Building a JointGrid and jointplot
Seaborn's JointGrid combines univariate plots such as histograms, rug plots and kde plots
with bivariate plots such as scatter and regression plots. The process for creating these plots
should be familiar to you now. These plots also demonstrate how Seaborn provides convenient
functions to combine multiple plots together.
For these exercises, we will use the bike share data that we reviewed earlier. In this exercise, we will look at the relationship between humidity levels and total rentals to see if there is an interesting relationship we might want to explore later.
This exercise is part of the course
Intermediate Data Visualization with Seaborn
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Build a JointGrid comparing humidity and total_rentals
sns.___("whitegrid")
g = sns.___(___="hum",
___="total_rentals",
data=df,
xlim=(0.1, 1.0))
g.plot(sns.___, sns.histplot)
plt.show()
plt.clf()