[otoburb]

Thanks for your work on Buckaroo! Jupyter print() and IPython display() have limitations given their dead static output and feels like printf debugging of yore, which I know Buckaroo was written to address.

What are your thoughts on Visidata's hotkeys and controls? I used Visidata in the past and always wondered why it couldn't be added into Jupyter (eventually) for dataframe explorations.

>It looks like they are almost building a "grammar of tables" similar to a grammar of graphics.

Agreed that Great Tables seems to be taking annother crack at formalizing a "grammar of tables", and I welcome this approach too given the power of tabular formats and wider adoption of the dataframe concept via the R/pandas/Arrows/polars ecosystem, although I believe the term was initially referred to in the 90s[1] from the statistical S language.

[1] https://towardsdatascience.com/preventing-the-death-of-the-d...

[paddy_m]

Buckaroo started as a lowcode UI with an accompanying table. The low code UI lets you click on columns and perform actions (drop, fillNA, groupby). The dataframe is then modified, AND python code to perform the same action is emitted. Controlling the lowcode UI through keyboard shortcuts should be fairly straightforward.

The other feature I have played with in this area is auto-cleaning. Auto-cleaning looks at individual columns and emits cleaning commands to the low-code UI. Different cleaning strategies can be implemented and toggled through.

Buckaroo takes the view that being opinionated is good, so long as you can toggle through opinions to get the right combination of cleaning, display, or post-processing that you are looking for quickly. All of the features of buckaroo are also built to be easily extendable by users.

This feature saw very little use, so I haven't developed it much (I had to disable it after some refactorings). The lowcode UI is demonstrated at the end of the youtube video linked above.