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Hands-On Data Preprocessing in Python

You're reading from   Hands-On Data Preprocessing in Python Learn how to effectively prepare data for successful data analytics

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Product type Paperback
Published in Jan 2022
Publisher Packt
ISBN-13 9781801072137
Length 602 pages
Edition 1st Edition
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Author (1):
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Roy Jafari Roy Jafari
Author Profile Icon Roy Jafari
Roy Jafari
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Table of Contents (24) Chapters Close

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

Frequent challenges regarding data fusion and integration

While every data integration task is unique, there are a few challenges that you will face frequently. In this chapter, you will learn about those challenges and, through examples, you will pick up the skills to handle them. First, let's learn about each. Then, through examples that feature one or more of them, we will pick up valuable skills to handle them.

Challenge 1 – entity identification

The entity identification challenge – or as it is known in the literature, the entity identification problem – may occur when the data sources are being integrated by adding attributes. The challenge is that the data objects in all the data sources are the same real-world entities with the same definitions of data objects, but they are not easy to connect due to the unique identifiers in the data sources. For instance, in the data integration example section, the sales department and the marketing department...

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