将 Pandas 数据框从多列导出到嵌套字典

Question

Its a similar question to this here

But in this case I want something different. I have the next dataframe example:

from pandas import DataFrame

df = DataFrame([
           ['A', 123, 1], 
           ['B', 345, 5], 
           ['C', 712, 4],
           ['B', 768, 2], 
           ['B', 768, 3], 
           ['A', 123, 9], 
           ['C', 178, 6], 
           ['C', 178, 5],  
           ['A', 321, 3]], 
           columns=['maingroup', 'subgroup', 'selectedCol'])

And I want to extract a nested dictionary, where the main keys correspond to the unique names in 'maingroup' , and the subkeys correspond to unique names of 'subgroup' and the values store arrays of values from 'selectedCol' with common 'maingroup' and 'subgroup' keys, like so:

{
 'A': {'123':[1, 9], '321':[3]},
 'B': {'345':[5], '768':[2, 3]},
 'C': {'712':[4], '178':[6, 5]}
}

Answer 1

Using dict comprehension with nested groupby:

d = {k: f.groupby('subgroup')['selectedCol'].apply(list).to_dict()
     for k, f in df.groupby('maingroup')}

[out]

{'A': {123: [1, 9], 321: [3]},
 'B': {345: [5], 768: [2, 3]},
 'C': {178: [6, 5], 712: [4]}}

Answer 2

Create MultiIndex Series and then in dictionary comprehension nested dict s:

s = df.groupby(['maingroup','subgroup'], sort=False)['selectedCol'].apply(list)
d = {l: s.xs(l).to_dict() for l in s.index.levels[0]}
print (d)
{'A': {123: [1, 9], 321: [3]}, 
 'B': {345: [5], 768: [2, 3]}, 
 'C': {712: [4], 178: [6, 5]}}