def __clean_bespoke_indicators(missing_value):
    """
    A number of indicators in the WHO GHO database use spaces instead of commas,
    e.g. a number is formatted as 10 453 000 000.00 instead of 10,453,000,000.00
    or even 10453000000.00 as a float type
    
    This function handles those cases upfront, rather than editing them later
    via regex. This isn't particularly needed, as we don't need to fill these
    columns for these indicators

    Parameters
    ----------
    missing_value : pd.DataFrame()
        The ingested data where 'NumericValue' is missing

    Returns
    -------
    missing_value : pd.DataFrame()
        The same frame, where the 'Value' entries for these specific cases has
        had ' ' replaced by ','
    """
    targets = ['HIV_0000000022',
               'Rev_excise',
               'Rev_govt_total',
               'Rev_imp_other',
               'Rev_VAT',
               'R_Price_lowest_cost_estimate',
               'R_Price_premium_estimate']
               
    unchanged = missing_value.loc[~missing_value['IndicatorCode'].isin(targets)]
    messy_df = missing_value.loc[missing_value['IndicatorCode'].isin(targets)]
    messy_df['Value'] = messy_df['Value'].str.strip()
    messy_df['Value'] = messy_df['Value'].str.replace(' ',',')
    messy_df['Value'] = messy_df['Value'].replace({'Not,applicable':'Not applicable',
                                               'No,data':'No data',
                                               'Data,not,available':'Data not available'})
    missing_value = unchanged.append(messy_df)    
    return missing_value