code: def generate_map_from_dataset(self, l_dataset):
          
          l_map = []
          headers = l_dataset.get_header()
          print(headers)
          for row_num, col in enumerate(headers):
              if col != '':
                  l_map.append('column:name:' + str(row_num) + '=' + l_dataset.force_to_string(col))
          
          for row_num, col in enumerate(headers):
              if col != '':
                  vals = l_dataset.get_distinct_values_from_cols([col])
                  l_map.append('column:count:distinct:' + col + '=' + str(len(vals[0])) )
                  
          for row_num, col in enumerate(headers):
              if col != '':
                  col_vals = l_dataset.count_unique_values(row_num, col, 10)
                  for val_num, v in enumerate(col_vals):
                      l_map.append('column:topvalues:' + col + ':' + str(val_num) + '='  + v )
                  #l_map.append('column:values:top5:' + str(row_num) + '=' + col_vals)
                  
                  
          return l_map
docs: creates a map file (in the standard CSV format) based on 
          columns of a dataset. 
          1. read column names, lookup names in list
          2. read column content, get highest match of distinct values 
              from ontology lists (eg, Years, countries, cities, ages)