根据分组数据标记列

Question

我正在尝试创建一个由每个ID的唯一值组成的列（每个ID都有与其关联的许多行），如果该ID的任何行都与已回答的标签相关联，则所有与该ID关联的行都应标记为回答。如果与ID关联的所有行都具有未回答的标记，则所有行都应标记为未回答（当前发生的情况）

这是我编写的代码：

将numpy导入为np

conds = [file.data__answered_at.isna(),file.data__answered_at.notna()]
choices = ["not answered","answered"]
file['call_status'] = np.select(conds,choices,default=np.nan)

 data__id   call_status       rank
  1            answered        1
  1          not_answered      2
  1            answered        3
  2          not_answered      1
  2             answered       2
  3          not_answered      1
  4            answered        1
  4          not_answered      2
  5          not_answered      1
  5          not_answered      2

在这种情况下，期望的结果将是

   data__id   call_status       rank
  1            answered        1
  1            answered        2
  1            answered        3
  2            answered        1
  2            answered        2
  3          not_answered      1
  4            answered        1
  4            answered        2
  5          not_answered      1
  5          not_answered      2

Answer 1

每组至少要GroupBy.transform与with一起使用，并通过以下方式设置值：GroupBy.anyansweredDataFrame.loc

mask = df['call_status'].eq('answered').groupby(df['data__id']).transform('any')

或data__id通过另一列过滤所有内容并通过Series.isin以下方式测试成员资格：

mask = df['data__id'].isin(df.loc[df['call_status'].eq('answered'), 'data__id'].unique())

df.loc[mask, 'call_status'] = 'answered'
print (df)
   data__id   call_status  rank
0         1      answered     1
1         1      answered     2
2         1      answered     3
3         2      answered     1
4         2      answered     2
5         3  not_answered     1
6         4      answered     1
7         4      answered     2
8         5  not_answered     1
9         5  not_answered     2