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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.

Cet exercice fait partie du cours

Intermediate Data Visualization with Seaborn

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Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de 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()
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