![]() ![]() This plot is a bit hard to read because all of the points are of the same color. As this example demonstrates, varying point size is best used if the variable is either a quantitative variable or a categorical variable that represents different levels of something, like "small", "medium", and "large". To do this, we'll set the "size" parameter equal to the variable name "size" from our dataset. We want each point on the scatter plot to be sized based on the number of people in the group, with larger groups having bigger points on the plot. Here, we're creating a scatter plot of total bill versus tip amount. The first customization we'll talk about is point size. ![]() Use with both scatterplot() and relplot() Show relationship between two quantitative variables For the rest of this post, we'll use the tips dataset to learn how to use each customization and cover best practices for deciding which customizations to use. All of these options can be used in both the "scatterplot()" and "relplot()" functions, but we'll continue to use "relplot()" for the rest of the course since it's more flexible and allows us to create subplots. In addition to these, Seaborn allows you to add more information to scatter plots by varying the size, the style, and the transparency of the points. Seaborn has six variations of its default color palette: deep, muted, pastel, bright, dark, and colorblind. We've seen a few ways to add more information to them as well, by creating subplots or plotting subgroups with different colored points. So far, we've only scratched the surface of what we're able to do with scatter plots in Seaborn.Īs a reminder, scatter plots are a great tool for visualizing the relationship between two quantitative variables.
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