store complex dictionary in pandas dataframe

Question

This question follows my previous one.it's a mother dictionary of the one before store dictionary in pandas dataframe

I have a dictionary

  dictionary_example={'New York':{1234:{'choice':0,'city':'New York','choice_set':{0:{'A':100,'B':200,'C':300},1:{'A':200,'B':300,'C':300},2:{'A':500,'B':300,'C':300}}},
   234:{'choice':1,'city':'New York','choice_set':{0:{'A':100,'B':400},1:{'A':100,'B':300,'C':1000}}},
   1876:{'choice':2,'city':'New York','choice_set':{0:{'A': 100,'B':400,'C':300},1:{'A':100,'B':300,'C':1000},2:{'A':600,'B':200,'C':100}}
  }},
    'London':{1534:{'choice':0,'city':'London','choice_set':{0:{'A':100,'B':400,'C':300},1:{'A':200,'B':300,'C':300},2:{'A':500,'B':300,'C':300}}},  
   2134:{'choice':1,'city':'London','choice_set':{0:{'A':100,'B':600},1:{'A':170,'B':300,'C':1000}}},
   1776:{'choice':2,'city':'London','choice_set':{0:{'A':100,'B':400,'C':500},1:{'A':100,'B':300},2:{'A':600,'B':200,'C':100}}}},

    'Paris':{1534:{'choice':0,'city':'Paris','choice_set':{0:{'A':100,'B':400,'C':300},1:{'A':200,'B':300,'C':300},2:{'A':500,'B':300,'C':300}}},
   2134:{'choice':1,'city':'Paris','choice_set':{0:{'A':100,'B':600},1:{'A':170,'B':300,'C':1000}}},
   1776:{'choice':1,'city':'Paris','choice_set':{0:{'A': 100,'B':400,'C':500},1:{'A':100,'B':300}}}
  }}

I want it become a pandas data frame like this (some specific value inside maybe not exactly accurate)

id choice  A_0  B_0  C_0  A_1  B_1  C_1  A_2  B_2  C_2 New York London Paris
1234  0     100  200 300  200  300  300  500  300  300    1      0      0
234  1      100  400  -   100  300  1000  -    -    -    1       0      0
1876  2     100  400  300  100  300  1000 600 200 100    1      0       0
1534  0     100  200 300  200  300  300  500  300  300    0      1      0
2134  1      100  400  -   100  300  1000  -    -    -    0       1      0
2006  2     100  400  300  100  300  1000 600 200 100    0      1       0
1264  0     100  200 300  200  300  300  500  300  300    0      0      1
1454  1      100  400  -   100  300  1000  -    -    -    0      0      1
1776  1     100  400  300  100  300     -   -    -    -   0      0       1

In the old question the nice guy provide a way for the sub_dictionary:

df = pd.read_json(json.dumps(dictionary_example)).T


def to_s(r):
    return pd.read_json(json.dumps(r)).unstack()

flattened_choice_set = df["choice_set"].apply(to_s)

flattened_choice_set.columns = ['_'.join((str(col[0]), col[1])) for col in flattened_choice_set.columns] 

result = pd.merge(df, flattened_choice_set, 
         left_index=True, right_index=True).drop("choice_set", axis=1)

Any way to do for the large dictionary?

All the best, Kevin

Answer 1

The previously provided solution, as you quote, is not a very neat one. This one is more readable and provides the solution for your current problem. If possible you should reconsider your data structure though...

df = pd.DataFrame()
question_ids = [0,1,2]

Create a dataframe with a row for every city-choice combination, with dictionary in choice set column

for _, city_value in dictionary_example.iteritems():
    city_df = pd.DataFrame.from_dict(city_value).T
    city_df = city_df.join(pd.DataFrame(city_df["choice_set"].to_dict()).T)
    df = df.append(city_df)

Join the weird column names from choice set to your df

for i in question_ids:
    choice_df = pd.DataFrame(df[i].to_dict()).T
    choice_df.columns = map(lambda x: "{}_{}".format(x,i), choice_df.columns)
    df = df.join(choice_df)

Fix the city columns

df = pd.get_dummies(df, prefix="", prefix_sep="", columns=['city'])
df.drop(question_ids + ['choice_set'], axis=1, inplace=True)
# Optional to remove NaN from questions:
# df = df.fillna(0)
df