You're reading from Hands-On Data Preprocessing in Python Learn how to effectively prepare data for successful data analytics

Product type Paperback

Published in Jan 2022

Publisher Packt

ISBN-13 9781801072137

Length 602 pages

Edition 1st Edition

Languages

Python

Tools

Azure Functions

Concepts

Big Data

Author (1):

Roy Jafari

View More author details

Table of Contents (24) Chapters

Preface

1. Part 1:Technical Needs

2. Chapter 1: Review of the Core Modules of NumPy and Pandas FREE CHAPTER

3. Chapter 2: Review of Another Core Module – Matplotlib

4. Chapter 3: Data – What Is It Really?

5. Chapter 4: Databases

6. Part 2: Analytic Goals

7. Chapter 5: Data Visualization

8. Chapter 6: Prediction

9. Chapter 7: Classification

10. Chapter 8: Clustering Analysis

11. Part 3: The Preprocessing

12. Chapter 9: Data Cleaning Level I – Cleaning Up the Table

13. Chapter 10: Data Cleaning Level II – Unpacking, Restructuring, and Reformulating the Table

14. Chapter 11: Data Cleaning Level III – Missing Values, Outliers, and Errors

15. Chapter 12: Data Fusion and Data Integration

16. Chapter 13: Data Reduction

17. Chapter 14: Data Transformation and Massaging

18. Part 4: Case Studies

19. Chapter 15: Case Study 1 – Mental Health in Tech

20. Chapter 16: Case Study 2 – Predicting COVID-19 Hospitalizations

21. Chapter 17: Case Study 3: United States Counties Clustering Analysis

22. Chapter 18: Summary, Practice Case Studies, and Conclusions

23. Other Books You May Enjoy

Chapter 5: Data Visualization

Being able to visualize data is the backbone of data analysis. The area of data visualization is very exciting, as there are endless possibilities for novelty and creativity in drawing visualizations that tell better stories about your data. However, the core mechanisms of even the most innovative graphs are similar. In this chapter, we will cover these fundamental mechanisms of visualizations that give life to the data and allow us to compare, analyze, and see patterns in it.

As you will learn these fundamental mechanisms, you will also be developing a better backbone/skillset for your data preprocessing goals. If you can fully understand the connection between the data and its visualizations, you will be more effective at preprocessing data for effective visuals. In this chapter, you will work with the data that I have already preprocessed, but in later chapters, we will cover the concepts and techniques that lead to these preprocessed datasets.

...