call_report.fca.get_domain_dataset_codes#

call_report.fca.get_domain_dataset_codes(*, domain_dataset: FCADomainDataset | str, dataframe_type: None = None) → NativeDataFrame[source]#

call_report.fca.get_domain_dataset_codes(*, domain_dataset: FCADomainDataset | str, dataframe_type: Literal['pandas']) → pandas.DataFrame

call_report.fca.get_domain_dataset_codes(*, domain_dataset: FCADomainDataset | str, dataframe_type: Literal['pyarrow_table']) → pyarrow.Table

call_report.fca.get_domain_dataset_codes(*, domain_dataset: FCADomainDataset | str, dataframe_type: Literal['polars_dataframe']) → polars.DataFrame

call_report.fca.get_domain_dataset_codes(*, domain_dataset: FCADomainDataset | str, dataframe_type: Literal['polars_lazyframe']) → polars.LazyFrame

Return a curated domain dataset’s codes and what each one means.

The reshaped frame keys its rows by code, so this is the lookup that turns those codes into names. is_total marks a code the source reports as a subtotal, which a caller aggregating over codes has to exclude.

Parameters:

domain_datasetFCADomainDataset or str: The domain dataset to look up. A string is matched case-insensitively.
dataframe_type{“pandas”, “pyarrow_table”, “polars_lazyframe”, “polars_dataframe”}, optional: The dataframe type to convert the result to as a final step. Leave this None (the default) to get back whatever backend call_report.config.get_config currently has configured.

Returns:

NativeDataFrame: Columns code, label, and is_total, one row per code, of the configured backend or of dataframe_type if it was supplied.

Raises:

DomainDatasetNotFoundError: If domain_dataset does not name a shipped dataset.

Examples

>>> from call_report.fca import get_domain_dataset_codes
>>> codes = get_domain_dataset_codes(domain_dataset="loan_portfolio")
>>> codes.shape
(13, 3)
>>> row = codes[codes["code"] == 110].iloc[0]
>>> row["label"], bool(row["is_total"])
('Agribusiness', False)