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+# Seaborn Function Reference
+
+This document provides a comprehensive reference for all major seaborn functions, organized by category.
+
+## Relational Plots
+
+### scatterplot()
+
+**Purpose:** Create a scatter plot with points representing individual observations.
+
+**Key Parameters:**
+- `data` - DataFrame, array, or dict of arrays
+- `x, y` - Variables for x and y axes
+- `hue` - Grouping variable for color encoding
+- `size` - Grouping variable for size encoding
+- `style` - Grouping variable for marker style
+- `palette` - Color palette name or list
+- `hue_order` - Order for categorical hue levels
+- `hue_norm` - Normalization for numeric hue (tuple or Normalize object)
+- `sizes` - Size range for size encoding (tuple or dict)
+- `size_order` - Order for categorical size levels
+- `size_norm` - Normalization for numeric size
+- `markers` - Marker style(s) (string, list, or dict)
+- `style_order` - Order for categorical style levels
+- `legend` - How to draw legend: "auto", "brief", "full", or False
+- `ax` - Matplotlib axes to plot on
+
+**Example:**
+```python
+sns.scatterplot(data=df, x='height', y='weight',
+                hue='gender', size='age', style='smoker',
+                palette='Set2', sizes=(20, 200))
+```
+
+### lineplot()
+
+**Purpose:** Draw a line plot with automatic aggregation and confidence intervals for repeated measures.
+
+**Key Parameters:**
+- `data` - DataFrame, array, or dict of arrays
+- `x, y` - Variables for x and y axes
+- `hue` - Grouping variable for color encoding
+- `size` - Grouping variable for line width
+- `style` - Grouping variable for line style (dashes)
+- `units` - Grouping variable for sampling units (no aggregation within units)
+- `estimator` - Function for aggregating across observations (default: mean)
+- `errorbar` - Method for error bars: "sd", "se", "pi", ("ci", level), ("pi", level), or None
+- `n_boot` - Number of bootstrap iterations for CI computation
+- `seed` - Random seed for reproducible bootstrapping
+- `sort` - Sort data before plotting
+- `err_style` - "band" or "bars" for error representation
+- `err_kws` - Additional parameters for error representation
+- `markers` - Marker style(s) for emphasizing data points
+- `dashes` - Dash style(s) for lines
+- `legend` - How to draw legend
+- `ax` - Matplotlib axes to plot on
+
+**Example:**
+```python
+sns.lineplot(data=timeseries, x='time', y='signal',
+             hue='condition', style='subject',
+             errorbar=('ci', 95), markers=True)
+```
+
+### relplot()
+
+**Purpose:** Figure-level interface for drawing relational plots (scatter or line) onto a FacetGrid.
+
+**Key Parameters:**
+All parameters from `scatterplot()` and `lineplot()`, plus:
+- `kind` - "scatter" or "line"
+- `col` - Categorical variable for column facets
+- `row` - Categorical variable for row facets
+- `col_wrap` - Wrap columns after this many columns
+- `col_order` - Order for column facet levels
+- `row_order` - Order for row facet levels
+- `height` - Height of each facet in inches
+- `aspect` - Aspect ratio (width = height * aspect)
+- `facet_kws` - Additional parameters for FacetGrid
+
+**Example:**
+```python
+sns.relplot(data=df, x='time', y='measurement',
+            hue='treatment', style='batch',
+            col='cell_line', row='timepoint',
+            kind='line', height=3, aspect=1.5)
+```
+
+## Distribution Plots
+
+### histplot()
+
+**Purpose:** Plot univariate or bivariate histograms with flexible binning.
