我正在尝试绘制一条动态定位的垂直线，以便在发生过滤时，该线会相应移动。例如，使用下面的代码，我可以在 25K 处绘制一条固定垂直线，该线以完整数据集作为中位数，但是当数据仅过滤为“美洲”时，因为 x 轴范围现在为 45K，该线不再处于中间位置。
那么如何绘制一条位于 x 轴范围中值位置的垂直线呢？谢谢
import pandas as pd
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
df = pd.read_csv('https://raw.githubusercontent.com/yankev/test/master/life-expectancy-per-GDP-2007.csv')
americas = df[(df.continent=='Americas')]
europe = df[(df.continent=='Europe')]
trace_comp0 = go.Scatter(
x=americas.gdp_percap,
y=americas.life_exp,
mode='markers',
marker=dict(size=12,
line=dict(width=1),
color="navy"
),
name='Americas',
text=americas.country,
)
trace_comp1 = go.Scatter(
x=europe.gdp_percap,
y=europe.life_exp,
mode='markers',
marker=dict(size=12,
line=dict(width=1),
color="red"
),
name='Europe',
text=europe.country,
)
data_comp = [trace_comp0, trace_comp1]
layout_comp = go.Layout(
title='Life Expectancy v. Per Capita GDP, 2007',
hovermode='closest',
xaxis=dict(
title='GDP per capita (2000 dollars)',
ticklen=5,
zeroline=False,
gridwidth=2,
range=[0, 50_000],
),
yaxis=dict(
title='Life Expectancy (years)',
ticklen=5,
gridwidth=2,
range=[0, 90],
),
shapes=[
{
'type': 'line',
'x0': 25000,
'y0': 0,
'x1': 25000,
'y1': 85,
'line': {
'color': 'black',
'width': 1
}
}
]
)
fig_comp = go.Figure(data=data_comp, layout=layout_comp)
iplot(fig_comp)
在 @rpanai 的答案的帮助下并使用情节更新按钮，开发了以下解决方案。检查一下。
import pandas as pd
import plotly.graph_objs as go
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
df = pd.read_csv('https://raw.githubusercontent.com/yankev/test/master/life-expectancy-per-GDP-2007.csv')
americas = df[(df.continent=='Americas')]
europe = df[(df.continent=='Europe')]
# med_eur = europe["gdp_percap"].median()
# med_ame = americas["gdp_percap"].median()
# med_total=pd.DataFrame(list(europe["gdp_percap"])+list(americas["gdp_percap"])).median()[0]
med_eur = europe["gdp_percap"].max()/2
med_ame = americas["gdp_percap"].max()/2
med_total=25000
trace_median0 = go.Scatter(x=[med_total, med_total],
y=[0,85],
mode="lines",
legendgroup="a",
showlegend=False,
marker=dict(size=12,
line=dict(width=0.8),
color="green"
),
name="Median Total"
)
trace_comp1 = go.Scatter(
x=americas.gdp_percap,
y=americas.life_exp,
mode='markers',
marker=dict(size=12,
line=dict(width=1),
color="navy"
),
name='Americas',
text=americas.country
)
trace_median1 = go.Scatter(x=[med_ame, med_ame],
y=[0,90],
mode="lines",
legendgroup="a",
showlegend=False,
marker=dict(size=12,
line=dict(width=0.8),
color="navy"
),
name="Median Americas",
visible=False
)
trace_comp2 = go.Scatter(
x=europe.gdp_percap,
y=europe.life_exp,
mode='markers',
marker=dict(size=12,
line=dict(width=1),
color="red"
),
name='Europe',
text=europe.country,
)
trace_median2 = go.Scatter(x=[med_eur, med_eur],
y=[0,90],
mode="lines",
legendgroup="b",
showlegend=False,
marker=dict(size=12,
line=dict(width=0.8),
color="red"
),
name="Median Europe",
visible=False
)
data_comp = [trace_comp1,trace_median1]+[trace_comp2,trace_median2]+[trace_median0]
layout_comp = go.Layout(
title='Life Expectancy v. Per Capita GDP, 2007',
hovermode='closest',
xaxis=dict(
title='GDP per capita (2000 dollars)',
ticklen=5,
zeroline=False,
gridwidth=2,
range=[0, 50_000],
),
yaxis=dict(
title='Life Expectancy (years)',
ticklen=5,
gridwidth=2,
range=[0, 90],
),
showlegend=False
)
updatemenus = list([
dict(type="buttons",
active=-1,
buttons=list([
dict(label = 'Total Dataset ',
method = 'update',
args = [{'visible': [True,False,True,False,True]},
{'title': 'Life Expectancy v. Per Capita GDP, 2007'}]),
dict(label = 'Americas',
method = 'update',
args = [{'visible': [True,True, False, False,False]},
{'title': 'Americas'}]),
dict(label = 'Europe',
method = 'update',
args = [{'visible': [False, False,True,True,False]},
{'title': 'Europe'}])
]),
)
])
annotations = list([
dict(text='Trace type:', x=0, y=1.085, yref='paper', align='left', showarrow=False)
])
layout_comp['updatemenus'] = updatemenus
layout_comp['annotations'] = annotations
fig_comp = go.Figure(data=data_comp, layout=layout_comp)
iplot(fig_comp)
