Pandas - Creating a Heatmap Using Seaborn to Visualize Correlations

Last update on April 09 2025 12:56:07 (UTC/GMT +8 hours)

7. Heatmap Visualization with Seaborn

Write a Pandas program to create a Heatmap Visualization with Seaborn.

This exercise demonstrates how to create a heatmap using Seaborn to visualize a correlation matrix in a DataFrame.

Sample Solution :

Code :

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Create a sample DataFrame
df = pd.DataFrame({
    'Math': [90, 85, 80, 75, 70],
    'Science': [85, 80, 75, 70, 65],
    'English': [80, 75, 70, 65, 60]
})

# Compute the correlation matrix
corr_matrix = df.corr()

# Create a heatmap to visualize the correlation matrix
sns.heatmap(corr_matrix, annot=True)

# Add a title
plt.title('Subject Correlation Heatmap')

# Display the plot
plt.show()

Output:

Explanation:

• Created a DataFrame with grades in different subjects.
• Calculated the correlation matrix using df.corr().
• Used sns.heatmap() to plot the correlation matrix, adding annotations to show correlation values.
• Displayed the heatmap with a title.

For more Practice: Solve these Related Problems:

• Write a Pandas program to create a heatmap of a correlation matrix from a DataFrame using a custom color gradient.
• Write a Pandas program to generate a heatmap with annotated cells that display the correlation values.
• Write a Pandas program to create a clustered heatmap that groups similar rows and columns based on a similarity metric.
• Write a Pandas program to produce a heatmap from pivot table data and customize both the x- and y-axis labels.

Python-Pandas Code Editor:

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