+
+**Key Parameters:**
+- `data` - DataFrame, array, or dict
+- `x, y` - Variables (y optional for bivariate)
+- `hue` - Grouping variable
+- `weights` - Variable for weighting observations
+- `stat` - Aggregate statistic: "count", "frequency", "probability", "percent", "density"
+- `bins` - Number of bins, bin edges, or method ("auto", "fd", "doane", "scott", "stone", "rice", "sturges", "sqrt")
+- `binwidth` - Width of bins (overrides bins)
+- `binrange` - Range for binning (tuple)
+- `discrete` - Treat x as discrete (centers bars on values)
+- `cumulative` - Compute cumulative distribution
+- `common_bins` - Use same bins for all hue levels
+- `common_norm` - Normalize across hue levels
+- `multiple` - How to handle hue: "layer", "dodge", "stack", "fill"
+- `element` - Visual element: "bars", "step", "poly"
+- `fill` - Fill bars/elements
+- `shrink` - Scale bar width (for multiple="dodge")
+- `kde` - Overlay KDE estimate
+- `kde_kws` - Parameters for KDE
+- `line_kws` - Parameters for step/poly elements
+- `thresh` - Minimum count threshold for bins
+- `pthresh` - Minimum probability threshold
+- `pmax` - Maximum probability for color scaling
+- `log_scale` - Log scale for axis (bool or base)
+- `legend` - Whether to show legend
+- `ax` - Matplotlib axes
+
+**Example:**
+```python
+sns.histplot(data=df, x='measurement', hue='condition',
+             stat='density', bins=30, kde=True,
+             multiple='layer', alpha=0.5)
+```
+
+### kdeplot()
+
+**Purpose:** Plot univariate or bivariate kernel density estimates.
+
+**Key Parameters:**
+- `data` - DataFrame, array, or dict
+- `x, y` - Variables (y optional for bivariate)
+- `hue` - Grouping variable
+- `weights` - Variable for weighting observations
+- `palette` - Color palette
+- `hue_order` - Order for hue levels
+- `hue_norm` - Normalization for numeric hue
+- `multiple` - How to handle hue: "layer", "stack", "fill"
+- `common_norm` - Normalize across hue levels
+- `common_grid` - Use same grid for all hue levels
+- `cumulative` - Compute cumulative distribution
+- `bw_method` - Method for bandwidth: "scott", "silverman", or scalar
+- `bw_adjust` - Bandwidth multiplier (higher = smoother)
+- `log_scale` - Log scale for axis
+- `levels` - Number or values for contour levels (bivariate)
+- `thresh` - Minimum density threshold for contours
+- `gridsize` - Grid resolution
+- `cut` - Extension beyond data extremes (in bandwidth units)
+- `clip` - Data range for curve (tuple)
+- `fill` - Fill area under curve/contours
+- `legend` - Whether to show legend
+- `ax` - Matplotlib axes
+
+**Example:**
+```python
+# Univariate
+sns.kdeplot(data=df, x='measurement', hue='condition',
+            fill=True, common_norm=False, bw_adjust=1.5)
+
+# Bivariate
+sns.kdeplot(data=df, x='var1', y='var2',
+            fill=True, levels=10, thresh=0.05)
+```
+
+### ecdfplot()
+
+**Purpose:** Plot empirical cumulative distribution functions.
+
+**Key Parameters:**
+- `data` - DataFrame, array, or dict
+- `x, y` - Variables (specify one)
+- `hue` - Grouping variable
+- `weights` - Variable for weighting observations
+- `stat` - "proportion" or "count"
+- `complementary` - Plot complementary CDF (1 - ECDF)
+- `palette` - Color palette
+- `hue_order` - Order for hue levels
+- `hue_norm` - Normalization for numeric hue
+- `log_scale` - Log scale for axis
+- `legend` - Whether to show legend
+- `ax` - Matplotlib axes
+
+**Example:**
+```python
+sns.ecdfplot(data=df, x='response_time', hue='treatment',
+             stat='proportion', complementary=False)
+```
+
+### rugplot()
+
+**Purpose:** Plot tick marks showing individual observations along an axis.
+
+**Key Parameters:**
+- `data` - DataFrame, array, or dict
+- `x, y` - Variable (specify one)
+- `hue` - Grouping variable
+- `height` - Height of ticks (proportion of axis)
+- `expand_margins` - Add margin space for rug
+- `palette` - Color palette
+- `hue_order` - Order for hue levels
+- `hue_norm` - Normalization for numeric hue
+- `legend` - Whether to show legend
+- `ax` - Matplotlib axes
+
+**Example:**
+```python
+sns.rugplot(data=df, x='value', hue='category', height=0.05)
+```
+
+### displot()
+
+**Purpose:** Figure-level interface for distribution plots onto a FacetGrid.
