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

To get the most out of this book

The book assumes basic programming skills such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python. Other than that, you can start your journey from the beginning of the book and start learning.

The Jupyter Notebook is an excellent UI for learning and practicing programming and data analytics. It can be downloaded and installed easily using Anaconda Navigator. Visit this page for installation: https://docs.anaconda.com/anaconda/navigator/install/.

While Anaconda has most of the modules that the book uses already installed, you will need to install a few other modules such as Seaborn and Graphviz. Don't worry; when the time comes, the book will instruct you on how to go about these installations.

If you are using the digital version of this book, we advise you to type the code yourself or access the code from the book's GitHub repository (a link is available in the next section). Doing so will help you avoid any potential errors related to the copying and pasting of code.

While learning, keep a file of your own code from each chapter. This learning repository can be used in the future for deeper learning and real projects. The Jupyter Notebook is especially great for this purpose as it allows you to take notes along with the code.