lets_plot.ggmarginal

lets_plot.ggmarginal(sides: str, *, size=None, layer: lets_plot.plot.core.LayerSpec) → lets_plot.plot.core.FeatureSpec

Convert a given geometry layer to a marginal layer. You can add one or more marginal layers to a plot to create a marginal plot.

Parameters

sidesstr

A string specifying which sides of the plot the marginal layer will appear on. It should be set to a string containing any of “trbl”, for top, right, bottom, and left.

sizenumber or list of numbers, default=0.1

Size of marginal geometry (width or height, depending on the margin side) as a fraction of the entire plotting area of the plot. The value should be in range [0.01..0.95].

layerLayerSpec

A marginal geometry layer. The result of calling of the geom_xxx() / stat_xxx() function. Marginal plot works best with density,`histogram`,`boxplot`,`violin` and freqpoly geometry layers.

Returns

FeatureSpec

An object specifying a marginal geometry layer or a list of marginal geometry layers.

Notes

A marginal plot is a scatterplot (sometimes a 2D density plot or other bivariate plot) that has histograms, boxplots, or other distribution visualization layers in the margins of the x- and y-axes.

Examples

import numpy as np
from lets_plot import *
from lets_plot.mapping import as_discrete
LetsPlot.setup_html()
LetsPlot.set_theme(theme_light())

np.random.seed(0)

cov0=[[1, -.8],
     [-.8, 1]]
cov1=[[ 10, .1],
       [.1, .1]]

x0, y0 = np.random.multivariate_normal(mean=[-2,0], cov=cov0, size=200).T
x1, y1 = np.random.multivariate_normal(mean=[0,1], cov=cov1, size=200).T

data = dict(
    x = np.concatenate((x0,x1)),
    y = np.concatenate((y0,y1)),
    c = ["A"]*200 + ["B"]*200
)

p = ggplot(data, aes("x", "y", color="c", fill="c")) + geom_point()
p + ggmarginal("tr", layer=geom_density(alpha=0.3, show_legend=False))