Pandas 将函数应用于列中的唯一值

Question

我一直被熊猫问题困住，我似乎无法弄清楚。 我有一个这样的数据框：

ref, value, rule, result, new_column
a100, 25, high, fail, nan
a100, 25, high, pass, nan
a100, 25, medium, fail, nan
a100, 25, medium, pass, nan
a101, 15, high, fail, nan
a101, 15, high, pass, nan
a102, 20, high, pass, nan

我想使用以下伪代码向此数据框添加一个新列

对于 ref 中的每个唯一值，如果为 result = fail，则对于具有相同“ref”值的所有后续行，则为 new_column = no。

这就是新数据框的外观。

ref, value, rule, result, new_column
a100, 25, high, fail, no
a100, 25, high, pass, no
a100, 25, medium, fail, no
a100, 25, medium, pass, no
a101, 15, high, fail, no
a101, 15, high, pass, no
a102, 20, high, pass, yes

我已经设法做到以下几点：

ref, value, rule, result, new_column
a100, 25, high, fail, no
a100, 25, high, pass, yes

这是通过df.loc 函数实现的。 但我需要将函数应用于唯一值，而不是每一行。

Answer 1

我觉得你可以用transform:

print (df)
    ref  value    rule result  new_column
0  a100     25    high   pass         NaN
1  a100     25    high   fail         NaN
2  a100     25  medium   fail         NaN
3  a100     25  medium   pass         NaN
4  a101     15    high   fail         NaN
5  a101     15    high   pass         NaN
6  a102     20    high   pass         NaN

df['new_column']=df.groupby('ref')['result']
                   .transform(lambda x: 'no' if ((x=='fail').any()) else 'yes')
print (df)
    ref  value    rule result new_column
0  a100     25    high   pass         no
1  a100     25    high   fail         no
2  a100     25  medium   fail         no
3  a100     25  medium   pass         no
4  a101     15    high   fail         no
5  a101     15    high   pass         no
6  a102     20    high   pass        yes

感谢Jon Clementsreplace 提供另一个解决方案：

df['new_column'] = df.groupby('ref')['result']
                     .transform(lambda L: (L == 'fail').any())
                     .replace({True: 'no', False: 'yes'})

print (df)
    ref  value    rule result new_column
0  a100     25    high   pass         no
1  a100     25    high   fail         no
2  a100     25  medium   fail         no
3  a100     25  medium   pass         no
4  a101     15    high   fail         no
5  a101     15    high   pass         no
6  a102     20    high   pass        yes