How to Use Seaborn for Statistical Plotting & Data Visualization | Python Tutorial

Want to create stunning statistical plots in Python? In this quick tutorial, you’ll learn how to visualize data using Seaborn—a powerful library built on top of Matplotlib for creating beautiful and informative plots. We’ll walk through real-world examples using the Penguins dataset and showcase how to create scatter plots, box plots, and histograms.

✅ What You’ll Learn in This Video:

How to load datasets with sns.load_dataset().
Creating scatter plots to visualize relationships between features.
Building box plots to compare distributions by categories.
Plotting histograms to display the distribution of numerical data.
Customizing plots with titles, labels, hues, and more for clarity.
Why This Tutorial? Seaborn makes it simple to create professional, publication-quality visualizations with minimal code. Whether you're performing exploratory data analysis or preparing reports, this tutorial is your guide to creating plots that impress.

📊 Who Is This For? Perfect for data analysts, Python enthusiasts, and anyone looking to enhance their data visualization skills.

🔗 Topics Covered:

Introduction to Seaborn in Python
Scatter plots for relationships between variables
Box plots for category-wise distribution
Histograms with KDE overlays for statistical insight
Customizing plots with Seaborn and Matplotlib
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Code from Tutorial:
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import seaborn as sns
import matplotlib.pyplot as plt

df = sns.load_dataset('penguins')

#Scatter Plot
plt.figure(figsize = (12,8))
sns.scatterplot(x='bill_length_mm', y = 'bill_depth_mm', hue = 'species', data = df)
plt.title('Bill Length vs Bill Depth')
plt.show()

#Boxplot
plt.figure(figsize = (12,8))
sns.boxplot(x = 'species', y = 'flipper_length_mm', hue = 'species', data = df)
plt.title('Flipper Length by Species')
plt.show()

Histogram
plt.figure(figsize = (12,8))
sns.histplot(x = 'body_mass_g', hue = 'species', kde = True, bins = 20, data = df)
plt.title('Body Mass by Species')
plt.show()
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