列名与预期的行值不匹配

Question

data = pd.DataFrame({'id':[1,  2 , 3],

                   'question': ['first country visited?', 'first city visited?' , 'two cities we love?'],
                   'answer1': ['UK', 'Paris', 'CA'],
                   'answer2': ['US', 0.4, 'Paris'],
                   'answer3': ['CA', 'London', 'London'],
                   'correct': [['UK'], [0.4], ['London, Paris, 0.4']]
                   })

数据：

    id  question                 answer1    answer2   answer3   correct
0   1   first country visited?      UK       US        CA       [UK]
1   2   first city visited?         Paris   0.4       London    [0.4]
2   3   two cities we love?         CA     Paris      London    [London, Paris, 0.4]

我正在创建一个新列来检查是否在 answer1 或 answer2 或 answer3 列中找到正确列中的值。

cols = data.filter(like='answer').columns
data['correct_column'] = data[cols].apply(lambda s: ','.join((m:=s.isin(data.loc[s.name, 'correct']))[m].index), axis=1)

输出：

id  question                   answer1    answer2   answer3       correct                  correct_column
0   1   first country visited?        UK        US        CA        [UK]                     answer1
1   2   first city visited?           Paris     0.4       London    [0.4                     answer2
2   3   two cities we love?           CA        Paris     London    [London, Paris, 0.4]    

我在第三行得到一个空值。我已经尝试了几个小时，但我的原始数据没有成功！有没有更好的方法来实现这一目标？考虑我原始 df 中的不同数据类型，例如 floats、int & Str ..

Answer 1

这是一个更长的版本：

cols = data.filter(like='answer').columns

def app(s):
    (m:=[s[col] in (data.loc[s.name, 'correct']) for col in cols])
    return ', '.join(cols[m])

data['correct_column'] = data[cols].apply(app, axis=1)
data['correct_column']

和更短的版本可以完成同样的事情：

data['correct_column'] = data[cols].apply(lambda s: ', '.join(cols[(m:=[s[col] in (data.loc[s.name, 'correct']) for col in cols])]) , axis=1)
data['correct_column']

这将产生：

0             answer1
1             answer2
2    answer2, answer3
Name: correct_column, dtype: object