Learn Enhancing Plots with Rugs

The rugplot is a plot intended to complement other plots by showing the location of individual observations in an unobtrusive way.

While kdeplot shows a smooth probability curve, it can sometimes hide the fact that there are very few data points in a certain area. Adding a rugplot draws small "ticks" (like fringes on a rug) for every single data point along the x or y axis.

Why Combine Them?

KDE: shows the abstract shape (trend).
Rugplot: shows the actual data density (reality).

Key Parameters

To make the rugplot effective and compatible with other plots, you should know these parameters:

height: controls the length of the ticks relative to the plot area. A value like 0.05 or 0.1 is usually best to keep it subtle;
hue: groups data by color, allowing you to match the categories in your main plot;
x or y: determines which axis the data lies on.


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import seaborn as sns
import matplotlib.pyplot as plt

# Load the dataset
df = sns.load_dataset('tips')

# 1. Main plot (Abstract shape)
sns.kdeplot(data=df, x='total_bill', fill=True, alpha=0.3)

# 2. Rug plot (Real data points)
sns.rugplot(data=df, x='total_bill', height=0.1, color='black')

plt.show()

Task

Swipe to start coding

Now, let's apply this to create a styled visualization for the tips dataset.

Set the style to 'darkgrid'. Pass a dictionary as the second argument to configure the grid: disable 'axes.grid' (False) and set 'axes.facecolor' to 'aliceblue'.
Create a KDE plot for 'total_bill', grouped by 'sex':
- Use the 'magma' palette;
- Stack the layers (multiple='layer');
- Fill the curves (fill=True).
Add a Rugplot to show individual data points:
- Use the same x-axis ('total_bill') and grouping ('sex');
- Set the height to 0.05;
- Use the same 'magma' palette.
Display the plot.

Solution

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Section 1. Chapter 6

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