+
+**Key Parameters:**
+All parameters from `histplot()`, `kdeplot()`, and `ecdfplot()`, plus:
+- `kind` - "hist", "kde", "ecdf"
+- `rug` - Add rug plot on marginal axes
+- `rug_kws` - Parameters for rug plot
+- `col` - Categorical variable for column facets
+- `row` - Categorical variable for row facets
+- `col_wrap` - Wrap columns
+- `col_order` - Order for column facets
+- `row_order` - Order for row facets
+- `height` - Height of each facet
+- `aspect` - Aspect ratio
+- `facet_kws` - Additional parameters for FacetGrid
+
+**Example:**
+```python
+sns.displot(data=df, x='measurement', hue='treatment',
+            col='timepoint', kind='kde', fill=True,
+            height=3, aspect=1.5, rug=True)
+```
+
+### jointplot()
+
+**Purpose:** Draw a bivariate plot with marginal univariate plots.
+
+**Key Parameters:**
+- `data` - DataFrame
+- `x, y` - Variables for x and y axes
+- `hue` - Grouping variable
+- `kind` - "scatter", "kde", "hist", "hex", "reg", "resid"
+- `height` - Size of the figure (square)
+- `ratio` - Ratio of joint to marginal axes
+- `space` - Space between joint and marginal axes
+- `dropna` - Drop missing values
+- `xlim, ylim` - Axis limits (tuples)
+- `marginal_ticks` - Show ticks on marginal axes
+- `joint_kws` - Parameters for joint plot
+- `marginal_kws` - Parameters for marginal plots
+- `hue_order` - Order for hue levels
+- `palette` - Color palette
+
+**Example:**
+```python
+sns.jointplot(data=df, x='var1', y='var2', hue='group',
+              kind='scatter', height=6, ratio=4,
+              joint_kws={'alpha': 0.5})
+```
+
+### pairplot()
+
+**Purpose:** Plot pairwise relationships in a dataset.
+
+**Key Parameters:**
+- `data` - DataFrame
+- `hue` - Grouping variable for color encoding
+- `hue_order` - Order for hue levels
+- `palette` - Color palette
+- `vars` - Variables to plot (default: all numeric)
+- `x_vars, y_vars` - Variables for x and y axes (non-square grid)
+- `kind` - "scatter", "kde", "hist", "reg"
+- `diag_kind` - "auto", "hist", "kde", None
+- `markers` - Marker style(s)
+- `height` - Height of each facet
+- `aspect` - Aspect ratio
+- `corner` - Plot only lower triangle
+- `dropna` - Drop missing values
+- `plot_kws` - Parameters for non-diagonal plots
+- `diag_kws` - Parameters for diagonal plots
+- `grid_kws` - Parameters for PairGrid
+
+**Example:**
+```python
+sns.pairplot(data=df, hue='species', palette='Set2',
+             vars=['sepal_length', 'sepal_width', 'petal_length'],
+             corner=True, height=2.5)
+```
+
+## Categorical Plots
+
+### stripplot()
+
+**Purpose:** Draw a categorical scatterplot with jittered points.
+
+**Key Parameters:**
+- `data` - DataFrame, array, or dict
+- `x, y` - Variables (one categorical, one continuous)
+- `hue` - Grouping variable
+- `order` - Order for categorical levels
+- `hue_order` - Order for hue levels
+- `jitter` - Amount of jitter: True, float, or False
+- `dodge` - Separate hue levels side-by-side
+- `orient` - "v" or "h" (usually inferred)
+- `color` - Single color for all elements
+- `palette` - Color palette
+- `size` - Marker size
+- `edgecolor` - Marker edge color
+- `linewidth` - Marker edge width
+- `native_scale` - Use numeric scale for categorical axis
+- `formatter` - Formatter for categorical axis
+- `legend` - Whether to show legend
+- `ax` - Matplotlib axes
+
+**Example:**
+```python
+sns.stripplot(data=df, x='day', y='total_bill',
+              hue='sex', dodge=True, jitter=0.2)
+```
+
+### swarmplot()
+
+**Purpose:** Draw a categorical scatterplot with non-overlapping points.
