Lots of unexpected behavior using resample after groupby
#### Code Sample, a copy-pastable example if possible
# PANDAS 0.18 code :

```
df=pd.DataFrame(np.ones((150,4)),columns=['A','B','C','D'],
index=pd.date_range('2014-01-01',freq='D',periods=150))
df2=pd.DataFrame(np.zeros((150,4)),columns=['A','B','C','D'],
index=pd.date_range('2014-01-01',freq='D',periods=150))

df=pd.concat([df,df2])

print df.groupby('B').mean()
print df.groupby('B').resample('MS').mean().head()
print 'shape : ',df.groupby('B').resample('MS').mean().shape
print df.groupby('B').apply(lambda x:x.resample('MS').mean()).head()
print 'shape : ',df.groupby('B').apply(lambda x:x.resample('MS').mean()).shape
print df.groupby('B').mean()
print df.groupby('B').resample('H').mean().head()
print 'shape : ',df.groupby('B').resample('H').mean().shape
print df.groupby('B').apply(lambda x:x.resample('H').mean()).head()
print 'shape : ',df.groupby('B').apply(lambda x:x.resample('H').mean()).shape
print 'pd version', pd.__version__

```
# PANDAS 0.17 equivalent  code:

```
df=pd.DataFrame(np.ones((150,4)),columns=['A','B','C','D'],index=pd.date_range('2014-01-01',freq='D',periods=150))
df2=pd.DataFrame(np.zeros((150,4)),columns=['A','B','C','D'],index=pd.date_range('2014-01-01',freq='D',periods=150))

df=pd.concat([df,df2])

print df.groupby('B').mean()
print df.groupby('B').resample('MS').head()
print 'shape : ',df.groupby('B').resample('MS').shape
print df.groupby('B').apply(lambda x:x.resample('MS')).head()
print 'shape : ',df.groupby('B').apply(lambda x:x.resample('MS')).shape
print df.groupby('B').mean()
print df.groupby('B').resample('H').head()
print 'shape : ',df.groupby('B').resample('H').shape
print df.groupby('B').apply(lambda x:x.resample('H')).head()
print 'shape : ',df.groupby('B').apply(lambda x:x.resample('H')).shape
print 'pd version', pd.__version__
```
#### Expected Output
# Pandas 0.18 code Output :

```
   A    C    D
```

B  
0.0  0.0  0.0  0.0
1.0  1.0  1.0  1.0
                  A    B    C    D
B  
0.0 2014-01-01  0.0  0.0  0.0  0.0
    2014-02-01  0.0  0.0  0.0  0.0
    2014-03-01  0.0  0.0  0.0  0.0
    2014-04-01  0.0  0.0  0.0  0.0
    2014-05-01  0.0  0.0  0.0  0.0
shape :  (10, 4)
                  A    B    C    D
B  
0.0 2014-01-01  0.0  0.0  0.0  0.0
    2014-02-01  0.0  0.0  0.0  0.0
    2014-03-01  0.0  0.0  0.0  0.0
    2014-04-01  0.0  0.0  0.0  0.0
    2014-05-01  0.0  0.0  0.0  0.0
shape :  (10, 4)
       A    C    D
B  
0.0  0.0  0.0  0.0
1.0  1.0  1.0  1.0
                  A    B    C    D
B  
0.0 2014-01-01  0.0  0.0  0.0  0.0
    2014-01-02  0.0  0.0  0.0  0.0
    2014-01-03  0.0  0.0  0.0  0.0
    2014-01-04  0.0  0.0  0.0  0.0
    2014-01-05  0.0  0.0  0.0  0.0
shape :  (300, 4)
                           A    B    C    D
B  
0.0 2014-01-01 00:00:00  0.0  0.0  0.0  0.0
    2014-01-01 01:00:00  NaN  NaN  NaN  NaN
    2014-01-01 02:00:00  NaN  NaN  NaN  NaN
    2014-01-01 03:00:00  NaN  NaN  NaN  NaN
    2014-01-01 04:00:00  NaN  NaN  NaN  NaN
shape :  (7154, 4)
pd version 0.18.0
# Pandas 0.17 equivalent code Output :

   A  C  D
B  
0  0  0  0
1  1  1  1
              A  C  D
B  
0 2014-01-01  0  0  0
  2014-02-01  0  0  0
  2014-03-01  0  0  0
  2014-04-01  0  0  0
  2014-05-01  0  0  0
shape :  (10, 3)
              A  B  C  D
B  
0 2014-01-01  0  0  0  0
  2014-02-01  0  0  0  0
  2014-03-01  0  0  0  0
  2014-04-01  0  0  0  0
  2014-05-01  0  0  0  0
shape :  (10, 4)
   A  C  D
B  
0  0  0  0
1  1  1  1
                        A   C   D
B  
0 2014-01-01 00:00:00   0   0   0
  2014-01-01 01:00:00 NaN NaN NaN
  2014-01-01 02:00:00 NaN NaN NaN
  2014-01-01 03:00:00 NaN NaN NaN
  2014-01-01 04:00:00 NaN NaN NaN
shape :  (7154, 3)
                        A   B   C   D
B  
0 2014-01-01 00:00:00   0   0   0   0
  2014-01-01 01:00:00 NaN NaN NaN NaN
  2014-01-01 02:00:00 NaN NaN NaN NaN
  2014-01-01 03:00:00 NaN NaN NaN NaN
  2014-01-01 04:00:00 NaN NaN NaN NaN
shape :  (7154, 4)
pd version 0.17.1
# ISSUES :

in pandas 0.18.0 the column B is not dropped when applying resample afterwards (it should be dropped and put in index like with the simple example using .mean() after groupby). 
in pandas 0.18.0 the behavior is correct when downsampling (example with 'MS') but is wrong when upsampling (example with 'H') The dataframe is not upsampled in that case and stays at freq='D'

A workaround is to use df.groupby('B').apply(lambda x: x.resample.mean()) but it's inelegant to say the least and does not solve the issue of B being not dropped in columns. 
