py-shiny VS tidytable

I really like this idea of using Python to create both the frontend and backend. Another lib doing this is https://solara.dev/ . Something I particularly like about Solara is that you can interactively build your app in a Jupyter Notebook, since behind the scenes it's using ipywidgets.

Has anyone compared Solara and Reflex and can comment on pros/cons? Are there other options in this space? Maybe https://shiny.posit.co/py/ ?

Would you consider giving Shiny (for Python) a try? https://shiny.posit.co/py/ It's (I hope) pretty close to Streamlit in ease of use for getting started, but reactive programming runs all the way through it. The kind of app you're talking about are extremely natural to write in Shiny, you don't have to keep track of state yourself at all.

If you decide to give it a try and have trouble, please email me (email in profile) or drop by the Discord (https://discord.gg/yMGCamUMnS).

Code is here: https://github.com/rstudio/py-shiny/tree/duckdb-example/examples/duckdb

The equivalent of shiny in python is shiny for python: https://shiny.posit.co/py/

I'm not sure how usable it is, but Shiny for Python exists: https://shiny.rstudio.com/py/

There's Shiny for Python - originally for R - but it's only Alpha status: https://shiny.rstudio.com/py/

If you need speed, you should consider using dtplyr (or tidytable), or even dbplyr with duckdb.

What do you think of this instead?

Here's an example - I use this in a package I've built called tidytable. Here is the as_tidytable() function I use that uses method dispatch.

If you're having performance issues with dplyr you can also try out tidytable

I’d say tidytable is a better option these days as it supports more functions. Although I think dtplyr has improved on this front recently, but still lags. The author of tidytable contributes to dtplyr as well.

I know you said it 'feels much faster' which isn't exactly a data oriented comparison, but tidymodels performs very well. You can see one of the dplyr functions as step_* in tidymodels, for example mutate vs. step_mutate under recipes library. The author of tidytable, which uses data.table, had some revisions due to this conversation, just as an example.

Tidytable is what you might be looking for: https://markfairbanks.github.io/tidytable/, this will require a bit of refactoring (e.g group-bys happen as arguments in summarise/mutate). You'll get data.table like speed in a very compact & complete package.

People can also use tidytable and keep the same workflow they're already used to 😄