plotnine.geoms.geom_density¶

class plotnine.geoms.geom_density(mapping=None, data=None, **kwargs)[source]¶

Smooth density estimate

Usage

geom_density(mapping=None, data=None, stat='density', position='identity',
             na_rm=False, inherit_aes=True, show_legend=None, **kwargs)

Only the mapping and data can be positional, the rest must be keyword arguments. **kwargs can be aesthetics (or parameters) used by the stat.

Parameters

mappingaes, optional

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
x
y
alpha	1
color	'black'
fill	None
group
linetype	'solid'
size	0.5
weight	1

The bold aesthetics are required.

datadataframe, optional

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.

statstr or stat, optional (default: stat_density)

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.

positionstr or position, optional (default: position_identity)

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

na_rmbool, optional (default: False)

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

inherit_aesbool, optional (default: True)

If False, overrides the default aesthetics.

show_legendbool or dict, optional (default: 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.

See also

plotnine.geoms.geom_ribbon

Examples¶

[1]:

import pandas as pd
import numpy as np

from plotnine import *
from plotnine.data import *

%matplotlib inline

Density Plot¶

[2]:

mpg.head()

[2]:

	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
0	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
1	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
2	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
3	audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
4	audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact

The defaults are not exactly beautiful, but still quite clear.

[3]:

(
    ggplot(mpg, aes(x='cty'))
    + geom_density()
)

[3]:

<ggplot: (97654321012345679)>

Plotting multiple groups is straightforward, but as each group is plotted as an independent PDF summing to 1, the relative size of each group will be normalized.

[4]:

(
    ggplot(mpg, aes(x='cty', color='drv', fill='drv'))
    + geom_density(alpha=0.1)
)

[4]:

<ggplot: (97654321012345679)>

To plot multiple groups and scale them by their relative size, you can pass stat(count) to the mapping.

[5]:

(
    ggplot(mpg, aes(x='cty', color='drv', fill='drv'))
    + geom_density(
        aes(y='stat(count)'),
        alpha=0.1
    )
)

[5]:

<ggplot: (97654321012345679)>

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