How to convert pandas dataframe to nested dictionary

Question

I am running Python 3.6 and Pandas 0.19.2 and have a DataFrame which looks as follows:

Name      Chain        Food       Healthy  

George    McDonalds    b         


        

Answer 1

Solution using dictionary comprehension and groupby:

{n: grp.loc[n].to_dict('index')
 for n, grp in df.set_index(['Name', 'Chain']).groupby(level='Name')}

{'George': {'KFC': {'Food': 'chicken', 'Healthy': False},
  'McDonalds': {'Food': 'burger', 'Healthy': False}},
 'John': {'McDonalds': {'Food': 'salad', 'Healthy': True},
  'Wendys': {'Food': 'burger', 'Healthy': False}}}

Solution using defaultdict:

from collections import defaultdict

d = defaultdict(dict)

for i, row in df.iterrows():
    d[row.Name][row.Chain] = row.drop(['Name', 'Chain']).to_dict()

dict(d)

{'George': {'KFC': {'Food': 'chicken', 'Healthy': False},
  'McDonalds': {'Food': 'burger', 'Healthy': False}},
 'John': {'McDonalds': {'Food': 'salad', 'Healthy': True},
  'Wendys': {'Food': 'burger', 'Healthy': False}}}

Answer 2

I think you was very close.

Use groupby and to_dict:

df = df.groupby('Name')[['Chain','Food','Healthy']]
       .apply(lambda x: x.set_index('Chain').to_dict(orient='index'))
       .to_dict()

print (df)
{'George': {'KFC': {'Healthy': False, 'Food': 'chicken'}, 
           'McDonalds': {'Healthy': False, 'Food': 'burger'}}, 
'John': {'McDonalds': {'Healthy': True, 'Food': 'salad'},
         'Wendys': {'Healthy': False, 'Food': 'burger'}}}