# holoviz/holoviews

With Holoviews, your data visualizes itself.

Repository: https://github.com/holoviz/holoviews
Canonical: https://ross.abutalabs.com/products/holoviews
Homepage: https://holoviews.org
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: holoviz, holoviews, plotting
Last push: 2026-08-26T17:37:13+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 4501, "days_push": 7, "days_rel": 9, "gap_med": 77.5, "n_releases_24m": 9}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2906, forks 419 (observed 2026-08-28T04:07:29.059438+00:00)

## What it is
HoloViews is an open-source Python library for data analysis and visualization that lets you annotate data with dimensions and let it visualize itself in very few lines of code. It supports Matplotlib, Bokeh, and Plotly backends and integrates with NumPy, pandas, dask, xarray, and Jupyter.

## Use cases
- visualize pandas dataframes with minimal code
- create interactive plots in jupyter notebooks
- explore high-dimensional datasets declaratively
- build reproducible research visualizations
- switch between matplotlib bokeh and plotly backends
- make interactive dashboards with widgets and streams

## When to choose
- you want declarative, annotation-based plotting with minimal code
- you work in Jupyter with the PyData ecosystem (pandas, xarray, dask)
- you need interactive visualizations with multiple plotting backends
- you want reproducible research workflows

## When to avoid
- you need pixel-perfect static publication graphics with fine-grained control
- you want a simple one-function plotting wrapper (consider hvPlot instead)
- your project is outside the Python/PyData ecosystem

## Facets
- artifact type: library
- maturity: stable
- function: data-visualization, charts
- domain: data-visualization, data-science, analytics
- platform: python, cross-platform, windows
- tags: plotting, jupyter, bokeh, matplotlib, plotly, declarative-visualization, pydata, interactive-visualization, linux, macos

## Member repositories
- holoviz/holoviews (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:29.059438+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 2026-08-30T07:34:26.366144+00:00, confidence not recorded.
  - readme: https://github.com/holoviz/holoviews (fetched 2026-08-28T04:07:29.059438+00:00, sha dd382baa9350)
  - homepage: https://holoviews.org (fetched 2026-08-29T09:49:49.461151+00:00, sha 0aa135228ec8)
  - site_page: https://holoviews.org/features.html (fetched 2026-08-29T09:49:49.470395+00:00, sha a1c975a7eca0)
  - site_page: https://holoviews.org/about.html (fetched 2026-08-29T09:49:49.480797+00:00, sha bf294eac42cf)
  - site_page: https://holoviews.org/install.html (fetched 2026-08-29T09:49:49.482407+00:00, sha e46d3d4e80a2)
  - site_page: https://holoviews.org/reference/features/index.html (fetched 2026-08-29T09:49:49.483910+00:00, sha 54fbfe5c0c05)
  - site_page: https://holoviews.org/reference/features/bokeh/index.html (fetched 2026-08-29T09:49:49.485300+00:00, sha d6f648cdb69f)
  - site_page: https://holoviews.org/reference/features/bokeh/Scalebar.html (fetched 2026-08-29T09:49:49.486733+00:00, sha 7ea1ced73365)
  - registry_pypi: https://pypi.org/pypi/holoviews/json (fetched 2026-08-29T09:49:49.488147+00:00, sha c50e3d950973)
  - site_page: https://holoviews.org/releases.html (fetched 2026-08-29T09:49:49.472794+00:00, sha 242fa66fa44c)
- Data as of 2026-08-30T08:39:29.467469+00:00.