Pandas 根据 GroupBy 值在另一列中填充值

Question

How can we create a new column, ie error_flag_type , whose value depends upon the groupby at student_id

The data looks like this:

student_id	subject_id	error	team
1	1	yes	A
1	2		A
1	3		A
1		yes	A
2	4		B
2	5		B
2		yes	B
3	6		B
3	7		B
3	8		B
3	9	yes	B
4	10		A
4	11		A
4	12		A

And step by step operation looks like this:

1/4) If both student_id and subject_id has value yes in the error column

student_id	subject_id	error	team
1	1	yes	A
1	2		A
1	3		A
1		yes	A

Thus, outcome:

student_id	error	team	error_flag_type
1	yes	A	both

2/4) If only subject_id has value yes _ in the error column

student_id	subject_id	error	team
2	4		B
2	5		B
2		yes	B

Becomes:

student_id	error	team	error_flag_type
2	yes	B	student_id_level

3/4)

student_id	subject_id	error	team
3	6		B
3	7		B
3	8		B
3	9	yes	B

Becomes

student_id	error	team	error_flag_type
3	yes	B	subject_id_level

4/4)

student_id	subject_id	error	team
4	10		A
4	11		A
4	12		A

Becomes

student_id	error	team	error_flag_type
4	no_error	A	no_error

Looking at all the individual steps together as:

student_id	error	team	error_flag_type
1	yes	A	both
2	yes	B	student_id_level
3	yes	B	subject_id_level
4	no_error	A	no_error

Answer 1

I would approach this using ordered Categorical , this will give you flexibility to chose the order of the errors/flags. Then a simple groupby.agg with max gives you the highest warning:

m1 = df['student_id'].eq(df['subject_id'])
m2 = df['error'].eq('yes')

(df.assign(error=pd.Categorical(df['error'].fillna('no_error'),
                                categories=['no_error', 'yes'],
                                ordered=True),
           error_flag_type=pd.Categorical(np.select([m1&m2, m2],
                                                    ['both', 'student_id_level'],
                                                    'no_error'
                                                   ),
                                          categories=['no_error',
                                                      'student_id_level',
                                                      'both'],
                                          ordered=True
                                         )
          )
    .groupby('student_id', as_index=False)
    .agg({'error': 'max', 'team': 'first', 'error_flag_type': 'max'})
)

output:

   student_id     error team   error_flag_type
0           1       yes    A              both
1           2       yes    B  student_id_level
2           3       yes    B  student_id_level
3           4  no_error    A          no_error