You're reading from Hands-On Data Analysis with Pandas A Python data science handbook for data collection, wrangling, analysis, and visualization

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800563452

Length 788 pages

Edition 2nd Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Author (1):

Stefanie Molin

Preface

1. Section 1: Getting Started with Pandas

2. Chapter 1: Introduction to Data Analysis FREE CHAPTER

3. Chapter 2: Working with Pandas DataFrames

4. Section 2: Using Pandas for Data Analysis

5. Chapter 3: Data Wrangling with Pandas

6. Chapter 4: Aggregating Pandas DataFrames

7. Chapter 5: Visualizing Data with Pandas and Matplotlib

8. Chapter 6: Plotting with Seaborn and Customization Techniques

9. Section 3: Applications – Real-World Analyses Using Pandas

10. Chapter 7: Financial Analysis – Bitcoin and the Stock Market

11. Chapter 8: Rule-Based Anomaly Detection

12. Section 4: Introduction to Machine Learning with Scikit-Learn

13. Chapter 9: Getting Started with Machine Learning in Python

14. Chapter 10: Making Better Predictions – Optimizing Models

15. Chapter 11: Machine Learning Anomaly Detection

16. Section 5: Additional Resources

17. Chapter 12: The Road Ahead

18. Solutions

19. Other Books You May Enjoy

Appendix

Exercises

Complete the following exercises to practice the concepts covered in this chapter:

Run the simulation for December 2018 into new log files without making the user base again. Be sure to run python3 simulate.py -h to review the command-line arguments. Set the seed to 27. This data will be used for the remaining exercises.
Find the number of unique usernames, attempts, successes, and failures, as well as the success/failure rates per IP address, using the data simulated from exercise 1.
Create two subplots with failures versus attempts on the left, and failure rate versus distinct usernames on the right. Draw decision boundaries for the resulting plots. Be sure to color each data point by whether or not it is a hacker IP address.
Build a rule-based criteria using the percentage difference from the median that flags an IP address if the failures and attempts are both five times their respective medians, or if the distinct usernames count is five times its...