lamindb.curators.DataFrameCatManager¶

class lamindb.curators.DataFrameCatManager(df, columns=FieldAttr(Feature.name), categoricals=None, verbosity='hint', organism=None, sources=None, exclude=None)¶

Bases: CatManager

Curation flow for a DataFrame object.

See also Curator.

Parameters:

df (DataFrame | Artifact) – The DataFrame object to curate.
columns (DeferredAttribute, default: FieldAttr(Feature.name)) – The field attribute for the feature column.
categoricals (dict[str, DeferredAttribute] | None, default: None) – A dictionary mapping column names to registry_field.
verbosity (str, default: 'hint') – The verbosity level.
organism (str | None, default: None) – The organism name.
sources (dict[str, Record] | None, default: None) – A dictionary mapping column names to Source records.
exclude (dict | None, default: None) – A dictionary mapping column names to values to exclude from validation. When specific Source instances are pinned and may lack default values (e.g., “unknown” or “na”), using the exclude parameter ensures they are not validated.

Returns:

A curator object.

Examples

>>> import bionty as bt
>>> curator = ln.Curator.from_df(
...     df,
...     categoricals={
...         "cell_type_ontology_id": bt.CellType.ontology_id,
...         "donor_id": ULabel.name
...     }
... )

Attributes¶

property categoricals: dict¶: Return the columns fields to validate against.

property non_validated: dict[str, list[str]]¶: Return the non-validated features and labels.

Class methods¶

classmethod from_anndata(data, var_index, categoricals=None, obs_columns=FieldAttr(Feature.name), verbosity='hint', organism=None, sources=None)¶

Return type:: AnnDataCatManager

classmethod from_df(df, categoricals=None, columns=FieldAttr(Feature.name), verbosity='hint', organism=None)¶

Return type:: DataFrameCatManager

classmethod from_mudata(mdata, var_index, categoricals=None, verbosity='hint', organism=None)¶

Return type:: MuDataCatManager

classmethod from_spatialdata(sdata, var_index, categoricals=None, organism=None, sources=None, exclude=None, verbosity='hint', *, sample_metadata_key='sample')¶

classmethod from_tiledbsoma(experiment_uri, var_index, categoricals=None, obs_columns=FieldAttr(Feature.name), organism=None, sources=None, exclude=None)¶

Return type:: TiledbsomaCatManager

Methods¶

add_new_from(key, **kwargs)¶

Add validated & new categories.

Parameters:

key (str) – The key referencing the slot in the DataFrame from which to draw terms.
organism – The organism name.
**kwargs – Additional keyword arguments to pass to create new records

add_new_from_columns(organism=None, **kwargs)¶

clean_up_failed_runs()¶: Clean up previous failed runs that don’t save any outputs.

lookup(public=False)¶

Lookup categories.

Parameters:: public (bool, default: False) – If “public”, the lookup is performed on the public reference.
Return type:: CurateLookup

save_artifact(*, key=None, description=None, revises=None, run=None)¶

Save an annotated artifact.

Parameters:

key (str | None, default: None) – A path-like key to reference artifact in default storage, e.g., "myfolder/myfile.fcs". Artifacts with the same key form a version family.
description (str | None, default: None) – A description.
revises (Artifact | None, default: None) – Previous version of the artifact. Is an alternative way to passing key to trigger a new version.
run (Run | None, default: None) – The run that creates the artifact.

Return type:

Artifact

Returns:

A saved artifact record.

standardize(key)¶

Replace synonyms with standardized values.

Modifies the input dataset inplace.

Parameters:: key (str) – The key referencing the column in the DataFrame to standardize.
Return type:: None

validate()¶

Validate variables and categorical observations.

This method also registers the validated records in the current instance: - from public sources

Parameters:: organism – The organism name.
Return type:: bool
Returns:: Whether the DataFrame is validated.