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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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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

Plotting with pandas

Both Series and DataFrame objects have a plot() method that allows us to create several different plots and control some aspects of their formatting, such as subplot layout, figure size, titles, and whether to share an axis across subplots. This makes plotting our data much more convenient, as the bulk of the work to create presentable plots is achieved with a single method call. Under the hood, pandas is making several calls to matplotlib to produce our plot. Some of the most frequently used arguments to the plot() method include the following:

Figure 5.10 – Frequently used pandas plotting arguments

Rather than having separate functions for each plot type, as we saw during our discussion of matplotlib, the plot() method from pandas allows us to specify the type of plot we want using the kind argument. The choice of plot will determine which other arguments are required. We can use the Axes object that's returned by the plot...

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