python - 基于分组数据的标签列

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 都有与之关联的许多行)，如果该 ID 的标签已回答与其任何行关联，则所有与该 ID 关联的行应标记为已回答。如果与一个 id 关联的所有行都有一个未回答的标签，所有的行都应该被标记为未回答(这是目前发生的情况)

这是我写的代码:

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

使用GroupBy.transform与 GroupBy.any每组至少测试一个 answered 并通过 DataFrame.loc 设置值:

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

或者通过另一列筛选所有 data__id 并通过 Series.isin 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