Seaborn vs Matplotlib

Matplotlib vs Seaborn (simple comparison)

Matplotlib

• Low-level building blocks
• Maximum control
• More code required for common charts

Seaborn

• High-level statistical plots
• Beautiful defaults
• Works naturally with DataFrames

In practice, you often use both together:

• Seaborn creates the plot
• Matplotlib is used to customize labels, titles, and layout

  flowchart LR
  A["DataFrame"] --> B["sns.barplot / boxplot / etc.
(the statistical chart)"]
  B --> C["plt.title / xlabel / ylabel
(Matplotlib finishing touches)"]
  C --> D["plt.tight_layout(); plt.show()"]

Example: same idea, different style

Matplotlib

import matplotlib.pyplot as plt
 
x = [1, 2, 3, 4]
y = [10, 6, 8, 4]
 
plt.plot(x, y, marker="o")
plt.title("Matplotlib line")
plt.xlabel("x")
plt.ylabel("y")
plt.tight_layout()
plt.show()

Seaborn

import seaborn as sns
import matplotlib.pyplot as plt
 
sns.set_theme(style="whitegrid")
 
x = [1, 2, 3, 4]
y = [10, 6, 8, 4]
 
sns.lineplot(x=x, y=y, marker="o")
plt.title("Seaborn line")
plt.xlabel("x")
plt.ylabel("y")
plt.tight_layout()
plt.show()

Rule of thumb

• Use Seaborn for quick exploration (EDA)
• Use Matplotlib for fine-grained control or custom layouts

Good habit

Even when using Seaborn, always add:

• Title
• Axis labels
• Reasonable figure size
• Tight layout

Check your understanding

Next

Continue to Distribution Plots (displot, histplot) to start exploring how a single numeric variable is spread out.

🧪 Try It Yourself

Exercise 1 – Switch the Seaborn Theme

Press Run to see what your code prints.

Exercise 2 – Same Data, Two Libraries

Press Run to see what your code prints.

Exercise 3 – Pick the Right Tool

Press Run to see what your code prints.