+
+**Key Parameters:**
+Same as `stripplot()`, except:
+- No `jitter` parameter
+- `size` - Marker size (important for avoiding overlap)
+- `warn_thresh` - Threshold for warning about too many points (default: 0.05)
+
+**Note:** Computationally intensive for large datasets. Use stripplot for >1000 points.
+
+**Example:**
+```python
+sns.swarmplot(data=df, x='day', y='total_bill',
+              hue='time', dodge=True, size=5)
+```
+
+### boxplot()
+
+**Purpose:** Draw a box plot showing quartiles and outliers.
+
+**Key Parameters:**
+- `data` - DataFrame, array, or dict
+- `x, y` - Variables (one categorical, one continuous)
+- `hue` - Grouping variable
+- `order` - Order for categorical levels
+- `hue_order` - Order for hue levels
+- `orient` - "v" or "h"
+- `color` - Single color for boxes
+- `palette` - Color palette
+- `saturation` - Color saturation intensity
+- `width` - Width of boxes
+- `dodge` - Separate hue levels side-by-side
+- `fliersize` - Size of outlier markers
+- `linewidth` - Box line width
+- `whis` - IQR multiplier for whiskers (default: 1.5)
+- `notch` - Draw notched boxes
+- `showcaps` - Show whisker caps
+- `showmeans` - Show mean value
+- `meanprops` - Properties for mean marker
+- `boxprops` - Properties for boxes
+- `whiskerprops` - Properties for whiskers
+- `capprops` - Properties for caps
+- `flierprops` - Properties for outliers
+- `medianprops` - Properties for median line
+- `native_scale` - Use numeric scale
+- `formatter` - Formatter for categorical axis
+- `legend` - Whether to show legend
+- `ax` - Matplotlib axes
+
+**Example:**
+```python
+sns.boxplot(data=df, x='day', y='total_bill',
+            hue='smoker', palette='Set3',
+            showmeans=True, notch=True)
+```
+
+### violinplot()
+
+**Purpose:** Draw a violin plot combining boxplot and KDE.
+
+**Key Parameters:**
+Same as `boxplot()`, plus:
+- `bw_method` - KDE bandwidth method
+- `bw_adjust` - KDE bandwidth multiplier
+- `cut` - KDE extension beyond extremes
+- `density_norm` - "area", "count", "width"
+- `inner` - "box", "quartile", "point", "stick", None
+- `split` - Split violins for hue comparison
+- `scale` - Scaling method: "area", "count", "width"
+- `scale_hue` - Scale across hue levels
+- `gridsize` - KDE grid resolution
+
+**Example:**
+```python
+sns.violinplot(data=df, x='day', y='total_bill',
+               hue='sex', split=True, inner='quartile',
+               palette='muted')
+```
+
+### boxenplot()
+
+**Purpose:** Draw enhanced box plot for larger datasets showing more quantiles.
+
+**Key Parameters:**
+Same as `boxplot()`, plus:
+- `k_depth` - "tukey", "proportion", "trustworthy", "full", or int
+- `outlier_prop` - Proportion of data as outliers
+- `trust_alpha` - Alpha for trustworthy depth
+- `showfliers` - Show outlier points
+
+**Example:**
+```python
+sns.boxenplot(data=df, x='day', y='total_bill',
+              hue='time', palette='Set2')
+```
+
+### barplot()
+
+**Purpose:** Draw a bar plot with error bars showing statistical estimates.
