如何用for循环匹配DataFrame索引值与元素值并替换列内容

最优方案：用Pandas向量化操作（不推荐for循环） #

Pandas原生向量化操作比for循环效率高得多，优先推荐这种方法：

1. 先把wiki_adj的ind_num转为整数类型（匹配poss的整数索引），再将ind_num设为df的索引：

import pandas as pd

# 整理原wiki_adj的换行问题，确保代码可运行
wiki_adj = {
    'ind_num': ['11','20','25','27','35','41','42','48','49','51','54','56','60','61','62'],
    'designation': [
        'Illegal (Decriminalized)', 'Illegal (unenforced)', 'Illegal (unenforced)',
        'Illegal (unenforced)', 'Illegal (unenforced)', 'Legal', 'Legal',
        'Illegal', 'Illegal', 'Illegal (Decriminalized)', 'Illegal (Decriminalized)',
        'Illegal (unenforced)', 'Illegal', 'Illegal (Decriminalized)', 'Illegal (unenforced)'
    ]
}

df = pd.DataFrame(wiki_adj)
# 转换索引类型并设置为df的索引
df['ind_num'] = df['ind_num'].astype(int)
df.set_index('ind_num', inplace=True)

# 匹配索引并替换boss列
poss['boss'] = poss.index.map(df['designation'])

如果只想替换匹配到的行，保留未匹配行的原有值，可用update：

# 生成索引-职位的映射字典
designation_map = df['designation'].to_dict()
# 仅更新poss中存在匹配索引的boss值
poss['boss'].update(poss.index.map(designation_map))

你要的for循环实现方法 #

如果一定要用for循环遍历，代码如下：

import pandas as pd

# 整理wiki_adj数据
wiki_adj = {
    'ind_num': ['11','20','25','27','35','41','42','48','49','51','54','56','60','61','62'],
    'designation': [
        'Illegal (Decriminalized)', 'Illegal (unenforced)', 'Illegal (unenforced)',
        'Illegal (unenforced)', 'Illegal (unenforced)', 'Legal', 'Legal',
        'Illegal', 'Illegal', 'Illegal (Decriminalized)', 'Illegal (Decriminalized)',
        'Illegal (unenforced)', 'Illegal', 'Illegal (Decriminalized)', 'Illegal (unenforced)'
    ]
}

df = pd.DataFrame(wiki_adj)

# 遍历df的每一行
for idx, row in df.iterrows():
    # 将ind_num转为整数，匹配poss的索引类型
    ind_num = int(row['ind_num'])
    # 检查索引是否存在于poss中
    if ind_num in poss.index:
        # 替换对应行的boss值
        poss.loc[ind_num, 'boss'] = row['designation']

注：如果poss的索引是字符串类型，无需转换ind_num的类型，直接用row['ind_num']匹配即可。

内容的提问来源于stack exchange，提问作者dane w