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Hands-On Data Preprocessing in Python

You're reading from  Hands-On Data Preprocessing in Python

Product type Book
Published in Jan 2022
Publisher Packt
ISBN-13 9781801072137
Pages 602 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Roy Jafari Roy Jafari
Profile icon Roy Jafari
Toc

Table of Contents (24) Chapters close

Preface 1. Part 1:Technical Needs
2. Chapter 1: Review of the Core Modules of NumPy and Pandas 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

Binary coding, ranking transformation, and discretization

In our analytics journey, there will be many instances in which we want to transform our data from numerical representation to categorical representation, or vice versa. To do these transformations, we will have to use one of three tools: binary coding, ranking transformation, and discretization.

As the following figure shows, to switch from Categories to Numbers, we either have to use Binary Coding or Ranking Transformation, and to switch from numbers to categories, we need to use Discretization:

Figure 14.3 – Direction of application for binary coding, ranking transformation, and discretization

One question that the preceding figure might bring to mind is, how do we know which one we choose when we want to move from categories to numbers: binary coding or ranking transformation? The answer is simple.

If the categories are nominal, we can only use binary coding; if they are ordinal, both...

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