如何使用 altair 突出显示散点图中的标记？

Question

I am trying to replicate the graph below, constructed in seaborn, to altair. Where I can mark certain points ie predicted point in a cluster.

The layer functionality in altair seems to be the direction.

Altair Example

import altair as alt
import pandas as pd

source = pd.DataFrame({
    'x': [1, 3, 5, 7, 9],
    'y': [1, 3, 5, 7, 9],
    'label': ['A', 'B', 'C', 'D', 'E']
})

bars = alt.Chart(source).mark_point().encode(
    x='x:Q',
    y='y:Q'
)

text = bars.mark_text(
    align='left',
    baseline='middle',
    dx=7
).encode(
    text='label'
)

bars + text

However I am not able to choose just some of the points to mark with a black dot in the middle.

Thank you

Answer 1

This can be done by layering two charts containing the data you want to display. Here's an example with some data generated by scikit-learn, since you didn't provide any example data:

import altair as alt
import pandas as pd
from sklearn.datasets import make_blobs

X, labels = make_blobs(20, random_state=1)
points = pd.DataFrame({
    'x': X[:, 0],
    'y': X[:, 1],
    'labels': labels
})
centers = points.groupby('labels').mean()
data = pd.concat([points , centers.reset_index()])

chart1 = alt.Chart(data).mark_point(filled=True, size=150).encode(
    x='x',
    y='y',
    color='labels:N'
)

chart2 = alt.Chart(centers).mark_point(filled=True, size=50).encode(
    x='x',
    y='y',
    color=alt.value('black')
)

chart1 + chart2