Bar chart

In [1]:

from plotnine import ggplot, aes, geom_bar, labs, coord_flip, theme_classic
from plotnine.data import mpg

geom_bar() makes the height of the bar proportional to the number of cases in each group

In [2]:

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

Bar Plot with Counts

In [3]:

# Gallery, distributions
ggplot(mpg) + geom_bar(aes(x="class"))

Sorting the bars

We can use the internal function reorder to sort the bars depending on the size of each group. In this case we create a variable class2 with values equal to those of class so because class is a reserved name and:

reorder(class, class, len)

is not a valid python expression, while:

reorder(class2, class2, len)

is a valid python expression.

In [4]:

mpg["class2"] = mpg["class"]

(
    ggplot(mpg)
    + geom_bar(aes(x="reorder(class2, class2, len)"))
    + labs(x="class")
)

And in descending order

In [5]:

(
    ggplot(mpg)
    + geom_bar(
        aes(x="reorder(class2, class2, len, ascending=False)")
    )
    + labs(x="class")
)

In [6]:

(
    ggplot(mpg)
    + geom_bar(aes(x="class", fill="drv"))
)

Horizontal Bar Plot with Counts

In [7]:

# Gallery, distributions
(
    ggplot(mpg) 
    + geom_bar(aes(x="class", fill="drv"))
    + coord_flip()
    + theme_classic()
)