Data Mapping and Cleanup

`pdb.apply_mapping_and_cleanup`

Applies an external mapping file to convert IDs in one column of a DataFrame, creates a new column, and optionally performs cleanup on specified columns.

Parameter	Type	Description
`df`	pd.DataFrame	The input DataFrame to be mapped.
`mapping_file_path`	str	Path to the external mapping file (e.g., HGNC file).
`mapping_sep`	str	Separator (delimiter) used in the mapping file (e.g., '\t').
`origin_col`	str	Name of the column in `df` containing the IDs to be mapped (the key).
`target_col`	str	Name of the new column to be created with the mapped values.
`origin_ref_col`	str	Name of the column in the mapping file corresponding to the key.
`target_ref_col`	str	Name of the column in the mapping file corresponding to the value.
`cleanup_subset_cols`	Optional[List[str]]	Columns to use for the final dropna operation. Defaults to only `target_col`.
`origin_col_dtype`	Union[type,str]	Data type hint for preprocessing the `origin_col` in `df`. (Currently unused internally, defaults to str conversion).
`origin_ref_dtype`	Union[type,str]	Data type hint for preprocessing the `origin_ref_col` in the mapping file. (Currently unused internally, defaults to str conversion).

Returns

pd.DataFrame: The DataFrame with the new target_col added and rows with missing mapped values removed.

Usage

# Map UniProt IDs in 'gene' column to HGNC symbols in new 'symbol' column
df_mapped = pdb.apply_mapping_and_cleanup(
    df=hmdb_gc_df,
    mapping_file_path="hgnc_complete_set.txt",
    mapping_sep="\t",
    origin_col="gene",
    target_col="symbol",
    origin_ref_col="UniProt ID(s)",
    target_ref_col="Approved symbol",
    cleanup_subset_cols=["gene", "symbol", "compound"],
)