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Mercury looks rather neat for quick dashboards.

For dynamic data I build my dashboards with Flask and Plotly. However that is certainly a more involved process to migrate Jupyter notebook Python plots to JS plots.

For static data one can just export html(with embedded JS) from Jupyter notebook that uses plotly.

The main difference with quick solutions is the level of customizability.

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Re your HTML point, we've been hacking on a framework which lets you programmatically create and share data reports in Python which you might find helpful (https://github.com/datapane/datapane). It supports Plotly, Pandas, Altair, Folium, MPL, etc., and provides some neat layout components like pages, selects, columns, and dropdowns.

If you need anything or want some help, feel free to make an issue or ping me on leo [-at-] datapane.com

Mercury works very well with Plotly (here is a demo https://mercury.mljar.com/app/7). If you need to choose between Flask+Plotly and Mercury I would strongly recommend Mercury because of the speed of development.

What do you mean by dynamic data? Do you need automatically fetch new data in time intervals? (Mercury has scheduling and auto-update built-in with 1 minute lowest time interval).

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