import pandas as pd
import numpy as np
from plotnine import ggplot, aes, after_stat, geom_bar, labs
after_stat
geom_bar
uses stat_count
which by default maps the y
aesthetic to the count
which is the number of observations at a position.
= pd.DataFrame({
df "var1": [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5]
})
("var1"))
ggplot(df, aes(+ geom_bar()
)
Using the after_stat
function, we can instead map to the prop
which is the ratio of points in the panel at a position.
("var1"))
ggplot(df, aes(+ geom_bar(aes(y=after_stat("prop"))) # default is after_stat('count')
)
With after_stat
you can used the variables calculated by the stat in expressions. For example we use the count
to calculated the prop
.
("var1"))
ggplot(df, aes(+ geom_bar(aes(y=after_stat("count / np.sum(count)")))
+ labs(y="prop")
)
By default geom_bar
uses stat_count
to compute a frequency table with the x
aesthetic as the key column. As a result, any mapping (other than the x
aesthetic is lost) to a continuous variable is lost (if you have a classroom and you compute a frequency table of the gender, you lose any other information like height of students).
For example, below fill='var1'
has no effect, but the var1
variable has not been lost it has been turned into x
aesthetic.
"var1")) + geom_bar(aes(fill="var1"))) (ggplot(df, aes(
We use after_stat
to map fill
to the x
aesthetic after it has been computed.
("var1"))
ggplot(df, aes(+ geom_bar(aes(fill=after_stat("x")))
+ labs(fill="var1")
)