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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 this exercise, we will be using Universities_imputed_reduced.csv. Draw the following visualizations:

    a) Use boxplots to compare the student/faculty ratio (stud./fac. ratio) for the two populations of public and private universities.

    b) Use a histogram to compare the student/faculty ratio (stud./fac. ratio) for the two populations of public and private universities.

    c) Use subplots to put the results of a) and b) on top of one another to create a visual that compares the two populations even better.

  2. In this exercise, we will continue using Universities_imputed_reduced.csv. Draw the following visualizations:

    a) Use a bar chart to compare the private/public ratio of all the states in the dataset. In this example, the populations we are comparing are the states.

    b) Improve the visualizations by sorting the states on the visuals based on the total number of universities they have.

    c) Create a stacked bar chart that shows and compares the percentages of public and private...

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