anndata.acc.MetaAcc

class anndata.acc.MetaAcc(dim, *, ref_class)

Bases: RefAcc[R, str | None, MuData | AnnData]

Reference accessor for arrays from metadata containers (A.obs/A.var).

Examples

You can refer to columns or the index in the way you would access them in a DataFrame:

>>> from anndata.acc import A, MetaAcc
>>> assert isinstance(A.obs, MetaAcc)
>>> A.obs["type"]
A.obs['type']
>>> A.var.index
A.var.index

Attributes

index

Index AdRef, i.e. A.obs.index or A.var.index.

dim: Literal['obs', 'var']

Axis this accessor refers to, e.g. A.obs.dim == 'obs'.

ref_class: type[AdRef[Hashable, MuData | AnnData]]

Methods

dims(k, /)

Get along which dimensions the referenced array is.

Return type:

Collection[Literal['obs', 'var']]

get(data, k=NO_IDX, /)

Overloads:

• self, data (MuData | AnnData) → DataFrameLike

• self, data (MuData | AnnData), k (str | None) → pd.api.extensions.ExtensionArray | XVariable

Get the indexed array from the AnnData object at idx.

When idx is omitted (i.e., idx is NO_IDX), return the full array one level up instead. This has the same semantics as the AdRef path but one level up: adata[A.obs] returns the full DataFrame and adata[A.obsm["pca"]] the full numpy.ndarray. These both have defined shape-like properties (or awkward.Array), unlike, for example, obsm or similar.

idx_repr(k)

Get a string representation of the index.

Return type:

str

isin(data, idx=None)

Check if the referenced array is in the AnnData object.

Return type:

bool

process_idx(k, /)

Return type:

str | None