Seaborn vs Matplotlib
Matplotlib vs Seaborn (simple comparison)
Section titled “Matplotlib vs Seaborn (simple comparison)”Matplotlib
Section titled “Matplotlib”- Low-level building blocks
- Maximum control
- More code required for common charts
Seaborn
Section titled “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
Section titled “Example: same idea, different style”Matplotlib
Section titled “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
Section titled “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
Section titled “Rule of thumb”- Use Seaborn for quick exploration (EDA)
- Use Matplotlib for fine-grained control or custom layouts
Good habit
Section titled “Good habit”Even when using Seaborn, always add:
- Title
- Axis labels
- Reasonable figure size
- Tight layout
Continue to Distribution Plots (displot, histplot) to start exploring how a single numeric variable is spread out.
🧪 Try It Yourself
Section titled “🧪 Try It Yourself”Exercise 1 – Switch the Seaborn Theme
Section titled “Exercise 1 – Switch the Seaborn Theme”Exercise 2 – Same Data, Two Libraries
Section titled “Exercise 2 – Same Data, Two Libraries”Exercise 3 – Pick the Right Tool
Section titled “Exercise 3 – Pick the Right Tool”pch.coffeeTagline
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