| Name | buckaroo JSON |
| Version |
0.11.0
JSON |
| download |
| home_page | None |
| Summary | Buckaroo - GUI Data wrangling for pandas |
| upload_time | 2025-07-18 13:27:47 |
| maintainer | None |
| docs_url | None |
| author | Paddy Mullen |
| requires_python | >=3.9 |
| license | Copyright (c) 2019 Bloomberg
All rights reserved.
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| keywords |
ipython
jupyter
widgets
pandas
|
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# 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.
<img width="947" alt="Screenshot 2025-05-12 at 3 54 33 PM" src="https://github.com/user-attachments/assets/9238c893-8dd4-47e4-8215-b5450c8c7b3a" />
## Try it now with Marimo in your browser
Play with Buckaroo without any installation.
[Full Tour](https://marimo.io/p/@paddy-mullen/buckaroo-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](https://marimo.io/p/@paddy-mullen/buckaroo-full-tour)
- `VS Code notebooks` (with extra install)
- [Jupyter Lite](https://paddymul.github.io/buckaroo-examples/lab/index.html)
- `Google colab`
Buckaroo works with the following DataFrame libraries
- `pandas` (version >=1.3.5)
- `polars` optional
- `geopandas` optional (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](https://py.cafe/app/paddymul/buckaroo-gallery) lets you see different styling configurations. You can live edit code and play with different configs.
## Videos
- [Buckaroo Full Tour](https://youtu.be/t-wk24F1G3s) 6m50s A broad introduction to Buckaroo
- [Autocleaning quick demo](https://youtube.com/shorts/4Jz-Wgf3YDc) 2m38s
- [Writing your own autocleaning functions](https://youtu.be/A-GKVsqTLMI) 10m10s
- [Extending Buckaroo](https://www.youtube.com/watch?v=GPl6_9n31NE) 12m56s
- [Styling Buckaroo](https://www.youtube.com/watch?v=cbwJyo_PzKY) 8m18s
- [Understanding JLisp in Buckaroo](https://youtu.be/3Tf3lnuZcj8) 12m42s
- [GeoPandas Support](https://youtu.be/8WBhoNjDJsA)
## Example Notebooks
The following examples are loaded into a jupyter lite environment with Buckaroo installed.
- [Full Tour Marimo Pyodide](https://marimo.io/p/@paddy-mullen/buckaroo-full-tour) Start here. This gives a broad overview of Buckaroo's features. [Jupyterlite (old)](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=Full-tour.ipynb) [Google Colab](https://colab.research.google.com/github/paddymul/buckaroo/blob/main/docs/example-notebooks/Full-tour-colab.ipynb)
[Notebook on Github](https://github.com/paddymul/buckaroo/blob/main/docs/example-notebooks/Full-tour.ipynb)
- [Live Styling Gallery](https://marimo.io/p/@paddy-mullen/buckaroo-styling-gallery) [ipynb](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=styling-gallery.ipynb) Examples of all of the different formatters and styling available for the table
- [Live Autocleaning](https://marimo.io/p/@paddy-mullen/buckaroo-auto-cleaning) Marimo notebook explaining how autocleaning works and showing how to implement your own cleaning commands and heuristic strategies.
- [Live Histogram Demo](https://marimo.io/p/@paddy-mullen/buckaroo-histogram-demo) [ipynb](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=Histograms-demo.ipynb) Explanation of the embedded histograms of Buckaroo.
- [Live JLisp overview](https://marimo.io/p/@paddy-mullen/jlisp-in-buckaroo) 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](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=Extending.ipynb) Broad overview of how to add post processing methods and custom styling methods to Buckaroo
- [Styling Howto](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=styling-howto.ipynb) In depth explanation of how to write custom styling methods
- [Pluggable Analysis Framework](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=Pluggable-Analysis-Framework.ipynb) How to add new summary stats to Buckaroo
- [Solara Buckaroo](https://github.com/paddymul/buckaroo/blob/main/docs/example-notebooks/Solara-Buckaroo.ipynb) Using Buckaroo with Solara
- [GeoPandas with Bucakroo](https://github.com/paddymul/buckaroo/blob/main/docs/example-notebooks/GeoPandas.ipynb)
# Features
## High performance table
The core data grid of buckaroo is based on [AG-Grid](https://www.ag-grid.com/). 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](https://buckaroo-data.readthedocs.io/en/latest/articles/histograms.html) 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](https://buckaroo-data.readthedocs.io/en/latest/articles/pluggable.html) 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](https://buckaroo-data.readthedocs.io/en/latest/articles/auto_clean.html) 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:
```bash
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:
```bash
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](./examples/ex).
