Tableau follows this drag-and-drop pattern, but in a much more explicit way than Kibana, with the ability to drop columns onto slots with (more-or-less) predictable effects. It's immensely polished - and priced accordingly.

https://help.tableau.com/current/guides/get-started-tutorial...

I'm actually not sure there's a better UI pattern for the nontechnical user. If you want to visualize both the slots and the options at once to give people a sense of what they can do with their current schema... you don't just want a dropdown for each slot that's going to forget your state, move all over the place, etc.

Maybe that's my problem: I'm a very technical user, and I find the drag thing baffling. Perhaps if I had less depth of knowledge I'd find it more intuitive?

Perhaps if you read the paper about the underlying algebra you'd find it more intuitive: https://graphics.stanford.edu/papers/polaris/polaris.pdf

I've always loved this UI. I think of visualisations as data dimension bound to visual dimensions (eg x position, y position, z position, colour, size, group, etc). So dragging the data dimension to the corresponding visual dimension afforded by the visualization maps perfectly with my mental model. How do you think of visualisations? Do you have an example of a UI that better matches your mental model?

One really really cool option I've seen is this research project called Aprtus: http://aprt.us/ . It creates this cool two-way binding between the properties of the visual elements and your data; supports recursive rendering/composition; etc. It's a little hard to explain, but the video in their homepage is worth a watch. That's probably my most ideal visualisations UX; easy to use, whilst still making it easy to use code to make complicated visualisations.

Long time lurker here, just created an account since my friend had the same issue as you, so I started working on https://github.com/wodend/pysimpleplotter. It is still in development, but please give it a try and let me know if it better suits your use-case, keep in mind this was specifically designed to work with CSV/TSV output from scientific instruments. We plan on supporting exporting charts with styles for specific journals, and I will add better error handling when I get time.

I’ve seen pyDatView: https://github.com/ebranlard/pyDatView which I use for time-series csv files generally. Might be some good inspiration for you too!

I'm one of the developers of Lens, and I agree that it's not the right fit for every problem. The way I categorize the different types of interfaces in my mind is a spectrum from:

* "shelf based" drag & drop like Lens, Tableau and most spreadsheets

* Query-based, where you type a query using SQL or another grammar and choose from a template

* High level code like ggplot, Vega or Observable's new Plot

* Low level code like D3

Since you're a developer, maybe you'd benefit from looking at one of the high level visualization grammars.

Power bi and Tableau offer a number of ways to transform data including python and R. There are a ton of plug-ins out there for custom d3 graphs or even using matplotlib right inside power bi. Check out "the charticulator."

I've also been using Google data studio and it is great and offers tons of way to connect to data and refresh for free!