How do I access embedded json objects in a Pandas

Question

TL;DR If loaded fields in a Pandas DataFrame contain JSON documents themselves, how can they be worked with in a Pandas like fashion?

Currently I'm directly dumping json/dictionary results from a Twitter library (twython) into a Mongo collection (called users here).

from twython import Twython
from pymongo import MongoClient

tw = Twython(...<auth>...)

# Using mongo as object storage 
client = MongoClient()
db = client.twitter
user_coll = db.users

user_batch = ... # collection of user ids
user_dict_batch = tw.lookup_user(user_id=user_batch)

for user_dict in user_dict_batch:
    if(user_coll.find_one({"id":user_dict['id']}) == None):
        user_coll.insert(user_dict)

After populating this database I read the documents into Pandas:

# Pull straight from mongo to pandas
cursor = user_coll.find()
df = pandas.DataFrame(list(cursor))

Which works like magic:

I'd like to be able to mangle the 'status' field Pandas style (directly accessing attributes). Is there a way?

EDIT: Something like df['status:text']. Status has fields like 'text', 'created_at'. One option could be flattening/normalizing this json field like this pull request Wes McKinney was working on.

Answer 1

One solution is just to smash it with the Series constructor:

In [1]: df = pd.DataFrame([[1, {'a': 2}], [2, {'a': 1, 'b': 3}]])

In [2]: df
Out[2]: 
   0                   1
0  1           {u'a': 2}
1  2  {u'a': 1, u'b': 3}

In [3]: df[1].apply(pd.Series)
Out[3]: 
   a   b
0  2 NaN
1  1   3

In some cases you'll want to concat this to the DataFrame in place of the dict row:

In [4]: dict_col = df.pop(1)  # here 1 is the column name

In [5]: pd.concat([df, dict_col.apply(pd.Series)], axis=1)
Out[5]: 
   0  a   b
0  1  2 NaN
1  2  1   3

If the it goes deeper, you can do this a few times...