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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

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800563452
Length 788 pages
Edition 2nd Edition
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Author (1):
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Stefanie Molin Stefanie Molin
Author Profile Icon Stefanie Molin
Stefanie Molin
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Toc

Table of Contents (21) Chapters Close

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

Data resources

As with any skill, to get better we need to practice, which for us means we need to find data to practice on. There is no best dataset to practice with; rather, each person should find data that they are interested in exploring. While this section is by no means comprehensive, it contains resources for data from various topics in the hopes that everyone will find something they want to use.

Tip

Unsure of what kind of data to look for? What are some of the things you have wondered about related to a topic that you find interesting? Has data been collected on this topic, and can you access it? Let your curiosity guide you.

Python packages

Both seaborn and scikit-learn provide built-in sample datasets that you can experiment with in order to get more practice with the material we've covered in the book and to try out new techniques. These datasets are often very clean and thus easy to work with. Once you're comfortable with the techniques, you can...

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