API

The central class:

AnnData([X, obs, var, uns, obsm, varm, …])

An annotated data matrix.

Combining

Combining AnnData objects. See also the section on concatenation.

concat(adatas, *[, axis, join, merge, …])

Concatenates AnnData objects along an axis.

Reading anndata’s native file format .h5ad.

read_h5ad(filename[, backed, as_sparse, …])

Read .h5ad-formatted hdf5 file.

Reading other file formats.

`read_csv`(filename[, delimiter, …])	Read `.csv` file.
`read_excel`(filename, sheet[, dtype])	Read `.xlsx` (Excel) file.
`read_hdf`(filename, key)	Read `.h5` (hdf5) file.
`read_loom`(filename, *[, sparse, cleanup, …])	Read `.loom`-formatted hdf5 file.
`read_mtx`(filename[, dtype])	Read `.mtx` file.
`read_text`(filename[, delimiter, …])	Read `.txt`, `.tab`, `.data` (text) file.
`read_umi_tools`(filename[, dtype])	Read a gzipped condensed count matrix from umi_tools.
`read_zarr`(_, *__)

Writing to anndata’s native file format .h5ad.

AnnData.write([filename, compression, …])

Write .h5ad-formatted hdf5 file.

Writing to other formats.

`AnnData.write_csvs`(dirname[, skip_data, sep])	Write annotation to `.csv` files.
`AnnData.write_loom`(filename[, write_obsm_varm])	Write `.loom`-formatted hdf5 file.
`AnnData.write_zarr`(store[, chunks])	Write a hierarchical Zarr array store.

Warning

API’s in the experimenal module are currently in development and subject to change at any time.

Two classes for working with batched access to collections of many AnnData objects or h5ad files. In paritcular, for pytorch-based models.

`experimental.AnnCollection`(adatas[, …])	Lazily concatenate AnnData objects along the `obs` axis.
`experimental.AnnLoader`(adatas[, batch_size, …])	PyTorch DataLoader for AnnData objects.

Raised whenever initializing an object or assigning a property changes the type of a part of a parameter or the value being assigned.