7 Visualizing DataFrames with Seaborn
This chapter covers
- Using the prepare-collect-plot-reduce pattern to overcome the challenges of visualizing large datasets
- Visualizing continuous relationships using
seaborn.scatterplot
andseaborn.regplot
- Visualizing groups of continuous data using Seaborn
seaborn.violinplot
- Visualizing patterns in categorical data using
seaborn.heatmap
In the previous chapter, we performed some basic analyses of the NYC Parking Ticket data by looking at descriptive statistics and some other numerical properties of the dataset. While describing data numerically is precise, the results can be somewhat difficult to interpret and are generally not intuitive. On the other hand, we humans are very good at detecting and understanding patterns in visual information. Incorporating visualization into our analyses can help us better understand the general makeup of our dataset as well as how different variables interact with one another.