+
+**Key Parameters:**
+- `data` - DataFrame, array, or dict
+- `x, y` - Variables (one categorical, one continuous)
+- `hue` - Grouping variable
+- `order` - Order for categorical levels
+- `hue_order` - Order for hue levels
+- `estimator` - Aggregation function (default: mean)
+- `errorbar` - Error representation: "sd", "se", "pi", ("ci", level), ("pi", level), or None
+- `n_boot` - Bootstrap iterations
+- `seed` - Random seed
+- `units` - Identifier for sampling units
+- `weights` - Observation weights
+- `orient` - "v" or "h"
+- `color` - Single bar color
+- `palette` - Color palette
+- `saturation` - Color saturation
+- `width` - Bar width
+- `dodge` - Separate hue levels side-by-side
+- `errcolor` - Error bar color
+- `errwidth` - Error bar line width
+- `capsize` - Error bar cap width
+- `native_scale` - Use numeric scale
+- `formatter` - Formatter for categorical axis
+- `legend` - Whether to show legend
+- `ax` - Matplotlib axes
+
+**Example:**
+```python
+sns.barplot(data=df, x='day', y='total_bill',
+            hue='sex', estimator='median',
+            errorbar=('ci', 95), capsize=0.1)
+```
+
+### countplot()
+
+**Purpose:** Show counts of observations in each categorical bin.
+
+**Key Parameters:**
+Same as `barplot()`, but:
+- Only specify one of x or y (the categorical variable)
+- No estimator or errorbar (shows counts)
+- `stat` - "count" or "percent"
+
+**Example:**
+```python
+sns.countplot(data=df, x='day', hue='time',
+              palette='pastel', dodge=True)
+```
+
+### pointplot()
+
+**Purpose:** Show point estimates and confidence intervals with connecting lines.
+
+**Key Parameters:**
+Same as `barplot()`, plus:
+- `markers` - Marker style(s)
+- `linestyles` - Line style(s)
+- `scale` - Scale for markers
+- `join` - Connect points with lines
+- `capsize` - Error bar cap width
+
+**Example:**
+```python
+sns.pointplot(data=df, x='time', y='total_bill',
+              hue='sex', markers=['o', 's'],
+              linestyles=['-', '--'], capsize=0.1)
+```
+
+### catplot()
+
+**Purpose:** Figure-level interface for categorical plots onto a FacetGrid.
+
+**Key Parameters:**
+All parameters from categorical plots, plus:
+- `kind` - "strip", "swarm", "box", "violin", "boxen", "bar", "point", "count"
+- `col` - Categorical variable for column facets
+- `row` - Categorical variable for row facets
+- `col_wrap` - Wrap columns
+- `col_order` - Order for column facets
+- `row_order` - Order for row facets
+- `height` - Height of each facet
+- `aspect` - Aspect ratio
+- `sharex, sharey` - Share axes across facets
+- `legend` - Whether to show legend
+- `legend_out` - Place legend outside figure
+- `facet_kws` - Additional FacetGrid parameters
+
+**Example:**
+```python
+sns.catplot(data=df, x='day', y='total_bill',
+            hue='smoker', col='time',
+            kind='violin', split=True,
+            height=4, aspect=0.8)
+```
+
+## Regression Plots
+
+### regplot()
+
+**Purpose:** Plot data and a linear regression fit.
+
+**Key Parameters:**
+- `data` - DataFrame
+- `x, y` - Variables or data vectors
+- `x_estimator` - Apply estimator to x bins
+- `x_bins` - Bin x for estimator
+- `x_ci` - CI for binned estimates
+- `scatter` - Show scatter points
+- `fit_reg` - Plot regression line
+- `ci` - CI for regression estimate (int or None)
+- `n_boot` - Bootstrap iterations for CI
+- `units` - Identifier for sampling units
+- `seed` - Random seed
+- `order` - Polynomial regression order
+- `logistic` - Fit logistic regression
+- `lowess` - Fit lowess smoother
+- `robust` - Fit robust regression
+- `logx` - Log-transform x
+- `x_partial, y_partial` - Partial regression (regress out variables)
+- `truncate` - Limit regression line to data range
+- `dropna` - Drop missing values
+- `x_jitter, y_jitter` - Add jitter to data
+- `label` - Label for legend
+- `color` - Color for all elements
+- `marker` - Marker style
+- `scatter_kws` - Parameters for scatter
+- `line_kws` - Parameters for regression line
+- `ax` - Matplotlib axes
+
+**Example:**
+```python
+sns.regplot(data=df, x='total_bill', y='tip',
+            order=2, robust=True, ci=95,
+            scatter_kws={'alpha': 0.5})
+```
+
+### lmplot()
+
+**Purpose:** Figure-level interface for regression plots onto a FacetGrid.
