Smoothed conditional means

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

theme_set(theme_matplotlib())

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")
)