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

Product type Paperback

Published in Jul 2019

Publisher

ISBN-13 9781789615326

Length 740 pages

Edition 1st 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 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

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Appendix

Section 1: Getting Started with Pandas

Our journey begins with an introduction to data analysis and statistics, which will lay a strong foundation for the concepts we will cover throughout the book. Then, we will set up our Python data science environment, which contains everything we will need to work through the examples, and get started with learning the basics of pandas.

The following chapters are included in this section:

Chapter 1, Introduction to Data Analysis
Chapter 2, Working with Pandas DataFrames

You have been reading a chapter from

Hands-On Data Analysis with Pandas

Published in: Jul 2019

Publisher:

ISBN-13: 9781789615326

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Authors (1)

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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