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# Categorical Plots
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Visualize distributions and statistical summaries for categorical data using strip plots, swarm plots, box plots, violin plots, bar plots, and count plots. These functions reveal patterns across categorical groups and provide statistical insights.
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## Capabilities
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### Figure-level Categorical Plotting
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Create multi-panel figures with categorical plots across subsets of data.
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```python { .api }
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def catplot(
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    data,
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    *,
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    x=None,
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    y=None,
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    hue=None,
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    row=None,
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    col=None,
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    col_wrap=None,
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    estimator="mean",
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    errorbar=("ci", 95),
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    n_boot=1000,
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    seed=None,
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    order=None,
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    hue_order=None,
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    row_order=None,
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    col_order=None,
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    kind="strip",
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    height=5,
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    aspect=1,
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    orient=None,
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    color=None,
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    palette=None,
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    hue_norm=None,
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    legend="auto",
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    legend_out=True,
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    sharex=True,
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    sharey=True,
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    margin_titles=False,
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    facet_kws=None,
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    **kwargs
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):
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    """
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    Figure-level interface for drawing categorical plots onto a FacetGrid.
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    Parameters:
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    - data: DataFrame, dict, or array of data
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    - x, y: str, names of variables in data
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    - hue: str, grouping variable for color mapping
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    - row, col: str, variables for faceting into subplots
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    - kind: str, plot type ("strip", "swarm", "box", "violin", "boxen", "point", "bar", "count")
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    - estimator: str or callable, statistical function for bar/point plots
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    - errorbar: str or tuple, error bar representation method
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    - height: float, height of each facet in inches
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    - aspect: float, aspect ratio of each facet
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    Returns:
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    FacetGrid object
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    """
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```
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### Strip Plots
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Draw categorical scatter plots with non-overlapping points.
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```python { .api }
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def stripplot(
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    data=None,
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    *,
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    x=None,
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    y=None,
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    hue=None,
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    order=None,
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    hue_order=None,
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    orient=None,
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    color=None,
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    palette=None,
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    size=5,
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    edgecolor="gray",
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    linewidth=0,
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    hue_norm=None,
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    native_scale=False,
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    formatter=None,
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    legend="auto",
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    ax=None,
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    **kwargs
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):
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    """
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    Draw a scatterplot where one variable is categorical.
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    Parameters:
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    - data: DataFrame, dict, or array of data
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    - x, y: str or array-like, input data variables
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    - hue: str, grouping variable for color mapping
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    - order: list, order for categorical levels
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    - size: float, marker size
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    - edgecolor: str, marker edge color
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    - native_scale: bool, use native scale for numeric categories
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    Returns:
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    matplotlib Axes object
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    """
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```
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### Swarm Plots
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Draw categorical scatter plots with adjusted point positions to avoid overlap.
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```python { .api }
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def swarmplot(
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    data=None,
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    *,
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    x=None,
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    y=None,
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    hue=None,
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    order=None,
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    hue_order=None,
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    orient=None,
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    color=None,
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    palette=None,
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    size=5,
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    edgecolor="gray",
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    linewidth=0,
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    hue_norm=None,
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    native_scale=False,
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    formatter=None,
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    legend="auto",
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    warn_thresh=0.05,
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    ax=None,
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    **kwargs
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):
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    """
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    Draw a categorical scatterplot with points adjusted to be non-overlapping.
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    Parameters:
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    - data: DataFrame, dict, or array of data
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    - x, y: str or array-like, input data variables
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    - hue: str, grouping variable for color mapping
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    - size: float, marker size
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    - warn_thresh: float, threshold for warning about point overlap
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    Returns:
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    matplotlib Axes object
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    """
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```
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### Box Plots
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Show distributions with quartiles, medians, and outliers.
