Seaborn Multi-Panel Categorical Plots

Categorical data can be visualized using two plot types: the pointplot() function or the higher-level factorplot() function.

Factorplot

Factorplot draws a categorical plot on a FacetGrid. Using the ‘kind’ parameter, we can choose between boxplot, violinplot, barplot, and stripplot plots. FacetGrid uses dotplots by default.

Examples

import pandas as pd
import seaborn as sb
from matplotlib import pyplot as plt
df = sb.load_dataset('exercise')
sb.factorplot(x = "time", y = pulse", hue = "kind",data = df);
plt.show()

Output

We can visualize the same data using different plots using the category argument.

Example

import pandas as pd
import seaborn as sb
from matplotlib import pyplot as plt
df = sb.load_dataset('exercise')
sb.factorplot(x = "time", y = "pulse", hue = "kind", kind = 'violin', data = df);
plt.show()

Output

In factorplot, data is plotted on a faceted grid.

What is a faceted grid?

Facet grid A facet grid forms a matrix of panels defined by rows and columns by partitioning the variables. Because of the panels, a single plot looks like multiple plots. It is very helpful for analyzing all possible combinations of two discrete variables.

Let’s illustrate the above definition with an example.

Example

import pandas as pd
import seaborn as sb
from matplotlib import pyplot as plt
df = sb.load_dataset('exercise')
sb.factorplot(x = "time", y = "pulse", hue = "kind", kind = 'violin', col = "diet", data = df);
plt.show()

Output

The benefit of using Facet is that we can include another variable in the plot. The plot above is split into two plots based on a third variable, “diet,” using the “col” argument.

We can create many column facets and align them with the rows of the grid:

Example

import pandas as pd
import seaborn as sb
from matplotlib import pyplot as plt
df = sb.load_dataset('titanic')
sb.factorplot("alive", col = "deck", col_wrap = 3, data = df[df.deck.notnull()], kind = "count")
plt.show()

Output: