seaborn.
catplot
(x=None, y=None, hue=None, data=None, row=None, col=None, col_wrap=None, estimator=<function mean>, ci=95, n_boot=1000, units=None, order=None, hue_order=None, row_order=None, col_order=None, kind='strip', height=5, aspect=1, orient=None, color=None, palette=None, legend=True, legend_out=True, sharex=True, sharey=True, margin_titles=False, facet_kws=None, **kwargs)¶Figurelevel interface for drawing categorical plots onto a FacetGrid.
This function provides access to several axeslevel 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 axeslevel 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 kindspecific options.
Note that unlike when using the axeslevel functions directly, data must be
passed in a longform DataFrame with variables specified by passing strings
to x
, y
, hue
, etc.
As in the case with the underlying plot functions, if variables have a
categorical
data type, the the levels of the categorical variables, and
their order will be inferred from the objects. Otherwise you may have to
use alter the dataframe sorting or use the function parameters (orient
,
order
, hue_order
, etc.) to set up the plot correctly.
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:  x, y, hue : names of variables in
data : DataFrame
row, col : names of variables in
col_wrap : int, optional
estimator : callable that maps vector > scalar, optional
ci : float or “sd” or None, optional
n_boot : int, optional
units : name of variable in
order, hue_order : lists of strings, optional
row_order, col_order : lists of strings, optional
kind : string, optional
height : scalar, optional
aspect : scalar, optional
orient : “v”  “h”, optional
color : matplotlib color, optional
palette : palette name, list, or dict, optional
legend : bool, optional
legend_out : bool, optional
share{x,y} : bool, ‘col’, or ‘row’ optional
margin_titles : bool, optional
facet_kws : dict, optional
kwargs : key, value pairings


Returns:  g :

Examples
Draw a single facet to use the FacetGrid
legend placement:
>>> import seaborn as sns
>>> sns.set(style="ticks")
>>> exercise = sns.load_dataset("exercise")
>>> g = sns.catplot(x="time", y="pulse", hue="kind", data=exercise)
Use a different plot kind to visualize the same data:
>>> g = sns.catplot(x="time", y="pulse", hue="kind",
... data=exercise, kind="violin")
Facet along the columns to show a third categorical variable:
>>> g = sns.catplot(x="time", y="pulse", hue="kind",
... col="diet", data=exercise)
Use a different height and aspect ratio for the facets:
>>> g = sns.catplot(x="time", y="pulse", hue="kind",
... col="diet", data=exercise,
... height=5, aspect=.8)
Make many column facets and wrap them into the rows of the grid:
>>> titanic = sns.load_dataset("titanic")
>>> g = sns.catplot("alive", col="deck", col_wrap=4,
... data=titanic[titanic.deck.notnull()],
... kind="count", height=2.5, aspect=.8)
Plot horizontally and pass other keyword arguments to the plot function:
>>> g = sns.catplot(x="age", y="embark_town",
... hue="sex", row="class",
... data=titanic[titanic.embark_town.notnull()],
... orient="h", height=2, aspect=3, palette="Set3",
... kind="violin", dodge=True, cut=0, bw=.2)
Use methods on the returned FacetGrid
to tweak the presentation:
>>> g = sns.catplot(x="who", y="survived", col="class",
... data=titanic, saturation=.5,
... kind="bar", ci=None, aspect=.6)
>>> (g.set_axis_labels("", "Survival Rate")
... .set_xticklabels(["Men", "Women", "Children"])
... .set_titles("{col_name} {col_var}")
... .set(ylim=(0, 1))
... .despine(left=True))
<seaborn.axisgrid.FacetGrid object at 0x...>