lamindb.curators.DataFrameCatManager¶

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

Returns:

A curator object.

Example:

import lamindb as ln
import bionty as bt

curator = ln.curators.DataFrameCatManager(
    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, verbosity='hint', *, sample_metadata_key='sample')¶

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