useful. It is also important to emphasize again that all aggregate functions can also be used as window functions (AVG, SUM, COUNT, and so on):

Figure 3.25: Statistical window functions

Normally, a call to any of these functions inside a SQL statement would be followed by the OVER keyword. This keyword will then have brackets with PARTITION BY and ORDER BY statements, either of which may be optional, depending on which function you are using.

For example, the ROW_NUMBER() function will look like this:

ROW_NUMBER() OVER(
  PARTITION BY column_1, column_2
  ORDER BY column_3,column_4
)

We will show you how to use these statistical functions in the next exercise...

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