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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
diagram A typical Seaborn + Matplotlib script mermaid
Seaborn draws the statistical chart; Matplotlib finishes the presentation details.

Example: same idea, different style

Matplotlib

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()
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

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()
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

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

Exercise 2 – Same Data, Two Libraries

Exercise 3 – Pick the Right Tool

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