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Visualize qualitative data9/22/2023 There are actually two different categorical scatter plots in seaborn. The default representation of the data in catplot() uses a scatterplot. Remember that this function is a higher-level interface each of the functions above, so we’ll reference them when we show each kind of plot, keeping the more verbose kind-specific API documentation at hand. In this tutorial, we’ll mostly focus on the figure-level interface, catplot(). The unified API makes it easy to switch between different kinds and see your data from several perspectives. When deciding which to use, you’ll have to think about the question that you want to answer. These families represent the data using different levels of granularity. Stripplot() (with kind="strip" the default) It’s helpful to think of the different categorical plot kinds as belonging to three different families, which we’ll discuss in detail below. There are a number of axes-level functions for plotting categorical data in different ways and a figure-level interface, catplot(), that gives unified higher-level access to them. Similar to the relationship between relplot() and either scatterplot() or lineplot(), there are two ways to make these plots. In seaborn, there are several different ways to visualize a relationship involving categorical data. If one of the main variables is “categorical” (divided into discrete groups) it may be helpful to use a more specialized approach to visualization. In the examples, we focused on cases where the main relationship was between two numerical variables. In the relational plot tutorial we saw how to use different visual representations to show the relationship between multiple variables in a dataset.
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