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Hands-On Data Analysis with Pandas

You're reading from   Hands-On Data Analysis with Pandas Efficiently perform data collection, wrangling, analysis, and visualization using Python

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Product type Paperback
Published in Jul 2019
Publisher
ISBN-13 9781789615326
Length 740 pages
Edition 1st 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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Table of Contents (21) Chapters Close

Preface 1. Section 1: Getting Started with Pandas FREE CHAPTER
2. Introduction to Data Analysis 3. Working with Pandas DataFrames 4. Section 2: Using Pandas for Data Analysis
5. Data Wrangling with Pandas 6. Aggregating Pandas DataFrames 7. Visualizing Data with Pandas and Matplotlib 8. Plotting with Seaborn and Customization Techniques 9. Section 3: Applications - Real-World Analyses Using Pandas
10. Financial Analysis - Bitcoin and the Stock Market 11. Rule-Based Anomaly Detection 12. Section 4: Introduction to Machine Learning with Scikit-Learn
13. Getting Started with Machine Learning in Python 14. Making Better Predictions - Optimizing Models 15. Machine Learning Anomaly Detection 16. Section 5: Additional Resources
17. 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 python 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, failures, and the success/failure rates per IP address, using the data simulated in 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 percentage difference from the median that flags an IP address if the failures and attempts are both five times their...
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