Creating multiple boxplots on the same graph from a dictionary, using Matplotlib

To create multiple boxplots on the same graph from a dictionary, we can use Matplotlib's boxplot() function. This is useful for comparing distributions of different datasets side by side.

Basic Setup

First, let's understand the steps involved ?

• Set the figure size and adjust the padding between and around the subplots
• Create a dictionary with multiple datasets
• Create a figure and a set of subplots
• Make a box and whisker plot using boxplot()
• Set the x-tick labels using set_xticklabels() method
• Display the figure using show() method

Example

Here's how to create multiple boxplots from a dictionary ?

import matplotlib.pyplot as plt

# Set figure configuration
plt.rcParams["figure.figsize"] = [7.50, 3.50]
plt.rcParams["figure.autolayout"] = True

# Create sample data dictionary
data = {'Dataset A': [3, 5, 2, 9, 1, 8, 6], 
        'Dataset B': [2, 6, 1, 3, 4, 7, 5]}

# Create figure and subplot
fig, ax = plt.subplots()

# Create boxplots from dictionary values
ax.boxplot(data.values())

# Set x-axis labels from dictionary keys
ax.set_xticklabels(data.keys())

# Add title and labels
ax.set_title('Multiple Boxplots from Dictionary')
ax.set_ylabel('Values')

plt.show()

Advanced Example with More Datasets

You can also create boxplots with more datasets and customize the appearance ?

import matplotlib.pyplot as plt
import numpy as np

# Create more complex sample data
np.random.seed(42)
data = {
    'Group A': np.random.normal(10, 2, 50),
    'Group B': np.random.normal(12, 1.5, 50),
    'Group C': np.random.normal(8, 3, 50),
    'Group D': np.random.normal(15, 2.5, 50)
}

# Create figure
fig, ax = plt.subplots(figsize=(10, 6))

# Create boxplots with customization
box_plot = ax.boxplot(data.values(), 
                      patch_artist=True,  # Fill boxes with color
                      notch=True)         # Add notches

# Customize box colors
colors = ['lightblue', 'lightgreen', 'lightcoral', 'lightyellow']
for patch, color in zip(box_plot['boxes'], colors):
    patch.set_facecolor(color)

# Set labels and title
ax.set_xticklabels(data.keys())
ax.set_title('Multiple Boxplots with Custom Colors')
ax.set_ylabel('Values')
ax.grid(True, alpha=0.3)

plt.show()

Key Parameters

Conclusion

Creating multiple boxplots from a dictionary is straightforward using boxplot(data.values()) and set_xticklabels(data.keys()). This approach allows easy comparison of distributions across different groups or categories.