I'm guessing the las one should link here https://rise.readthedocs.io/en/stable/ and not to voila two times?

You're right, thanks!

To add to this, shamelessly self-promoting, Notebooker (https://github.com/man-group/notebooker) is a neat way of scheduling your Jupyter notebooks as parametrisable reports whose results are presented in a little web GUI (either as static HTML, PDF, or as reveal.js slideshow renders)

While I have no need for its online functionality and the SAAS part of plotly, I really do like plotly python + cufflinks [1]. It lets you make interactive plots in html/js format. Which means you can save the notebook as html, and while people won't be able to rerun the code, they can still zoom in on graphs, hover to see annotations etc, which is a really nice way to share the outcome of your work in a more accessible way.

[1] https://github.com/santosjorge/cufflinks

If you're interested in an easier way to create reports using Python and Plotly/Pandas, you should check out our open-source library, Datapane: https://github.com/datapane/datapane - you can create a standalone, redistributable HTML file in a few lines of Python.

Cufflinks seems to be stale, maybe it is not needed anymore to bind plotly and pandas? I don't think these options existed in 2021 when cufflinks was last updated:

- Plotly can be used directly as pandas backend: https://plotly.com/python/pandas-backend/

- The plotly.express module makes it easy to create interactive plots in html/js format from pandas dataframes: https://plotly.com/python/plotly-express/#gallery

I think the ideal would be a link directly to the notebook which allows user-local editing/fiddling, with an option to fork the master copy and save it into their own workspace— basically the Github model.

But it sounds from both your comment and the many sibling replies that there are a number of tools now directly addressing this space, so I should definitely re-evaluate what is available.

Do you have an example of how this works with another tool/language?

I don't know if I understood it correctly but maybe you could:

- Upload your notebook to Github, then create a url with Binder (part of the jupyter ecosystem) directly to an editing/fiddling playground: https://mybinder.org/

- If by user-local you mean on their own machine, they can clone your repo and run their own jupyterlab to fiddle

- If everything should stay on your own computer/server, you could share a link to your own jupyterlab and collaborate with others in real-time: https://jupyterlab-realtime-collaboration.readthedocs.io/en/... (doing this securely might be a bit of a hassle)