You're reading from Python and SQL Bible

Product type Book

Published in Jun 2024

Publisher Packt

ISBN-13 9781836206279

Pages 519 pages

Edition 1st Edition

Languages

SQL Python

Concepts

Programming Language

Author (1):

Cuantum Technologies LLC

Table of Contents (29) Chapters

1. Who we are

2. Introduction

3. Chapter 1: Python: An Introduction

4. Chapter 2: Python Building Blocks

5. Chapter 3: Controlling the Flow

6. Chapter 4: Functions, Modules, and Packages

7. Chapter 5: Deep Dive into Data Structures

8. Chapter 6: Object-Oriented Programming in Python

9. Chapter 7: File I/O and Resource Management

10. Chapter 8: Exceptional Python

11. Chapter 9: Python Standard Library

12. Chapter 10: Python for Scientific Computing and Data Analysis

13. Chapter 11: Testing in Python

14. Chapter 12: Introduction to SQL

15. Chapter 13: SQL Basics

16. Chapter 14: Deep Dive into SQL Queries

17. Chapter 15: Advanced SQL

18. Chapter 16: SQL for Database Administration

19. Chapter 17: Python Meets SQL

20. Chapter 18: Data Analysis with Python and SQL

21. Chapter 19: Advanced Database Operations with SQLAlchemy

22. References

23. Conclusion

24. Where to continue?

25. Know more about us

Appendix A: Python Interview Questions

1. Appendix B: SQL Interview Questions

2. Appendix C: Python Cheat Sheet

3. Appendix D: SQL Cheat Sheet

CRUD Operations

18.2 Data Transformation in Python and SQL

Data transformation is a fundamental process in data analysis. It involves converting data from one form or structure to another in order to make it suitable for further analysis. This step is critical because the format of your data can have a significant impact on the accuracy and reliability of your analysis results.

In this section, we will delve deeper into the process of data transformation and explore the various techniques that can be employed to achieve it. We will focus on two of the most popular tools for data transformation – Python and SQL – and examine how each tool can be used to its advantage in this process.

Using Python, you can easily manipulate and transform data by leveraging the built-in functions and libraries. For instance, you can use the pandas library to perform operations such as filtering, sorting, and grouping on your data. You can also use NumPy for numerical operations, and Matplotlib for data...