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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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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

Create the following visualizations using what you have learned up to this point in this book. Use the data from this chapter's data/ directory:

  1. Plot the rolling 20-day minimum of the Facebook closing price using pandas.
  2. Create a histogram and KDE of the change from open to close in the price of Facebook stock.
  3. Using the earthquake data, create box plots for the magnitudes of each magType used in Indonesia.
  4. Make a line plot of the difference between the weekly maximum high price and the weekly minimum low price for Facebook. This should be a single line.
  5. Plot the 14-day moving average of the daily change in new COVID-19 cases in Brazil, China, India, Italy, Spain, and the USA:

    a) First, use the diff() method that was introduced in the Working with time series data section of Chapter 4, Aggregating Pandas DataFrames, to calculate the day-over-day change in new cases. Then, use rolling() to calculate the 14-day moving average.

    b) Make three subplots...

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