New Variables ..sumprop.., ..sumpct..
in the count and count2d Statistics

Computed variables ..sumprop.. and ..sumpct.. take the value of the share of observations at a given location relative to the total number of observations.

This is in contrast to computed variables ..prop.. and ..proppct.. which take the value of the share of observations belonging to a given group relative to the number of observations at a given location.

import pandas as pd

from lets_plot import *
from lets_plot.mapping import as_discrete

LetsPlot.setup_html()

data = {
    'x': ['a', 'a', 'a', 'a', 'b', 'b'],
    'group': ['A', 'A', 'A', 'B', 'A', 'B'],
}

tooltip_options = layer_tooltips(["..sumprop..", "..sumpct..", "..prop..", "..proppct.."])

1. Use New ..sumprop.. and ..sumpct.. for Plots without Grouping

Note: compare vaues shown in the tooltip.

ggplot(data, aes('x')) + \
    geom_bar(tooltips=tooltip_options, labels=layer_labels().line('@..sumprop.. (@..sumpct..)'))

2. However "..prop..", "..proppct.." Better Suit for Grouped Plots

Note: again, compare vaues shown in the tooltip.

ggplot(data, aes('x', fill='group')) + \
    geom_bar(tooltips=tooltip_options, labels=layer_labels().line('@..prop.. (@..proppct..)'))

3. The count2d Stat Works Similarly

df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/master/data/mpg.csv")
print(df.shape)
df.head()

(234, 12)

	Unnamed: 0	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
0	1	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
1	2	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
2	3	audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
3	4	audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
4	5	audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact

ggplot(df, aes("drv", as_discrete("year"))) + \
    geom_pie(aes(fill="class", size='..sum..'), tooltips=tooltip_options) + \
    scale_size(guide='none')