从熊猫数据框格式化json

Question

I'm trying to build out a JSON file from my dataframe that looks similar to this:

{'249' : [
          {'candidateId': 751,
           'votes':7528,
           'vote_pct':0.132
          },
          {'candidateId': 803,
           'votes':7771,
           'vote_pct':0.138
          }...
          ],
'274': [
         {'candidateId': 891,
         ....

My dataframe looks like this:

         officeId  candidateId    votes  vote_pct
0        249          751         7528  0.132198
1        249          803         7771  0.136465
2        249          818         7569  0.132918
3        249          827         9089  0.159610
4        249          856         2271  0.039881
5        249          877         7491  0.131548
6        249          878         8758  0.153798 
7        249          895         6267  0.110054
8        249         1161          201  0.003530
9        274          736         4664  0.073833
10       274          737         6270  0.099256
11       274          757         4953  0.078407
12       274          769         5239  0.082935
13       274          770         7134  0.112933
14       274          783         7673  0.121466
15       274          862         6361  0.100697
16       274          901         7671  0.121434

Using a function I can flip the dataframe's index and return it as a JSON string for each office ID, like this:

def clean_results(votes):
    #trying to get a well structured json file
    return votes.reset_index().to_json(orient='index', double_precision=2)

res_json = results.groupby(['officeId']).apply(clean_results)

But when I do that I end up with a new dataframe, with a JSON object for each officeID, and the JSON uses the numbered index as the top level, like so:

{"0":{"index":0.0,"officeId":249.0,"candidateId":751.0,"total_votes":7528.0,"vote_pct":0.13},"1":{"index":1.0,"officeId":249.0,"candidateId":803.0,"total_votes":7771.0,"vote_pct":0.14},"2":...

Answer 1

This is one approach, there may be something cleaner.

results = {}
for key, df_gb in df.groupby('officeId'):
    results[str(key)] = df_gb.to_dict('records')


import json
print json.dumps(results, indent=4)
####
{
    "274": [
        {
            "votes": 4664.0, 
            "candidateId": 736.0, 
            "vote_pct": 0.07383300000000001, 
            "officeId": 274.0
        }, 
        {
            "votes": 6270.0, 
            "candidateId": 737.0, 
            "vote_pct": 0.099255999999999997, 
            "officeId": 274.0
 ......