lamindb.curators.core.DataFrameCatManager¶

class lamindb.curators.core.DataFrameCatManager(df, columns_field=FieldAttr(Feature.name), columns_names=None, categoricals=None, sources=None, index=None, slot=None, maximal_set=False)¶

Bases: object

Manage categoricals by updating registries.

This class is accessible from within a DataFrameCurator via the .cat attribute.

If you find non-validated values, you have two options:

new values found in the data can be registered via DataFrameCurator.cat.add_new_from() add_new_from()
non-validated values can be accessed via DataFrameCurator.cat.add_new_from() non_validated() and addressed manually

Attributes¶

property categoricals: list[Feature]¶: The categorical features.

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

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.
**kwargs – Additional keyword arguments to pass to create new records

lookup(public=False)¶

Lookup categories.

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

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.

Return type:: bool