Plotly saving multiple plots into a single html

In the Plotly API there is a function to_html which returns HTML of the figure. Moreover, you can set option param full_html=False which will give you just DIV containing figure.

You can just write multiple figures to one HTML by appending DIVs containing figures:

with open('p_graph.html', 'a') as f:
    f.write(fig1.to_html(full_html=False, include_plotlyjs='cdn'))
    f.write(fig2.to_html(full_html=False, include_plotlyjs='cdn'))
    f.write(fig3.to_html(full_html=False, include_plotlyjs='cdn'))

https://plot.ly/python-api-reference/generated/plotly.io.to_html.html

You can also use Beautiful Soup to do DOM manipulation and insert DIV exactly where you need it in the HTML.

https://beautiful-soup-4.readthedocs.io/en/latest/#append

Here is an example that looks pretty good:

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

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')
plotly.offline.plot(fig, filename='test.html')
#fig.show()

It depend how do you build the html page. If it is with from plotly.offline.plot(fig, filename='name.html') than it is not possible. As you mentioned than subplot are too small, you can play with play with height and weight variable in layout:

On layout:

from plotly.offline import plot
from plotly.subplots import make_subplots
import plotly.graph_objects as go

fig = make_subplots(
rows=3, cols=1, shared_xaxes=True, 
vertical_spacing=0.02)

fig.add_trace(go.Scatter(x=[0, 1, 2], y=[10, 11, 12]),
          row=3, col=1)

fig.add_trace(go.Scatter(x=[2, 3, 4], y=[100, 110, 120]),
          row=2, col=1)

fig.add_trace(go.Scatter(x=[3, 4, 5], y=[1000, 1100, 1200]),
          row=1, col=1)

fig.update_layout(height=1200, width=600,
              title_text="Stacked Subplots with Shared X-Axes")
fig['layout']['yaxis1'].update(domain=[0, 0.2])
fig['layout']['yaxis2'].update(domain=[0.3, 0.7])
fig['layout']['yaxis3'].update(domain=[0.8, 1])

plotly.offline.plot(fig, filename='name.html')

If you build by yourself the html page you can render the html divs as http://www.codingwithricky.com/2019/08/28/easy-django-plotly/ and play on height and width variable of layout to make it bigger or smaller.