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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

Preprocessing the data

The very first step in preprocessing for clustering analysis is to be clear about which data objects will be clustered, and that is clear here: counties. So, at the end of the data preprocessing, we will need to have a dataset whose rows are counties, and with columns based on how we want to group the counties. As shown in the following screenshot, which is a summary of the data preprocessing that we will perform during this chapter, we will get to county_df, which has the characteristics that were just described.

Figure 17.2 – Schematic of the data preprocessing

As shown in the preceding summarizing screenshot, we will first transform election_df into partisan_df, and then integrate the partisan_df, edu_df, pov_df, pop_df, and employ_df DataFrames. Of course, there will be more detail to all of these steps than the preceding screenshot shows; however, this serves as a great summary and a general map for our understanding.

Let...

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