如何在Python中使用plotly制作混合统计子图？

Question

我在 csv 文件中有一些数据集（总共 3 个），需要以不同的方式表示它。它们必然是带有 kde（核密度估计）的折线图、箱线图和直方图。

我知道如何单独绘制它们，但为了更方便，我需要将它们合并到一个输出中。在查阅参考资料后，我确实编写了一些代码，但它没有运行。

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.figure_factory as ff
import numpy as np

y1 = np.random.randn(200) - 1
y2 = np.random.randn(200)
y3 = np.random.randn(200) + 1
x = np.linspace(0, 1, 200)

fig = make_subplots(
    rows=3, cols=2,
    column_widths=[0.6, 0.4],
    row_heights=[0.3, 0.6],
    specs=[[{"type": "scatter"}, {"type": "box"}],
           [{"type": "scatter"}, {"type": "dist", "rowspan": 2}]
           [{"type": "scatter"},            None           ]])

fig.add_trace(
    go.Scatter(x = x, 
                y = y1,
                hoverinfo = 'x+y',
                mode='lines',
                line=dict(color='rgb(0, 0, 0)',
                width=1),
                showlegend=False,
                )
    row=1, col=1
)

fig.add_trace(
    go.Scatter(x = x, 
                y = y2,
                hoverinfo = 'x+y',
                mode='lines',
                line=dict(color='rgb(246, 52, 16)',
                width=1),
                showlegend=False,
                )
    row=2, col=1
)

fig.add_trace(
    go.Scatter(x = x, 
                y = y3,
                hoverinfo = 'x+y',
                mode='lines',
                line=dict(color='rgb(16, 154, 246)',
                width=1),
                showlegend=False,
                )
    row=3, col=1
)

fig.add_trace(
    go.Box(x=y1)
    go.Box(x=y2)
    go.Box(x=y3)
    row=1, col=2
)

hist_data = [y1, y2, y3]

fig.add_trace(
    ff.create_distplot(hist_data,
                         bin_size=.02, show_rug=False)

    row=2, col=2
)

fig.show()

上面的代码有什么问题，或者如何使用独特的输出绘制这些图表？

PS：为了更好的可视化，需要将折线图分开。

Answer 1

我在plotly论坛上发布了同样的问题，用户empet优雅地回答了。

正如我怀疑的那样， make_subplots() 无法处理图形对象，解决方法是“一次将图形数据添加为单个跟踪”。

阴谋： 代码：

import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.figure_factory as ff
import numpy as np

y1 = np.random.randn(200) - 1
y2 = np.random.randn(200)
y3 = np.random.randn(200) + 1
x = np.linspace(0, 1, 200)

colors = ['#3f3f3f', '#00bfff', '#ff7f00']

fig = make_subplots(
    rows=3, cols=2,
    column_widths=[0.55, 0.45],
    row_heights=[1., 1., 1.],
    specs=[[{"type": "scatter"}, {"type": "xy"}],
           [{"type": "scatter"}, {"type": "xy", "rowspan": 2}],
           [{"type": "scatter"},            None           ]])

fig.add_trace(
    go.Scatter(x = x, 
                y = y1,
                hoverinfo = 'x+y',
                mode='lines',
                line=dict(color='#3f3f3f',
                width=1),
                showlegend=False,
                ),
    row=1, col=1
)

fig.add_trace(
    go.Scatter(x = x, 
                y = y2,
                hoverinfo = 'x+y',
                mode='lines',
                line=dict(color='#00bfff',
                width=1),
                showlegend=False,
                ),
    row=2, col=1
)

fig.add_trace(
    go.Scatter(x = x, 
                y = y3,
                hoverinfo = 'x+y',
                mode='lines',
                line=dict(color='#ff7f00',
                width=1),
                showlegend=False,
                ),
    row=3, col=1
)

boxfig= go.Figure(data=[go.Box(x=y1, showlegend=False, notched=True, marker_color="#3f3f3f", name='3'),
                        go.Box(x=y2, showlegend=False, notched=True, marker_color="#00bfff", name='2'),
                        go.Box(x=y3, showlegend=False, notched=True, marker_color="#ff7f00", name='1')])

for k in range(len(boxfig.data)):
     fig.add_trace(boxfig.data[k], row=1, col=2)

group_labels = ['Group 1', 'Group 2', 'Group 3']
hist_data = [y1, y2, y3]

distplfig = ff.create_distplot(hist_data, group_labels, colors=colors,
                         bin_size=.2, show_rug=False)

for k in range(len(distplfig.data)):
    fig.add_trace(distplfig.data[k],
    row=2, col=2
)
fig.update_layout(barmode='overlay')
fig.show()