apply function in pandas hierarchical index

I have a pandas dataframe as below.

df = pd.DataFrame({'team' : ['A', 'B', 'A', 'B', 'A', 'B'],
                   'tiger' : [87, 159, 351, 140, 72, 119],
                   'lion' : [1843, 3721, 6905, 1667, 2865, 1599],
                   'bear' : [1.9, 3.3, 6.3, 2.3, 1.2, 4.1],
                   'points' : [425, 425, 441, 441, 1048, 1048]})

grouped = df.groupby(['points', 'team'])[['tiger', 'lion', 'bear']].median()

print(grouped)

                tiger       lion    bear
points team                             
425    A     87.00000 1843.00000 1.90000
       B    159.00000 3721.00000 3.30000
441    A    351.00000 6905.00000 6.30000
       B    140.00000 1667.00000 2.30000
1048   A     72.00000 2865.00000 1.20000
       B    119.00000 1599.00000 4.10000

I would like to take the difference between teams A and B for each of the animal (tiger, lion, bear) and points levels. So the difference between team A (87) and B (159) within points 425 and tiger. I’m not sure how to do this with an hierarchical index. It would look something like below. Thanks.

   points  tiger  lion     bear
0     425     72  1878  1.40000
1     441   -211 -5238 -4.00000
2    1048     47 -1266  2.90000

>Solution :

You can swaplevel and slice:

grouped = (df.groupby(['points', 'team'])[['tiger', 'lion', 'bear']].median()
            .swaplevel()
          )

grouped.loc['A']-grouped.loc['B']

Or use xs:

grouped = df.groupby(['points', 'team'])[['tiger', 'lion', 'bear']].median()

grouped.xs('A', level='team')-grouped.xs('B', level='team')

Output:

        tiger    lion  bear
points                     
425     -72.0 -1878.0  -1.4
441     211.0  5238.0   4.0
1048    -47.0  1266.0  -2.9