Buckaroo – Data table UI for Notebooks
Buckaroo is my open source project. It is a dataframe viewer that has the basic features we expect in a modern table - scroll, search, sort. In addition there are summary stats, and histograms available. Buckaroo support Pandas and Polars dataframes and works on Jupter, Marimo, VSCode and Google Colab notebooks. All of this is extensible. I think of Buckaroo as a framework for building table UIs, and an initial data exploration app built on top of that framework. AG-Grid is used for the core table display and it has been customized with a declarative layer so you don't have to pass JS…
In plain words
Buckaroo is an open-source dataframe viewer for Jupyter, Marimo, VSCode, and Google Colab notebooks that displays Pandas and Polars data with scrolling, searching, and sorting. It includes summary statistics and histograms for initial data exploration. Built as an extensible framework using AG-Grid for table display, Buckaroo offers a declarative customization layer and a low-code UI for common operations like column dropping, with generated Python functions for reproducibility.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Buckaroo is my open source project. It is a dataframe viewer that has the basic features we expect in a modern table - scroll, search, sort. In addition there are summary stats, and histograms available. Buckaroo support Pandas and Polars dataframes and works on Jupter, Marimo, VSCode and Google Colab notebooks. All of this is extensible. I think of Buckaroo as a framework for building table UIs, and an initial data exploration app built on top of that framework. AG-Grid is used for the core table display and it has been customized with a declarative layer so you don't have to pass JS functions around for customizations. On the python side there is a framework for adding summary stats (with a small DAG for dependencies). There is also an entire Low Code UI for point and click selection of common commands (drop column). The lowcode UI also generates a python function that accomplishes the same tasks. This is built on top of JLisp - a small lisp interpreter that reads JSON flavored lisp. 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.
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