scverse/scanpy
Some data may be outdated — see provenance labels.
Single-cell analysis in Python. Scales to >100M cells. observed · 2026-09-28
Health v2 · maintenance only
92/100
- Activity 97
- Release rhythm 80
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-10-21. Adoption (stars, forks) is never an input.
- gap_med: 58
- age_days: 3551
- days_rel: 54
- days_push: 23
- n_releases_24m: 12
Adoption not part of the score
2574 stars · 781 forks observed · 2026-09-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
Scanpy is a scalable Python toolkit for analyzing single-cell gene expression data, built alongside the anndata data structure. It provides preprocessing, visualization, clustering, trajectory inference, and differential expression testing, efficiently handling datasets of over a million cells with experimental Dask support for out-of-memory data.
Use cases
- analyze single-cell RNA-seq gene expression data in Python
- cluster and visualize millions of single cells
- perform trajectory inference on single-cell datasets
- run differential expression testing between cell groups
- preprocess and quality-control single-cell count matrices
- analyze single-cell datasets too large to fit in memory
- build scRNA-seq analysis pipelines with anndata
When to choose
- you need a mature, widely adopted Python toolkit for single-cell transcriptomics
- your datasets scale to hundreds of thousands or millions of cells
- you want preprocessing, clustering, visualization, and differential expression in one package
- you prefer Python over R-based single-cell tools like Seurat
- you want GPU-accelerated workflows via compatible ecosystem tools like rapids-singlecell
When to avoid
- you need bulk RNA-seq or non-single-cell genomics analysis
- you require a stable internal API - only the documented public API is supported
- you need out-of-memory processing at scale - Dask compatibility is still experimental
- your workflow is R-centric and you prefer Bioconductor ecosystems
Facets
library · maturity stable
data-science machine-learning data-visualization analytics bioinformatics data-science machine-learning data-visualization python cross-platform single-cell transcriptomics genomics anndata clustering trajectory-inference differential-expression scverse dask bioinformatics gpu
6 sources
- readme: https://github.com/scverse/scanpy · fetched 2026-09-28 · 0fff44b44915
- homepage: https://scanpy.scverse.org · fetched 2026-08-29 · 8693e0b62fdc
- site_page: https://scanpy.scverse.org/en/stable/installation.html · fetched 2026-08-29 · c3f0aa4c6e35
- site_page: https://scanpy.scverse.org/en/stable/dev/documentation.html · fetched 2026-08-29 · f38b9df0cebc
- site_page: https://scverse.org/about/roles · fetched 2026-08-29 · d2e0a43e216d
- registry_pypi: https://pypi.org/pypi/scanpy/json · fetched 2026-08-29 · 9d5b068408f0
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| scverse/scanpy | main | 92 |
For agents
Data as of 2026-10-19T08:05:51.212177+00:00 · Report a problem