This looks excellent. The ability to generate the Python code for the pandas dataframe transformations looks to be more useful than OpenRefine, TBH. How much work would it be to use Dask (and Dask-ML) as a backend? I see the OneHotEncoder button. Have you considered integration with Yellowbrick? They've probably already implemented a few of your near-future and someday roadmap items involving hyperparameter selection…
Thank you for your feedback and support :) Are you currently using OpenRefine? We are currently thinking about providing other dataframe libraries like dask or pyspark and similar. However, we are a little bit unsure on how to make sure that there is user demand before we implement it. It is not a complete rewrite but it would require some additional abstractions at some points in the library. And we need to check if…
Pandas-profiling has a number of cool descriptive statistics features as well. https://github.com/pandas-profiling/pandas-profiling
There's a new IterativeImputer in Scikit-learn 0.22 that it'd be cool to see visualizations of. https://twitter.com/TedPetrou/status/1197150813707108352 https://scikit-learn.org/stable/modules/impute.html
A plugin model would be cool; though configuring the container every time wouldn't be fun. Some ideas about how we could create a desktop version of binderhub in order to launch REES-compatible environments on our own resources: https://github.com/westurner/nbhandler/issues/1