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

View More author details

Table of Contents (21) Chapters

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

Chapter materials

In this chapter, we will be working with three datasets. The first two come from data on wine quality that was donated to the UCI Machine Learning Data Repository (http://archive.ics.uci.edu/ml/index.php) by P. Cortez, A. Cerdeira, F. Almeida, T. Matos, and J. Reis, which contains information on the chemical properties of various wine samples, along with a rating of the quality from a blind tasting by a panel of wine experts. These files can be found in the data/ folder inside this chapter's folder in the GitHub repository (https://github.com/stefmolin/Hands-On-Data-Analysis-with-Pandas-2nd-edition/tree/master/ch_09) as winequality-red.csv and winequality-white.csv for red and white wine, respectively.

Our third dataset was collected using the Open Exoplanet Catalogue database, which can be found at https://github.com/OpenExoplanetCatalogue/open_exoplanet_catalogue/. This database provides data in eXtensible Markup Language (XML) format, which is similar to...