plotnine.geom_smooth

geom_smooth(
    mapping=None,
    data=None,
    *,
    stat="smooth",
    position="identity",
    na_rm=False,
    inherit_aes=True,
    show_legend=None,
    raster=False,
    legend_fill_ratio=0.5,
    **kwargs
)

A smoothed conditional mean

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
x
y
alpha	`0.4`
color	`'black'`
fill	`'#999999'`
group
linetype	`'solid'`
size	`1`
ymax	`None`
ymin	`None`

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 = "smooth"

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 = "identity"

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.

legend_fill_ratio : float = 0.5

How much (vertically) of the legend box should be filled by the color that indicates the confidence intervals. Should be in the range [0, 1].

**kwargs : Any = {}

Aesthetics or parameters used by the stat.

Examples


from plotnine import ggplot, aes, geom_point, geom_smooth, labs, theme_matplotlib, theme_set
from plotnine.data import mpg

theme_set(theme_matplotlib())

Smoothed conditional means

Aids the eye in seeing patterns in the presence of overplotting.

mpg.head()

	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

(
    ggplot(mpg, aes(x="displ", y="hwy"))
    + geom_point()
    + geom_smooth()
    + labs(x="displacement", y="horsepower")
)

Use span to control the “wiggliness” of the default loess smoother. The span is the fraction of points used to fit each local regression: small numbers make a wigglier curve, larger numbers make a smoother curve.

(
    ggplot(mpg, aes(x="displ", y="hwy"))
    + geom_point()
    + geom_smooth(span=0.3)
    + labs(x="displacement", y="horsepower")
)

You can remove confidence interval around smooth with se=False:

(
    ggplot(mpg, aes(x="displ", y="hwy"))
    + geom_point()
    + geom_smooth(span=0.3, se=False)
    + labs(x="displacement", y="horsepower")
)

Instead of a loess smooth, you can use any other modelling function:

(
    ggplot(mpg, aes(x="displ", y="hwy"))
    + geom_point()
    + geom_smooth(method="lm")
    + labs(x="displacement", y="horsepower")
)

Points & Linear Models

# Gallery, points

(
    ggplot(mpg, aes(x="displ", y="hwy", color="factor(drv)"))
    + geom_point()
    + geom_smooth(method="lm")
    + labs(x="displacement", y="horsepower")
)

Source: Smoothed conditional means