How to Make Grouped Violinplot with Seaborn in Python?

Creating Grouped Violin Plots with Seaborn in Python

Grouped violin plots provide a rich way to compare distributions across multiple categories and subgroups. By combining kernel density estimates with categorical grouping, Seaborn lets you reveal subtle patterns and differences in your data. In this tutorial, you'll learn how to build and customize grouped violin plots using Seaborn.

What Is a Grouped Violin Plot?

A violin plot combines the visual elements of box plots and density plots, displaying the full data distribution. Grouped violin plots add another layer by splitting distribution curves based on a categorical variable using the hue parameter. This enables side-by-side comparisons of subgroups within each main category.

Creating a Basic Grouped Violin Plot

Use Seaborn’s violinplot() with hue to visualize subgroup distributions. Here’s how to create a grouped violin plot using the tips dataset:

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")

sns.violinplot(x="day", y="total_bill", hue="sex", data=tips, palette="Set2")
plt.title("Total Bill Distribution by Day and Gender")
plt.show()

This plot shows separate density shapes for males and females within each day, enabling visual comparison.

Splitting Violins for Clearer Comparison

Use split=True to overlay male and female distributions within each violin for a clearer side-by-side comparison:

sns.violinplot(x="day", y="total_bill", hue="sex", data=tips,
               split=True, palette="Set1")
plt.title("Split Violin Plot by Gender")
plt.show()

This creates a mirrored violin with each half representing a subgroup—ideal for directly comparing two categories.

Customizing the Interior and Bandwidth

Enhance detail using inner and bw parameters. For instance:

sns.violinplot(x="day", y="total_bill", hue="sex", data=tips,
               split=True, inner="quartile", bw=0.3, palette="pastel")
plt.title("Violin Plot with Quartiles and Bandwidth Tuning")
plt.show()

Here, inner="quartile" draws quartile lines, and bw=0.3 adjusts the smoothness of the density curve.

Switching Orientation

When category names are long, or a landscape format is preferred, use horizontal orientation by swapping axes:

sns.violinplot(y="day", x="total_bill", hue="sex", data=tips,
               split=True, palette="viridis", orient="h")
plt.title("Horizontal Grouped Violin Plot")
plt.show()

This layout often improves readability, especially with long labels.

Fine‑Tuning Appearance

Seaborn offers additional styling options like scale, linewidth, and fliersize. For example:

sns.violinplot(x="day", y="total_bill", hue="sex", data=tips,
               split=True, palette="coolwarm", bw=0.4,
               scale="count", linewidth=2, orient="h")
plt.title("Styled Horizontal Grouped Violin Plot")
plt.show()

This plot uses scale="count" to weight the violin width by group sample size, adds stronger borders, and applies a coolwarm palette.

Conclusion

Grouped violin plots are a versatile tool for comparing data distributions across multiple categories and subgroups. With Seaborn’s violinplot() function, you can easily control orientation, splitting, density details, and styling. Experiment with these features to create clear and insightful visualizations.