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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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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 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

Exercise

  1. In your own words, what is the difference between data fusion and data integration? Provides examples other than the ones given in this chapter.
  2. Answer the following question about Challenge 4 – aggregation mismatch. Is this challenge a data fusion one, a data integration one, or both? Explain why.
  3. How come Challenge 2 – unwise data collection is somehow both a data cleaning step and a data integration step? Do you think it is essential that we categorize an unwise data collection under data cleaning or data integration?
  4. In Example 1 of this chapter, we used multi-level indexing using Date and Hour to overcome the index mismatched formatting challenge. For this exercise, repeat this example but this time, use single-level indexing using the Python DataTime object instead.
  5. Recreate Figure 5.20 from Chapter 5, Data Visualization, but instead of using WH Report_preprocessed.csv, integrate the following three files yourself first: WH Report.csv...
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