Instructions
```bash
npm install
npm run dev
```
### UV Instructions
```sh
cd buckaroo
uv venv
source ~/buckaroo/.venv/bin/activate
uv sync -q
```
### adding a package
```sh
cd ~/buckaroo
uv add $PACKAGE_NAME
```
#### adding a package to a subgroup
```sh
cd ~/buckaroo
uv add --group $GROUP_NAME --quiet $PACKAGE_NAME
```
### Release instructions
[github release instructions](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository)
```bash
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](https://github.com/paddymul/buckaroo/releases/new)
Follow instructions
## Contributions
We :heart: 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](../../issues); be sure to choose the proper issue template for your issue, so that we can be sure you're providing the necessary information.
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"description": "# Buckaroo - The Data Table for Jupyter\n\nBuckaroo 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.\n\n<img width=\"947\" alt=\"Screenshot 2025-05-12 at 3 54 33 PM\" src=\"https://github.com/user-attachments/assets/9238c893-8dd4-47e4-8215-b5450c8c7b3a\" />\n\n## Try it now with Marimo in your browser\nPlay with Buckaroo without any installation.\n[Full Tour](https://marimo.io/p/@paddy-mullen/buckaroo-full-tour)\n\n\n## Quick start\n\nrun `pip install buckaroo` then restart your jupyter server\n\nThe following code shows Buckaroo on a simple dataframe\n\n```\nimport pandas as pd\nimport buckaroo\npd.DataFrame({'a':[1, 2, 10, 30, 50, 60, 50], 'b': ['foo', 'foo', 'bar', pd.NA, pd.NA, pd.NA, pd. NA]})\n\n```\n\nWhen you run `import buckaroo` in a Jupyter notebook, Buckaroo becomes the default display method for Pandas and Polars DataFrames\n\n\n## Compatibility\n\nBuckaroo works in the following notebook environments\n\n- `jupyter lab` (version >=3.6.0)\n- `jupyter notebook` (version >=7.0) \n- [Marimo](https://marimo.io/p/@paddy-mullen/buckaroo-full-tour)\n- `VS Code notebooks` (with extra install)\n- [Jupyter Lite](https://paddymul.github.io/buckaroo-examples/lab/index.html)\n- `Google colab` \n\n\nBuckaroo works with the following DataFrame libraries\n- `pandas` (version >=1.3.5)\n- `polars` optional\n- `geopandas` optional (deprecated, if you are interested in geopandas, please get in touch)\n\n\n# Learn More\n\nBuckaroo has extensive docs and tests, the best way to learn about the system is from feature example videos on youtube\n\n## Interactive Styling Gallery\n\nThe interactive [styling gallery](https://py.cafe/app/paddymul/buckaroo-gallery) lets you see different styling configurations. You can live edit code and play with different configs.\n\n## Videos \n- [Buckaroo Full Tour](https://youtu.be/t-wk24F1G3s) 6m50s A broad introduction to Buckaroo\n- [Autocleaning quick demo](https://youtube.com/shorts/4Jz-Wgf3YDc) 2m38s\n- [Writing your own autocleaning functions](https://youtu.be/A-GKVsqTLMI) 10m10s\n- [Extending Buckaroo](https://www.youtube.com/watch?v=GPl6_9n31NE) 12m56s\n- [Styling Buckaroo](https://www.youtube.com/watch?v=cbwJyo_PzKY) 8m18s\n- [Understanding JLisp in Buckaroo](https://youtu.be/3Tf3lnuZcj8) 12m42s\n- [GeoPandas Support](https://youtu.be/8WBhoNjDJsA)\n\n## Example Notebooks\n\nThe following examples are loaded into a jupyter lite environment with Buckaroo installed.\n- [Full Tour Marimo Pyodide](https://marimo.io/p/@paddy-mullen/buckaroo-full-tour) Start here. This gives a broad overview of Buckaroo's features. [Jupyterlite (old)](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=Full-tour.ipynb) [Google Colab](https://colab.research.google.com/github/paddymul/buckaroo/blob/main/docs/example-notebooks/Full-tour-colab.ipynb)\n[Notebook on Github](https://github.com/paddymul/buckaroo/blob/main/docs/example-notebooks/Full-tour.ipynb)\n\n\n- [Live Styling Gallery](https://marimo.io/p/@paddy-mullen/buckaroo-styling-gallery) [ipynb](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=styling-gallery.ipynb) Examples of all of the different formatters and styling available for the table\n- [Live Autocleaning](https://marimo.io/p/@paddy-mullen/buckaroo-auto-cleaning) Marimo notebook explaining how autocleaning works and showing how to implement your own cleaning commands and heuristic strategies.\n- [Live Histogram Demo](https://marimo.io/p/@paddy-mullen/buckaroo-histogram-demo) [ipynb](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=Histograms-demo.ipynb) Explanation of the embedded histograms of Buckaroo.