Context
Based on the Plotly descriptions it seems one can self-host plotly and the dash. However, I did not find an MWE to "self-host" plotly inside a GitHub pages website. (To reduce the number of accounts used in such a visualisation (from 2; 1 for GitHub 1 for Plotly, to just one for GitHub)).
Note
"Self-hosted" is in quotations because hosting a page on GitHub is not self-hosting, however, often one can take a self-hosted webpage and host it on GitHub pages. I assume if one can host plotly on GitHub pages, one can also self-host it. And my first approach would consist of simply pushing the self-hosted website with an interactive plotly graph, to GitHub pages. I did not yet find an MWE for this.
Question
How can one host an interactive plotly graph with a slider, inside a GitHub pages website without Plotly account?
Example
This is an example of an interactive Plotly graph with a slider:
Related
I have some basic knowledge of python and I am using a Django. I am trying to create drill down operation for various chart in Plotly Dash. I have tried multiple demos from online but I am not able to find any proper tutorials which provides. I have uploaded post in Plotly support, but don't gives a proper links or tutorials.
I was trying to this demo : Drill through bar chart Dash plotly , but this demo is in flask app but i need a code in Django. How do I put this code into Django?. Anyone please share useful demo links or some other tutorials with details which can I use. I need a code in Django project for drill down operation for various chart in Plotly Dash
I have a Python script that ingests GFS model data and plots it using matplotlib. How do I get those exact plots to display on a website? I am currently using PythonAnywhere to build my webpage.
It looks like Python Anywhere has a variety of options for how you can host your website/web application--so the details of how to do this will depend on how you are hosting your site.
One option is Flask, and I'll just point you to Matplotlib's example on embedding Matplotlib within Flask.
Another option is to generate the plots statically somewhere, and then upload them into your static website content. I'm not sure how exactly one one would go about that with Python Anywhere.
I built a Flask app and i would like to embed a chart for my data using Plotly. Making some research on Plotly i found about Dash, and i'm having some troubles deciding which one of the two i need to use for my project. What's the difference between them?
Actually Dash was made by Plotly's creators as a way to easily implement a web interface and create dashboards with Plotly without having to learn javascript, html and other web technologies.
With Dash you don't make visualizations, you build an interface to display Plotly's visualizations.
It uses Flask under the hood so it is specially compatible with other Flask apps. For more info refer to Dash's deployment reference.
Edit:
The reference I point to used to have more information about how to work with Flask, but not so much anymore. There are plenty other tutorials out there though, e.g. this one.
I have asked pretty much the same question on Dash forums and Chris Parmer, the creator of Dash, answered it (how awesome is that?).
You can read the full discussion here:
https://community.plotly.com/t/plotly-ecosystem-explanation/53826/2
But the summary is:
You might want to use Plotly / Plotly Express if you want to add interactive charts to your existing pages here and there and your backend is using some server side language.
You might want to use Dash if you want to build a web app with interactive charting capabilities where everything (frontend and backend) is handled with the tools that Dash provides.
PS: Dash can also inject interactive charts to your existing pages using either iframes or Dash enterprise solution (paid).
I have created a Bokeh Document that currently hosts two plots. The first employs two widgets that control a barchart while the other is a standalone choropleth map. Using the Bokeh server, the plots are completely interactive and dropdown and button widgets are able to dynamically update the source data and sort barchart respectively, but I am trying to share this Document with colleagues that simply want to visualize these data in the same fashion on a web page independent of the Bokeh server.
Is this possible?
Thanks in advance!
I have a chart that is a time based chart. However when I try to publish it as a image - it doesn't seem to physically publish. I get link and when I inspect link I can see the image, but the image is just a blank when you look at the site. I thought this was a browser specific issue - but alas, tried it on linux/microsoft O/S and different browsers - same result - the image is blank.
The interactive version of the graph seems to be working from the get go, in all o/s and browsers.
Is there way to bypass this and have a time-based chart display as an image?
Addendum:
The type of graph is as follows:
The x axis is time and y axis is any relevant value. The graph events that has happened at a given time instant. However with most if not all Google charts, when you click the publish button you have the option to publish the graph as an image or an interactive graph:
However with time-based graphs you still get this option but when you choose to publish it as an image, and go to the published website - there is nothing to be seen there - it's just blank. I have tried different o/s and browsers but no success.
Is there a way to publish the time-based graph as an image that can actually be seen?
This should not have a google-visualization tag. It looks like you are have created a chart in Google docs and are trying to use their 'publish to the web' feature. That is separate from the Google visualization API.