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Hands-On Data Analysis with Pandas

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

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Product type Paperback
Published in Jul 2019
Publisher
ISBN-13 9781789615326
Length 740 pages
Edition 1st 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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Table of Contents (21) Chapters Close

Preface 1. Section 1: Getting Started with Pandas
2. Introduction to Data Analysis FREE CHAPTER 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 Appendix

Summary

Now we are well-equipped to quickly create a variety of visualizations in Python using pandas and matplotlib. We understand the basics of how matplotlib works and the main components of a plot. Additionally, we discussed various plot types and the situations in which to use them—a crucial component of data visualization is choosing the appropriate plot. Be sure to check out the Choosing the appropriate visualization section in the appendix for future reference.

Note that the best practices for visualization don't just apply to the plot type, but also to the formatting of the plot, which we will discuss in the next chapter. In addition to this, we will build upon the foundation we laid here to discuss additional plots using seaborn and how to customize our plots using matplotlib.

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