Combining Two Boxplots With the Same Axes

Combining two boxplots with the same axes into one boxplot in Python is a common task in data visualization, especially when you want to compare distributions from different datasets or categories. This article will guide you through the process of creating combined boxplots using Python libraries such as Matplotlib and Seaborn. We'll cover the basics of boxplots, how to prepare your data, and how to implement the solution step-by-step.

Why Combine Boxplots?

Combining boxplots allows you to:

• Compare the spread and center of two datasets.
• Visualize the differences in distribution, outliers, and symmetry between datasets.
• Detect variations in central tendency or variability.

Combining Two Boxplots on the Same Axes Using Matplotlib

Before combining boxplots, it's important to understand how to create a basic boxplot. Python provides multiple libraries for visualization, with Matplotlib and Seaborn being the most popular.

Here’s a simple boxplot using Matplotlib:

import matplotlib.pyplot as plt
import numpy as np

# Generate sample data
data = np.random.normal(100, 10, 200)

# Create a boxplot
plt.boxplot(data)
plt.title('Single Boxplot')
plt.show()

Output:

When comparing two datasets, you often want to place their boxplots side by side on the same axes. In Matplotlib, this can be done by passing the datasets as a list to the boxplot() function.

Example: Combining Two Boxplots

import matplotlib.pyplot as plt
import numpy as np

# Generating two datasets
data1 = np.random.normal(100, 10, 200)
data2 = np.random.normal(90, 15, 200)

# Creating a boxplot for both datasets
plt.boxplot([data1, data2], labels=['Data 1', 'Data 2'])

# Adding a title and labels
plt.title('Combined Boxplot of Two Datasets')
plt.ylabel('Values')

# Display the plot
plt.show()

Output:

Explanation:

• boxplot([data1, data2]): This combines both datasets in a single boxplot.
• labels=['Data 1', 'Data 2']: Adds labels to the x-axis to identify the datasets.

The boxplots are placed side by side on the same axes, making it easy to compare the distribution of both datasets.

Combining Two Boxplots Using Seaborn

Seaborn is a more high-level library built on top of Matplotlib, designed to make visualization easier and more aesthetically pleasing. Seaborn's boxplot() function automatically handles much of the layout and styling, which simplifies the process of combining boxplots.

Example: Combining Two Boxplots with Seaborn

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

# Generating two datasets
data1 = np.random.normal(100, 10, 200)
data2 = np.random.normal(90, 15, 200)

# Creating a DataFrame for plotting
df = pd.DataFrame({
    'Values': np.concatenate([data1, data2]),
    'Group': ['Data 1'] * len(data1) + ['Data 2'] * len(data2)
})

# Plotting the combined boxplot
sns.boxplot(x='Group', y='Values', data=df)

# Adding title and labels
plt.title('Combined Boxplot of Two Datasets')
plt.show()

Output:

Explanation:

• pd.DataFrame(): A DataFrame is created, with one column for the values and another column for the group labels. This makes it easier to plot both datasets on the same axes.
• sns.boxplot(): Plots the boxplots based on the DataFrame, where x='Group' defines the categories on the x-axis and y='Values' plots the values.

Seaborn automatically handles the layout, making the visualization more elegant with minimal effort.

Conclusion

Combining boxplots on the same axes is an excellent way to compare distributions between datasets. Both Matplotlib and Seaborn provide convenient methods to achieve this.

• Matplotlib offers flexibility and control for users who want to customize every aspect of their plot.
• Seaborn is ideal for users who prefer simplicity and built-in aesthetic improvements.