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 that displays up to 10k rows, is sortable, has value formatting, and scrolls. 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.

• Buckaroo full tour Notebook

run pip install buckaroo in a notebook execute the following to see Buckaroo

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

Buckaroo works in the following notebook environments

• jupyter lab (version >=3.6.0)
• jupyter notebook (version >=7.0)
• VS Code notebooks (with extra install)
• Google colab (with special initiation code)

Buckaroo works with the following DataFrame libraries

• pandas (version >=1.3.5)
• polars optional
• geopandas optional

Buckaroo has extensive docs and tests, the best way to learn about the system is from feature example videos on youtube

• Extending Buckaroo
• Styling Buckaroo
• GeoPandas Support

The following notebooks must executed in an environemnt with Buckaroo installed.

• Full Tour Start here. This gives a broad overview of Buckaroo's features.
• Histogram Demo Explantion of the embedded histograms of Buckaroo.
• Styling Gallery Examples of all of the different formatters and styling available for the table
• 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
• GeoPandas with Bucakroo

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. You no longer have to use df.head() to poke at portions of your data.

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 for every column give you a very quick overview of the distribution of values, including uniques and N/A.

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.

Buckaroo will display entire DataFrames up to 10k rows. Displaying more than that would run into performance problems that would make display too slow. When a DataFrame has more than 10k rows, Buckaroo samples a random set of 10k rows, and also adds in the rwos with the 5 most extreme values for each column.

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.

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.

Buckaroo has a simple low code UI with python code gen. This view can be toggled by clicking on the 0 below the λ icon.

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)

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. `

There are a series of examples of the components in examples/ex.

Instructions

npm install
npm run dev

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 here; be sure to choose the proper issue template for your issue, so that we can be sure you're providing the necessary information.