# comet-ml/kangas

🦘 Explore multimedia datasets at scale

Repository: https://github.com/comet-ml/kangas
Canonical: https://ross.abutalabs.com/products/kangas
Homepage: https://github.com/comet-ml/kangas/wiki
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: data-analysis, data-exploration, dataframe, datagrid, machine-learning
Last push: 2024-12-07T13:52:25+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1421, "days_push": 634, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1077, forks 50 (observed 2026-08-28T04:03:29.587496+00:00)

## What it is
Kangas is a Python library and tool for exploring, analyzing, and visualizing large-scale multimedia datasets via its DataGrid format. It offers a Python API for logging large tables of data plus a visual interface for querying, grouping, sorting, and filtering millions of rows, with built-in computer vision support.

## Use cases
- explore large multimedia datasets with images and bounding boxes
- visualize and filter millions of rows of data in a UI
- analyze computer vision datasets with labels and metadata
- load CSV or data files into an interactive datagrid
- run a local server to browse a dataset in a browser
- inspect ML training data from a notebook

## When to choose
- you need to explore image-heavy or multimedia datasets at scale
- you want a fast visual UI for grouping, sorting, and filtering large tables
- you work in Jupyter notebooks and want integrated dataset exploration
- you need built-in visualization of bounding boxes and CV labels

## When to avoid
- you need a general-purpose dataframe library for heavy numeric computation (use pandas or polars)
- you need production dashboards or BI reporting tools
- your data is purely tabular text without multimedia
- you need actively developed features - the project has seen limited recent activity

## Facets
- artifact type: library
- maturity: maintenance
- function: data-visualization, analytics, computer-vision, data-science
- domain: data-science, machine-learning, data-visualization, computer-vision
- platform: python, cli, cross-platform
- tags: dataframe, datagrid, multimedia-datasets, exploratory-data-analysis, jupyter, web-server

## Member repositories
- comet-ml/kangas (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:29.587496+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:53:14.086472+00:00, confidence not recorded.
  - readme: https://github.com/comet-ml/kangas (fetched 2026-08-28T04:03:29.587496+00:00, sha f602a77427aa)
  - homepage: https://github.com/comet-ml/kangas/wiki (fetched 2026-08-29T12:54:45.697691+00:00, sha dd47c0f1287b)
  - registry_pypi: https://pypi.org/pypi/kangas/json (fetched 2026-08-29T12:54:45.700330+00:00, sha 30ddbbf35935)
- Data as of 2026-08-30T08:39:29.467469+00:00.