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.