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