Reshaping Pandas groupby data row values into column headers

Question

I am trying to extract grouped row data from a pandas groupby object so that the primary group data ('course' in the example below) act as a row index, the secondary grouped row values act as column headers ('student') and the aggregate values as the corresponding row data ('score').

So, for example, I would like to transform:

import pandas as pd
import numpy as np

data = {'course_id':[101,101,101,101,102,102,102,102] ,
    'student_id':[1,1,2,2,1,1,2,2],
    'score':[80,85,70,60,90,65,95,80]}

df = pd.DataFrame(data, columns=['course_id', 'student_id','score'])

Which I have grouped by course_id and student_id:

group = df.groupby(['course_id', 'student_id']).aggregate(np.mean)
g = pd.DataFrame(group)

Into something like this:

data = {'course':[101,102],'1':[82.5,77.5],'2':[65.0,87.5]}
g3 = pd.DataFrame(data, columns=['course', '1', '2'])

I have spent some time looking through the groupby documentation and I have trawled stack overflow and the like but I'm still not sure how to approach the problem. I would be very grateful if anyone would suggest a sensible way of achieving this for a largish dataset.

Many thanks!

• Edited: to fix g3 example typo

Answer 1

>>> g.reset_index().pivot('course_id', 'student_id', 'score')
student_id     1     2
course_id             
101         82.5  65.0
102         77.5  87.5