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

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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 7: Financial Analysis – Bitcoin and the Stock Market

It's time to switch gears and work on an application. In this chapter, we will explore a financial application by performing an analysis of bitcoin and the stock market. This chapter builds upon everything we have learned so far—we will extract data from the Internet; perform some exploratory data analysis; create visualizations with pandas, seaborn, and matplotlib; calculate important metrics for analyzing the performance of financial instruments using pandas; and get a taste of building some models. Note that we are not trying to learn financial analysis here, but rather walk through an introduction of how the skills we have learned in this book can be applied to financial analysis.

This chapter is also a departure from the standard workflow in this book. Up until this point, we have been working with Python as more of a functional programming language. However, Python also supports object-oriented...