How to convert python dataframe to nested dictionary based on column values?

I have 2 dataframes

df1 = pd.DataFrame(data={'ID': ['0','1'], 'col1': [0.73, 0.58], 'col2': [0.51, 0.93], 'Type': ['mean', 'mean'] })
df2 = pd.DataFrame(data={'ID': ['0','1'], 'col1': [0.44, 0.49], 'col2': [0.50, 0.24], 'Type': ['std', 'std'] })
print(df1)
print(df2)

I need to convert to nested dictionary like

mydict = {0: {'col1': {'mean': 0.73, 'std': 0.44}, 'col2': {'mean': 0.51, 'std': 0.5}},
 1: {'col1': {'mean': 0.58, 'std': 0.49}, 'col2': {'mean': 0.93, 'std': 0.24}}}

where ‘ID’ as key, column names as nested key and ‘Type’ as nested keys and column values as values

>Solution :

Use concat with DataFrame.pivot for MultiIndex DataFrame and then convert to nested dict:

df = pd.concat([df1, df2]).pivot('Type', 'ID')

d = {level: df.xs(level, axis=1, level=1).to_dict() for level in df.columns.levels[1]}
print (d)
{'0': {'col1': {'mean': 0.73, 'std': 0.44}, 
       'col2': {'mean': 0.51, 'std': 0.5}}, 
 '1': {'col1': {'mean': 0.58, 'std': 0.49}, 
       'col2': {'mean': 0.93, 'std': 0.24}}}