divide/split a row into multiple rows in pandas data frame

Question

I have the following dataset;

     Subject                                         Student ID  Student Number
0      Cit11  [S95, S96, S97, S98, S99, S100, S101, S102, S1...              45
1  EngLang11  [S95, S96, S97, S98, S99, S100, S101, S102, S1...              45
2   EngLit11  [S110, S111, S112, S113, S114, S115, S116, S11...              21
3      Fre11  [S95, S96, S97, S99, S100, S101, S102, S103, S...              26
4      Ger11  [S114, S115, S116, S117, S118, S124, S125, S12...              13
5      His11  [S95, S96, S97, S98, S99, S100, S101, S102, S1...              45
6      Mat11  [S95, S96, S97, S98, S99, S100, S101, S102, S1...              45
7      Spa11  [S95, S97, S98, S99, S100, S102, S103, S104, S...              23

where 'Student Number' is the total number of 'Student ID' in each 'Subject' .

Let's say the maximum 'Student Number' should be 30 (classroom_Max_Capacity returns the value), and the following code returns indexes where 'Student Number' exceeds the maximum number.

idx = filtered_Group[filtered_Group['Student Number'] > classroom_Max_Capacity].index.tolist()
Output: [0, 1, 5, 6]

I am wondering if I can split those rows into two by changing 'Subject' name and 'Student ID' to fit the maximum student number; for example,

Subject                                         Student ID  Student Number
0      Cit11_1  [S95, S96, S97, S98, S99, S100, S101, S102, S1...              30
1      Cit11_2  [S110, S115, S116...                                           15
2  EngLang11_1  [S95, S96, S97, S98, S99, S100, S101, S102, S1...              30
3  EngLang11_2  [S110, S115, S116...                                           15
4     EngLit11  [S110, S111, S112, S113, S114, S115, S116, S11...              21
5        Fre11  [S95, S96, S97, S99, S100, S101, S102, S103, S...              26
6        Ger11  [S114, S115, S116, S117, S118, S124, S125, S12...              13
7      His11_1  [S95, S96, S97, S98, S99, S100, S101, S102, S1...              30
8      His11_2  [S110, S115, S116...                                           15
9      Mat11_1  [S95, S96, S97, S98, S99, S100, S101, S102, S1...              30
10     Matt11_2 [S110, S115, S116...                                           15
11       Spa11  [S95, S97, S98, S99, S100, S102, S103, S104, S...              23

Is this even possible by not specifically writing modified 'Subject' name to add in the data frame?

--edit

I attempted to solve the problem by doing something like;

filtered = filtered_Group.iloc[idx]

student_list = filtered['Student ID'].explode().str.split(', ')
subject_list = filtered['Subject']

for i in idx:
    for number in range(classroom_Max_Capacity):
        df.append({temp_subject_list[i]: temp_student_list[number]})

But of course, this doesn't work so any help would be much appreciated.

Answer 1

You can use explode and enumerate the students, and then groupby :

# randome data
np.random.seed(1)
df = pd.DataFrame({
    'Subject': list('abcdef'),
    'Student Number': [np.random.choice(np.arange(20), 
                                        np.random.randint(3,10),
                                        replace=None)
                       for _ in range(6)]
})

# maximum number of students allowed
max_students = 5

# output:
(df.explode('Student Number')
   .assign(section=lambda x: x.groupby('Subject')
                              .cumcount()//max_students + 1
          )
   .groupby(['Subject','section'])
   ['Student Number'].agg([list, 'count'])
)

Output:

                                list  count
Subject section                            
a       1        [15, 10, 3, 18, 17]      5
        2                [14, 16, 4]      3
b       1           [3, 2, 5, 8, 17]      5
        2                   [13, 10]      2
c       1        [11, 18, 2, 12, 16]      5
        2                 [17, 0, 4]      3
d       1               [16, 19, 11]      3
e       1         [16, 5, 4, 12, 15]      5
        2                       [19]      1
f       1          [18, 17, 3, 0, 1]      5
        2                [9, 14, 13]      3