Let's say I have the following dataframe
df = pd.DataFrame({0: {('A', 'a'): 1, ('A', 'b'): 6, ('B', 'a'): 2, ('B', 'b'): 7},
1: {('A', 'a'): 2, ('A', 'b'): 7, ('B', 'a'): 3, ('B', 'b'): 8},
2: {('A', 'a'): 3, ('A', 'b'): 8, ('B', 'a'): 4, ('B', 'b'): 9},
3: {('A', 'a'): 4, ('A', 'b'): 9, ('B', 'a'): 5, ('B', 'b'): 1},
4: {('A', 'a'): 5, ('A', 'b'): 1, ('B', 'a'): 6, ('B', 'b'): 2}})
which looks this:
0 1 2 3 4
A a 1 2 3 4 5
b 6 7 8 9 1
B a 2 3 4 5 6
b 7 8 9 1 2
When I convert this to a dictionary via to_dict (regardless of stacking, unstacking), I get a dictionary whose keys are tuples:
df.transpose().to_dict()
{('A', 'a'): {0: 1, 1: 2, 2: 3, 3: 4, 4: 5},
('A', 'b'): {0: 6, 1: 7, 2: 8, 3: 9, 4: 1},
('B', 'a'): {0: 2, 1: 3, 2: 4, 3: 5, 4: 6},
('B', 'b'): {0: 7, 1: 8, 2: 9, 3: 1, 4: 2}}
What I'd like instead is a nested dict like this:
{'A':{'a': {0: 1, 1:2, 2:3, 3:4, 4:5}, 'b':{0:6, 1:7, 2:8, 3:9,4:1}...
You can use a dictionary comprehension to iterate through the outer levels (values 'A' and 'B') and use the xs method to slice the frame by those levels.
{level: df.xs(level).to_dict('index') for level in df.index.levels[0]}
{'A': {'a': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5},
'b': {0: 6, 1: 7, 2: 8, 3: 9, 4: 1}},
'B': {'a': {0: 2, 1: 3, 2: 4, 3: 5, 4: 6},
'b': {0: 7, 1: 8, 2: 9, 3: 1, 4: 2}}}