How to make a grouped boxplot graph in matplotlib?

A grouped boxplot displays the distribution of a continuous variable across different categories, with each group subdivided by another categorical variable. In matplotlib, we can create grouped boxplots using Seaborn, which provides a high-level interface for statistical visualizations.

Basic Grouped Boxplot

Here's how to create a grouped boxplot using the tips dataset ?

import seaborn as sns
import matplotlib.pyplot as plt

# Set the figure size
plt.rcParams["figure.figsize"] = [7.00, 3.50]
plt.rcParams["figure.autolayout"] = True

# Load the tips dataset
data = sns.load_dataset('tips')

# Create a grouped boxplot
sns.boxplot(x='day', y='total_bill', hue='sex', data=data)

# Add title and labels
plt.title('Total Bill Distribution by Day and Gender')
plt.xlabel('Day of Week')
plt.ylabel('Total Bill ($)')

plt.show()

Customizing Grouped Boxplots

You can customize the appearance with different colors and styling ?

import seaborn as sns
import matplotlib.pyplot as plt

# Set style and color palette
sns.set_style("whitegrid")
plt.rcParams["figure.figsize"] = [8.00, 4.50]

# Load dataset
data = sns.load_dataset('tips')

# Create customized grouped boxplot
sns.boxplot(x='day', y='total_bill', hue='sex', 
           data=data, palette='Set2', linewidth=1.5)

# Customize the plot
plt.title('Restaurant Tips Analysis', fontsize=14, fontweight='bold')
plt.xlabel('Day of Week', fontsize=12)
plt.ylabel('Total Bill ($)', fontsize=12)
plt.legend(title='Gender', loc='upper right')

plt.tight_layout()
plt.show()

Multiple Grouped Variables

You can create more complex groupings by using different categorical variables ?

import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
data = sns.load_dataset('tips')

# Create subplots for multiple groupings
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# First plot: Day vs Total Bill grouped by Sex
sns.boxplot(x='day', y='total_bill', hue='sex', data=data, ax=ax1)
ax1.set_title('By Gender')
ax1.set_xlabel('Day')
ax1.set_ylabel('Total Bill ($)')

# Second plot: Day vs Total Bill grouped by Time
sns.boxplot(x='day', y='total_bill', hue='time', data=data, ax=ax2)
ax2.set_title('By Meal Time')
ax2.set_xlabel('Day')
ax2.set_ylabel('Total Bill ($)')

plt.tight_layout()
plt.show()

Key Parameters

Important parameters for sns.boxplot() include:

• x, y − Variables for x and y axes
• hue − Grouping variable for different colors
• data − DataFrame containing the data
• palette − Color palette for groups
• order, hue_order − Order of categories

Comparison of Approaches

Conclusion

Grouped boxplots effectively compare distributions across multiple categorical variables. Use sns.boxplot() with the hue parameter to create groups, and customize with palettes and styling for better visual appeal.