如何在Python API中使用plotly在x轴范围中间位置绘制垂直线？

Question

我正在尝试绘制一个动态定位的垂直线，以便在进行过滤时，线会相应移动。 例如，使用下面的代码，我可以绘制一个25K的固定垂直线，它与完整数据集一起作为中位数，但是当数据被过滤到“美洲”时，因为x轴范围现在是45K，所以不再处于中间位置。

那么如何绘制位于x轴范围中间位置的垂直线？ 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)

Answer 1

您需要向程序添加所谓的callbacks ，以便在数据库更改时更新整个数字。 然后在x1和x0形状定义的定义中包含mean() 。 但是，这需要您使用破折号 。

Answer 2

借助@ rpanai的答案并使用图表更新按钮，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)

Answer 3

这不完全是你问的。 正如我怀疑你可以实现显示明显的痕迹的中位数只是没dash作为正确地Mike_H指出。 无论如何，如果你想使用只有plotly解决方案，它会很有用。 所以如果你对这个输出感到满意的话

您可以使用以下代码。 主要区别在于我们使用垂直线而不是形状的轨迹，我们使用legendgroup和showlegend参数

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()

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,
    legendgroup="a",
    )

trace_median0 =  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",
                            )


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,
    legendgroup="b",
        )

trace_median1 =  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",
                            )
data_comp = [trace_comp0, trace_median0,
             trace_comp1, trace_median1]

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],
    ),
)
fig_comp = go.Figure(data=data_comp, layout=layout_comp)
iplot(fig_comp)