Libraries & Dataset
First, we need to load a few libraries and the iris dataset:
# libraries
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="darkgrid")
# dataset
df = sns.load_dataset("iris")Histogram + rug + kernel density curve
Rugs are small lines or marks at the edge of a plot that represent the distribution of individual data points along an axis.
sns.displot(data=df["sepal_length"], kde=True, rug=True)
plt.show()
Customizing rugs
Thanks to the rug_kws argument, we can easily change how the rugs look like!
sns.displot(df["sepal_length"],
kde=True,
rug=True,
rug_kws={"alpha": 0.2, "linewidth": 4, "height": 0.2})
plt.show()
Customizing the density curve
It's also pretty straight forward to customize the density curve in a chart:
sns.displot(df["sepal_length"],
kind="kde",
fill=True,
rug=True,
rug_kws={"alpha": 0.9, "height": 0.1},
color="skyblue",
alpha=0.6,)
plt.show()
Going further
This post explains how to customize a distribution plot by adding rugs and density to it using seaborn!
For more examples of how to create or customize your distribution plots, see the density plot section. You may also be interested in how to create a density chart with multiple groups.
