KDE overlay concept in Matplotlib - Mini Project: Build & Apply

KDE Overlay Concept

📖 Scenario: You are analyzing the heights of two groups of people: Group A and Group B. You want to see how their height distributions compare visually.

🎯 Goal: Create two sets of height data, then plot their Kernel Density Estimates (KDE) on the same graph to compare their distributions.

📋 What You'll Learn

Create two lists called group_a_heights and group_b_heights with given height values

Create a variable called bandwidth to control the smoothness of the KDE

Use matplotlib and seaborn to plot KDE curves for both groups on the same figure

Label the plot with a title and legend

Display the plot

💡 Why This Matters

🌍 Real World

KDE overlays help compare distributions visually, useful in fields like health, marketing, and social sciences.

💼 Career

Data scientists often use KDE plots to understand and compare data distributions before modeling.

1

Create height data for two groups

Create two lists called group_a_heights and group_b_heights with these exact values: group_a_heights = [160, 162, 165, 168, 170, 172, 175] and group_b_heights = [158, 159, 161, 163, 166, 169, 171].

Matplotlib

# Create two lists for heights of Group A and Group B
# Your code here

Need a hint?

Use square brackets to create lists with the exact numbers given.

2

Set the bandwidth for KDE smoothing

Create a variable called bandwidth and set it to 1.0 to control the smoothness of the KDE curves.

Matplotlib

group_a_heights = [160, 162, 165, 168, 170, 172, 175]
group_b_heights = [158, 159, 161, 163, 166, 169, 171]
# Create a variable called bandwidth and set it to 1.0
# Your code here

Need a hint?

Just write bandwidth = 1.0.

3

Plot KDE curves for both groups

Import matplotlib.pyplot as plt and seaborn as sns. Use sns.kdeplot to plot KDE curves for group_a_heights and group_b_heights on the same figure. Use the bandwidth variable for the bw_adjust parameter. Add labels 'Group A' and 'Group B' to each curve.

Matplotlib

group_a_heights = [160, 162, 165, 168, 170, 172, 175]
group_b_heights = [158, 159, 161, 163, 166, 169, 171]
bandwidth = 1.0
# Import matplotlib.pyplot as plt and seaborn as sns
# Plot KDE for group_a_heights and group_b_heights with bw_adjust=bandwidth
# Add labels 'Group A' and 'Group B'
# Your code here

Need a hint?

Use sns.kdeplot twice, once for each group, with the label parameter.

4

Add title, legend, and display the plot

Add a title 'Height Distribution KDE Overlay' to the plot using plt.title(). Show the legend with plt.legend(). Finally, display the plot with plt.show().

Matplotlib

import matplotlib.pyplot as plt
import seaborn as sns

group_a_heights = [160, 162, 165, 168, 170, 172, 175]
group_b_heights = [158, 159, 161, 163, 166, 169, 171]
bandwidth = 1.0

sns.kdeplot(group_a_heights, bw_adjust=bandwidth, label='Group A')
sns.kdeplot(group_b_heights, bw_adjust=bandwidth, label='Group B')
# Add title 'Height Distribution KDE Overlay'
# Show legend
# Display the plot
# Your code here

Need a hint?

Use plt.title(), plt.legend(), and plt.show() in that order.