Show HN: Buckaroo – The data table UI for Notebooks
Buckaroo – The Data Table for Jupyter
Buckaroo is a modern data table for Jupyter that expedites the most common exploratory data analysis tasks. The most basic data analysis task – looking at the raw data, is cumbersome with the existing pandas tooling. Buckaroo starts with a modern performant data table, is sortable, has value formatting, and scrolls infinitely. On top of the core table experience extra features like summary stats, histograms, smart sampling, auto-cleaning, and a low code UI are added. All of the functionality has sensible defaults that can be overridden to customize the experience for your workflow.
Try it now with Marimo in your browser
Play with Buckaroo without any installation.
Full Tour
Quick start
run pip install buckaroo then restart your jupyter server
The following code shows Buckaroo on a simple dataframe
import pandas as pd
import buckaroo
pd.DataFrame({'a':[1, 2, 10, 30, 50, 60, 50], 'b': ['foo', 'foo', 'bar', pd.NA, pd.NA, pd.NA, pd. NA]})
When you run import buckaroo in a Jupyter notebook, Buckaroo becomes the default display method for Pandas and Polars DataFrames
Compatibility
Buckaroo works in the following notebook environments
jupyter lab(version >=3.6.0)jupyter notebook(version >=7.0)- Marimo
VS Code notebooks(with extra install)- Jupyter Lite
Google colab
Buckaroo works with the following DataFrame libraries
pandas(version >=1.3.5)polarsoptionalgeopandasoptional (deprecated, if you are interested in geopandas, please get in touch)
Learn More
Buckaroo has extensive docs and tests, the best way to learn about the system is from feature example videos on youtube
Interactive Styling Gallery
The interactive styling gallery lets you see different styling configurations. You can live edit code and play with different configs.
Videos
- Buckaroo Full Tour 6m50s A broad introduction to Buckaroo
- Autocleaning quick demo 2m38s
- Writing your own autocleaning functions 10m10s
- Extending Buckaroo 12m56s
- Styling Buckaroo 8m18s
- Understanding JLisp in Buckaroo 12m42s
- GeoPandas Support
Example Notebooks
The following examples are loaded into a jupyter lite environment with Buckaroo installed.
-
Full Tour Marimo Pyodide Start here. This gives a broad overview of Buckaroo’s features. Jupyterlite (old) Google Colab
Notebook on Github -
Live Styling Gallery ipynb Examples of all of the different formatters and styling available for the table
-
Live Autocleaning Marimo notebook explaining how autocleaning works and showing how to implement your own cleaning commands and heuristic strategies.
-
Live Histogram Demo ipynb Explanation of the embedded histograms of Buckaroo.
-
Live JLisp overview Buckaroo embeds a small lisp interpreter to power the lowcode UI. You don’t have to understand lisp to use buckaroo, but if you want to geek out on programming language UI, check this out.
-
Extending Buckaroo Broad overview of how to add post processing methods and custom styling methods to Buckaroo
-
Styling Howto In depth explanation of how to write custom styling methods
-
Pluggable Analysis Framework How to add new summary stats to Buckaroo
-
Solara Buckaroo Using Buckaroo with Solara
Features
High performance table
The core data grid of buckaroo is based on AG-Grid. This loads 1000s of cells in less than a second, with highly customizable display, formatting and scrolling. Data is loaded lazily into the browser as you scroll, and serialized with parquet. You no longer have to use df.head() to poke at portions of your data.
Fixed width formatting by default
By default numeric columns are formatted to use a fixed width font and commas are added. This allows quick visual confirmation of magnitudes in a column.
Histograms
Histograms for every column give you a very quick overview of the distribution of values, including uniques and N/A.
Summary stats
The summary stats view can be toggled by clicking on the 0 below the Σ icon. Summary stats are similar to df.describe and extensible.
Sorting
All of the data visible in the table (rows shown), is sortable by clicking on a column name, further clicks change sort direction then disable sort for that column. Because extreme values are included with sample rows, you can see outlier values too.
Search
Search is built into Buckaroo so you can quickly find the rwos you are looking for.
Lowcode UI
Buckaroo has a simple low code UI with python code gen. This view can be toggled by clicking the checkbox below the λ(lambda) icon.
Autocleaning
Select a cleaning method from the status bar. Buckaroo has heuristic autocleaning. The autocleaning system inspects each column and runs statistics to decide if a cleaning methods should be applied to the column (parsing as dates, stripping non integer characters and treating as an integer, parsing implied booleans “yes” “no” to booleans), then adds those cleaning operations to the low code UI. Different cleaning methods can be tried because dirty data isn’t deterministic and there are multiple approaches that could properly apply to any situation.
Extensibility at the core
Buckaroo summary stats are built on the Pluggable Analysis Framework that allows individual summary stats to be overridden, and new summary stats to be built in terms of existing summary stats. Care is taken to prevent errors in summary stats from preventing display of a dataframe.
Auto cleaning (beta)
Buckaroo can automatically clean dataframes to remove common data errors (a single string in a column of ints, recognizing date times…). This feature is in beta. You can access it by invoking buckaroo as BuckarooWidget(df, auto_clean=True)
Development installation
For a development installation:
git clone https://github.com/paddymul/buckaroo.git cd buckaroo #we need to build against 3.6.5, jupyterlab 4.0 has different JS typing that conflicts # the installable still works in JL4 pip install build twine pytest sphinx polars mypy jupyterlab==3.6.5 pandas-stubs geopolars pyarrow pip install -ve .
Enabling development install for Jupyter notebook:
Enabling development install for JupyterLab:
jupyter labextension develop . --overwrite
Note for developers: the --symlink argument on Linux or OS X allows one to modify the JavaScript code in-place. This feature is not available with Windows.
`
Developing the JS side
There are a series of examples of the components in examples/ex.
Instructions
UV Instructions
cd buckaroo uv venv source ~/buckaroo/.venv/bin/activate uv sync -q
adding a package
cd ~/buckaroo uv add $PACKAGE_NAME
adding a package to a subgroup
cd ~/buckaroo uv add --group $GROUP_NAME --quiet $PACKAGE_NAME
Release instructions
update CHANGELOG.md git commit -m "updated changelog for release $VERSION_NUMBER" git tag $VERSION_NUMBER # no leading v in the version number git push origin tag $VERSION_NUMBER
navigate to create new buckaroo release
Follow instructions
Contributions
We ❤️ contributions.
Have you had a good experience with this project? Why not share some love and contribute code, or just let us know about any issues you had with it?
We welcome issue reports; be sure to choose the proper issue template for your issue, so that we can be sure you’re providing the necessary information.
