Creating Violin Plots with Quartile Lines Using Python’s Pandas and Seaborn

💡 Problem Formulation: Data scientists often need to visualize distributions to understand their datasets better. A violin plot is a method of plotting numeric data and can show the density of the data points at different values. However, just a violin plot is not enough if we want to display quartile information. The challenge is to combine the visual appeal of a violin plot with the informational clarity of quartiles. This article describes how to draw a violin plot with quartile lines using Python’s Pandas library and Seaborn for enhanced data visualization.

Method 1: Basic Seaborn Violin Plot with Quartile Lines

This method involves creating a violin plot with Seaborn’s violinplot() function and overlaying horizontal lines for quartiles. It assumes proficiency with Python’s Pandas for data manipulation and Seaborn for plotting.

Here’s an example:

import seaborn as sns
import numpy as np

# Sample data
data = sns.load_dataset("tips")

# Create the violin plot
sns.violinplot(x=data["total_bill"])

# Calculate and add horizontal lines for quartiles
quartiles = np.percentile(data["total_bill"], [25, 50, 75])
for quartile in quartiles:
    plt.axhline(quartile, color='r', linestyle='--')
plt.show()

The output is a violin plot with red dashed lines indicating the quartiles.

This example uses Seaborn to draw a basic violin plot for the ‘total_bill’ column of the ‘tips’ dataset. Quartiles are calculated using NumPy’s percentile() function and overlaid using Matplotlib’s axhline() function. The resulting plot is a clear visual representation of the distribution of total bills, with horizontal lines marking the quartiles.

Method 2: Advanced Customization of Quartile Lines

Advanced customization includes altering the appearance of quartile lines to improve readability and aesthetics. This might involve changing colors, line styles, and adding labels to the quartile lines.

Here’s an example:

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

# Sample data
data = sns.load_dataset("tips")

# Create the violin plot
sns.violinplot(x=data["total_bill"])

# Calculate and add horizontal lines for quartiles with customization
quartiles = np.percentile(data["total_bill"], [25, 50, 75])
colors = ['blue', 'green', 'magenta']
linestyles = ['-', '--', ':']
for q, color, linestyle in zip(quartiles, colors, linestyles):
    plt.axhline(q, color=color, linestyle=linestyle, label=f'Q{colors.index(color)+1}')
plt.legend()
plt.show()

The output is a violin plot with blue solid, green dashed, and magenta dotted lines at the 25th, 50th, and 75th percentiles, respectively, with corresponding labels.

In the provided snippet, we not only include the quartile calculation and overlay but also customize each line’s color, style, and add a legend with clear labeling. These modifications contribute to a more informative and visually distinct representation of the quartiles over the violin plot.

Method 3: Combining Multiple Violin Plots with Quartile Lines

This method shows how to visualize quartiles across different categories by combining multiple violin plots on the same chart. This is particularly useful for comparative analysis.

Here’s an example:

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

# Sample data
data = sns.load_dataset("tips")

# Create multiple violin plots
sns.violinplot(x="day", y="total_bill", data=data)

# Add horizontal quartile lines for each violin plot
for day in data["day"].unique():
    day_data = data[data["day"] == day]["total_bill"]
    quartiles = np.percentile(day_data, [25, 50, 75])
    for quartile in quartiles:
        plt.axhline(quartile, color='k', alpha=0.5)
plt.show()

The output is multiple violin plots for each day of the week, with quartiles marked as semi-transparent horizontal lines across all violins.

This code snippet creates a violin plot for the ‘total_bill’ column grouped by ‘day’ and then overlays quartile lines that span across all violin plots. This staging enables a visual comparison of distributions and their respective quartiles between different days of the week.

Method 4: Annotating Quartile Lines with Text

Annotating quartile lines involves adding text labels directly on the plot to indicate quartile values, which can further clarify the data points for the audience.

Here’s an example:

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

# Sample data
data = sns.load_dataset("tips")

# Create the violin plot
sns.violinplot(x=data["total_bill"])

# Calculate quartiles and add annotated lines
quartiles = np.percentile(data["total_bill"], [25, 50, 75])
for quartile in quartiles:
    plt.axhline(quartile, color='purple', linestyle=':')
    plt.text(data["total_bill"].max() + 1, quartile, f'{quartile:.2f}', va='center', color='purple')
plt.show()

The output is a violin plot with purple dotted lines at the quartile levels, each accompanied by a text label indicating the precise quartile value.

The code creates a violin plot for the ‘total_bill’ column and adds dotted purple lines for quartiles. Next to each line, there’s a text annotation showing the exact numeric value of the quartile. This method ensures the viewer can identify the exact quartile values at a glance without referencing outside data.

Bonus One-Liner Method 5: Plot with Integrated Quartiles

Seaborn has integrated support for showing quartiles within violin plots. This one-liner method simplifies the process.

Here’s an example:

import seaborn as sns

# Sample data
data = sns.load_dataset("tips")

# Create the violin plot with built-in quartile lines
sns.violinplot(x=data["total_bill"], inner="quartile")
plt.show()

The output is a violin plot with quartile lines drawn inside the violin plot area itself.

This approach is the simplest, using the inner parameter set to “quartile” in Seaborn’s violinplot() function to include quartiles directly inside the violin plot. It provides a clean, integrated way to display quartile information without the need for additional lines or annotations.

Summary/Discussion

Method 1: Basic. It is straightforward and easy to implement while overlaying clear quartile information. However, it provides minimal customization options.
Method 2: Advanced Customization. Offers a richer visualization with color-coded and labeled quartile lines. It requires more code but is much more informative.
Method 3: Multiple Violin Plots. Ideal for comparative analysis but may become cluttered if there are many categories or quartile lines are too prominent.
Method 4: Annotated Quartile Lines. This method is explicit and educational, immediately providing quartile values, but might overcrowd the plot if too much text is added.
Method 5: Integrated Quartiles. The simplest method with clean integration, but it allows for the least amount of customization or emphasis on quartile lines.