A boxplot is a graphical representation used to display the distribution of a dataset, showing key statistics such as the median, quartiles, and potential outliers. It provides a concise summary of the data's central tendency and spread.
Creating boxplots with Matplotlib allows us to effectively visualize the distribution of data points. In this post, we will explore how to use Matplotlib to create a grouped and customized boxplot.

## Libraries

First, you need to install the following librairies:

• matplotlib is used for creating the plot
• pandas for data manipulation
• numpy for data generation
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

## Dataset

We use np.random.normal() to create normally distributed data for 'Group A' and 'Group B', but with different means.

# Generate data for Group A (Normal Distribution)
group_a = np.random.normal(loc=50, scale=10, size=1000)

# Generate data for Group B (Skewed Distribution)
group_b = np.random.normal(loc=20, scale=10, size=1000)

# Create a DataFrame
df = pd.DataFrame({'Group': ['Group A'] * 1000 + ['Group B'] * 1000,
'Value': np.concatenate((group_a, group_b))})

## Basic grouped boxplot

Once we've opened our dataset, we'll now create the graph. The following displays the distribution of both groups using the boxplot() function.

• Data Grouping: Groups the data in a DataFrame 'df' based on the 'Group' column. Selects the 'Value' column from each group, creating a series of grouped values.
• Box Plot Creation: Creates a boxplot on the 'ax' axes. Uses data from the grouped values to form the boxes. Labels each box with the unique group names from the 'Group' column.
# Group our dataset with our 'Group' variable
grouped = df.groupby('Group')['Value']

# Init a figure and axes
fig, ax = plt.subplots(figsize=(8,6))

# Create the plot
ax.boxplot(x=[group.values for name, group in grouped],
labels=grouped.groups.keys())

# Display it
plt.show()

## Different color for each group

• We use the patch_artist=True parameter in the boxplot function to enable the ability to set individual colors for each box
• We define a list of colors that correspond to each group
• We iterate through the boxes in the boxplot and set their face colors based on the defined colors for each group

This will color each group with a different color in your boxplot. You can adjust the colors list to specify the colors you want for each group.

# Group our dataset with our 'Group' variable
grouped = df.groupby('Group')['Value']

# Init a figure and axes
fig, ax = plt.subplots(figsize=(8, 6))

# Create the plot with different colors for each group
boxplot = ax.boxplot(x=[group.values for name, group in grouped],
labels=grouped.groups.keys(),
patch_artist=True,
medianprops={'color': 'black'}
)

# Define colors for each group
colors = ['orange', 'purple']

# Assign colors to each box in the boxplot
for box, color in zip(boxplot['boxes'], colors):
box.set_facecolor(color)

# Display it
plt.show()

## Custom legend

We first create legend labels (legend_labels) and legend handles (legend_handles) for each group. Then, we use ax.legend() to add the legend to the plot, specifying the handles and labels. This will help viewers understand which color corresponds to each group in your boxplot.

# Group our dataset with our 'Group' variable
grouped = df.groupby('Group')['Value']

# Init a figure and axes
fig, ax = plt.subplots(figsize=(6, 6))

# Create the plot with different colors for each group
boxplot = ax.boxplot(x=[group.values for name, group in grouped],
labels=grouped.groups.keys(),
patch_artist=True,
medianprops={'color': 'black'}
)

# Define colors for each group
colors = ['orange', 'purple']

# Assign colors to each box in the boxplot
for box, color in zip(boxplot['boxes'], colors):
box.set_facecolor(color)

# Create a legend for the groups
legend_labels = ['Group 1', 'Group 2']
legend_handles = [plt.Rectangle((0,0),1,1, color=color) for color in colors]
ax.legend(legend_handles, legend_labels)

# Display it
plt.show()

## Going further

This post explains how to create a grouped boxplot with matplotlib.

For more examples of how to create or customize your boxplots, see the boxplot section. You may also be interested in how to created an boxplot with multiple groups.

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