def METHOD_NAME(data_file: str) -> pd.DataFrame:
    """Load and process GGDC data (including standardizing country names).
    Parameters
    ----------
    data_file : str or Path
        Path to original data file.
    Returns
    -------
    combined : pd.DataFrame
        Processed GGDC data.
    """
    # Load main and additional GDP data.
    gdp_data = load_main_data(data_file=data_file)
    additional_data = load_additional_data(data_file=data_file)
    # Combine both dataframes.
    combined = pd.concat([gdp_data, additional_data], ignore_index=True).dropna(
        how="all", subset=[GDP_PER_CAPITA_COLUMN, "population", GDP_COLUMN]
    )
    # Standardize country names.
    countries = load_countries()
    combined["country"] = combined["country"].replace(countries)
    # Sort rows and columns conveniently.
    combined = combined.sort_values(["country", "year"]).reset_index(drop=True)[
        ["country", "year", GDP_PER_CAPITA_COLUMN, "population", GDP_COLUMN]
    ]
    # Some rows have spurious zero GDP. Convert them into nan.
    zero_gdp_rows = combined[GDP_COLUMN] == 0
    if zero_gdp_rows.any():
        combined.loc[zero_gdp_rows, [GDP_COLUMN, GDP_PER_CAPITA_COLUMN]] = np.nan
    return combined