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.

  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)"]

Install and import

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

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 (meltmelt, pivot_tablepivot_table) is useful.

A 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()

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

Next

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

🧪 Try It Yourself

Exercise 1 – Import and Check the Version

Exercise 2 – A First Line Plot

Exercise 3 – Tidy Data Shape