So I think the answer you needed was “it cannot be done”. To clarify the repeated ask of converting a dashboard to HTML: HTML is a markup language; it displays text in aesthetic ways, you can use CSS to improve the aesthetics. The idea of interactivity like a dashboard where onClick() leads to changes in … Read more
You could place the Component you need to hide inside an html.div([]) and change its ‘display’ option to ‘none’ in a callback. The callback should have e.g a Dropdown as Input and the Component inside the html.div([]) as Output. The following is a web app containing only a Dropdown and an Input Component that is … Read more
First, if an explanation of the broader differences between go and px is required, please take a look here and here. And if absolutely no explanations are needed, you’ll find a complete code snippet at the very end of the answer which will reveal many of the powers with colors in plotly.express Part 1: The … Read more
A little late to the party but I have found this to be a straightforward way of doing it: You create a separate script called ‘callbacks.py’ (for example) Define a function within callbacks.py which takes a dash.Dash object (i.e. the app) as a parameter (you can of course pass more arguments if necessary) and within … Read more
Alternatively, you can change the viewport sizing in the parent container like: dcc.Graph(id=’my-graph’,style={‘width’: ’90vh’, ‘height’: ’90vh’}) That will change the graph to be 90% of the viewport height of the browser. You can see more info of viewport on this link.
Updating traces of a Graph component without generating a new graph object can be achieved via the extendData property. Here is a small example that appends data each second, import dash import dash_html_components as html import dash_core_components as dcc import numpy as np from dash.dependencies import Input, Output # Example data (a circle). resolution = … Read more
(Disclaimer, I help maintain Dash) See https://github.com/plotly/jupyterlab-dash. This is a JupyterLab extension that embeds Dash within Jupyter. Also see alternative solutions in the Dash Community Forum like the Can I run dash app in jupyter topic.
Disclaimer: I wrote Dash 🙂 I’d recommend just trying both of them. Dash takes about 30 minutes to run through the tutorial. I’d also recommend checking out: The Dash announcement letter. This is a comprehensive introduction to Dash including examples, architecture, and a discussion about licensing (MIT). Live examples of Dash Apps in the Dash … Read more
From the docs: The underlying Flask app is available at app.server. import dash app = dash.Dash(__name__) server = app.server You can also pass your own Flask app instance into Dash: import flask server = flask.Flask(__name__) app = dash.Dash(__name__, server=server) Now that you have the Flask instance, you can add whatever routes and other functionality you … Read more