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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

Example 2 – restructuring the table

In this example, we will use the Customer Churn.csv dataset. This dataset contains the records of 3,150 customers of a telecommunication company. The rows are described by demographic columns such as gender and age, and activity columns such as the distinct number of calls in 9 months. The dataset also specifies whether each customer was churned or not 3 months after the 9 months of collecting the activity data of the customers. Customer churning, from a telecommunication company's point of view, means the customer stops using the company's services and receives the services from the company's competition.

We would like to use box plots to compare the two populations of churning customers and non-churning customers for the following activity columns: Call Failure, Subscription Length, Seconds of Use, Frequency of use, Frequency of SMS, and Distinct Called Numbers.

Let's start by reading the Customer Churn.csv file...

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