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```python { .api }
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def boxplot(
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    data=None,
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    *,
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    x=None,
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    y=None,
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    hue=None,
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    order=None,
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    hue_order=None,
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    orient=None,
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    color=None,
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    palette=None,
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    saturation=0.75,
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    fill=True,
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    dodge="auto",
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    width=0.8,
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    gap=0,
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    whis=1.5,
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    linecolor="auto",
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    linewidth=None,
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    boxprops=None,
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    whiskerprops=None,
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    capprops=None,
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    medianprops=None,
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    flierprops=None,
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    hue_norm=None,
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    native_scale=False,
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    formatter=None,
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    legend="auto",
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    ax=None,
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    **kwargs
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):
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    """
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    Draw a box plot to show distributions with respect to categories.
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    Parameters:
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    - data: DataFrame, dict, or array of data
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    - x, y: str or array-like, input data variables
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    - hue: str, grouping variable for color mapping
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    - dodge: bool or "auto", separate hue levels along categorical axis
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    - width: float, width of boxes
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    - whis: float, whisker length as multiple of IQR
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    - boxprops, whiskerprops, etc.: dict, styling properties
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    Returns:
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    matplotlib Axes object
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    """
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```
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### Violin Plots
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Combine box plots with kernel density estimation to show full distributions.
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```python { .api }
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def violinplot(
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    data=None,
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    *,
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    x=None,
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    y=None,
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    hue=None,
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    order=None,
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    hue_order=None,
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    orient=None,
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    color=None,
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    palette=None,
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    saturation=0.75,
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    fill=True,
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    dodge="auto",
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    width=0.8,
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    gap=0,
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    inner="box",
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    split=False,
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    linecolor="auto",
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    linewidth=None,
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    cut=2,
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    gridsize=100,
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    bw_method="scott",
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    bw_adjust=1,
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    density_norm="area",
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    common_norm=False,
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    hue_norm=None,
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    formatter=None,
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    legend="auto",
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    ax=None,
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    **kwargs
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):
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    """
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    Draw a combination of boxplot and kernel density estimation.
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    Parameters:
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    - data: DataFrame, dict, or array of data
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    - x, y: str or array-like, input data variables
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    - hue: str, grouping variable for color mapping
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    - inner: str, representation inside violins ("box", "quart", "point", "stick", None)
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    - split: bool, split violins when hue nesting
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    - cut: float, distance to extend density beyond data extremes
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    - bw_method: str or scalar, bandwidth estimation method
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    Returns:
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    matplotlib Axes object
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    """
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```
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### Enhanced Box Plots
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Draw enhanced box plots optimized for larger datasets.
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```python { .api }
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def boxenplot(
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    data=None,
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    *,
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    x=None,
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    y=None,
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    hue=None,
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    order=None,
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    hue_order=None,
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    orient=None,
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    color=None,
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    palette=None,
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    saturation=0.75,
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    fill=True,
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    dodge="auto",
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    width=0.8,
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    gap=0,
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    linecolor=None,
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    linewidth=None,
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    width_method="exponential",
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    k_depth="tukey",
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    outlier_prop=0.007,
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    trust_alpha=0.05,
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    showfliers=True,
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    hue_norm=None,
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    native_scale=False,
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    formatter=None,
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    legend="auto",
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    ax=None,
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    **kwargs
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):
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    """
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    Draw an enhanced box plot for larger datasets.
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    Parameters:
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    - data: DataFrame, dict, or array of data
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    - x, y: str or array-like, input data variables
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    - hue: str, grouping variable for color mapping
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    - k_depth: str or int, number of boxes to draw
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    - outlier_prop: float, proportion of data as outliers
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    - showfliers: bool, whether to show outliers
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    Returns:
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    matplotlib Axes object
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    """
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```
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### Point Plots
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Show point estimates and confidence intervals with connecting lines.
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```python { .api }
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def pointplot(
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    data=None,
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    *,
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    x=None,
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    y=None,
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    hue=None,
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    order=None,
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    hue_order=None,
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    estimator="mean",
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    errorbar=("ci", 95),
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    n_boot=1000,
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    seed=None,
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    orient=None,
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    color=None,
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    palette=None,
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    markers="o",
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    linestyles="-",
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    dodge=False,
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    join=True,
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    scale=1,
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    hue_norm=None,
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    native_scale=False,
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    formatter=None,
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    legend="auto",
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    capsize=0,
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    err_kws=None,
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    ax=None,
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    **kwargs
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):
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    """
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    Show point estimates and confidence intervals using scatter plot glyphs.
