Multi-Plot Grids, Figure-Level vs Axes-Level

FacetGrid, PairGrid, and JointGrid, and how figure-level and axes-level functions differ in what they return and how they are composed with Matplotlib.

Multi-Plot Grids

Seaborn provides grid objects for creating complex multi-panel figures:

FacetGrid

Create subplots based on categorical variables. Most useful when called through figure-level functions (relplot, displot, catplot), but can be used directly for custom plots.

g = sns.FacetGrid(df, col='time', row='sex', hue='smoker')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()

PairGrid

Show pairwise relationships between all variables in a dataset.

g = sns.PairGrid(df, hue='species')
g.map_upper(sns.scatterplot)
g.map_lower(sns.kdeplot)
g.map_diag(sns.histplot)
g.add_legend()

JointGrid

Combine bivariate plot with marginal distributions.

g = sns.JointGrid(data=df, x='total_bill', y='tip')
g.plot_joint(sns.scatterplot)
g.plot_marginals(sns.histplot)

Figure-Level vs Axes-Level Functions

Understanding this distinction is crucial for effective seaborn usage:

Axes-Level Functions

• Plot to a single matplotlib Axes object
• Integrate easily into complex matplotlib figures
• Accept ax= parameter for precise placement
• Return Axes object
• Examples: scatterplot, histplot, boxplot, regplot, heatmap

When to use:

• Building custom multi-plot layouts
• Combining different plot types
• Need matplotlib-level control
• Integrating with existing matplotlib code

fig, axes = plt.subplots(2, 2, figsize=(10, 10))
sns.scatterplot(data=df, x='x', y='y', ax=axes[0, 0])
sns.histplot(data=df, x='x', ax=axes[0, 1])
sns.boxplot(data=df, x='cat', y='y', ax=axes[1, 0])
sns.kdeplot(data=df, x='x', y='y', ax=axes[1, 1])

Figure-Level Functions

• Manage entire figure including all subplots
• Built-in faceting via col and row parameters
• Return FacetGrid, JointGrid, or PairGrid objects
• Use height and aspect for sizing (per subplot)
• Cannot be placed in existing figure
• Examples: relplot, displot, catplot, lmplot, jointplot, pairplot

When to use:

• Faceted visualizations (small multiples)
• Quick exploratory analysis
• Consistent multi-panel layouts
• Don't need to combine with other plot types

# Automatic faceting
sns.relplot(data=df, x='x', y='y', col='category', row='group',
            hue='type', height=3, aspect=1.2)