Question

我一直被困在熊猫问题上，我似乎无法弄明白。我有这样的数据框：

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

为此数据框添加新列

对于ref中的每个唯一值，如果result = fail，则new_column = no用于相同＆＃34; ref＆＃34;的所有后续行。值。

这就是新数据框的样子。

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

我设法做的是以下内容：

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

这是通过df.loc功能实现的。但我需要将函数应用于唯一值，而不是每行。

Answer 1

我认为您可以使用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 Clements了解replace的其他解决方案：

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

Pandas将函数应用于列中的唯一值

1 个答案: