# holoviz/datashader

Quickly and accurately render even the largest data.

Repository: https://github.com/holoviz/datashader
Canonical: https://ross.abutalabs.com/products/datashader
Homepage: http://datashader.org
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: holoviz, datashader, data-visualizations, rasterization
Last push: 2026-08-19T11:59:54+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 72, longevity 100
- inputs: {"age_days": 3906, "days_push": 14, "days_rel": 106, "gap_med": 69, "n_releases_24m": 6}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3558, forks 376 (observed 2026-08-28T04:08:09.756048+00:00)

## What it is
Datashader is a Python data rasterization pipeline that renders very large datasets into fixed-size images by projecting, aggregating, and shading data into grids. It is part of the HoloViz ecosystem and uses Numba, Dask, and CUDA to scale across CPU cores and GPUs.

## Use cases
- plot millions of data points without subsampling
- render large scatter plots of geographic coordinates
- visualize distributions of huge datasets as images
- rasterize large time series or line charts
- create density heatmaps from big data
- visualize large datasets in a browser dashboard

## When to choose
- your dataset is too large for conventional plotting libraries and you are subsampling or chunking just to plot it
- you need faithful, accurate representation of the full data distribution
- you want GPU or distributed rendering of large data
- you are already using HoloViz tools like HoloViews, hvPlot, or Panel

## When to avoid
- you have only hundreds or thousands of points where standard tools like Bokeh or Matplotlib suffice
- you need per-point interactivity, metadata, or hover on individual data points
- you need a CLI tool (the CLI was removed in 0.19.0)

## Facets
- artifact type: library
- maturity: active
- function: data-visualization, image-processing, gpu-computing
- domain: data-visualization, data-science, big-data
- platform: python, cross-platform
- tags: rasterization, numba, dask, holoviz, large-datasets, plotting, geospatial, gpu

## Member repositories
- holoviz/datashader (main) score 89

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:09.756048+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 2026-08-29T18:34:14.752140+00:00, confidence not recorded.
  - readme: https://github.com/holoviz/datashader (fetched 2026-08-28T04:08:09.756048+00:00, sha da46ec663ae8)
  - homepage: http://datashader.org (fetched 2026-08-29T09:28:01.222800+00:00, sha 442251b559e3)
  - site_page: https://datashader.org/about.html (fetched 2026-08-29T09:28:01.237462+00:00, sha 4ff7d6c22b29)
  - registry_pypi: https://pypi.org/pypi/datashader/json (fetched 2026-08-29T09:28:01.239181+00:00, sha 8db24099c821)
  - site_page: https://datashader.org/releases.html (fetched 2026-08-29T09:28:01.231938+00:00, sha 4cbfc9f08043)
  - site_page: https://datashader.org/FAQ.html (fetched 2026-08-29T09:28:01.235359+00:00, sha f216c4a8dec1)
- Data as of 2026-08-30T08:39:29.467469+00:00.