Introduction to Seaborn

What is Seaborn?

Seaborn is a Python visualization library built on top of Matplotlib.

It provides:

Better default styles
High-level charts for statistics
Easy integration with Pandas DataFrames

Seaborn is especially useful in EDA because you can quickly create informative plots with fewer lines of code.

diagram Where Seaborn sits mermaid

  flowchart LR
  A["Your DataFrame"] --> B["seaborn.barplot / histplot / etc."]
  B --> C["Aggregation + styling
(handled automatically)"]
  C --> D["Matplotlib Figure/Axes
(rendered under the hood)"]

Seaborn is a high-level layer over Matplotlib that understands DataFrames and statistics.

Install and import

Import seaborn

import seaborn as sns
import matplotlib.pyplot as plt
 
print(sns.__version__)

Seaborn’s core idea

Most Seaborn functions work well with tidy (long) data:

One row per observation
One column per variable

That’s why Pandas reshaping (melt, pivot_table) is useful.

A first Seaborn plot

First Seaborn plot

import seaborn as sns
import matplotlib.pyplot as plt
 
x = [1, 2, 3, 4, 5]
y = [1, 4, 9, 16, 25]
 
sns.lineplot(x=x, y=y)
plt.title("Seaborn line plot")
plt.tight_layout()
plt.show()

Why analysts like Seaborn

Built-in confidence intervals and aggregations in some plots
Simple API for categorical comparisons
Great for quickly exploring distributions and relationships

Continue to Seaborn vs Matplotlib to see exactly when to reach for each library — and how they work together in the same script.

Introduction to Seaborn

What is Seaborn?

Install and import

Seaborn’s core idea

A first Seaborn plot

Why analysts like Seaborn

Next

🧪 Try It Yourself

Exercise 1 – Import and Check the Version

Exercise 2 – A First Line Plot

Exercise 3 – Tidy Data Shape

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