Stacked Density Estimates

To plot a stacked graph of estimates, use a shared extent and a fixed number of subdivision steps to ensure that the points for each area align well. Density estimates of measurements for each iris flower feature are plot in a stacked method. In addition, setting counts to true multiplies the densities by the number of data points in each group, preserving proportional differences.

import altair as alt
from vega_datasets import data

source = data.iris()

alt.Chart(source).transform_fold(
    ['petalWidth',
     'petalLength',
     'sepalWidth',
     'sepalLength'],
    as_ = ['Measurement_type', 'value']
).transform_density(
    density='value',
    bandwidth=0.3,
    groupby=['Measurement_type'],
    extent= [0, 8],
    counts = True,
    steps=200
).mark_area().encode(
    alt.X('value:Q'),
    alt.Y('density:Q', stack='zero'),
    alt.Color('Measurement_type:N')
).properties(width=400, height=100)