+
+**Key Parameters:**
+All parameters from `regplot()`, plus:
+- `hue` - Grouping variable
+- `col` - Column facets
+- `row` - Row facets
+- `palette` - Color palette
+- `col_wrap` - Wrap columns
+- `height` - Facet height
+- `aspect` - Aspect ratio
+- `markers` - Marker style(s)
+- `sharex, sharey` - Share axes
+- `hue_order` - Order for hue levels
+- `col_order` - Order for column facets
+- `row_order` - Order for row facets
+- `legend` - Whether to show legend
+- `legend_out` - Place legend outside
+- `facet_kws` - FacetGrid parameters
+
+**Example:**
+```python
+sns.lmplot(data=df, x='total_bill', y='tip',
+           hue='smoker', col='time', row='sex',
+           height=3, aspect=1.2, ci=None)
+```
+
+### residplot()
+
+**Purpose:** Plot residuals of a regression.
+
+**Key Parameters:**
+Same as `regplot()`, but:
+- Always plots residuals (y - predicted) vs x
+- Adds horizontal line at y=0
+- `lowess` - Fit lowess smoother to residuals
+
+**Example:**
+```python
+sns.residplot(data=df, x='x', y='y', lowess=True,
+              scatter_kws={'alpha': 0.5})
+```
+
+## Matrix Plots
+
+### heatmap()
+
+**Purpose:** Plot rectangular data as a color-encoded matrix.
+
+**Key Parameters:**
+- `data` - 2D array-like data
+- `vmin, vmax` - Anchor values for colormap
+- `cmap` - Colormap name or object
+- `center` - Value at colormap center
+- `robust` - Use robust quantiles for colormap range
+- `annot` - Annotate cells: True, False, or array
+- `fmt` - Format string for annotations (e.g., ".2f")
+- `annot_kws` - Parameters for annotations
+- `linewidths` - Width of cell borders
+- `linecolor` - Color of cell borders
+- `cbar` - Draw colorbar
+- `cbar_kws` - Colorbar parameters
+- `cbar_ax` - Axes for colorbar
+- `square` - Force square cells
+- `xticklabels, yticklabels` - Tick labels (True, False, int, or list)
+- `mask` - Boolean array to mask cells
+- `ax` - Matplotlib axes
+
+**Example:**
+```python
+# Correlation matrix
+corr = df.corr()
+mask = np.triu(np.ones_like(corr, dtype=bool))
+sns.heatmap(corr, mask=mask, annot=True, fmt='.2f',
+            cmap='coolwarm', center=0, square=True,
+            linewidths=1, cbar_kws={'shrink': 0.8})
+```
+
+### clustermap()
+
+**Purpose:** Plot a hierarchically-clustered heatmap.
+
+**Key Parameters:**
+All parameters from `heatmap()`, plus:
+- `pivot_kws` - Parameters for pivoting (if needed)
+- `method` - Linkage method: "single", "complete", "average", "weighted", "centroid", "median", "ward"
+- `metric` - Distance metric for clustering
+- `standard_scale` - Standardize data: 0 (rows), 1 (columns), or None
+- `z_score` - Z-score normalize data: 0 (rows), 1 (columns), or None
+- `row_cluster, col_cluster` - Cluster rows/columns
+- `row_linkage, col_linkage` - Precomputed linkage matrices
+- `row_colors, col_colors` - Additional color annotations
+- `dendrogram_ratio` - Ratio of dendrogram to heatmap
+- `colors_ratio` - Ratio of color annotations to heatmap
+- `cbar_pos` - Colorbar position (tuple: x, y, width, height)
+- `tree_kws` - Parameters for dendrogram
+- `figsize` - Figure size
+
+**Example:**
+```python
+sns.clustermap(data, method='average', metric='euclidean',
+               z_score=0, cmap='viridis',
+               row_colors=row_colors, col_colors=col_colors,
+               figsize=(12, 12), dendrogram_ratio=0.1)
+```
+
+## Multi-Plot Grids
+
+### FacetGrid
+
+**Purpose:** Multi-plot grid for plotting conditional relationships.
