Nested groupby using sum of columns changes return type unexpectedly in 0.13.0-111-ge29c8e8
I believe this to be a regression error, and could not find information on this change in the release notes (so I don't think it is intentional).

With some DataFrame in python 0.12.0 I could group by timestamp and another column using the following:

``` python
mydf.set_index('timestamp').sort_index().groupby(pandas.TimeGrouper('1D')).apply(__grouped_by_time)
```

See this message by Jeff on StackOverflow: http://stackoverflow.com/questions/20788932/out-of-memory-when-dataframe-resample-is-called-in-pandas-on-small-dataset

The results of this call on a given dataframe differ on pandas 0.12 and pandas 0.13.  In particular, the results in 0.13 are all quoted strings, but in 0.12 are all numeric.  If the aggregation function is something like sum(), which returns numeric, the results probably shouldn't change into strings (or at least an explanation would be good).  Here is a full test, first using pandas 0.12.0 and then using 0.13

``` python
>>> import pandas
>>> pandas.__version__
'0.12.0'
>>> def __grouped_by_time(df):
...     return df.groupby(["hashed_username"]).sum()
... 
>>> mydf=pandas.read_pickle("sample.pik")
>>> mydf
   cnt                   hashed_username           timestamp
0    1  cfc2ff00f9ff635a368f230329a3a464 2012-10-08 00:00:00
1    1  1babe8cf174b20951147ea80ecf2a251 2012-11-16 00:00:00
2    1  fdafdb2e21c47ff967b2705c31457d61 2012-11-20 00:00:00
3    1  f0afdad49d7b693f680dc26b9e1f0fc7 2012-10-02 00:00:00
4    1  febaa2c77250cf7a78ea1aef3c9b3527 2012-10-25 00:00:00
5    1  0cbaabd42eccb0dce44d8070f1d4e7b1 2012-10-07 00:00:00
6    1  ca0efe0e195035d193b78cb0ab1975ee 2012-10-25 00:00:00
7    1  6852cc5b3a9c08a3f675aea075d226a8 2012-10-08 00:00:00
8    1  f731fd87e810c399598b702928392089 2012-11-01 00:00:00
9    1  2792e61e451d0fd63da951759763f52f 2012-10-16 00:00:00
10   0                           special 2013-01-01 00:00:00
>>> df_result = mydf.set_index('timestamp').sort_index().groupby(pandas.TimeGrouper('1D')).apply(__grouped_by_time)
>>> df_result["cnt"].unique()
array([1, 0])
```

And now, 0.13.0-111-ge29c8e8:

``` pandas
>>> import pandas
>>> pandas.__version__
'0.13.0-111-ge29c8e8'
>>> def __grouped_by_time(df):
...     return df.groupby(["hashed_username"]).sum()
... 
>>> mydf=pandas.read_pickle("sample.pik")
>>> mydf
   cnt                   hashed_username           timestamp
0    1  cfc2ff00f9ff635a368f230329a3a464 2012-10-08 00:00:00
1    1  1babe8cf174b20951147ea80ecf2a251 2012-11-16 00:00:00
2    1  fdafdb2e21c47ff967b2705c31457d61 2012-11-20 00:00:00
3    1  f0afdad49d7b693f680dc26b9e1f0fc7 2012-10-02 00:00:00
4    1  febaa2c77250cf7a78ea1aef3c9b3527 2012-10-25 00:00:00
5    1  0cbaabd42eccb0dce44d8070f1d4e7b1 2012-10-07 00:00:00
6    1  ca0efe0e195035d193b78cb0ab1975ee 2012-10-25 00:00:00
7    1  6852cc5b3a9c08a3f675aea075d226a8 2012-10-08 00:00:00
8    1  f731fd87e810c399598b702928392089 2012-11-01 00:00:00
9    1  2792e61e451d0fd63da951759763f52f 2012-10-16 00:00:00
10   0                           special 2013-01-01 00:00:00

[11 rows x 3 columns]
>>> df_result = mydf.set_index('timestamp').sort_index().groupby(pandas.TimeGrouper('1D')).apply(__grouped_by_time)
>>> df_result["cnt"].unique()
array(['1', '0'], dtype=object)
```
