Violin Plot (Python)

Author

Modified

2026-04-04

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A violin plot combines a box plot and a kernel density estimation to show the distribution of continuous data across categories. In biomedical research, violin plots are ideal for comparing gene expression distributions, drug response measurements, or clinical biomarker levels across patient groups. Python’s seaborn library makes it simple to create beautiful violin plots.

Example

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming Language: Python
Dependencies: matplotlib, seaborn, pandas, numpy

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

Data Preparation

iris = sns.load_dataset("iris")

np.random.seed(42)
n_per_group = 80
groups = ['Tumor', 'Normal', 'Adjacent']
gene_expr = pd.DataFrame({
    'Expression': np.concatenate([
        np.random.normal(8, 1.5, n_per_group),
        np.random.normal(5, 1.2, n_per_group),
        np.random.normal(6.5, 1.8, n_per_group)
    ]),
    'Group': np.repeat(groups, n_per_group),
    'Gene': np.tile(np.repeat(['TP53', 'BRCA1'], n_per_group // 2), 3)
})

Visualization

Basic Violin Plot

fig, ax = plt.subplots(figsize=(8, 6))
sns.violinplot(data=iris, x='species', y='sepal_length', palette='Set2',
               inner='box', ax=ax)
ax.set_xlabel('Species')
ax.set_ylabel('Sepal Length (cm)')
ax.set_title('Distribution of Sepal Length by Species')
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

/mnt/TMP/ipykernel_54033/2407574281.py:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.violinplot(data=iris, x='species', y='sepal_length', palette='Set2',

Figure 1: Basic Violin Plot with Iris Data

Split Violin Plot

fig, ax = plt.subplots(figsize=(8, 6))
tumor_normal = gene_expr[gene_expr['Group'].isin(['Tumor', 'Normal'])]
sns.violinplot(data=tumor_normal, x='Gene', y='Expression', hue='Group',
               split=True, palette={'Tumor': '#e63946', 'Normal': '#457b9d'},
               inner='quart', ax=ax)
ax.set_title('Gene Expression: Tumor vs Normal')
ax.set_ylabel('Expression Level')
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

Violin with Strip Plot Overlay

fig, ax = plt.subplots(figsize=(9, 6))
sns.violinplot(data=gene_expr, x='Group', y='Expression', palette='pastel',
               inner=None, alpha=0.7, ax=ax)
sns.stripplot(data=gene_expr, x='Group', y='Expression', color='black',
              size=3, alpha=0.4, jitter=True, ax=ax)
ax.set_title('Gene Expression Distribution with Individual Points')
ax.set_ylabel('Expression Level')
ax.spines[['top', 'right']].set_visible(False)
plt.tight_layout()
plt.show()

/mnt/TMP/ipykernel_54033/1230588679.py:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.violinplot(data=gene_expr, x='Group', y='Expression', palette='pastel',

Figure 3: Violin Plot with Individual Data Points

References

Hintze, J. L., & Nelson, R. D. (1998). Violin plots: a box plot-density trace synergism. The American Statistician, 52(2), 181-184.
Waskom, M. L. (2021). seaborn: statistical data visualization. JOSS, 6(60), 3021.

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