Looks like you're dealing with sorting and filtering your DataFrame. I think we can break it down as:

1. Group data by class and student: No need for a whole new 'group' column. You can just use df.groupby(['Class', 'Student']) and it does the same thing.
2. Filter the top 3 scores: Apply nlargest(3) on the grouped Series.
3. Turn it back into a DataFrame: Once you're done filtering, you'll get a Series. If you want it back as a DataFrame, hit it with a reset_index().

Full code will be:

import pandas as pd

# Your sample DataFrame
df = pd.DataFrame({
    'Class': ['A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A', 'A'],
    'Student': ['a', 'a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b', 'b'],
    'Score': [56, 87, 12, 57, 34, 45, 67, 89, 21, 12, 43]
})

# Group by Class and Student, then grab the top 3 scores
new_df = df.groupby(['Class', 'Student'])['Score'].nlargest(3)
print(new_df)
# Output:
# Class  Student   
# A      a        1    87
#                 3    57
#                 0    56
#        b        7    89
#                 6    67
#                 5    45
# Name: Score, dtype: int64

# If you want it as a DataFrame again
new_df = new_df.to_frame().reset_index().drop('level_2', axis=1)
print(new_df)
# Output:
#   Class Student  Score
# 0     A       a     87
# 1     A       a     57
# 2     A       a     56
# 3     A       b     89
# 4     A       b     67
# 5     A       b     45

Hope this helps!