anndata - Annotated data
anndata is a Python package for handling annotated data matrices in memory and on disk, positioned between pandas and xarray. anndata offers a broad range of computationally efficient features including, among others, sparse data support, and lazy operations. A separate data loader package, annbatch, offers minibatch data loading functionality for applications ranging from linear models to foundation models, scaling all the way up from small in-memory matrices to terabyte-scale disk-backed anndata datasets.
- Discuss development on GitHub.
- Read the documentation.
- Ask questions on the scverse Discourse.
- Install via
pip install anndataorconda install anndata -c conda-forge. - See Scanpy's documentation for usage related to single cell data. anndata was initially built for Scanpy.
anndata is part of the scverse® project (website, governance) and is fiscally sponsored by NumFOCUS. If you like scverse® and want to support our mission, please consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs.
Public API
Our public API is documented in the API section of these docs.
We cannot guarantee the stability of our internal APIs, whether it's the location of a function, its arguments, or something else.
In other words, we do not officially support (or encourage users to do) something like from anndata._core import AnnData as _core is both not documented and contains a leading underscore.
However, we are aware that many users do use these internal APIs and thus encourage them to open an issue or migrate to the public API.
That is, if something is missing from our public API as documented, for example a feature you wish to be exported publicly, please open an issue.
Citation
If you use anndata in your work, please cite the anndata publication as follows:
anndata: Annotated data
Isaac Virshup, Sergei Rybakov, Fabian J. Theis, Philipp Angerer, F. Alexander Wolf
JOSS 2024 Sep 16. doi: 10.21105/joss.04371.
You can cite the scverse publication as follows:
The scverse project provides a computational ecosystem for single-cell omics data analysis
Isaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor Sturm, Adam Gayoso, Ilia Kats, Mikaela Koutrouli, Scverse Community, Bonnie Berger, Dana Pe’er, Aviv Regev, Sarah A. Teichmann, Francesca Finotello, F. Alexander Wolf, Nir Yosef, Oliver Stegle & Fabian J. Theis
Nat Biotechnol. 2023 Apr 10. doi: 10.1038/s41587-023-01733-8.
Metadata
Release files for anndata 0.13.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| anndata-0.13.4.tar.gz | 2.4 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| anndata-0.13.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.6 MB
Release files / anndata-0.13.4.tar.gz
| Download URL | anndata-0.13.4.tar.gz |
|---|---|
| Size | 2.4 MB |
| Tags | Source |
|
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Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
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Transparency logRelease files / anndata-0.13.4-py3-none-any.whl
| Download URL | anndata-0.13.4-py3-none-any.whl |
|---|---|
| Size | 190.2 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 18, 2026.
Transparency log
