chapter seven

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 and seaborn.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.

7.1 The prepare-reduce-collect-plot pattern

7.2 Visualizing continuous relationships with scatterplot and regplot

7.2.1 Creating a scatterplot with Dask and Seaborn

7.2.2 Adding a linear regression line to the scatterplot

7.2.3 Adding a nonlinear regression line to a scatterplot

7.3 Visualizing categorical relationships with violinplot

7.3.1 Creating a violinplot with Dask and Seaborn

7.3.2 Randomly sampling data from a Dask DataFrame

7.4 Visualizing two categorical relationships with heatmap

Summary