Pairplot in Matplotlib

Last Updated : 23 Jul, 2025

Pair Plot is a type of chart that shows how different numbers in a dataset relate to each other. It creates multiple small scatter plots, comparing two variables at a time. While Seaborn has a ready-made pairplot() function to quickly create this chart, Matplotlib allows more control to customize how the plot looks and behaves. A Pair Plot (also called a scatterplot matrix) consists of:

  • Scatter plots for each pair of numerical variables.
  • Histograms (or kernel density plots) on the diagonal, representing the distribution of individual variables.

This visualization helps in identifying:

  • Linear and non-linear relationships between features.
  • Clusters or groups within data.
  • Potential outliers.

Creating a pair plot using matplotlib

To get started, we first need to import the necessary libraries.

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

Implementation:

Python 
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

np.random.seed(42)
data = pd.DataFrame({
    'Feature 1': np.random.rand(50),
    'Feature 2': np.random.rand(50),
    'Feature 3': np.random.rand(50),
    'Feature 4': np.random.rand(50)
})

# Number of features
num_features = len(data.columns)

# Create Subplots Grid
fig, axes = plt.subplots(num_features, num_features, figsize=(10, 10))

# Loop through each pair of features
for i in range(num_features):
    for j in range(num_features):
        ax = axes[i, j]
        
        if i == j:
            # Diagonal: Histogram of the feature
            ax.hist(data.iloc[:, i], bins=15, color='skyblue', edgecolor='black')
        else:
            # Scatter plot for feature pairs
            ax.scatter(data.iloc[:, j], data.iloc[:, i], alpha=0.7, s=10, color="blue")

        # Set labels on the left and bottom axes
        if j == 0:
            ax.set_ylabel(data.columns[i], fontsize=10)
        if i == num_features - 1:
            ax.set_xlabel(data.columns[j], fontsize=10)

        # Remove ticks for a cleaner look
        ax.set_xticks([])
        ax.set_yticks([])

# Adjust layout
plt.tight_layout()
plt.show()

Output