Half Violin Plot in Matplotlib

A half violin plot is a variation of the traditional violin plot where only one half of the violin (either left or right) is displayed. This type of plot is particularly useful when you want to focus on one side of the distribution while keeping the plot clean and easier to read. Half violin plots are often paired with other visualizations such as box plots, making them a valuable tool in exploratory data analysis.

In this article, we will explore how to create half violin plots in Matplotlib using Seaborn, which simplifies the process with easy-to-use functionality.

Why Use Half Violin Plots?

A violin plot combines elements of a box plot and a kernel density plot. It not only shows the summary statistics of the data (like the median and interquartile range) but also displays the distribution’s density, offering more insight into how data points are spread across different values.

A half violin plot is a useful variation when:

• You want to reduce clutter in the visualization.
• You want to pair the violin plot with another visualization (e.g., a box plot or swarm plot) for better comparison.
• You’re interested in showing the distribution on one side only, either for aesthetic reasons or for highlighting particular aspects of the data.

Half violin plots are often used in modern data visualization to create clean, clear, and aesthetically pleasing plots while still providing detailed information about the data distribution.

Creating a Basic Violin Plot in Matplotlib

Step 1: Import Necessary Libraries

We’ll be using Matplotlib and Seaborn, as Seaborn’s violin plot functionality makes it easy to create the desired plot.

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

Step 2: Generate or Load Data

For demonstration purposes, we can generate some synthetic data. You can replace this with your own dataset.

# Generate synthetic data
np.random.seed(42)
data = np.random.normal(loc=0, scale=1, size=100)

Step 3: Create a Full Violin Plot

Before creating a half violin plot, it’s important to understand how to create a full violin plot using Seaborn.

# Create a full violin plot
sns.violinplot(data=data)
plt.show()

Output:

Modifying the Violin Plot to Display Half

A half violin plot requires us to modify the output from the violinplot() function. By default, Matplotlib creates symmetric violins. To make a half violin plot, we need to manipulate the split parameter and control the drawing of one side.

We also use bw to adjust the bandwidth for a smooth distribution and cut to restrict the plot to the observed data.

# Create a half violin plot by splitting and displaying only one side
sns.violinplot(data=data, cut=0, bw=0.2, inner=None, linewidth=1)
plt.gca().collections[0].set_clip_on(False)  # Show only one side
plt.gca().collections[0].set_alpha(0.7)      # Adjust transparency for better visibility

plt.show()

Output:

Explanation:

• Violin Plot: We use the violinplot() function with the cut parameter to restrict the range and bw to smooth the density estimate.
• Half Violin: We manipulate the plot’s drawing to display only the right half of the violin.
• Box Plot: A small, narrow box plot is added over the half violin plot to show the quartiles and median more clearly.
• Customization: Transparency and line width are adjusted for better aesthetics.

Customizing the Half Violin Plot

Matplotlib provides many options for customizing violin plots. Here are some ways you can modify the appearance of your half violin plot. You can change the color of the half violin plot by using the set_facecolor() method.

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(42)
data = np.random.normal(loc=0, scale=1, size=100)

# Create the violin plot
fig, ax = plt.subplots()
parts = ax.violinplot(data, showmeans=False, showmedians=True)

# Customize the violin plot appearance
for pc in parts['bodies']:
    pc.set_facecolor('skyblue')  # Set the fill color
    pc.set_edgecolor('black')    # Set the edge color
    pc.set_alpha(0.8)            # Set the transparency level

plt.title("Customized Violin Plot")
plt.show()

Output:

Comparing Multiple Distributions with Half Violin Plots

Half violin plots are particularly useful when comparing multiple distributions side by side. You can visualize different categories or datasets by positioning half violin plots next to each other.

# Generate sample data for two distributions
data1 = np.random.normal(loc=-2, scale=1, size=100)
data2 = np.random.normal(loc=2, scale=1, size=100)

fig, ax = plt.subplots()

# Plot the first half violin plot
parts1 = ax.violinplot(data1, positions=[1], showmeans=False, showmedians=True)
for pc in parts1['bodies']:
    pc.set_clip_path(plt.Rectangle((0, -np.inf), np.inf, np.inf))

# Plot the second half violin plot
parts2 = ax.violinplot(data2, positions=[2], showmeans=False, showmedians=True)
for pc in parts2['bodies']:
    pc.set_clip_path(plt.Rectangle((0, -np.inf), np.inf, np.inf))

plt.title("Comparison of Two Distributions with Half Violin Plots")
plt.show()

Output:

This code displays two half violin plots for two different distributions side by side. This is particularly useful for comparative analyses.

Conclusion

A half violin plot is a versatile and visually appealing tool for exploring and presenting data distributions. It reduces clutter while still providing detailed information about the data. By combining it with a box plot or other visualization types, you can create more informative and cleaner plots.