py-shiny VS dtplyr

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/

Can’t say I’ve used it, but isn’t that what dtplyr is supposed to provide?

https://dtplyr.tidyverse.org/

# I am trying to follow the example at https://dtplyr.tidyverse.org/

FYI the developer of tidytable has been developing dtplyr for the Tidyverse. You might like that too!

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

https://dtplyr.tidyverse.org/ ?

If you want data.table speed, but using dplyr/tidy then dtplyr is a good package to have handy. Personally I love R, and choose R + NodeJS as my gotos for everything I do, and use Python only when I have to.

Also, that dataset is quite big, and the "base" Tidyverse will be excessively slow. You should supplement the "base" Tidyverse packages (i.e. dplyr and tidyr) with either dtplyr or dbplyr (+ duckDB). I'd suggest starting with dtplyr, which should handle 10M+ rows fine.

If you want, there's a "substitute" for dplyr called dtplyr (also part of the Tidyverse), which "translates" your dplyr/tidyr code into data.table behind the scenes, and allows you to make your modifications apply directly to the original dataset by default:

Install the dtplyr package and change your code to:

Get the best of both worlds (tidyverse + data.tables) with dtplyr, a data.table backend for dplyr.