+
+**Initialization:**
+```python
+g = sns.FacetGrid(data, row=None, col=None, hue=None,
+                  col_wrap=None, sharex=True, sharey=True,
+                  height=3, aspect=1, palette=None,
+                  row_order=None, col_order=None, hue_order=None,
+                  hue_kws=None, dropna=False, legend_out=True,
+                  despine=True, margin_titles=False,
+                  xlim=None, ylim=None, subplot_kws=None,
+                  gridspec_kws=None)
+```
+
+**Methods:**
+- `map(func, *args, **kwargs)` - Apply function to each facet
+- `map_dataframe(func, *args, **kwargs)` - Apply function with full DataFrame
+- `set_axis_labels(x_var, y_var)` - Set axis labels
+- `set_titles(template, **kwargs)` - Set subplot titles
+- `set(kwargs)` - Set attributes on all axes
+- `add_legend(legend_data, title, label_order, **kwargs)` - Add legend
+- `savefig(*args, **kwargs)` - Save figure
+
+**Example:**
+```python
+g = sns.FacetGrid(df, col='time', row='sex', hue='smoker',
+                  height=3, aspect=1.5, margin_titles=True)
+g.map(sns.scatterplot, 'total_bill', 'tip', alpha=0.7)
+g.add_legend()
+g.set_axis_labels('Total Bill ($)', 'Tip ($)')
+g.set_titles('{col_name} | {row_name}')
+```
+
+### PairGrid
+
+**Purpose:** Grid for plotting pairwise relationships in a dataset.
+
+**Initialization:**
+```python
+g = sns.PairGrid(data, hue=None, vars=None,
+                 x_vars=None, y_vars=None,
+                 hue_order=None, palette=None,
+                 hue_kws=None, corner=False,
+                 diag_sharey=True, height=2.5,
+                 aspect=1, layout_pad=0.5,
+                 despine=True, dropna=False)
+```
+
+**Methods:**
+- `map(func, **kwargs)` - Apply function to all subplots
+- `map_diag(func, **kwargs)` - Apply to diagonal
+- `map_offdiag(func, **kwargs)` - Apply to off-diagonal
+- `map_upper(func, **kwargs)` - Apply to upper triangle
+- `map_lower(func, **kwargs)` - Apply to lower triangle
+- `add_legend(legend_data, **kwargs)` - Add legend
+- `savefig(*args, **kwargs)` - Save figure
+
+**Example:**
+```python
+g = sns.PairGrid(df, hue='species', vars=['a', 'b', 'c', 'd'],
+                 corner=True, height=2.5)
+g.map_upper(sns.scatterplot, alpha=0.5)
+g.map_lower(sns.kdeplot)
+g.map_diag(sns.histplot, kde=True)
+g.add_legend()
+```
+
+### JointGrid
+
+**Purpose:** Grid for bivariate plot with marginal univariate plots.
+
+**Initialization:**
+```python
+g = sns.JointGrid(data=None, x=None, y=None, hue=None,
+                  height=6, ratio=5, space=0.2,
+                  dropna=False, xlim=None, ylim=None,
+                  marginal_ticks=False, hue_order=None,
+                  palette=None)
+```
+
+**Methods:**
+- `plot(joint_func, marginal_func, **kwargs)` - Plot both joint and marginals
+- `plot_joint(func, **kwargs)` - Plot joint distribution
+- `plot_marginals(func, **kwargs)` - Plot marginal distributions
+- `refline(x, y, **kwargs)` - Add reference line
+- `set_axis_labels(xlabel, ylabel, **kwargs)` - Set axis labels
+- `savefig(*args, **kwargs)` - Save figure
+
+**Example:**
+```python
+g = sns.JointGrid(data=df, x='x', y='y', hue='group',
+                  height=6, ratio=5, space=0.2)
+g.plot_joint(sns.scatterplot, alpha=0.5)
+g.plot_marginals(sns.histplot, kde=True)
+g.set_axis_labels('Variable X', 'Variable Y')
+```