Python 如果 altair 中有多个 y 轴，如何增加标签间距和刻度间距

从技术上讲，可以使用offset参数移动第三个y轴:

import altair as alt
from vega_datasets import data


source = data.seattle_weather()
base = alt.Chart(source).encode( alt.X('month(date):T', axis=alt.Axis(title=None)))
area = base.mark_area(opacity=0.3, color='#57A44C').encode(
    alt.Y('average(temp_max)',
          axis=alt.Axis(title='Avg. Temperature (°C)', titleColor='#57A44C')),
    alt.Y2('average(temp_min)'))

line = base.mark_line(stroke='#5276A7', interpolate='monotone').encode(
    alt.Y('average(precipitation)',
          axis=alt.Axis(title='Precipitation (inches)', titleColor='#5276A7')))

line2 = base.mark_line(stroke='coral', interpolate='monotone').encode(
    alt.Y('average(wind)',
          axis=alt.Axis(title='wind', titleColor='coral', offset=60)))

alt.layer(area, line,line2).resolve_scale(y = 'independent').configure_axisY()

然而，我会考虑绘制单独的图，因为读取具有多个y轴的图表可能会变得困难.如果垂直叠加图，仍然很容易比较三种不同测量的趋势:

base = alt.Chart(source, height=150).encode(alt.X('month(date):T', axis=None))
area = base.mark_area(opacity=0.3, color='#57A44C').encode(
    alt.Y('average(temp_max)',
          axis=alt.Axis(title='Avg. Temperature (°C)', titleColor='#57A44C')),
    alt.Y2('average(temp_min)'))

line = base.mark_line(stroke='#5276A7', interpolate='monotone').encode(
    alt.Y('average(precipitation)',
          axis=alt.Axis(title='Precipitation (inches)', titleColor='#5276A7')))

line2 = base.mark_line(stroke='coral', interpolate='monotone').encode(
    alt.Y('average(wind)',
          axis=alt.Axis(title='wind', titleColor='coral')),
    alt.X('month(date):T')
)

alt.vconcat(area, line, line2, spacing=0).resolve_scale(x='shared')