cellxgene-census
API to facilitate the use of the CZ CELLxGENE Discover Census. For more information about the API and the project visit https://github.com/chanzuckerberg/cellxgene-census/
Decision gist · record as of 2026-08-14
Yes, if you work with single-cell genomics and need programmatic access to the CELLxGENE Census. The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and installs with low friction. It is appropriate for research and production use. Install only if you have network access to the Census service and require Python 3.10 or later.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and network connectivity to the CZ CELLxGENE Discover Census service.
- Low friction installation with a pure-Python wheel.
- Actively maintained as of 2026-08-04 with recent releases.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions—suitable for most research and production contexts.
last release 2026-06-24 (51 days) · last repo commit 2026-08-04 · 129 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 76,903 downloads/mo, #14,581 on PyPI
Alternatives
Verify before relying
pip install cellxgene_census
import cellxgene_census
with cellxgene_census.open_soma() as census:
cell_metadata = cellxgene_census.get_obs(
census,
"homo_sapiens",
value_filter="cell_type == 'neuron'",
column_names=["cell_type", "tissue"]
)- Whether the package supports offline access or requires live network connectivity to the Census service
- Performance characteristics when querying large cell populations or complex filters
- Data versioning and reproducibility guarantees across different Census releases
What it is and what it does
cellxgene_census is a client library for accessing the CZ CELLxGENE Discover Census, a curated repository of single-cell genomics data. It abstracts the complexity of querying and retrieving cell metadata and gene expression measurements from a large distributed dataset, allowing researchers to filter cells by biological attributes (tissue, cell type, disease status, etc.) and fetch results as pandas DataFrames or other standard formats.
The package depends on tiledbsoma for efficient sparse array access, anndata for in-memory representation of single-cell data, and s3fs for cloud storage integration. It is designed for bioinformaticians and computational biologists who need programmatic access to reference single-cell datasets without managing raw files locally.
Use it for
- Query cell metadata across tissues and organisms to identify cells matching specific biological criteria for downstream analysis
- Retrieve gene expression matrices for a subset of cells and genes to train machine learning models on reference data
- Integrate Census data with local analysis pipelines by fetching pre-filtered cell populations as DataFrames
- Explore cell type distributions and tissue composition across the human and mouse reference atlases
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with single-cell genomics and need programmatic access to the CELLxGENE Census.
The package is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and installs with low friction. It is appropriate for research and production use. Install only if you have network access to the Census service and require Python 3.10 or later.
Install
cellxgene-census on PyPI
Before you install
Low friction installation with a pure-Python wheel. Actively maintained as of 2026-08-04 with recent releases. Requires Python 3.10 or later.
Requires Python 3.10 or later and network connectivity to the CZ CELLxGENE Discover Census service.
License in practice
MIT license permits commercial and private use with minimal restrictions—suitable for most research and production contexts.
Quickstart
pip install cellxgene_census
import cellxgene_census
with cellxgene_census.open_soma() as census:
cell_metadata = cellxgene_census.get_obs(
census,
"homo_sapiens",
value_filter="cell_type == 'neuron'",
column_names=["cell_type", "tissue"]
)Verify before relying
- Whether the package supports offline access or requires live network connectivity to the Census service
- Performance characteristics when querying large cell populations or complex filters
- Data versioning and reproducibility guarantees across different Census releases
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagestiledbsomaanndatanumpyrequeststyping_extensionss3fs |
| Maintenance | Actively maintained 51 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 76,903 / month, #14,581 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: MacOS :: MacOS XOperating System :: POSIX :: LinuxProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/Engineering :: Bio-Informatics |
Evidence: cellxgene_census-1.18.0-py3-none-any.whl
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See also tiledbsoma · somacore · cg · bionty · biothings-client · scanpy · pydeseq2 · pyranges · refgenie · census