lets_plot.stat_sum

lets_plot.stat_sum(mapping=None, *, data=None, geom=None, position=None, show_legend=None, sampling=None, tooltips=None, color_by=None, fill_by=None, **other_args)

Sum unique values.

Parameters

mappingFeatureSpec

Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

datadict or Pandas or Polars DataFrame

The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

geomstr, default=’point’

The geometry to display the sum stat for this layer, as a string.

positionstr or FeatureSpec, default=’identity’

Position adjustment, either as a string (‘identity’, ‘stack’, ‘dodge’, …), or the result of a call to a position adjustment function.

show_legendbool, default=True

False - do not show legend for this layer.

samplingFeatureSpec

Result of the call to the sampling_xxx() function. To prevent any sampling for this layer pass value “none” (string “none”).

tooltipslayer_tooltips

Result of the call to the layer_tooltips() function. Specify appearance, style and content.

color_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’color’

Define the color aesthetic for the geometry.

fill_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’fill’

Define the fill aesthetic for the geometry.

other_args

Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.

Returns

LayerSpec

Geom object specification.

Notes

stat_sum() understands the following aesthetics mappings:

• x : x-axis coordinates.

• y : y-axis coordinates.

In addition, you can use any aesthetics, available for the geometry defined by the geom parameter.

Examples

import numpy as np
from lets_plot import *
from lets_plot.mapping import as_discrete
LetsPlot.setup_html()
n = 50
np.random.seed(42)
x = [round(it) for it in np.random.normal(0, 1.5, size=n)]
y = [round(it) for it in np.random.normal(0, 1.5, size=n)]
ggplot({'x': x, 'y': y}, aes(x=as_discrete('x', order=1), y=as_discrete('y', order=1))) + \
    stat_sum()

import numpy as np
from lets_plot import *
from lets_plot.mapping import as_discrete
LetsPlot.setup_html()
n = 50
np.random.seed(42)
x = [round(it) for it in np.random.normal(0, 1.5, size=n)]
y = [round(it) for it in np.random.normal(0, 1.5, size=n)]
ggplot({'x': x, 'y': y}, aes(x=as_discrete('x', order=1), y=as_discrete('y', order=1))) + \
    stat_sum(aes(size='..prop..', group='x'))