We’ve been focused primarily on the static display of visualizations because that’s what viewers see first, and often that’s often the only thing they see. Relying too heavily on interaction places an onus on the user to find the insights; a good display of data should be opinionated about what it shows and guide the user to what is interesting.
We’re not trying to convince you to switch to JavaScript here — a main value prop of Observable Framework is that you can write data loaders in any language (Python, R, Go, Julia, etc.). So do all your data preparation and analysis in whatever language you like, and then do your front-end in JavaScript to leverage the graphics and interactive compute capabilities of modern browsers. It’s pipes and child_process.spawn under the hood. And you still get instant reactivity when you save changes to your data loaders (when you edit Python) because Framework watches files and pushes new data to the client with reactive hot data & module replacement.
And you can compress (aggregate or filter) the data as much as you like, so it’s up to you how much data you send to the client. For example your data loader could be a minimal CSV file that’s just the numbers you need for a bar chart. Or it could be a Parquet file and you use DuckDB (https://observablehq.com/framework/lib/duckdb) on the client to generate dynamic visualizations.
