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 2: Review of Another Core Module – Matplotlib

Matplotlib is our go-to module for creating visualizations from data. Not only can this module draw many different plots, but it also gives us the capability to design and tailor the plots to our needs. Matplotlib will serve our data analytics and data preprocessing journey by providing a great number of functions for effective visualizations.

Before we start reviewing this valuable module, I would like to let you know that this chapter is not meant to be a comprehensive teaching guide for Matplotlib, but rather a collection of concepts, functions, and examples that will be invaluable as we cover data analytics and data preprocessing in future chapters.

We have actually started using this module in the previous chapter. The Pandas plot functions that we introduced in Chapter 1, Review of the Core Modules of NumPy and Pandas, under the Pandas functions to explore a DataFrame are section, actually Matplotlib visuals that...