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Hands-On Data Analysis with Pandas - Second Edition

You're reading from Hands-On Data Analysis with Pandas - Second Edition

Product type Book

Published in Apr 2021

Publisher Packt

ISBN-13 9781800563452

Pages 788 pages

Edition 2nd Edition

Languages

Concepts

Author (1):

Stefanie Molin

Table of Contents (21) Chapters

Preface

1. Section 1: Getting Started with Pandas

2. Chapter 1: Introduction to Data Analysis

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

Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Python Data Analysis, Third Edition

Avinash Navlani, Armando Fandango, Ivan Idris

ISBN: 978-1-78995-524-8

Explore data science and its various process models
Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values
Create interactive visualizations using Matplotlib, Seaborn, and Bokeh
Retrieve, process, and store data in a wide range of formats
Understand data preprocessing and feature engineering using pandas and scikit-learn

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

Tarek Amr

ISBN: 978-1-83882-604-8

Understand when to use supervised, unsupervised, or reinforcement learning algorithms
Find out how to collect and prepare your data for machine learning tasks
Tackle imbalanced data and optimize your algorithm for a bias or variance...

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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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