lamindb.core.DataFrameCurator¶

class lamindb.core.DataFrameCurator(df, columns=FieldAttr(Feature.name), categoricals=None, using_key=None, verbosity='hint', organism=None, sources=None, exclude=None, check_valid_keys=True)¶

Bases: BaseCurator

Curation flow for a DataFrame object.

See also Curator.

Parameters:

df (DataFrame) – 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.
using_key (str | None, default: None) – The reference instance containing registries to validate against.
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": ln.ULabel.name
...     }
... )

Attributes¶

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

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

Methods¶

add_new_from(key, organism=None, **kwargs)¶

Add validated & new categories.

Parameters:

key (str) – The key referencing the slot in the DataFrame from which to draw terms.
organism (str | None, default: None) – The organism name.
**kwargs – Additional keyword arguments to pass to create new records

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

lookup(using_key=None, public=False)¶

Lookup categories.

Parameters:: using_key (str | None, default: None) – The instance where the lookup is performed. if “public”, the lookup is performed on the public reference.
Return type:: CurateLookup

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

Save the validated DataFrame and metadata.

Parameters:

description (str | None, default: None) – Description of the DataFrame object.
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 revision family.
revises (Artifact | None, default: None) – Previous version of the artifact. Triggers a revision.
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(organism=None)¶

Validate variables and categorical observations.

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

Parameters:: organism (str | None, default: None) – The organism name.
Return type:: bool
Returns:: Whether the DataFrame is validated.