GroupBy Column1, then get all elements with the first/last element on Column2 (Python)

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],'survey_id':[1,1,1,1,2,2,3,4,4,4,5,5,6,6,7,8,8,9,9,9,9,10,10,11,11,12,12],
              'answer':["no","yes","no","no","yes","no","no","yes","no","yes","no","no","yes","no","no","no","yes","yes","yes","no","no","no","yes","no","yes","no","yes"]}))
df

    user_id     survey_id   answer
0   1   1   no
1   1   1   yes
2   1   1   no
3   1   1   no
4   1   2   yes
5   1   2   no
6   1   3   no
7   2   4   yes
8   2   4   no
9   2   4   yes
10  2   5   no
11  2   5   no
12  2   6   yes
13  2   6   no
14  3   7   no
15  3   8   no
16  3   8   yes
17  3   9   yes
18  3   9   yes
19  3   9   no
20  3   9   no
21  4   10  no
22  4   10  yes
23  4   11  no
24  4   11  yes
25  4   12  no
26  4   12  yes

I want to group by user_id, then get the first element of survey_id, and get all elements related to this selection

df_head=
    user_id     survey_id   answer
0   1   1   no
1   1   1   yes
2   1   1   no
3   1   1   no
4   2   4   yes
5   2   4   no
6   2   4   yes
7   3   7   no
8   4   10  no
9   4   10  yes

In the same way I want to group by user_id, then get the last element of survey_id, and get all elements related to this selection

df_tail=
    user_id     survey_id   answer
0   1   3   no
1   2   6   yes
2   2   6   no
3   3   9   yes
4   3   9   yes
5   3   9   no
6   3   9   no
7   4   12  no
8   4   12  yes

Is there a quick groupby command to get this? I can do this by merging dataframes but I think there is some better way to do this in less command lines. Thank you in advance

Answer

Solution with no merging:

df_head = df[df.survey_id.eq(df.groupby('user_id').transform('min').survey_id)]

result:

    user_id  survey_id answer
0         1          1     no
1         1          1    yes
2         1          1     no
3         1          1     no
7         2          4    yes
8         2          4     no
9         2          4    yes
14        3          7     no
21        4         10     no
22        4         10    yes

df_tail = df[df.survey_id.eq(df.groupby('user_id').transform('max').survey_id)]

result:

    user_id  survey_id answer
6         1          3     no
12        2          6    yes
13        2          6     no
17        3          9    yes
18        3          9    yes
19        3          9     no
20        3          9     no
25        4         12     no
26        4         12    yes

Idea is to calculate min / max of survey_id per user_id and compare it to survey_id at row level of df. Please note that original index of dataframe is preserved. If You need new index just add at the end:

df_head = df_head.reset_index(drop = True)
df_tail = df_tail.reset_index(drop = True)