plotnine.mapping.after_scale

plotnine.mapping.after_scale(x)

Evaluate mapping after variable has been mapped to the scale

This gives the user a chance to alter the value of a variable in the final units of the scale e.g. the rgb hex color.

Parameters:

xstr

An expression

See also

after_stat()

For how to map aesthetics to variable calculated by the stat

stage

For how to map to aesthetics at more than one stage of the plot building pipeline.

Examples

import pandas as pd
import numpy as np

from plotnine import (
    ggplot,
    aes,
    after_scale,
    geom_bar,
    theme_classic
)

after_scale

The bars in geom_bar have two aesthetics that control the coloring; fill for the interior and color for the boundary/edge. Using after_scale we can create a matching combination of these two.

Start off with a mapping to the color.

df = pd.DataFrame({
    'var1': [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 5]
})

(ggplot(df, aes('var1', color='factor(var1)'))
 + geom_bar(size=1)
)

<Figure Size: (640 x 480)>

We can match the color with the fill.

(ggplot(df, aes('var1', color='factor(var1)'))
 + geom_bar(aes(fill=after_scale('color')), size=1)
)

<Figure Size: (640 x 480)>

As after_scale takes an expression, for the fill aesthetic we can modify the color by adding to it an alpha channel i.e. '#AABBCC' to '#AABBCC66'.

(ggplot(df, aes('var1', color='factor(var1)'))
 + geom_bar(aes(fill=after_scale('color + "66"')), size=1)
)

<Figure Size: (640 x 480)>

We rely on the fact that you can append a string to all elements of a pandas series

pd.Series(['#AABBCC', '#112233']) + '66' == pd.Series(['#AABBCC66', '#11223366'])

With a fitting theme.

(ggplot(df, aes('var1', color='factor(var1)'))
 + geom_bar(aes(fill=after_scale('color + "66"')), size=1)
 + theme_classic()
)

<Figure Size: (640 x 480)>