如何从包含多列数据的 CSV 文件中合并特定列并计算各个选项出现的比例？

import pandas as pd from collections import Counter  # 读取 CSV 文件 df = pd.read_csv('test.csv', encoding='cp932')  # 获取 CMD 列作为主键 index_list_cmd = df['cmd'].tolist()  # 计算 CMD 出现的次数 idxLstCmd_cnt = Counter(list(index_list_cmd))  # 去除重复的 CMD index_list_cmd = sorted(set(index_list_cmd), key=list(index_list_cmd).index)  # 计算每个 CMD 出现的次数 cntLst = [] for lst_i in range(len(index_list_cmd)):     cntLst.append(idxLstCmd_cnt[index_list_cmd[lst_i]])  # 合并 OPT 列 index_list_opt_idx = []  # 删除之前保存的文件 if os.path.isfile(useRateF):     os.remove(useRateF)  # 遍历 CMD for cmd_i in range(len(index_list_cmd)):     # 获得 OPT 列的 index     index_list_opt_idx = list(df['opt'][df['cmd'] == index_list_cmd[cmd_i]].index.values)      index_list_opt = []      # 遍历 OPT     for opt_i in range(len(index_list_opt_idx)):         try:             index_list_opt.append(df.loc[int(index_list_opt_idx[opt_i]), 'opt'])         except:             print(index_list_cmd[cmd_i])             print(index_list_opt_idx[opt_i])             print(opt_i)      # 列表转字符串     str_idxLstOpt = ' '.join(index_list_opt)      # 字符串转回列表     lst_idxLstOpt = str_idxLstOpt.split(' ')      # 计算 OPT 出现的比例     for tag in sorted(set(lst_idxLstOpt), key=lst_idxLstOpt.index):         with open(useRateF, 'a', encoding='utf-8_sig') as uf:             uf.writelines(str(index_list_cmd[cmd_i]) + ',' + tag + ',' + str(lst_idxLstOpt.count(tag)/cntLst[cmd_i]))             uf.writelines(' ')