plotnine.geom_boxplot

geom_boxplot(
    mapping=None,
    data=None,
    *,
    stat="boxplot",
    position="dodge2",
    na_rm=False,
    inherit_aes=True,
    show_legend=None,
    raster=False,
    width=None,
    outlier_alpha=1,
    outlier_color=None,
    outlier_shape="o",
    outlier_size=1.5,
    outlier_stroke=0.5,
    notch=False,
    varwidth=False,
    notchwidth=0.5,
    fatten=2,
    **kwargs
)

Box and whiskers plot

Parameters

mapping : aes = None

Aesthetic mappings created with aes. If specified and inherit_aes=True, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

Aesthetic	Default value
lower
middle
upper
x
ymax
ymin
alpha	`1`
color	`'#333333'`
fill	`'white'`
group
linetype	`'solid'`
shape	`'o'`
size	`0.5`
weight	`1`

The bold aesthetics are required.

data : DataFrame = None

The data to be displayed in this layer. If None, the data from from the ggplot() call is used. If specified, it overrides the data from the ggplot() call.

stat : str | stat = "boxplot"

The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

position : str | position = "dodge2"

Position adjustment. If it is a string, it must be registered and known to Plotnine.

na_rm : bool = False

If False, removes missing values with a warning. If True silently removes missing values.

inherit_aes : bool = True

If False, overrides the default aesthetics.

show_legend : bool | dict = None

Whether this layer should be included in the legends. None the default, includes any aesthetics that are mapped. If a bool, False never includes and True always includes. A dict can be used to exclude specific aesthetis of the layer from showing in the legend. e.g show_legend={'color': False}, any other aesthetic are included by default.

raster : bool = False

If True, draw onto this layer a raster (bitmap) object even ifthe final image is in vector format.

width : float = None

Box width. If None, the width is set to 90% of the resolution of the data. Note that if the stat has a width parameter, that takes precedence over this one.

outlier_alpha : float = 1

Transparency of the outlier points.

outlier_color : str | tuple = None

Color of the outlier points.

outlier_shape : str = "o"

Shape of the outlier points. An empty string hides the outliers.

outlier_size : float = 1.5

Size of the outlier points.

outlier_stroke : float = 0.5

Stroke-size of the outlier points.

notch : bool = False

Whether the boxes should have a notch.

varwidth : bool = False

If True, boxes are drawn with widths proportional to the square-roots of the number of observations in the groups.

notchwidth : float = 0.5

Width of notch relative to the body width.

fatten : float = 2

A multiplicative factor used to increase the size of the middle bar across the box.

**kwargs : Any = {}

Aesthetics or parameters used by the stat.

Examples

import numpy as np
import pandas as pd

from plotnine import (
    ggplot,
    aes,
    geom_boxplot,
    geom_jitter,
    scale_x_discrete,
    coord_flip,
)
from plotnine.data import pageviews

A box and whiskers plot

The boxplot compactly displays the distribution of a continuous variable.

	year	month	passengers
0	1949	January	112
1	1949	February	118
2	1949	March	132
3	1949	April	129
4	1949	May	121

A Basic Boxplot

# Gallery, distributions
(
    ggplot(flights)
    + geom_boxplot(aes(x="factor(month)", y="passengers"))
    + scale_x_discrete(labels=months, name="month")  # change ticks labels on OX
)

Horizontal boxplot

(
    ggplot(flights)
    + geom_boxplot(aes(x="factor(month)", y="passengers"))
    + coord_flip()
    + scale_x_discrete(
        labels=months[::-1],
        limits=flights.month[11::-1],
        name="month",
    )
)

Boxplot with jittered points:

(
    ggplot(flights, aes(x="factor(month)", y="passengers"))
    + geom_boxplot()
    + geom_jitter()
    + scale_x_discrete(labels=months, name="month")  # change ticks labels on OX
)

Precomputed boxplots

For datasets that do not fit in memory, you can precompute the boxplot metrics (for example by aggregating the statistics using database queries) and then use geom_boxplot with stat="identity".

# Precompute the metrics
def q25(x):
    return x.quantile(0.25)

def q75(x):
    return x.quantile(0.75)
    
pageviews["hour"] = pageviews.date_hour.dt.hour
precomputed_metrics = pageviews.groupby("hour").agg({'pageviews': ["min", q25, "median", q75, "max"]})
precomputed_metrics.columns = [col_name[1] for col_name in precomputed_metrics.columns]  
precomputed_metrics = precomputed_metrics.reset_index()
precomputed_metrics.head()

	hour	min	q25	median	q75	max
0	0	8437.500380	8842.109077	9297.046035	9600.362430	11762.446233
1	1	8852.123978	9177.938537	9457.821814	10530.072887	11974.437292
2	2	8793.076686	9176.462389	9704.885172	10446.315276	12105.406628
3	3	8683.606449	9574.722286	10615.670464	11290.246605	11651.443193
4	4	8252.974951	9898.998785	10959.909095	11409.657288	11603.711837

(
    ggplot(precomputed_metrics)
    + geom_boxplot(
        aes(x="factor(hour)", ymin="min", lower="q25", middle="median", upper="q75", ymax="max"),
        stat="identity"
    )
)

Source: A box and whiskers plot