Python 创建列"；地位；，基于另一列中不同元素的存在，对于每一对（“用户id”和“日期”）

我只想创建下表中显示的“status”列。如果“问题”列中每对“用户id”和“日期”有4个元素（“a”、“b”、“c”、“d”），则本列指定标签“完成”或“不完整”

---

您可以使用

GroupBy.transform

和

set

来删除重复项，然后使用

len

函数来计算生成的唯一元素，这将允许我们查看“question”列中每对“user\u id”和“date”是否有4个元素（“a”、“b”、“c”、“d”）

尽量不要删除temp列，以清楚地看到它的作用。它将显示每个用户id每个日期回答了多少问题

df=(pd.DataFrame({'user_id':[1,1,1,1,1,1,1,2,2,2,2,2,2,2,3,3,3,3,3,3,3,4,4,4,4,4,4],
                  'date':["01-01-2020","01-01-2020","01-01-2020","01-01-2020","02-01-2020","02-01-2020","03-01-2020","04-01-2020",
                          "04-01-2020","04-01-2020","05-01-2020","05-01-2020","06-01-2020","06-01-2020","07-01-2020","08-01-2020",
                          "08-01-2020","09-01-2020","09-01-2020","09-01-2020","09-01-2020","10-01-2020","10-01-2020","11-01-2020",
                          "11-01-2020","12-01-2020","12-01-2020"],
    'question':["a","b","c","d","a","b","a","a","b","c","a","b","a","b","a","a","b","a","b","c","d","a","b","a","b","a","b"],
    'status': ["Complete","Complete","Complete","Complete","Incomplete","Incomplete","Incomplete","Incomplete","Incomplete","Incomplete",
               "Incomplete","Incomplete","Incomplete","Incomplete","Incomplete","Incomplete","Incomplete","Complete","Complete","Complete",
               "Complete","Incomplete","Incomplete","Incomplete","Incomplete","Incomplete","Incomplete"]}))
display_full(df)

|    |   user_id | date       | question   | status     |
|---:|----------:|:-----------|:-----------|:-----------|
|  0 |         1 | 01-01-2020 | a          | Complete   |
|  1 |         1 | 01-01-2020 | b          | Complete   |
|  2 |         1 | 01-01-2020 | c          | Complete   |
|  3 |         1 | 01-01-2020 | d          | Complete   |
|  4 |         1 | 02-01-2020 | a          | Incomplete |
|  5 |         1 | 02-01-2020 | b          | Incomplete |
|  6 |         1 | 03-01-2020 | a          | Incomplete |
|  7 |         2 | 04-01-2020 | a          | Incomplete |
|  8 |         2 | 04-01-2020 | b          | Incomplete |
|  9 |         2 | 04-01-2020 | c          | Incomplete |
| 10 |         2 | 05-01-2020 | a          | Incomplete |
| 11 |         2 | 05-01-2020 | b          | Incomplete |
| 12 |         2 | 06-01-2020 | a          | Incomplete |
| 13 |         2 | 06-01-2020 | b          | Incomplete |
| 14 |         3 | 07-01-2020 | a          | Incomplete |
| 15 |         3 | 08-01-2020 | a          | Incomplete |
| 16 |         3 | 08-01-2020 | b          | Incomplete |
| 17 |         3 | 09-01-2020 | a          | Complete   |
| 18 |         3 | 09-01-2020 | b          | Complete   |
| 19 |         3 | 09-01-2020 | c          | Complete   |
| 20 |         3 | 09-01-2020 | d          | Complete   |
| 21 |         4 | 10-01-2020 | a          | Incomplete |
| 22 |         4 | 10-01-2020 | b          | Incomplete |
| 23 |         4 | 11-01-2020 | a          | Incomplete |
| 24 |         4 | 11-01-2020 | b          | Incomplete |
| 25 |         4 | 12-01-2020 | a          | Incomplete |
| 26 |         4 | 12-01-2020 | b          | Incomplete |

import numpy as np
import pandas as pd

df['temp'] = df.groupby(['user_id','date'])['question'].transform(lambda x: len(set(x)))
df['status_new'] = np.where(df['temp'] <4,'Incomplete','Complete')
df.drop('temp',axis=1,inplace=True)

    user_id        date question      status  status_new
0         1  01-01-2020        a    Complete    Complete
1         1  01-01-2020        b    Complete    Complete
2         1  01-01-2020        c    Complete    Complete
3         1  01-01-2020        d    Complete    Complete
4         1  02-01-2020        a  Incomplete  Incomplete
5         1  02-01-2020        b  Incomplete  Incomplete
6         1  03-01-2020        a  Incomplete  Incomplete
7         2  04-01-2020        a  Incomplete  Incomplete
8         2  04-01-2020        b  Incomplete  Incomplete
9         2  04-01-2020        c  Incomplete  Incomplete
10        2  05-01-2020        a  Incomplete  Incomplete
11        2  05-01-2020        b  Incomplete  Incomplete
12        2  06-01-2020        a  Incomplete  Incomplete
13        2  06-01-2020        b  Incomplete  Incomplete
14        3  07-01-2020        a  Incomplete  Incomplete
15        3  08-01-2020        a  Incomplete  Incomplete
16        3  08-01-2020        b  Incomplete  Incomplete
17        3  09-01-2020        a    Complete    Complete
18        3  09-01-2020        b    Complete    Complete
19        3  09-01-2020        c    Complete    Complete
20        3  09-01-2020        d    Complete    Complete
21        4  10-01-2020        a  Incomplete  Incomplete
22        4  10-01-2020        b  Incomplete  Incomplete
23        4  11-01-2020        a  Incomplete  Incomplete
24        4  11-01-2020        b  Incomplete  Incomplete
25        4  12-01-2020        a  Incomplete  Incomplete
26        4  12-01-2020        b  Incomplete  Incomplete