GeezerVison

Interactive vs Static Export¶

Plotly figures are interactive when viewed in a web browser: you can hover over data points, pan and zoom axes, and show and hide traces by clicking or double-clicking on the legend. You can export figures either to static image file formats like PNG, JPEG, SVG or PDF or you can export them to HTML files which can be opened in a browser. This page explains how to do the latter.

Saving to an HTML file¶

Any figure can be saved as an HTML file using the write_html method. These HTML files can be opened in any web browser to access the fully interactive figure.

import plotly.express as px

fig = px.scatter(x=range(10), y=range(10))
fig.write_html("path/to/file.html")

Controlling the size of the HTML file¶

By default, the resulting HTML file is a fully self-contained HTML file which can be uploaded to a web server or shared via email or other file-sharing mechanisms. The downside to this approach is that the file is very large (5Mb+) because it contains an inlined copy of the Plotly.js library required to make the figure interactive. This can be controlled via the include_plotlyjs argument (see below).

Inserting Plotly Output into HTML using a Jinja2 Template¶

You can insert Plotly output and text related to your data into HTML templates using Jinja2. Use .to_html to send the HTML to a Python string variable rather than using write_html to send the HTML to a disk file. Use the full_html=False option to output just the code necessary to add a figure to a template. We do not want to output a full HTML page, as the template will define the rest of the page's structure — for example, the page's HTML and BODY tags. First create a template file containing HTML and a Jinja variable, {{ fig }}. We use Python to replace the Jinja variable with our graphic fig.

<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />   <!--It is necessary to use the UTF-8 encoding with plotly graphics to get e.g. negative signs to render correctly -->
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
</head>

<body>
<h1>Here's a Plotly graph!</h1>
{{ fig }}
<p>And here's some text after the graph.</p>
</body>
</html>

Then use the following Python to replace {{ fig }} in the template with HTML that will display the Plotly figure "fig":

import plotly.express as px
from jinja2 import Template

data_canada = px.data.gapminder().query("country == 'Canada'")
fig = px.bar(data_canada, x='year', y='pop')

output_html_path=r"/path/to/output.html"
input_template_path = r"/path/to/template.html"

plotly_jinja_data = {"fig":fig.to_html(full_html=False)}
#consider also defining the include_plotlyjs parameter to point to an external Plotly.js as described above

with open(output_html_path, "w", encoding="utf-8") as output_file:
    with open(input_template_path) as template_file:
        j2_template = Template(template_file.read())
        output_file.write(j2_template.render(plotly_jinja_data))

The height of the figure can be made responsive by setting autosize to True and height to None in the layout, and responsive to True in the config.

import plotly.express as px
from jinja2 import Template

data_canada = px.data.gapminder().query("country == 'Canada'")
fig = px.bar(data_canada, x='year', y='pop')

output_html_path=r"/path/to/output.html"
input_template_path = r"/path/to/template.html"

fig.update_layout(autosize=True, height=None)
fig_html = fig.to_html(
    config={"responsive": True},
    default_height="80vh",
    full_html=False,
)
plotly_jinja_data = {"fig": fig_html}
#consider also defining the include_plotlyjs parameter to point to an external Plotly.js as described above

with open(output_html_path, "w", encoding="utf-8") as output_file:
    with open(input_template_path) as template_file:
        j2_template = Template(template_file.read())
        output_file.write(j2_template.render(plotly_jinja_data))

HTML export in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & publish apps like this with Dash Enterprise or Plotly Cloud.

Reference¶

See the following links for parameter information and HTML figure export options:

• plotly.io.to_html()
• plotly.io.write_html()

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter