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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

Table of Contents (24) Chapters

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

Exercise

  1. In your own words, what are the differences and similarities between normalization and standardization? How come some use them interchangeably?
  2. There are two instances of data transformation done during the discussion of binary coding, ranking transformation, and discretization that can be labeled as massaging. Try to spot them and explain how come they can be labeled that way.
  3. Of course, we know that one of the ways that the color of a data object is presented is by using their names. This is why we would assume color probably should be a nominal attribute. However, you can transform this usually nominal attribute to a numerical one. What are the two possible approaches? (Hint: one of them is an attribute construction using RGB coding.) Apply the two approaches to the following small dataset. The data shown in the table below is accessible in the color_nominal.csv file:

    Figure 14.27 – color_nominal.csv

    Once after binary codding and once after RGB attribute...

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