Overlaying Box Plot on Swarm Plot in Seaborn

Seaborn is a powerful Python library built on top of Matplotlib, designed for creating attractive and informative statistical graphics. It provides a high-level interface for drawing attractive and informative statistical graphics, making it easier to create complex visualizations with less code. One of the key features of Seaborn is its ability to create various types of plots, including swarm plots and box plots, which are particularly useful for visualizing the distribution and relationships within datasets.

Understanding the Components

1. Box Plot:

A box plot, or whisker plot, is used to display the distribution of a dataset through quartiles. It highlights the median, upper and lower quartiles, and potential outliers. The key components are:

• Box: Represents the interquartile range (IQR) between the 25th and 75th percentiles.
• Whiskers: Extend from the box to the smallest and largest values within 1.5 times the IQR from the quartiles.
• Outliers: Data points outside the whiskers are considered outliers and are often plotted individually.

2. Swarm Plot:

A swarm plot visualizes each individual data point, avoiding overlap by positioning them to show the distribution clearly. This plot is particularly useful for categorical data, as it reveals the density of data points and allows for a detailed examination of distribution patterns.

Overlaying Swarm Plot on Box Plot

Why Overlay Swarm Plot on Box Plot?

Overlaying a swarm plot on top of a box plot combines the strengths of both plots: it shows individual data points while also displaying summary statistics such as medians and quartiles. This combination provides a more comprehensive view of the data distribution.

Steps to Overlay Swarm Plot on Box Plot

• Create the Box Plot First: Start by creating a box plot using seaborn.boxplot(). This will provide the background for your visualization.
• Add the Swarm Plot: Overlay the swarm plot using seaborn.swarmplot(), ensuring that it uses the same x and y variables as the box plot.
• Adjust Aesthetics: You may need to adjust colors or transparency to ensure both plots are visible and clear.

import seaborn as sns
import matplotlib.pyplot as plt

# Load example dataset
tips = sns.load_dataset("tips")
# Create box plot
sns.boxplot(x="day", y="total_bill", data=tips, whis=np.inf)

# Overlay swarm plot
sns.swarmplot(x="day", y="total_bill", data=tips, color=".2")

plt.show()

Output:

Handling Overlapping Issues

Sometimes, when overlaying plots, one may obscure important details of another. To address this:

• Order of Plotting: Ensure that you call boxplot() before swarmplot() so that the swarm plot appears on top.
• Adjust zorder: Use Matplotlib's zorder parameter to control which elements appear on top.
• Transparency: Adjust transparency using the alpha parameter if needed.

To effectively handle overlapping issues when overlaying plots, we need to demonstrate how the order of plotting, adjusting zorder, and modifying transparency can resolve these issues. We can create subplots that show the problem and then apply solutions using zorder and alpha for transparency.

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

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

# Create a figure with two subplots
fig, axes = plt.subplots(1, 2, figsize=(10, 4))

# Subplot 1: Default overlay without adjustments (shows overlapping issue)
sns.boxplot(ax=axes[0], x="day", y="total_bill", data=tips, whis=np.inf)
sns.swarmplot(ax=axes[0], x="day", y="total_bill", data=tips, color=".2")
axes[0].set_title('Without Adjustments')

# Subplot 2: Overlay with adjustments to handle overlapping
sns.boxplot(ax=axes[1], x="day", y="total_bill", data=tips, whis=np.inf, zorder=1)
sns.swarmplot(ax=axes[1], x="day", y="total_bill", data=tips, color=".2", zorder=2, alpha=0.7)
axes[1].set_title('With Adjustments')

# Display the plots
plt.tight_layout()
plt.show()

Output:

• Subplot 1 (Without Adjustments):
  • This subplot shows the default overlay of a swarm plot on a box plot without any adjustments. You might notice that the swarm plot points can obscure parts of the box plot.
• Subplot 2 (With Adjustments):
  • Order of Plotting: The boxplot() is called before swarmplot() to ensure the swarm plot appears on top.
  • Adjust zorder: The zorder parameter is set to ensure the swarm plot (zorder=2) is drawn above the box plot (zorder=1).
  • Transparency: The alpha parameter is set to 0.7 for the swarm plot to make it slightly transparent, allowing both plots to be visible simultaneously.

Plotting Swarm Plot on Box Plot : Advanced Customization

1. Customizing Colors and Styles

# Customizing colors
sns.boxplot(x="day", y="total_bill", data=tips, palette="Set2")
sns.swarmplot(x="day", y="total_bill", data=tips, color=".25")
plt.show()

Output:

2. Adding Additional Dimensions with Hue

The hue parameter can be used in both box plots and swarm plots to introduce an additional categorical variable, allowing you to compare distributions across different subsets.

# Adding hue for additional dimension
sns.boxplot(x="day", y="total_bill", hue="sex", data=tips)
sns.swarmplot(x="day", y="total_bill", hue="sex", data=tips, dodge=True)
plt.show()

Output:

Conclusion

Combining swarm plots with box plots in Seaborn provides a powerful way to visualize both individual observations and summary statistics within your data. This approach enhances your ability to interpret complex datasets by offering detailed insights into distributions while maintaining clarity through effective visualization techniques.