https://github.com/intake/akimbo

For when your data won't fit in your dataframe
https://github.com/intake/akimbo

awkward-array cudf data dataframe pandas polars python

Last synced: 5 months ago
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For when your data won't fit in your dataframe

• Host: GitHub
• URL: https://github.com/intake/akimbo
• Owner: intake
• License: bsd-3-clause
• Created: 2022-07-07T19:34:45.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2025-06-25T19:25:59.000Z (7 months ago)
• Last Synced: 2025-08-21T09:19:46.614Z (5 months ago)
• Topics: awkward-array, cudf, data, dataframe, pandas, polars, python
• Language: Python
• Homepage: https://akimbo.readthedocs.io
• Size: 419 KB
• Stars: 48
• Watchers: 5
• Forks: 6
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# akimbo

**For when your data won't fit in your dataframe**

[![Tests](https://github.com/intake/akimbo/actions/workflows/pypi.yml/badge.svg)](https://github.com/intake/akimbo/actions/workflows/pypi.yml)

[![Documentation Status](https://readthedocs.org/projects/akimbo/badge/?version=latest)](https://akimbo.readthedocs.io/en/latest/?badge=latest)

Akimbo provides fast, vectorized processing of nested, ragged data

in dataframes, using the ``.ak`` accessor.

### Features

- numpy-like API for slicing and accessing data deep in nested structures

- apply any function, ufunc or aggregation at any level

- use with different backends: pandas, polars (lazy and eager),

  dask-dataframe, pyspark, duckDB and cuDF

  exactly the same way (more backends may come in the future)

- CPU and GPU processing and support for ``numba``-jit

- attach object-like behaviours to your record (sub)structures

See the [quick

start](https://akimbo.readthedocs.io/en/latest/quickstart.html)

in the documentation for an introduction to akimbo.

Acknowledgements

----------------

Support for this work was provided by NSF grant [OAC-2103945](https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103945).

Work on this repository is supported in part by:

"Anaconda, Inc. - Advancing AI through open source."

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