Pair Plotis 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, Matplotliballows 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

• matplotlib.pyplot:Used for creating visualizations.
• pandas:Helps in handling structured data (dataframes).
• numpy:Useful for generating numerical data.

Implementation:

importmatplotlib.pyplotaspltimportpandasaspdimportnumpyasnpnp.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 featuresnum_features=len(data.columns)# Create Subplots Gridfig,axes=plt.subplots(num_features,num_features,figsize=(10,10))# Loop through each pair of featuresforiinrange(num_features):forjinrange(num_features):ax=axes[i,j]ifi==j:# Diagonal: Histogram of the featureax.hist(data.iloc[:,i],bins=15,color='skyblue',edgecolor='black')else:# Scatter plot for feature pairsax.scatter(data.iloc[:,j],data.iloc[:,i],alpha=0.7,s=10,color="blue")# Set labels on the left and bottom axesifj==0:ax.set_ylabel(data.columns[i],fontsize=10)ifi==num_features-1:ax.set_xlabel(data.columns[j],fontsize=10)# Remove ticks for a cleaner lookax.set_xticks([])ax.set_yticks([])# Adjust layoutplt.tight_layout()plt.show()

Output

Explanation:

• Data Generation:4 features × 50 values (0-1) stored in a Pandas DataFrame(np.random.seed(42)).
• Subplots Grid: 4×4 layout (plt.subplots()), with histograms on the diagonal (i == j) and scatter plots elsewhere (i ≠ j).
• Histograms: ax.hist()with 15 bins, skyblue fill, black edges for clarity.
• Scatter Plots: ax.scatter() with alpha=0.7, s=10, blue color to show relationships.
• Formatting:Labels only on leftmost column (j == 0) & bottom row (i == num_features - 1). Ticks removed for a clean look. plt.tight_layout() prevents overlap.
• plt.show()renders the final visualization.

Advantages of pair plot in matplotlib

• Customizability:Unlike Seaborn’s pairplot(), Matplotlib allows full control over plot styling.
• Better Integration: Works seamlessly within larger Matplotlib-based visualizations.
• Flexibility:Can modify elements like colors, markers, line styles, and annotations easily.

Enhancing the pair plot

To improve the visualization, consider:

• Adding regression lines to scatter plots.
• Using different colors to highlight categories in the dataset.
• Replacing histograms with kernel density estimation (KDE) plots.

Example:

importmatplotlib.pyplotaspltimportnumpyasnpimportpandasaspdnp.random.seed(42)data=pd.DataFrame(np.random.rand(50,4),columns=['Feature 1','Feature 2','Feature 3','Feature 4'])# Number of featuresnum_features=len(data.columns)# Create figurefig,axes=plt.subplots(num_features,num_features,figsize=(10,10))# Loop through each pair of featuresforiinrange(num_features):forjinrange(num_features):ax=axes[i,j]ifi==j:# Plot histogram on the diagonalax.hist(data.iloc[:,i],bins=10,color="skyblue",edgecolor="black")else:# Scatter plotx=data.iloc[:,j]y=data.iloc[:,i]ax.scatter(x,y,alpha=0.7,s=10,color="blue")# Add Regression Linem,b=np.polyfit(x,y,1)# Linear regressionax.plot(x,m*x+b,color="red",linewidth=1)# Labelsifj==0:ax.set_ylabel(data.columns[i],fontsize=10)ifi==num_features-1:ax.set_xlabel(data.columns[j],fontsize=10)# Hide ticks for cleaner lookax.set_xticks([])ax.set_yticks([])# Adjust layoutplt.tight_layout()plt.show()

Output:

Explanation:

• Data Preparation:Random values are generated for four features usingNumPyandPandas DataFrame stores the dataset.
• Creating Subplots:A 4×4 grid of subplots is created to display the pairwise relationships. plt.subplots(num_features, num_features, figsize=(10, 10)) sets up the grid layout.
• Plotting the Pair Plot: If i == j, a histogram is plotted on the diagonal using ax.hist(). If i ≠ j, a scatter plot is created using ax.scatter().
• Adding Regression Lines:The np.polyfit(x, y, 1) function computes the slope (m) and intercept (b) of the regression line. Theax.plot(x, m*x + b, color="red", linewidth=1)function overlays a red regression line on the scatter plot.
• Labels are added to only the leftmost and bottom plots. Ticks are hidden for a clean design.
• plt.tight_layout()ensures proper spacing for readability.