seaborn.boxenplot#

seaborn.boxenplot(data=None, *, x=None, y=None, hue=None, order=None, hue_order=None, orient=None, color=None, palette=None, saturation=0.75, fill=True, dodge='auto', width=0.8, gap=0, linewidth=None, linecolor=None, width_method='exponential', k_depth='tukey', outlier_prop=0.007, trust_alpha=0.05, showfliers=True, hue_norm=None, log_scale=None, native_scale=False, formatter=None, legend='auto', scale=<deprecated>, box_kws=None, flier_kws=None, line_kws=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.

See the tutorial for more information.

Note

By default, this function treats one of the variables as categorical and draws data at ordinal positions (0, 1, … n) on the relevant axis. As of version 0.13.0, this can be disabled by setting native_scale=True.

Parameters:
dataDataFrame, Series, dict, array, or list of arrays

Dataset for plotting. If x and y are absent, this is interpreted as wide-form. Otherwise it is expected to be long-form.

x, y, huenames of variables in data or vector data

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

order, hue_orderlists of strings

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

orient“v” | “h” | “x” | “y”

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.

Changed in version v0.13.0: Added ‘x’/’y’ as options, equivalent to ‘v’/’h’.

colormatplotlib color

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.

saturationfloat

Proportion of the original saturation to draw fill colors in. Large patches often look better with desaturated colors, but set this to 1 if you want the colors to perfectly match the input values.

fillbool

If True, use a solid patch. Otherwise, draw as line art.

New in version v0.13.0.

dodge“auto” or bool

When hue mapping is used, whether elements should be narrowed and shifted along the orient axis to eliminate overlap. If "auto", set to True when the orient variable is crossed with the categorical variable or False otherwise.

Changed in version 0.13.0: Added "auto" mode as a new default.

widthfloat

Width allotted to each element on the orient axis. When native_scale=True, it is relative to the minimum distance between two values in the native scale.

gapfloat

Shrink on the orient axis by this factor to add a gap between dodged elements.

New in version 0.13.0.

linewidthfloat

Width of the lines that frame the plot elements.

linecolorcolor

Color to use for line elements, when fill is True.

New in version v0.13.0.

width_method{“exponential”, “linear”, “area”}

Method to use for the width of the letter value boxes:

  • "exponential": Represent the corresponding percentile

  • "linear": Decrease by a constant amount for each box

  • "area": Represent the density of data points in that box

k_depth{“tukey”, “proportion”, “trustworthy”, “full”} or int

The number of levels to compute and draw in each tail:

  • "tukey": Use log2(n) - 3 levels, covering similar range as boxplot whiskers

  • "proportion": Leave approximately outlier_prop fliers

  • "trusthworthy": Extend to level with confidence of at least trust_alpha

  • "full": Use log2(n) + 1 levels and extend to most extreme points

outlier_propfloat

Proportion of data expected to be outliers; used when k_depth="proportion".

trust_alphafloat

Confidence threshold for most extreme level; used when k_depth="trustworthy".

showfliersbool

If False, suppress the plotting of outliers.

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.

New in version v0.12.0.

log_scalebool or number, or pair of bools or numbers

Set axis scale(s) to log. A single value sets the data axis for any numeric axes in the plot. A pair of values sets each axis independently. Numeric values are interpreted as the desired base (default 10). When None or False, seaborn defers to the existing Axes scale.

New in version v0.13.0.

native_scalebool

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

New in version v0.13.0.

formattercallable

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

New in version v0.13.0.

legend“auto”, “brief”, “full”, or False

How to draw the legend. If “brief”, numeric hue and size variables will be represented with a sample of evenly spaced values. If “full”, every group will get an entry in the legend. If “auto”, choose between brief or full representation based on number of levels. If False, no legend data is added and no legend is drawn.

New in version v0.13.0.

box_kws: dict

Keyword arguments for the box artists; passed to matplotlib.patches.Rectangle.

New in version v0.12.0.

line_kws: dict

Keyword arguments for the line denoting the median; passed to matplotlib.axes.Axes.plot().

New in version v0.12.0.

flier_kws: dict

Keyword arguments for the scatter denoting the outlier observations; passed to matplotlib.axes.Axes.scatter().

New in version v0.12.0.

axmatplotlib Axes

Axes object to draw the plot onto, otherwise uses the current Axes.

kwargskey, value mappings

Other keyword arguments are passed to matplotlib.patches.Rectangle, superceded by those in box_kws.

Returns:
axmatplotlib Axes

Returns the Axes object with the plot drawn onto it.

See also

violinplot

A combination of boxplot and kernel density estimation.

boxplot

A traditional box-and-whisker plot with a similar API.

catplot

Combine a categorical plot with a FacetGrid.

Notes

For a more extensive explanation, you can read the paper that introduced the plot: https://vita.had.co.nz/papers/letter-value-plot.html

Examples

Draw a single horizontal plot, assigning the data directly to the coordinate variable:

sns.boxenplot(x=diamonds["price"])
../_images/boxenplot_1_0.png

Group by a categorical variable, referencing columns in a datafame

sns.boxenplot(data=diamonds, x="price", y="clarity")
../_images/boxenplot_3_0.png

Group by another variable, representing it by the color of the boxes. By default, each boxen plot will be “dodged” so that they don’t overlap; you can also add a small gap between them:

large_diamond = diamonds["carat"].gt(1).rename("large_diamond")
sns.boxenplot(data=diamonds, x="price", y="clarity", hue=large_diamond, gap=.2)
../_images/boxenplot_5_0.png

The default rule for choosing each box width represents the percentile covered by the box. Alternatively, you can reduce each box width by a linear factor:

sns.boxenplot(data=diamonds, x="price", y="clarity", width_method="linear")
../_images/boxenplot_7_0.png

The width parameter itself, on the other hand, determines the width of the largest box:

sns.boxenplot(data=diamonds, x="price", y="clarity", width=.5)
../_images/boxenplot_9_0.png

There are several different approaches for choosing the number of boxes to draw, including a rule based on the confidence level of the percentie estimate:

sns.boxenplot(data=diamonds, x="price", y="clarity", k_depth="trustworthy", trust_alpha=0.01)
../_images/boxenplot_11_0.png

The linecolor and linewidth parameters control the outlines of the boxes, while the line_kws parameter controls the line representing the median and the flier_kws parameter controls the appearance of the outliers:

sns.boxenplot(
    data=diamonds, x="price", y="clarity",
    linewidth=.5, linecolor=".7",
    line_kws=dict(linewidth=1.5, color="#cde"),
    flier_kws=dict(facecolor=".7", linewidth=.5),
)
../_images/boxenplot_13_0.png

It is also possible to draw unfilled boxes. With unfilled boxes, all elements will be drawn as line art and follow hue, when used:

sns.boxenplot(data=diamonds, x="price", y="clarity", hue="clarity", fill=False)
../_images/boxenplot_15_0.png