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    Parameters:
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    - data: DataFrame, dict, or array of data
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    - x, y: str or array-like, input data variables
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    - hue: str, grouping variable for color mapping
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    - estimator: str or callable, statistical function
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    - errorbar: str or tuple, error bar representation method
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    - markers: str or list, marker styles
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    - linestyles: str or list, line styles for connections
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    - join: bool, connect points with lines
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    - scale: float, scaling factor for elements
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    Returns:
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    matplotlib Axes object
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    """
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```
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### Bar Plots
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Show point estimates and confidence intervals as rectangular bars.
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```python { .api }
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def barplot(
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    data=None,
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    *,
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    x=None,
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    y=None,
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    hue=None,
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    order=None,
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    hue_order=None,
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    estimator="mean",
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    errorbar=("ci", 95),
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    n_boot=1000,
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    seed=None,
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    orient=None,
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    color=None,
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    palette=None,
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    saturation=0.75,
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    fill=True,
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    hue_norm=None,
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    width=0.8,
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    dodge="auto",
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    gap=0,
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    log_scale=None,
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    native_scale=False,
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    formatter=None,
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    legend="auto",
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    capsize=0,
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    err_kws=None,
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    ax=None,
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    **kwargs
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):
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    """
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    Show point estimates and confidence intervals as rectangular bars.
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    Parameters:
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    - data: DataFrame, dict, or array of data
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    - x, y: str or array-like, input data variables
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    - hue: str, grouping variable for color mapping
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    - estimator: str or callable, statistical function
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    - errorbar: str or tuple, error bar representation method
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    - width: float, width of bars
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    - dodge: bool or "auto", separate hue levels
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    - capsize: float, cap width for error bars
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    Returns:
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    matplotlib Axes object
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    """
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```
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### Count Plots
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Show counts of observations in categorical bins using bars.
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```python { .api }
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def countplot(
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    data=None,
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    *,
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    x=None,
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    y=None,
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    hue=None,
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    order=None,
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    hue_order=None,
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    orient=None,
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    color=None,
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    palette=None,
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    saturation=0.75,
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    fill=True,
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    hue_norm=None,
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    stat="count",
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    width=0.8,
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    dodge="auto",
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    gap=0,
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    log_scale=None,
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    native_scale=False,
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    formatter=None,
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    legend="auto",
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    ax=None,
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    **kwargs
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):
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    """
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    Show the counts of observations in each categorical bin using bars.
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    Parameters:
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    - data: DataFrame, dict, or array of data
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    - x, y: str or array-like, input data variables (one should be categorical)
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    - hue: str, grouping variable for color mapping
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    - stat: str, statistic to compute ("count" or "percent")
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    - width: float, width of bars
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    - dodge: bool or "auto", separate hue levels
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    Returns:
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    matplotlib Axes object
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    """
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```
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## Usage Examples
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### Strip Plot
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```python
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import seaborn as sns
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import matplotlib.pyplot as plt
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tips = sns.load_dataset("tips")
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# Basic strip plot
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sns.stripplot(data=tips, x="day", y="total_bill")
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plt.show()
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```
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### Box Plot with Grouping
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```python
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# Box plot with hue grouping
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sns.boxplot(data=tips, x="day", y="total_bill", hue="smoker")
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plt.show()
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```
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### Violin Plot
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```python
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# Violin plot showing full distributions
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sns.violinplot(data=tips, x="day", y="total_bill", inner="box")
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plt.show()
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```
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### Bar Plot with Confidence Intervals
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```python
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# Bar plot showing means with confidence intervals
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sns.barplot(data=tips, x="day", y="total_bill", estimator="mean")
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plt.show()
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```
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### Multi-Panel Categorical Plot
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```python
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# Create subplots by smoker status
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sns.catplot(
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    data=tips,
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    x="day", y="total_bill",
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    col="smoker", kind="box"
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)
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plt.show()
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```