seaborn.
boxenplot
(x=None, y=None, hue=None, data=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, width=0.8, dodge=True, k_depth='proportion', linewidth=None, scale='exponential', outlier_prop=None, ax=None, **kwargs)¶Draw an enhanced box plot for larger datasets.
This style of plot was originally named a “letter value” plot because it shows a large number of quantiles that are defined as “letter values”. It is similar to a box plot in plotting a nonparametric representation of a distribution in which all features correspond to actual observations. By plotting more quantiles, it provides more information about the shape of the distribution, particularly in the tails. For a more extensive explanation, you can read the paper that introduced the plot:
https://vita.had.co.nz/papers/lettervalueplot.html
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
orient : “v”  “h”, optional
color : matplotlib color, optional
palette : palette name, list, or dict, optional
saturation : float, optional
width : float, optional
dodge : bool, optional
k_depth : “proportion”  “tukey”  “trustworthy”, optional
linewidth : float, optional
scale : “linear”  “exponential”  “area”
outlier_prop : float, optional
ax : matplotlib Axes, optional
kwargs : key, value mappings


Returns:  ax : matplotlib Axes

See also
violinplot
boxplot
Examples
Draw a single horizontal boxen plot:
>>> import seaborn as sns
>>> sns.set(style="whitegrid")
>>> tips = sns.load_dataset("tips")
>>> ax = sns.boxenplot(x=tips["total_bill"])
Draw a vertical boxen plot grouped by a categorical variable:
>>> ax = sns.boxenplot(x="day", y="total_bill", data=tips)
Draw a letter value plot with nested grouping by two categorical variables:
>>> ax = sns.boxenplot(x="day", y="total_bill", hue="smoker",
... data=tips, palette="Set3")
Draw a boxen plot with nested grouping when some bins are empty:
>>> ax = sns.boxenplot(x="day", y="total_bill", hue="time",
... data=tips, linewidth=2.5)
Control box order by passing an explicit order:
>>> ax = sns.boxenplot(x="time", y="tip", data=tips,
... order=["Dinner", "Lunch"])
Draw a boxen plot for each numeric variable in a DataFrame:
>>> iris = sns.load_dataset("iris")
>>> ax = sns.boxenplot(data=iris, orient="h", palette="Set2")
Use stripplot()
to show the datapoints on top of the boxes:
>>> ax = sns.boxenplot(x="day", y="total_bill", data=tips)
>>> ax = sns.stripplot(x="day", y="total_bill", data=tips,
... size=4, jitter=True, color="gray")
Use catplot()
to combine boxenplot()
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="boxen",
... height=4, aspect=.7);