KDE overlay concept in Matplotlib

Introduction

KDE overlay helps us see how two or more groups of data spread and compare on the same plot.

Comparing heights of boys and girls in a class.

Checking sales patterns of two products over time.

Seeing how temperatures differ between two cities.

Comparing exam scores of two different classes.

Syntax

Matplotlib

import matplotlib.pyplot as plt
import seaborn as sns

sns.kdeplot(data=dataset1, label='Group 1')
sns.kdeplot(data=dataset2, label='Group 2')
plt.legend()
plt.show()

Use label to name each group for the legend.

Call plt.legend() to show the labels on the plot.

Examples

Overlay KDE plots for boys and girls ages to compare their distributions.

Matplotlib

sns.kdeplot(data=ages_boys, label='Boys')
sns.kdeplot(data=ages_girls, label='Girls')
plt.legend()
plt.show()

Compare sales data of two products using KDE overlay.

Matplotlib

sns.kdeplot(data=sales_productA, label='Product A')
sns.kdeplot(data=sales_productB, label='Product B')
plt.legend()
plt.show()

Sample Program

This code creates two sets of random data with different averages and spreads. Then it draws KDE plots for both on the same graph to compare their distributions.

Matplotlib

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

# Create two groups of data
np.random.seed(0)
data_group1 = np.random.normal(loc=50, scale=5, size=100)
data_group2 = np.random.normal(loc=60, scale=7, size=100)

# Plot KDE overlays
sns.kdeplot(data=data_group1, label='Group 1')
sns.kdeplot(data=data_group2, label='Group 2')

plt.title('KDE Overlay of Two Groups')
plt.xlabel('Value')
plt.ylabel('Density')
plt.legend()
plt.show()

Important Notes

KDE plots show smooth curves estimating data distribution.

Overlaying helps compare multiple groups easily.

Make sure data is numeric and cleaned before plotting.

Summary

KDE overlay shows multiple data distributions on one plot.

It helps compare groups visually by their shape and spread.

Use labels and legends to keep the plot clear.