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Hands-On Data Analysis with Pandas

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

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800563452
Length 788 pages
Edition 2nd Edition
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Author (1):
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Stefanie Molin Stefanie Molin
Author Profile Icon Stefanie Molin
Stefanie Molin
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Toc

Table of Contents (21) Chapters Close

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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...
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