code: def gen_df_site(
          list_csv_in=list_table,
          url_base=url_repo_input_site)->pd.DataFrame:
      '''Generate description info of supy output results as a dataframe
      Parameters
      ----------
      path_csv_out : str, optional
          path to the output csv file (the default is 'df_output.csv')
      list_csv_in : list, optional
          list of file names for csv files with meta info (the default is url_repo_input_site, which is defined at the top of this file)
      url_base : URL, optional
          URL to the input dir of repo base (the default is url_repo_input, which is defined at the top of this file)
      Returns
      -------
      pd.DataFrame
          full path to the output csv file
      '''
      list_url_table = [
          url_base/table for table in list_csv_in
      ]
      try:
          df_var_info = pd.concat([pd.read_csv(f) for f in list_url_table])
      except:
          for url in list_url_table:
              if not url.get().ok:
                  print(f'{url} not existing!')
      else:
          df_var_info_x = df_var_info\
              .drop(['No.', 'Use'], axis=1)\
              .set_index('Column Name')
          df_var_info_x.index = df_var_info_x.index.map(
              lambda x: x.replace('`', ''))
          dict_var_full = sp.supy_load.exp_dict_full(
              sp.supy_load.dict_var2SiteSelect)
          dict_var_ref_suews = {
              k: extract_var_suews(dict_var_full, k)
              for k in dict_var_full
          }
          df_var_ref_suews = pd.DataFrame(
              {k: ', '.join(dict_var_ref_suews[k])
               for k in dict_var_ref_suews},
              index=[0]).T.rename({
                  0: 'SUEWS-related variables'
              }, axis=1)
          dict_var_desc = {
              k: '\n'.join(df_var_info_x.loc[v].values.flatten())
              for k, v in dict_var_ref_suews.items()
          }
          df_var_desc = pd.DataFrame(dict_var_desc, index=[0]).T\
              .rename(columns={0: 'Description'})
          df_var_dim = gen_df_dim(df_init_sample)
          df_var_site_raw = pd.concat(
              [df_var_dim, df_var_desc, df_var_ref_suews],
              axis=1, sort=False)
          df_var_site = df_var_site_raw.filter(items=set_input, axis=0).dropna()
          return df_var_site