Plot Multiple Features Histplot with Seaborn and Matplotlib

This article explores how to plot histograms for multiple features in a dataset using Seaborn and Matplotlib's gridspec.

Why Use Gridspec for Multiple Plots?

When dealing with multiple features, plotting individual histograms separately can be inefficient. Using gridspec helps:

Organize multiple subplots into a grid layout
Maintain consistent figure sizes
Improve readability by keeping plots aligned

Steps to Create Multiple Histograms for Features

Import required libraries (matplotlib, seaborn, pandas)
Load the credit card fraud dataset - source: creditcard.csv
Extract the relevant feature columns
Create a grid layout using gridspec.GridSpec
Loop through each feature and plot histograms for fraudulent (Class=1) and non-fraudulent (Class=0) transactions

More information can be found: Matplotlib GridSpec

Dataset: Credit Card Fraud Transactions

The dataset contains transactions with multiple features (V1–V28) extracted from PCA transformation. The Class column indicates whether a transaction is fraudulent (1) or legitimate (0).

source: creditcard.csv

import pandas as pd
data = pd.read_csv("creditcard.csv.zip")
print("Shape of input data: "+str(data.shape))
data.head()

Example: Plot Histograms for Fraud and Non-Fraud Transactions

import matplotlib.pyplot as plt
import seaborn as sns 
import matplotlib.gridspec as gridspec
%matplotlib inline

v_features = data.iloc[:,1:29].columns

plt.figure(figsize=(12,8*4))
gs = gridspec.GridSpec(7, 4)
for i, cn in enumerate(data[v_features]):
    ax = plt.subplot(gs[i])
    sns.histplot(data[cn][data.Class == 1], color="green", label="100% Equities", kde=True, stat="density", linewidth=0, bins=50)
    sns.histplot(data[cn][data.Class == 0], color="red", label="100% Equities", kde=True, stat="density", linewidth=0, bins=50)
    ax.set_xlabel('')
    ax.set_title('feature: ' + str(cn))
plt.show()

Output

Histograms are displayed for each feature (V1–V28).
Red color represents fraudulent transactions (Class = 1).
Blue color represents non-fraudulent transactions (Class = 0).
Grid layout ensures all features are visible and aligned.

Customizations

Adjust bins=50 for a smoother or more detailed histogram
Modify figsize=(12, 32) to increase/decrease plot size
Use alpha=0.5 in sns.histplot() for transparent overlapping bars

sns.histplot(train_df[cn][train_df.Class == 1], bins=50, color='red', alpha=0.5)
sns.histplot(train_df[cn][train_df.Class == 0], bins=50, color='blue', alpha=0.5)