Show HN: Buckaroo – Data table UI for Notebooks(github.com)
github.com
Show HN: Buckaroo – Data table UI for Notebooks
https://github.com/paddymul/buckaroo
12 comments
This looks really cool. I will say my default solution for this, and the default across my org, is Data Wrangler in VS Code[1]. My only wish list item is if the low code solution wrote polars instead of pandas. Any thoughts on how hard that might be to accomplish?
1: https://marketplace.visualstudio.com/items?itemName=ms-tools...
1: https://marketplace.visualstudio.com/items?itemName=ms-tools...
Thank you.
The Buckaroo lowcode UI is capable of working with Polars, but I don't currently have any commands plumbed in. I will work on that.
I'm aware of Data Wrangler and they did nice work, but it's closed source and from what I can tell non-extensible. What features do you like in Data Wrangler, what do you wish it did differently?
The Buckaroo lowcode UI is capable of working with Polars, but I don't currently have any commands plumbed in. I will work on that.
I'm aware of Data Wrangler and they did nice work, but it's closed source and from what I can tell non-extensible. What features do you like in Data Wrangler, what do you wish it did differently?
I made a Marimo WASM example that you can play with in your browser [1]
I need to make some updates to the polars functionality, I just completed some extensive refactorings of the Lowcode UI focussed on pandas, time to clean that up for polars too.
Also the python codegen for polars is non-idiomatic with multiple re-assignments to a dataframe, vs one big select block. I have some ideas for how to fix that, but they'll take time.
https://marimo.io/p/@paddy-mullen/notebook-sctuj8
I need to make some updates to the polars functionality, I just completed some extensive refactorings of the Lowcode UI focussed on pandas, time to clean that up for polars too.
Also the python codegen for polars is non-idiomatic with multiple re-assignments to a dataframe, vs one big select block. I have some ideas for how to fix that, but they'll take time.
https://marimo.io/p/@paddy-mullen/notebook-sctuj8
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This is really great, I'm looking forward to playing with it.
Currently I use a mix of quak (preferred) and itable (if starting fom a colab notebook). It will be interesting to compare for my use cases, which most consist of checking for the distribution of data in a new file, or verifying that a transform I did resulted in the right sort of stuff.
Currently I use a mix of quak (preferred) and itable (if starting fom a colab notebook). It will be interesting to compare for my use cases, which most consist of checking for the distribution of data in a new file, or verifying that a transform I did resulted in the right sort of stuff.
Congratulations on launching. Buckaroo looks great.
How does it compare to Data Wrangler ? I like Data Wrangler because it let us open up in a separate VS Code window.
Looks cool to me. I often just end out exporting and opening in excel to do this
Auto Cleaning looks at columns and heuristically suggests common cleaning operations. The operations are added to the lowcode UI where they can be edited. Multiple cleaning strategies can be applied and the best fit retained. Autocleaning without a UI and multiple strategies is very opaque. Since this runs heuristically (not with an LLM), it’s fast and data stays local.
I'm eager to hear feedback from data scientists and other users of dataframes/notebooks.