seaborn.
swarmplot
(x=None, y=None, hue=None, data=None, order=None, hue_order=None, dodge=False, orient=None, color=None, palette=None, size=5, edgecolor='gray', linewidth=0, ax=None, **kwargs)¶Draw a categorical scatterplot with nonoverlapping points.
This function is similar to stripplot()
, but the points are adjusted
(only along the categorical axis) so that they don’t overlap. This gives a
better representation of the distribution of values, but it does not scale
well to large numbers of observations. This style of plot is sometimes
called a “beeswarm”.
A swarm plot can be drawn on its own, but it is also a good complement to a box or violin plot in cases where you want to show all observations along with some representation of the underlying distribution.
Arranging the points properly requires an accurate transformation between data and point coordinates. This means that nondefault axis limits must be set before drawing the plot.
Input data can be passed in a variety of formats, including:
x
, y
, and/or hue
parameters.x
, y
, and hue
variables will determine how the data are plotted.In most cases, it is possible to use numpy or Python objects, but pandas objects are preferable because the associated names will be used to annotate the axes. Additionally, you can use Categorical types for the grouping variables to control the order of plot elements.
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.
Parameters:  x, y, hue : names of variables in
data : DataFrame, array, or list of arrays, optional
order, hue_order : lists of strings, optional
dodge : bool, optional
orient : “v”  “h”, optional
color : matplotlib color, optional
palette : palette name, list, or dict, optional
size : float, optional
edgecolor : matplotlib color, “gray” is specialcased, optional
linewidth : float, optional
ax : matplotlib Axes, optional


Returns:  ax : matplotlib Axes

See also
boxplot
violinplot
stripplot
catplot
Examples
Draw a single horizontal swarm plot:
>>> import seaborn as sns
>>> sns.set(style="whitegrid")
>>> tips = sns.load_dataset("tips")
>>> ax = sns.swarmplot(x=tips["total_bill"])
Group the swarms by a categorical variable:
>>> ax = sns.swarmplot(x="day", y="total_bill", data=tips)
Draw horizontal swarms:
>>> ax = sns.swarmplot(x="total_bill", y="day", data=tips)
Color the points using a second categorical variable:
>>> ax = sns.swarmplot(x="day", y="total_bill", hue="sex", data=tips)
Split each level of the hue
variable along the categorical axis:
>>> ax = sns.swarmplot(x="day", y="total_bill", hue="smoker",
... data=tips, palette="Set2", dodge=True)
Control swarm order by passing an explicit order:
>>> ax = sns.swarmplot(x="time", y="tip", data=tips,
... order=["Dinner", "Lunch"])
Plot using larger points:
>>> ax = sns.swarmplot(x="time", y="tip", data=tips, size=6)
Draw swarms of observations on top of a box plot:
>>> ax = sns.boxplot(x="tip", y="day", data=tips, whis=np.inf)
>>> ax = sns.swarmplot(x="tip", y="day", data=tips, color=".2")
Draw swarms of observations on top of a violin plot:
>>> ax = sns.violinplot(x="day", y="total_bill", data=tips, inner=None)
>>> ax = sns.swarmplot(x="day", y="total_bill", data=tips,
... color="white", edgecolor="gray")
Use catplot()
to combine a swarmplot()
and a
FacetGrid
. This allows grouping within additional categorical
variables. Using catplot()
is safer than using FacetGrid
directly, as it ensures synchronization of variable order across facets:
>>> g = sns.catplot(x="sex", y="total_bill",
... hue="smoker", col="time",
... data=tips, kind="swarm",
... height=4, aspect=.7);