Facet Grids
What are facet grids?
Section titled “What are facet grids?”Facet grids create small multiples:
- The same plot repeated for different subgroups
- Helps compare patterns across segments
Examples:
- Distribution by gender
- Trend by region
- Relationship by category
flowchart LR A["Dataset"] --> B["Split by col="] A --> C["Split by row= (optional)"] B --> D["One subplot
per column value"] C --> D D --> E["Same plot type
repeated in each cell"]
Facet with displot
Section titled “Facet with displot”import seaborn as sns
tips = sns.load_dataset("tips")
sns.displot(data=tips, x="total_bill", col="time", bins=20, kde=True)Facet with relplot
Section titled “Facet with relplot”import seaborn as sns
tips = sns.load_dataset("tips")
sns.relplot(data=tips, x="total_bill", y="tip", col="time", hue="sex")Using FacetGrid directly
Section titled “Using FacetGrid directly”import seaborn as sns
import matplotlib.pyplot as plt
tips = sns.load_dataset("tips")
g = sns.FacetGrid(tips, col="time", row="sex")
g.map_dataframe(sns.scatterplot, x="total_bill", y="tip")
plt.tight_layout()Visualize it
Section titled “Visualize it”Each small panel is the same chart type, just filtered to one combination of row/column categories — easy to compare shapes side by side:
- Facets can get crowded—keep category counts small.
- Facets are excellent for storytelling and comparisons.
This wraps up the Seaborn phase — revisit the Phase Overview to see how all these lessons connect, or move on to the next module.
🧪 Try It Yourself
Section titled “🧪 Try It Yourself”Exercise 1 – Facet a Distribution by Column
Section titled “Exercise 1 – Facet a Distribution by Column”Exercise 2 – Add a Row Dimension
Section titled “Exercise 2 – Add a Row Dimension”Exercise 3 – Map a Plot Function onto the Grid
Section titled “Exercise 3 – Map a Plot Function onto the Grid”pch.coffeeTagline
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