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 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).
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
import plotly.graph_objects as go help(go.Figure.write_html)
What About Dash?¶
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( ... ) import dash import dash_core_components as dcc import dash_html_components as html app = dash.Dash() app.layout = html.Div([ dcc.Graph(figure=fig) ]) app.run_server(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter