答案:
只需使用 px 构建一个图形,然后从那里“窃取”所有图形元素并在 graph_objects 图形中使用它来获得您需要的东西!
详情:
如果px 确实为您提供了所需的旭日图,如下所示:
情节 1:
代码 1:
# imports
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
# data
df = pd.DataFrame({'user': [23, 24, 25, 26, 27],
'age': [12, 13,15, 20, 21],
'gender': ['male','male', 'female','male', 'male'] })
# plotly express figure
fig = px.sunburst(df, path=["gender", "age"])
fig.show()
然后,据我所知,您必须重组数据才能使用graph_objects。目前,您的数据具有以下形式
而graph_objects 需要label = ['12', '13', '15', '20', '21', 'female', 'male']。所以现在怎么办?经历为每个元素找到正确数据结构的痛苦吗?不,只需使用px 构建一个 图形,然后从那里“窃取”所有图形元素并在graph_objects 图形中使用它:
代码 2:
# imports
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
# data
df = pd.DataFrame({'user': [23, 24, 25, 26, 27],
'age': [12, 13,15, 20, 21],
'gender': ['male','male', 'female','male', 'male'] })
# plotly express figure
fig = px.sunburst(df, path=["gender", "age"])
# plotly graph_objects figure
fig2 =go.Figure(go.Sunburst(
labels=fig['data'][0]['labels'].tolist(),
parents=fig['data'][0]['parents'].tolist(),
)
)
fig2.show()
情节 2:
现在,如果您想在同一张图中显示数据集的更多特征,只需添加 ids=fig['data'][0]['ids'].tolist() 即可:
情节 3:
完整代码:
# imports
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
# data
df = pd.DataFrame({'user': [23, 24, 25, 26, 27],
'age': [12, 13,15, 20, 21],
'gender': ['male','male', 'female','male', 'male'] })
# plotly express figure
fig = px.sunburst(df, path=["gender", "age"])
# plotly graph_objects figure
fig2 =go.Figure(go.Sunburst(
labels=fig['data'][0]['labels'].tolist(),
parents=fig['data'][0]['parents'].tolist(),
values=fig['data'][0]['values'].tolist(),
ids=fig['data'][0]['ids'].tolist(),
domain={'x': [0.0, 1.0], 'y': [0.0, 1.0]}
))
fig2.show()