Python 牛郎星子弹图

我试图在Altair中复制，但遇到了一些问题。以下是我目前掌握的情况：

# data import and prep
import json
import altair as alt
import pandas as pd

df = pd.read_json("""[{"title":"Revenue","subtitle":"US$, in thousands","ranges":[150,225,300],"measures":[220,270],"markers":250},
{"title":"Profit","subtitle":"%","ranges":[20,25,30],"measures":[21,23],"markers":26},
{"title":"Order Size","subtitle":"US$, average","ranges":[350,500,600],"measures":[100,320],"markers":550},
{"title":"New Customers","subtitle":"count","ranges":[1400,2000,2500],"measures":[1000,1650],"markers":2100},
{"title":"Satisfaction","subtitle":"out of 5","ranges":[3.5,4.25,5],"measures":[3.2,4.7],"markers":4.4}]""")

df[['measure1','measure2']] = pd.DataFrame(df.measures.values.tolist(), index=df.index)

df[['low', 'medium', 'high']] = pd.DataFrame(df.ranges.values.tolist())

# chart
base = alt.Chart(df).encode(row = 'title:O')
m1 = base.mark_bar().encode(x='measure1:Q')
m2 = base.mark_tick().encode(x='measure2:Q')

到目前为止还不错。但是，当我尝试将两个图表分层时：

m1 + m2

SchemaValidationError: Invalid specification

    altair.vegalite.v2.api.LayerChart->layer->items, validating 'anyOf'

    {'data': {'name': 'data-58353a9bcf31ee710e2a5cb2da21a143'}, 'mark': 'bar', 'encoding': {'row': {'type': 'nominal', 'field': 'title'}, 'x': {'type': 'quantitative', 'field': 'measure1'}}} is not valid under any of the given schemas

请注意，如果我在两个层和末尾都指定了

y

编码，那么这是可行的，但是这违背了创建面的目的（所有的y轴标记在所有面中都是重复的。如果我既没有在基本图表中指定

行

编码，也没有指定

y

编码，那么只会绘制一个条形图（最大的一个）

我想要facet的原因是，在给定不同数据域的情况下，我可以指定独立的x比例（参见原始示例）

---

感谢您的帮助！

在Altair和vega lite中，将两个面图表分层是无效的（一般来说，无法保证两个面图表在分层时会对齐）。如果您仔细查看vega lite图表，您会发现它不是分层面图表，而是分层图表

Altair也可以这样做：

import altair as alt
import pandas as pd

df = pd.DataFrame.from_records([
    {"title":"Revenue","subtitle":"US$, in thousands","ranges":[150,225,300],"measures":[220,270],"markers":[250]},
    {"title":"Profit","subtitle":"%","ranges":[20,25,30],"measures":[21,23],"markers":[26]},
    {"title":"Order Size","subtitle":"US$, average","ranges":[350,500,600],"measures":[100,320],"markers":[550]},
    {"title":"New Customers","subtitle":"count","ranges":[1400,2000,2500],"measures":[1000,1650],"markers":[2100]},
    {"title":"Satisfaction","subtitle":"out of 5","ranges":[3.5,4.25,5],"measures":[3.2,4.7],"markers":[4.4]}
])

alt.layer(
    alt.Chart().mark_bar(color='#eee').encode(alt.X("ranges[2]:Q", scale=alt.Scale(nice=False), title=None)),
    alt.Chart().mark_bar(color='#ddd').encode(x="ranges[1]:Q"),
    alt.Chart().mark_bar(color='#ccc').encode(x="ranges[0]:Q"),
    alt.Chart().mark_bar(color='lightsteelblue', size=10).encode(x='measures[1]:Q'),
    alt.Chart().mark_bar(color='steelblue', size=10).encode(x='measures[0]:Q'),
    alt.Chart().mark_tick(color='black').encode(x='markers[0]:Q'),
    data=df
).facet(
    row="title:O"
).resolve_scale(
    x='independent'
)

---

原始图表中的一些样式/配置选项缺失，但这是一个粗略的想法。

在Altair和vega lite中，将两个面图表分层是无效的（通常，无法保证两个面图表在分层时会对齐）.如果你仔细观察vega lite图表，你会发现它不是分层刻面图表，而是分层图表

Altair也可以这样做：

import altair as alt
import pandas as pd

df = pd.DataFrame.from_records([
    {"title":"Revenue","subtitle":"US$, in thousands","ranges":[150,225,300],"measures":[220,270],"markers":[250]},
    {"title":"Profit","subtitle":"%","ranges":[20,25,30],"measures":[21,23],"markers":[26]},
    {"title":"Order Size","subtitle":"US$, average","ranges":[350,500,600],"measures":[100,320],"markers":[550]},
    {"title":"New Customers","subtitle":"count","ranges":[1400,2000,2500],"measures":[1000,1650],"markers":[2100]},
    {"title":"Satisfaction","subtitle":"out of 5","ranges":[3.5,4.25,5],"measures":[3.2,4.7],"markers":[4.4]}
])

alt.layer(
    alt.Chart().mark_bar(color='#eee').encode(alt.X("ranges[2]:Q", scale=alt.Scale(nice=False), title=None)),
    alt.Chart().mark_bar(color='#ddd').encode(x="ranges[1]:Q"),
    alt.Chart().mark_bar(color='#ccc').encode(x="ranges[0]:Q"),
    alt.Chart().mark_bar(color='lightsteelblue', size=10).encode(x='measures[1]:Q'),
    alt.Chart().mark_bar(color='steelblue', size=10).encode(x='measures[0]:Q'),
    alt.Chart().mark_tick(color='black').encode(x='markers[0]:Q'),
    data=df
).facet(
    row="title:O"
).resolve_scale(
    x='independent'
)

原始图表中的一些样式/配置选项缺失，但这只是一个粗略的想法