# facebookresearch/hiplot

HiPlot makes understanding high dimensional data easy

Repository: https://github.com/facebookresearch/hiplot
Canonical: https://ross.abutalabs.com/products/hiplot
Homepage: https://facebookresearch.github.io/hiplot/
Language: TypeScript
License: MIT
License Family: permissive
Archived: true
Last push: 2024-01-10T07:43:27+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2490, "days_push": 966, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2799, forks 149 (observed 2026-08-28T04:07:22.514936+00:00)

## What it is
HiPlot is a lightweight interactive visualization library from Facebook Research for exploring high-dimensional data using parallel coordinates plots and other graphical views. It works as a Python library in Jupyter notebooks, a web server, a Streamlit component, and a CLI that renders standalone HTML.

## Use cases
- visualize hyperparameter sweep results to find which parameters affect the metric
- explore correlations in high-dimensional CSV data interactively
- display experiment data in a jupyter notebook
- render standalone HTML reports of experiment data from the command line
- add interactive parallel plots to a streamlit app
- compare multiple machine learning experiments side by side

## When to choose
- you need to analyze hyperparameter tuning or sweep results
- you want interactive parallel coordinates plots in notebooks or a web server
- you work with tabular experiment data in Python and need quick visual exploration

## When to avoid
- you need publication-quality static statistical charts
- you need general-purpose dashboards or BI-style visualizations
- your data is not tabular/experimental in nature

## Facets
- artifact type: library
- maturity: maintenance
- function: data-visualization, web-framework, cli
- domain: data-visualization, machine-learning, data-science
- platform: python, cli, cross-platform
- tags: parallel-coordinates, interactive-plots, hyperparameter-analysis, jupyter, streamlit, high-dimensional-data, web-server

## Member repositories
- facebookresearch/hiplot (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:22.514936+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 2026-08-30T08:14:36.758672+00:00, confidence not recorded.
  - readme: https://github.com/facebookresearch/hiplot (fetched 2026-08-28T04:07:22.514936+00:00, sha 732b9a8f9dde)
  - homepage: https://facebookresearch.github.io/hiplot/ (fetched 2026-08-29T09:54:56.974255+00:00, sha 67bb2632189e)
  - registry_npm: https://registry.npmjs.org/hiplot (fetched 2026-08-29T09:54:56.983512+00:00, sha c0ec91b1afbc)
- Data as of 2026-08-30T08:39:29.467469+00:00.