You're reading from Data Wrangling with SQL A hands-on guide to manipulating, wrangling, and engineering data using SQL

Product type Paperback

Published in Jul 2023

Publisher Packt

ISBN-13 9781837630028

Length 350 pages

Edition 1st Edition

Languages

SQL

Concepts

Data Engineering

Authors (2):

Raghav Kandarpa

Shivangi Saxena

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Table of Contents (21) Chapters

Preface

1. Part 1:Data Wrangling Introduction

2. Chapter 1: Database Introduction FREE CHAPTER

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

Why subscribe?

20. Other Books You May Enjoy

Pivoting Data Using SQL

Pivoting data in SQL is useful for data wrangling because it allows you to reshape the structure of your data in a way that is more useful for analysis and visualization. Some reasons why you might want to pivot data include the following:

Presenting data in a more intuitive format: Pivoting data can make it easier to understand and interpret by transforming it into a more familiar layout, such as a table with rows and columns
Facilitating data analysis: Pivoting data can make it easier to perform certain types of analysis, such as comparing the values of different groups or calculating aggregate values
Enhancing data visualization: Pivoting data can make it easier to create visualizations that are more meaningful and informative, such as bar charts, line graphs, and heat maps
Creating a consistent data structure: Pivoting data can be used to transform data into a consistent format, making it easier to join with other data sources, perform...

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Authors (2)

Raghav Kandarpa

Raghav Kandarpa is an experienced Data Scientist in Finance and logistics industry with expertise in SQL, Python, Building Machine Learning Models, Financial Data Modelling, and Statistical Analysis. He holds a Masters' degree in Business Analytics specializing in Data Science from the University of Texas at Dallas.

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Shivangi Saxena

Shivangi Saxena is an experienced BI Engineer with proficiency in SQL, Data Visualization, and Statistical Analysis. She holds a master's degree in Information Technology and Management from the University of Texas at Dallas. She has several years of experience building several BI tools and products using SQL and BI reporting tools which has helped stakeholders to get visibility to the right data points

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