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SQL for Data Analytics

You're reading from   SQL for Data Analytics Harness the power of SQL to extract insights from data

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Product type Paperback
Published in Aug 2022
Publisher Packt
ISBN-13 9781801812870
Length 540 pages
Edition 3rd Edition
Languages
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Authors (4):
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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
Jun Shan Jun Shan
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Jun Shan
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

Cleaning Data

Often, the raw data presented in a query output may not be in the desired form. You may want to remove values, substitute values, or map values to other values. To accomplish these tasks, SQL provides a wide variety of statements and functions. Functions are keywords that take in inputs (such as a column or a scalar value) and process those inputs into some sort of output. You will learn about some useful functions for data transformation and cleaning in the following sections.

The CASE WHEN Function

CASE WHEN is a function that allows a query to map various values in a column to other values. The general format of a CASE WHEN statement is as follows:

CASE 
  WHEN condition1 THEN value1
  WHEN condition2 THEN value2
  …
  WHEN conditionX THEN valueX
  ELSE else_value 
END;

Here, condition1 and condition2, through conditionX, are Boolean conditions; value1 and value2, through valueX, are values to map...

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