我的数据框看起来像这样:
user age gender
0 23 12 male
1 24 13 male
2 25 15 female
3 26 20 male
4 27 21 male
并使用
px.sunburst(df, path=["gender", "age"])
为我提供了正确的朝阳图,其中性别在饼图的中间部分,并且对于每个性别,它都具有相关的年龄。
我想使用graph_objects而不是通过图表示来执行此操作,因为我希望两个旭日图可以并排。
来自df,我上面有如何在graph_objects中使用它。我不知道要为标签,父母,身份证等添加什么值...
fig = go.Figure()
fig.add_trace(
go.Sunburst(
lables = df.age,
parents = df.gender,
domain=dict(column=0)
)
)
fig.show()
我正在阅读文档,但是我不明白它是如何工作的。如果有人知道,请告诉我如何使用带有以上df的graph_object创建旭日形图。
如果px
实际上确实为您提供了所需的朝阳图,则如下:
图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
。当前,您的数据的格式为
[graph_objects
将需要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的数据结构的痛苦痛苦?不,只需使用px
构建one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()
图2:
现在,如果您想在同一图中显示数据集的更多功能,只需将ids=fig['data'][0]['ids'].tolist()
添加到混合中:
图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()