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Data Wrangling with SQL

You're reading from  Data Wrangling with SQL

Product type Book
Published in Jul 2023
Publisher Packt
ISBN-13 9781837630028
Pages 350 pages
Edition 1st Edition
Languages
Authors (2):
Raghav Kandarpa Raghav Kandarpa
Profile icon Raghav Kandarpa
Shivangi Saxena Shivangi Saxena
Profile icon Shivangi Saxena
View More author details

Table of Contents (21) Chapters

Preface 1. Part 1:Data Wrangling Introduction
2. Chapter 1: Database Introduction 3. Chapter 2: Data Profiling and Preparation before Data Wrangling 4. Part 2:Data Wrangling Techniques Using SQL
5. Chapter 3: Data Wrangling on String Data Types 6. Chapter 4: Data Wrangling on the DATE Data Type 7. Chapter 5: Handling NULL Values 8. Chapter 6: Pivoting Data Using SQL 9. Part 3:SQL Subqueries, Aggregate And Window Functions
10. Chapter 7: Subqueries and CTEs 11. Chapter 8: Aggregate Functions 12. Chapter 9: SQL Window Functions 13. Part 4:Optimizing Query Performance
14. Chapter 10: Optimizing Query Performance 15. Part 5:Data Science And Wrangling
16. Chapter 11: Descriptive Statistics with SQL 17. Chapter 12: Time Series with SQL 18. Chapter 13: Outlier Detection 19. Index 20. Other Books You May Enjoy

Summary

This brings us to the end of this chapter, and by now, you should have learned about the following:

  • The different ways to store date and time information in a SQL database, specifically the DATETIME, DATE, and TIME data types
  • The syntax and format for each of these data types
  • Various built-in functions provided by SQL for manipulating and extracting information from DateTime data, such as NOW(), DATE(), and DATEDIFF()
  • How to perform calculations and comparisons with DateTime data using SQL functions
  • The importance of good data validation and integrity in order to ensure that the data stored in DateTime columns is accurate, consistent, and in the correct format

In the next chapter, we will learn about Null values in a dataset and will look at ways to avoid and handle such values in a dataset. Handling null values in SQL is an important step before introducing data wrangling, as it ensures that your data is accurate, consistent, and ready for data...

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