Assuming I have the following dataframe:
a b c Sce1 Sce2 Sce3 Sce4 Sce5 Sc6
Animal Ground Dog 0.0 0.9 0.5
With some liberal use of MAGIC
pd.concat([
df.assign(
**{x: 'Total' for x in 'abc'[i:]}
).groupby(list('abc')).sum() for i in range(4)
]).sort_index()
Sce1 Sce2 Sce3 Sce4 Sce5 Sc6
a b c
Animal Air Eagle 1.0 0.1 0.1 0.6 0.9 0.1
Owl 0.3 0.1 0.5 0.3 0.5 0.9
Total 1.3 0.2 0.6 0.9 1.4 1.0
Ground Cat 0.6 0.5 0.3 0.5 1.0 0.2
Dog 0.0 0.9 0.5 0.0 0.3 0.4
Total 0.6 1.4 0.8 0.5 1.3 0.6
Total Total 1.9 1.6 1.4 1.4 2.7 1.6
Object Metal Bike 0.5 0.1 0.4 0.7 0.4 0.2
Car 0.3 0.3 0.8 0.6 0.5 0.6
Total 0.8 0.4 1.2 1.3 0.9 0.8
Total Total 2.6 1.6 1.9 2.3 2.0 2.3
Wood Chair 0.9 0.6 0.1 0.9 0.2 0.8
Table 0.9 0.6 0.6 0.1 0.9 0.7
Total 1.8 1.2 0.7 1.0 1.1 1.5
Total Total Total 4.5 3.2 3.3 3.7 4.7 3.9
I can get exactly what you asked for with
pd.concat([
df.assign(
**{x: '' for x in 'abc'[i:]}
).groupby(list('abc')).sum() for i in range(1, 4)
]).sort_index()
Sce1 Sce2 Sce3 Sce4 Sce5 Sc6
a b c
Animal 1.9 1.6 1.4 1.4 2.7 1.6
Air 1.3 0.2 0.6 0.9 1.4 1.0
Eagle 1.0 0.1 0.1 0.6 0.9 0.1
Owl 0.3 0.1 0.5 0.3 0.5 0.9
Ground 0.6 1.4 0.8 0.5 1.3 0.6
Cat 0.6 0.5 0.3 0.5 1.0 0.2
Dog 0.0 0.9 0.5 0.0 0.3 0.4
Object 2.6 1.6 1.9 2.3 2.0 2.3
Metal 0.8 0.4 1.2 1.3 0.9 0.8
Bike 0.5 0.1 0.4 0.7 0.4 0.2
Car 0.3 0.3 0.8 0.6 0.5 0.6
Wood 1.8 1.2 0.7 1.0 1.1 1.5
Chair 0.9 0.6 0.1 0.9 0.2 0.8
Table 0.9 0.6 0.6 0.1 0.9 0.7
As for the how! I'll leave that as an exercise for the reader.
you need to do two group by
to get subtotals at every level of aggregation. Then add those back in to the initial DF. Here's a related question.