\n- [Live JLisp overview](https://marimo.io/p/@paddy-mullen/jlisp-in-buckaroo) 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.\n- [Extending Buckaroo](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=Extending.ipynb) Broad overview of how to add post processing methods and custom styling methods to Buckaroo\n- [Styling Howto](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=styling-howto.ipynb) In depth explanation of how to write custom styling methods\n- [Pluggable Analysis Framework](https://paddymul.github.io/buckaroo-examples/lab/index.html?path=Pluggable-Analysis-Framework.ipynb) How to add new summary stats to Buckaroo\n- [Solara Buckaroo](https://github.com/paddymul/buckaroo/blob/main/docs/example-notebooks/Solara-Buckaroo.ipynb) Using Buckaroo with Solara\n- [GeoPandas with Bucakroo](https://github.com/paddymul/buckaroo/blob/main/docs/example-notebooks/GeoPandas.ipynb)\n\n# Features\n\n## High performance table\nThe core data grid of buckaroo is based on [AG-Grid](https://www.ag-grid.com/). 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.\n\n## Fixed width formatting by default\n\nBy 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.\n\n## Histograms\n\n[Histograms](https://buckaroo-data.readthedocs.io/en/latest/articles/histograms.html) for every column give you a very quick overview of the distribution of values, including uniques and N/A.\n\n## Summary stats\nThe summary stats view can be toggled by clicking on the `0` below the `\u03a3` icon. Summary stats are similar to `df.describe` and extensible.\n\n## Sorting\n\nAll 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.\n\n## Search\nSearch is built into Buckaroo so you can quickly find the rwos you are looking for.\n\n## Lowcode UI\n\nBuckaroo has a simple low code UI with python code gen. This view can be toggled by clicking the checkbox below the ` \u03bb `(lambda) icon.\n\n## Autocleaning\n\nSelect 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.\n\n## Extensibility at the core\n\nBuckaroo summary stats are built on the [Pluggable Analysis Framework](https://buckaroo-data.readthedocs.io/en/latest/articles/pluggable.html) 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.\n\n\n## Auto cleaning (beta)\n\nBuckaroo can [automatically clean](https://buckaroo-data.readthedocs.io/en/latest/articles/auto_clean.html) 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)`\n\n## Development installation\n\nFor a development installation:\n\n```bash\ngit clone https://github.com/paddymul/buckaroo.git\ncd buckaroo\n#we need to build against 3.6.5, jupyterlab 4.0 has different JS typing that conflicts\n# the installable still works in JL4\npip install build twine pytest sphinx polars mypy jupyterlab==3.6.5 pandas-stubs geopolars pyarrow\npip install -ve .\n```\n\nEnabling development install for Jupyter notebook:\n\n\nEnabling development install for JupyterLab:\n\n```bash\njupyter labextension develop . --overwrite\n```\n\nNote 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.\n`\n### Developing the JS side\n\nThere are a series of examples of the components in [examples/ex](./examples/ex).\n\n\n\nInstructions\n```bash\nnpm install\nnpm run dev\n```\n\n\n### UV Instructions\n```sh\ncd buckaroo\nuv venv\nsource ~/buckaroo/.venv/bin/activate\nuv sync -q\n\n```\n\n### adding a package\n```sh\ncd ~/buckaroo\nuv add $PACKAGE_NAME\n```\n\n#### adding a package to a subgroup \n```sh\ncd ~/buckaroo\nuv add --group $GROUP_NAME --quiet $PACKAGE_NAME\n```\n\n### Release instructions\n[github release instructions](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository)\n\n```bash\nupdate CHANGELOG.md\ngit commit -m \"updated changelog for release $VERSION_NUMBER\"\ngit tag $VERSION_NUMBER # no leading v in the version number\ngit push origin tag $VERSION_NUMBER\n```\nnavigate to [create new buckaroo release](https://github.com/paddymul/buckaroo/releases/new)\nFollow instructions\n\n\n\n\n## Contributions\n\nWe :heart: contributions.\n\nHave 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?\n\nWe welcome [issue reports](../../issues); be sure to choose the proper issue template for your issue, so that we can be sure you're providing the necessary information.\n\n\n",
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