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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

Exercises

  1. Ask five colleagues or classmates to provide a definition for the term data.

    a) Record these definitions and notice the similarities among them.

    b) In your own words, define the all-encompassing definition of data put forth in this chapter.

    c) Indicate the two important aspects of the definition in b).

    d) Compare the five definitions of data from your colleagues with the all-encompassing definitions and indicate their similarities and differences.

  2. In this exercise, we are going to use covid_impact_on_airport_traffic.csv. Answer the following questions. This dataset is from Kaggle.com – use this link to see its page:

    https://www.kaggle.com/terenceshin/covid19s-impact-on-airport-traffic

    The key attribute of this dataset is PercentOfBaseline, which shows the ratio of air traffic in a specific day compared to a pre-pandemic time range (February 1 to March 15, 2020).

    a) What is the best definition of the data object for this dataset?

    b) Are there any attributes in the...

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