Splitting Violin Plots in Python Using Seaborn

A violin plot is a data visualization technique that combines aspects of a box plot and a kernel density plot. It is particularly useful for visualizing the distribution of data across different categories. Sometimes, it can be helpful to split each violin in a violin plot to compare two halves of the data (e.g., based on a grouping factor). This allows for side-by-side comparison of two subgroups within each category.

In this article, we’ll explore how you can split the violins in a violin plot using Seaborn and delve into how this feature enhances the interpretability of the data.

What Does Splitting a Violin Mean?

In Seaborn, the violinplot() function includes an option to split the violins. When violins are split, two distributions are shown on the same violin, making it easier to compare the distributions of different categories (e.g., gender) within each category of another variable (e.g., species).

Key Scenarios for Using Split Violin Plots

• Comparing Distributions: Splitting violins is helpful when you want to compare two related groups (e.g., male vs. female or treatment A vs. treatment B) within each category.
• Symmetry Comparison: Split violins can help visualize whether two groups have similar or different distribution patterns.

Steps to Split Every Violin in Seaborn

In Python, the Seaborn library provides a convenient way to create violin plots and to split them into mirrored halves using the split argument in the sns.violinplot() function. Before we dive into the example, you need to have the following libraries installed:

pip install seaborn matplotlib pandas

Now we will discuss step by step implementation of How Can Every Violin in a Violin Plot Be Split in Python Seaborn Library.

Step 1: Import Libraries and Prepare Data

Let's start by importing the required libraries: Seaborn, Matplotlib, and Pandas for data manipulation and visualization.

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

# Load sample dataset
tips = sns.load_dataset("tips")

# Display the first few rows of the dataset
print(tips.head())

Output:

   total_bill   tip     sex smoker  day    time  size
0       16.99  1.01  Female     No  Sun  Dinner     2
1       10.34  1.66    Male     No  Sun  Dinner     3
2       21.01  3.50    Male     No  Sun  Dinner     3
3       23.68  3.31    Male     No  Sun  Dinner     2
4       24.59  3.61  Female     No  Sun  Dinner     4

The tips dataset from Seaborn contains information on tips collected from waiters and waitresses, including:

• total_bill: The total bill for the meal.
• tip: The tip given by the customer.
• sex: The gender of the customer.
• smoker: Whether the customer is a smoker or not.
• day: The day of the week.
• time: Lunch or dinner.
• size: The size of the dining party.

Step 2: Create a Simple Violin Plot

We will create a basic violin plot to show the distribution of total_bill across different days of the week.

# Create a basic violin plot
sns.violinplot(x="day", y="total_bill", data=tips)
plt.title("Basic Violin Plot of Total Bill by Day")
plt.show()

Output:

• x="day": This sets the categorical axis (the days of the week).
• y="total_bill": This sets the numeric variable to be plotted (total bill).
• data=tips: The data source for the plot.

Step 3: Split the Violins by a Grouping Variable

To split the violins into mirrored halves, you can use the hue argument, which specifies the variable to use for splitting, and set split=True.

# Create a violin plot split by 'sex'
sns.violinplot(x="day", y="total_bill", hue="sex", data=tips, split=True)
plt.title("Violin Plot of Total Bill by Day (Split by Gender)")
plt.show()

Output:

• hue="sex": This argument adds a second grouping factor, splitting the violin plot by gender (Male and Female).
• split=True: This argument splits the violins into two mirrored halves for easy comparison.

Customizing Split Violin Plots

Seaborn allows for extensive customization of violin plots. You can further customize the plot by adding color palettes, adjusting the scale, and adding inner plots (like boxplots or points) to show additional statistics.

# Customize the violin plot with a palette and additional inner plot
sns.violinplot(x="day", y="total_bill", hue="sex", data=tips, split=True,
               palette="Set2", inner="quartile", scale="width")
plt.title("Customized Split Violin Plot with Quartiles")
plt.show()

Output:

• palette="Set2": A color palette is specified to give distinct colors to different categories.
• inner="quartile": Shows quartiles within the violins to add more detailed statistical information.
• scale="width": Adjusts the width of the violin based on the sample size.

Split Violin Plot with Multiple Variables

You can create split violin plots with different variables, such as smoker, time, or other categorical variables. This allows for in-depth comparison of data distribution across multiple factors.

# Create a violin plot split by 'smoker'
sns.violinplot(x="day", y="total_bill", hue="smoker", data=tips, split=True)
plt.title("Violin Plot of Total Bill by Day (Split by Smoker)")
plt.show()

Output:

It Includes additional features like quartiles, different color palettes, and width scaling to convey more information.

Conclusion

The Seaborn library makes it easy to create violin plots and customize them for more advanced data visualizations. Using the split argument, you can split violins to compare the distribution of subgroups within categories, making the plot informative and visually appealing. This is especially useful for understanding the distribution and relationships in multi-category data.