SlickGrid VS buckaroo

If anyone is looking for a great open source table, I recommend slick grid: https://github.com/6pac/SlickGrid it has all the features shown in the article and has proven itself capable of everything I've needed for the last 2+ years.

Leaving it unmaintained allows for a fork to emerge.

For example, "SlickGrid" https://github.com/mleibman/SlickGrid was popular but hadn't seen an update in many years. Someone else forked and a number of people migrated over to https://github.com/6pac/SlickGrid .

In a world where the original author instead decides to relicense and commercialize, the author suddenly has a different and much more contentious relationship with anyone who tries to keep a community fork.

> because I've longed for an open source, liberally licensed, spreadsheet component for the web

I assume SlickGrid (https://github.com/6pac/SlickGrid/wiki/Examples) didn't satisfy your requirements?

I created buckaroo [1] as a better dataframe viewer for jupyter with built in summary stats. It's built to bring a better dataframe experience to people already using pandas/polars. All of it is extensible [2] so that you can customize stats and transformations to your workflow.

[1] https://github.com/paddymul/buckaroo

Great tables has done some really nice work on python/jupyter tables. It looks like they are almost building a "grammar of tables" similar to a grammar of graphics. More projects should write about their philosophy and aims like this.

I have built a different table library for jupyter called buckaroo. My approach has been different. Buckaroo aims to allow you to interactively cycle through different formats and post-processing functions to quickly glean important insights from a table while working interactively. I took the view that I type the same commands over and over to perform rudimentary exploratory data analysis, those commands and insights should be built into a table.

Great tables seems built so that you can manually format a table for presentation.

https://github.com/paddymul/buckaroo

https://youtu.be/GPl6_9n31NE

JLisp.

3. It was very easy to define new lowcode commands, and have the frontend add them to the palette. Each command defines two methods "transform" which manipulates the dataframe, and "transform_to_py" which takes the same arguments but emits python code.

Adoption of my library in general, and the low code UI specifically has been very limited. I'm in the middle of plumbing the lowcode support back in after a refactor of other parts.

I would like to build a whole ecosystem around JLisp and Buckaroo. Specifically I have some "auto-cleaning" functionality that emits JLisp cleaning and normalization commands, these commands can then be editted in the UI (delete, edit parameters). It's easier to emit JLisp than raw python syntax, it's also much easier to make a UI to manipulate it.

Do you have a repo to look at? What usecase did you have in mind when you were building it?

If I were evaluating a low-code backend builder I'd be interested in the examples, and tests. Hopefully the tests would double as examples. For a Workflow type low-code-builder I'd be most interested in the cron functionality.

[1] https://github.com/paddymul/buckaroo

[2] http://norvig.com/lispy2.html

I used Norvig’s lisp2.py to build a low code UI. I modified the interpreter to accept JSON flavored lisp, basically replace parens with brackets. The upside is that it was very very easy to make a react front end that manipulates JSON (JLisp). My thinking was, I need a serialization format for operations from the front end, and a way to interpret them. I could write my own language that no one has heard of, or use lisp, which few have used.

https://github.com/paddymul/buckaroo/blob/main/buckaroo/jlis...

Location: Boston

Remote: Yes

Willing to relocate: Yes

Technologies: talking to users, python, pandas/numpy, jupyter, js/ts

Résumé/CV: https://www.linkedin.com/in/paddymullen/

Email: [email protected]

In my next role, I want a broad mandate to make a meaningful impact within an organization by developing products that address genuine business challenges, with a preference for data related problems.

Recently I built the data table for Jupyter/Pandas that I have wanted for over a decade. The open source Buckaroo (https://github.com/paddymul/buckaroo) data table combines a performant table, summary statistics, and a low code UI to expedite common data analysis tasks.

wanted for over a decade. The open source Buckaroo https://github.com/paddymul/buckaroo data table combines a performant table, summary statistics, and a low code UI to

This morning I added a "Related Projects" [3] Section to the Buckaroo docs. If Buckaroo doesn't solve your problem, look at one of the other linked projects (like Mito).

[1] https://github.com/approximatelabs/sketch

[2] https://github.com/paddymul/buckaroo

[3] https://buckaroo-data.readthedocs.io/en/latest/FAQ.html