# 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.

```python
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.

```python
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.

```python
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

```python
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

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