Correlation matrix visualization in Matplotlib - Mini Project: Build & Apply

Correlation matrix visualization

📖 Scenario: You work as a data analyst and want to understand how different features in a dataset relate to each other. Visualizing the correlation matrix helps you see which features move together.

🎯 Goal: Create a correlation matrix from a dataset and visualize it using a heatmap with matplotlib.

📋 What You'll Learn

Create a pandas DataFrame with given data

Calculate the correlation matrix using pandas

Visualize the correlation matrix as a heatmap using matplotlib

Label the heatmap axes with feature names

💡 Why This Matters

🌍 Real World

Correlation matrices help analysts understand relationships between variables in datasets, which is useful in fields like finance, healthcare, and marketing.

💼 Career

Data scientists and analysts often visualize correlations to select features for models or to explain data insights to stakeholders.

1

Create the dataset

Create a pandas DataFrame called df with these exact columns and values:
'Height': [150, 160, 170, 180, 190], 'Weight': [50, 60, 65, 80, 90], 'Age': [25, 30, 35, 40, 45]

Matplotlib

# Create the DataFrame called df with the given data
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where keys are column names and values are lists of numbers.

2

Calculate the correlation matrix

Create a variable called corr_matrix that stores the correlation matrix of df using the corr() method.

Matplotlib

import pandas as pd

df = pd.DataFrame({
    'Height': [150, 160, 170, 180, 190],
    'Weight': [50, 60, 65, 80, 90],
    'Age': [25, 30, 35, 40, 45]
})

# Calculate the correlation matrix and store it in corr_matrix
# Your code here

Need a hint?

Use df.corr() to get the correlation matrix.

3

Visualize the correlation matrix

Import matplotlib.pyplot as plt. Use plt.imshow() to display corr_matrix as a heatmap with the cmap set to 'coolwarm'. Add a color bar with plt.colorbar().

Matplotlib

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Height': [150, 160, 170, 180, 190],
    'Weight': [50, 60, 65, 80, 90],
    'Age': [25, 30, 35, 40, 45]
})

corr_matrix = df.corr()

# Visualize the correlation matrix as a heatmap
# Your code here

Need a hint?

Use plt.imshow() with cmap='coolwarm' and add a color bar with plt.colorbar().

4

Add labels and show the plot

Set the x-axis and y-axis ticks to the column names of df using plt.xticks() and plt.yticks(). Rotate the x-axis labels by 45 degrees. Finally, call plt.show() to display the heatmap.

Matplotlib

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Height': [150, 160, 170, 180, 190],
    'Weight': [50, 60, 65, 80, 90],
    'Age': [25, 30, 35, 40, 45]
})

corr_matrix = df.corr()

plt.imshow(corr_matrix, cmap='coolwarm')
plt.colorbar()

# Add axis labels and show the plot
# Your code here

Need a hint?

Use plt.xticks() and plt.yticks() with ticks=range(len(df.columns)) and labels=df.columns. Rotate x labels by 45 degrees. Then call plt.show().