seaborn.catplot

seaborn.catplot(data=None, *, x=None, y=None, hue=None, row=None, col=None, col_wrap=None, estimator='mean', errorbar=('ci', 95), n_boot=1000, units=None, seed=None, order=None, hue_order=None, row_order=None, col_order=None, height=5, aspect=1, kind='strip', native_scale=False, formatter=None, orient=None, color=None, palette=None, hue_norm=None, legend='auto', legend_out=True, sharex=True, sharey=True, margin_titles=False, facet_kws=None, ci='deprecated', **kwargs)

Figure-level interface for drawing categorical plots onto a FacetGrid.

This function provides access to several axes-level functions that show the relationship between a numerical and one or more categorical variables using one of several visual representations. The kind parameter selects the underlying axes-level function to use:

Categorical scatterplots:

• stripplot() (with kind="strip"; the default)

• swarmplot() (with kind="swarm")

Categorical distribution plots:

• boxplot() (with kind="box")

• violinplot() (with kind="violin")

• boxenplot() (with kind="boxen")

Categorical estimate plots:

• pointplot() (with kind="point")

• barplot() (with kind="bar")

• countplot() (with kind="count")

Extra keyword arguments are passed to the underlying function, so you should refer to the documentation for each to see kind-specific options.

Note that unlike when using the axes-level functions directly, data must be passed in a long-form DataFrame with variables specified by passing strings to x, y, hue, etc.

Note

This function always treats one of the variables as categorical and draws data at ordinal positions (0, 1, … n) on the relevant axis, even when the data has a numeric or date type.

See the tutorial for more information.

After plotting, the FacetGrid with the plot is returned and can be used directly to tweak supporting plot details or add other layers.

Parameters:

dataDataFrame

Long-form (tidy) dataset for plotting. Each column should correspond to a variable, and each row should correspond to an observation.

x, y, huenames of variables in data

Inputs for plotting long-form data. See examples for interpretation.

row, colnames of variables in data, optional

Categorical variables that will determine the faceting of the grid.

col_wrapint

“Wrap” the column variable at this width, so that the column facets span multiple rows. Incompatible with a row facet.

estimatorstring or callable that maps vector -> scalar, optional

Statistical function to estimate within each categorical bin.

errorbarstring, (string, number) tuple, callable or None

Name of errorbar method (either “ci”, “pi”, “se”, or “sd”), or a tuple with a method name and a level parameter, or a function that maps from a vector to a (min, max) interval, or None to hide errorbar.

n_bootint, optional

Number of bootstrap samples used to compute confidence intervals.

unitsname of variable in data or vector data, optional

Identifier of sampling units, which will be used to perform a multilevel bootstrap and account for repeated measures design.

seedint, numpy.random.Generator, or numpy.random.RandomState, optional

Seed or random number generator for reproducible bootstrapping.

order, hue_orderlists of strings, optional

Order to plot the categorical levels in; otherwise the levels are inferred from the data objects.

row_order, col_orderlists of strings, optional

Order to organize the rows and/or columns of the grid in, otherwise the orders are inferred from the data objects.

heightscalar

Height (in inches) of each facet. See also: aspect.

aspectscalar

Aspect ratio of each facet, so that aspect * height gives the width of each facet in inches.

kindstr, optional

The kind of plot to draw, corresponds to the name of a categorical axes-level plotting function. Options are: “strip”, “swarm”, “box”, “violin”, “boxen”, “point”, “bar”, or “count”.

native_scalebool, optional

When True, numeric or datetime values on the categorical axis will maintain their original scaling rather than being converted to fixed indices.

formattercallable, optional

Function for converting categorical data into strings. Affects both grouping and tick labels.

orient“v” | “h”, optional

Orientation of the plot (vertical or horizontal). This is usually inferred based on the type of the input variables, but it can be used to resolve ambiguity when both x and y are numeric or when plotting wide-form data.

colormatplotlib color, optional

Single color for the elements in the plot.

palettepalette name, list, or dict

Colors to use for the different levels of the hue variable. Should be something that can be interpreted by color_palette(), or a dictionary mapping hue levels to matplotlib colors.

hue_normtuple or matplotlib.colors.Normalize object

Normalization in data units for colormap applied to the hue variable when it is numeric. Not relevant if hue is categorical.

legendstr or bool, optional

Set to False to disable the legend. With strip or swarm plots, this also accepts a string, as described in the axes-level docstrings.

legend_outbool

If True, the figure size will be extended, and the legend will be drawn outside the plot on the center right.

share{x,y}bool, ‘col’, or ‘row’ optional

If true, the facets will share y axes across columns and/or x axes across rows.

margin_titlesbool

If True, the titles for the row variable are drawn to the right of the last column. This option is experimental and may not work in all cases.

facet_kwsdict, optional

Dictionary of other keyword arguments to pass to FacetGrid.

kwargskey, value pairings

Other keyword arguments are passed through to the underlying plotting function.

Returns:

gFacetGrid

Returns the FacetGrid object with the plot on it for further tweaking.

Examples

By default, the visual representation will be a jittered strip plot:

df = sns.load_dataset("titanic")
sns.catplot(data=df, x="age", y="class")

Use kind to select a different representation:

sns.catplot(data=df, x="age", y="class", kind="box")

One advantage is that the legend will be automatically placed outside the plot:

sns.catplot(data=df, x="age", y="class", hue="sex", kind="boxen")

Additional keyword arguments get passed through to the underlying seaborn function:

sns.catplot(
    data=df, x="age", y="class", hue="sex",
    kind="violin", bw=.25, cut=0, split=True,
)

Assigning a variable to col or row will automatically create subplots. Control figure size with the height and aspect parameters:

sns.catplot(
    data=df, x="class", y="survived", col="sex",
    kind="bar", height=4, aspect=.6,
)

For single-subplot figures, it is easy to layer different representations:

sns.catplot(data=df, x="age", y="class", kind="violin", color=".9", inner=None)
sns.swarmplot(data=df, x="age", y="class", size=3)

Use methods on the returned FacetGrid to tweak the presentation:

g = sns.catplot(
    data=df, x="who", y="survived", col="class",
    kind="bar", height=4, aspect=.6,
)
g.set_axis_labels("", "Survival Rate")
g.set_xticklabels(["Men", "Women", "Children"])
g.set_titles("{col_name} {col_var}")
g.set(ylim=(0, 1))
g.despine(left=True)