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

Product type Paperback

Published in Jan 2022

Publisher Packt

ISBN-13 9781801072137

Length 602 pages

Edition 1st Edition

Languages

Python

Tools

Azure Functions

Concepts

Big Data

Author (1):

Roy Jafari

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

Exercises

This question is about the difference between dataset reformulation and dataset restructuring. Answer the following questions:
a) In your own words, describe the difference between dataset reformulation and dataset restructuring.
b) In Example 3 of this chapter, we moved the data from month_df to predict_df. The text described the level II data cleaning for both table reformulation and table restructuring. Which of the two occurred? Is it possible that the distinction we provided for the difference between table restructuring and reformulation cannot specify which one happened? Would that matter?
For this exercise, we will be using LaqnData.csv, which can be found on the London Air website (https://www.londonair.org.uk/LondonAir/Default.aspx) and includes the hourly readings of five air particles (NO, NO2, NOX, PM2.5, and PM10) from a specific site. Perform the following steps for this dataset:
a) Read the dataset into air_df using pandas.
b) Use the .unique() function...