Creating clear and informative visualizations is key to effectively communicating data insights. In this post, we will explore how to customize legends in Seaborn boxplots, ensuring that the labels and colors are both informative and accurate. Specifically, we’ll look at how to manually set the legend labels while maintaining the correct color associations in a Seaborn boxplot.

Setting Up the Example

Let’s assume you are working with a dataset that contains information about individuals’ BMI (Body Mass Index), age group quantiles, and whether they have diabetes or not. You want to create a boxplot that shows the distribution of BMI across different age groups, with a distinction between those who have diabetes and those who do not.

Here’s how your data might look like:

import pandas as pd

# Sample data
data = {
    'total_age_quantils': ['20-29', '20-29', '30-39', '30-39', '40-49', '40-49'],
    'BMI': [22, 27, 31, 30, 29, 35],
    'Outcome': [0, 1, 0, 1, 0, 1]
}

df = pd.DataFrame(data)

Creating the Boxplot with Seaborn

To create a boxplot using Seaborn that differentiates by the ‘Outcome’ variable, you can use the sns.boxplot() function:

import seaborn as sns
import matplotlib.pyplot as plt

# Create the boxplot
sns.boxplot(x='total_age_quantils', y='BMI', hue='Outcome', data=df)
plt.title('BMI by Age Group and Diabetes Status')
plt.xlabel('Age Group')
plt.ylabel('BMI')
plt.show()

In the above plot, Outcome is the hue, indicating whether the person has diabetes (1) or not (0). Seaborn automatically assigns colors to these categories.

Customizing the Legend Labels

However, you may want to replace the labels ‘0’ and ‘1’ with more descriptive labels like ‘No’ and ‘Yes’ to indicate the absence or presence of diabetes. Additionally, you want to make sure that the colors in the legend match those used in the plot.

Here’s how you can do this:

from matplotlib.patches import Patch

# Create the boxplot
sns.boxplot(x='total_age_quantils', y='BMI', hue='Outcome', data=df)
plt.title('BMI by Age Group and Diabetes Status')
plt.xlabel('Age Group')
plt.ylabel('BMI')

# Custom legend
handles = [
    Patch(color=sns.color_palette()[0], label='No'),  # No Diabetes
    Patch(color=sns.color_palette()[1], label='Yes')  # Yes Diabetes
]
plt.legend(title='Diabetes', handles=handles)

# Show the plot
plt.show()

Explanation of the Custom Legend

• Using matplotlib.patches.Patch: We use Patch to create custom legend entries. Each Patch represents a category in the legend. We pass the desired color and label for each category.
• Extracting Colors with sns.color_palette(): Seaborn’s color palette is accessed to ensure that the colors in the legend match those in the plot. The function sns.color_palette()[0] gives the color assigned to ‘0’ (No diabetes), and sns.color_palette()[1] gives the color for ‘1’ (Yes diabetes).
• Adding the Legend: We use plt.legend() with the handles argument to apply our custom legend entries.

Conclusion

By following these steps, you can create customized legends in Seaborn plots that accurately represent your data, providing clearer and more descriptive visualizations. This method is particularly useful when working with categorical data and wanting to replace default labels with more meaningful descriptions.