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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

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

Section 3: Applications – Real-World Analyses Using Pandas

It's time to see how to bring together everything we have learned so far. In this section, we will take some real-world datasets and run through analyses from start to finish, combining all the concepts covered in the previous chapters and introducing some new material along the way.

This section comprises the following chapters:

Chapter 7, Financial Analysis – Bitcoin and the Stock Market
Chapter 8, Rule-Based Anomaly Detection

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Authors (1)

Stefanie Molin

Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

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