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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def agg_conc(original_countries, aggregates, missing_countries='test', merge_multiple_string='_&_', log_missing_countries=None, log_merge_multiple_strings=None, coco=None, as_dataframe='sparse', original_countries_class=None):
 """ Builds an aggregation concordance dict, vec or matrix Parameters original_countries: list or str List of countries to aggregated, also accepts and valid column name of CountryConverter.data aggregates: list of dict or str List of aggregation information. This can either be dict mapping the names of 'original_countries' to aggregates, or a valid column name of CountryConverter.data Aggregation happens in order given in this parameter. Thus, country assigned to an aggregate are not re-assigned by the following aggregation information. missing_countries: str, boolean, None Entry to fill in for countries in 'original_countries' which do not appear in 'aggregates'. str: Use the given name for all missing countries True: Use the name in original_countries for missing countries False: Skip these countries None: Use None for these countries merge_multiple_string: str or None, optional If multiple correspondance entries are given in one of the aggregates join them with the given string (default: '_&_'. To skip these enries, pass None. log_missing_countries: function, optional This function is called with country is country is in 'original_countries' but missing in all 'aggregates'. For example, pass lambda x: logging.error('Country {} missing'.format(x)) to log errors for such countries. Default: do nothing log_merge_multiple_strings: function, optional Function to call for logging multiple strings, see log_missing_countries Default: do nothing coco: instance of CountryConverter, optional CountryConverter instance used for the conversion. Pass a custom one if additional data is needed in addition to the custom country converter file. If None (default), the bare CountryConverter is used as_dataframe: boolean or st, optional If False, output as OrderedDict. If True or str, output as pandas dataframe. If str and 'full', output as a full matrix, otherwise only two collumns with the original and aggregated names are returned. original_countries_class: str, optional Valid column name of CountryConverter.data. This parameter is needed if a list of countries is passed to 'orginal_countries' and strings corresponding to data in CountryConverter.data are used subsequently. Can be omitted otherwise. Returns ------- OrderedDict or DataFrame (defined by 'as_dataframe') """


    if coco is None:
        coco = CountryConverter()

    if type(original_countries) is str:
        original_countries_class = original_countries
        original_countries = coco.data[original_countries].values
    else:
        original_countries_class = (original_countries_class or
                                    coco._get_input_format_from_name(
                                        original_countries[0]))

    if type(aggregates) is not list:
        aggregates = [aggregates]

    correspond = OrderedDict.fromkeys(original_countries)

    for agg in aggregates:
        if type(agg) is str:
            agg = coco.get_correspondance_dict(original_countries_class,
                                               agg)
        for country in original_countries:
            if correspond.get(country) is None:
                try:
                    entry = agg[country]
                except KeyError:
                    entry = None

                if type(entry) is list:
                    if 1 < len(entry):
                        if merge_multiple_string:
                            entry = merge_multiple_string.join([
                                str(e) for e in entry])
                        else:
                            entry = None
                        if log_merge_multiple_strings:
                            log_merge_multiple_strings(country)
                    else:
                        entry = entry[0]

                correspond[country] = entry

    for country in original_countries:
        if correspond.get(country) is None:
            if missing_countries is True:
                correspond[country] = country
            elif missing_countries is False:
                del correspond[country]
            else:
                correspond[country] = missing_countries
            if log_missing_countries:
                log_missing_countries(country)

    if as_dataframe:
        correspond = pd.DataFrame.from_dict(
            correspond, orient='index').reset_index()
        correspond.columns = ['original', 'aggregated']
        if ((type(as_dataframe) is str) and
                (as_dataframe[0].lower() == 'f')):
            _co_list = correspond.original
            correspond['val'] = 1
            correspond = correspond.set_index(
                ['original', 'aggregated']).unstack().fillna(0)['val']
            correspond = correspond.loc[_co_list]

    return correspond