groupby.cumsum fails, groupby.apply(np.cumsum) works on Decimal column

#### Problem description

Cumulative sum fails on a decimal column during a groupby. However if I apply `np.cumsum` to the same column, it works. Would expect cumsum to work on numeric object types.

#### Code Sample, a copy-pastable example if possible

```python
>>> import pandas as pd                                                                          
>>> df = pd.DataFrame({'x': ['a', 'b', 'a', 'b', 'a', 'b'], 'y': [10, 5, 20, 6, 30, 7]})         
>>> df.groupby('x').cumsum()
    y
0  10
1   5
2  30
3  11
4  60
5  18                          

>>> from decimal import Decimal                                                                  
>>> df['y'] = [Decimal(str(v)) for v in df['y'].tolist()]  
>>> df.groupby('x').cumsum()
---------------------------------------------------------------------------
DataError                                 Traceback (most recent call last)
<ipython-input-12-2d4e07f67976> in <module>()
----> 1 df.groupby(['x'])['y'].cumsum()

C:\Users\rpostelnik\AppData\Local\Continuum\miniconda3\envs\jsiops36\lib\site-packages\pandas\core\groupby.py in cumsum(self, axis, *args, **kwargs)
   1682             return self.apply(lambda x: x.cumsum(axis=axis, **kwargs))
   1683
-> 1684         return self._cython_transform('cumsum', **kwargs)
   1685
   1686     @Substitution(name='groupby')

C:\Users\rpostelnik\AppData\Local\Continuum\miniconda3\envs\jsiops36\lib\site-packages\pandas\core\groupby.py in _cython_transform(self, how, numeric_only)
    905
    906         if len(output) == 0:
--> 907             raise DataError('No numeric types to aggregate')
    908
    909         return self._wrap_transformed_output(output, names)

DataError: No numeric types to aggregate
                                   
>>> import numpy as np                                                                           
df.groupby('x')['y'].apply(np.cumsum) 
    y
0  10
1   5
2  30
3  11
4  60
5  18                                                                                                                                        
```
#### Expected Output

```python
>>> df.groupby('x')['y'].cumsum()
    y
0  10
1   5
2  30
3  11
4  60
5  18    
```

#### Output of ``pd.show_versions()``

<details>

INSTALLED VERSIONS                                                                  
------------------                                                                  
commit: None                                                                        
python: 3.6.4.final.0                                                               
python-bits: 64                                                                     
OS: Windows                                                                         
OS-release: 7                                                                       
machine: AMD64                                                                      
processor: Intel64 Family 6 Model 45 Stepping 2, GenuineIntel                       
byteorder: little                                                                   
LC_ALL: None                                                                        
LANG: en_US.UTF-8                                                                   
LOCALE: None.None                                                                   
                                                                                    
pandas: 0.22.0                                                                      
pytest: 3.5.0                                                                       
pip: 9.0.1                                                                          
setuptools: 38.5.1                                                                  
Cython: None                                                                        
numpy: 1.14.2                                                                       
scipy: None                                                                         
pyarrow: None                                                                       
xarray: None                                                                        
IPython: 6.2.1                                                                      
sphinx: 1.7.1                                                                       
patsy: None                                                                         
dateutil: 2.7.0                                                                     
pytz: 2018.3                                                                        
blosc: None                                                                         
bottleneck: None                                                                    
tables: None                                                                        
numexpr: None                                                                       
feather: None                                                                       
matplotlib: None                                                                    
openpyxl: None                                                                      
xlrd: None                                                                          
xlwt: None                                                                          
xlsxwriter: None                                                                    
lxml: None                                                                          
bs4: None                                                                           
html5lib: 1.0.1                                                                     
sqlalchemy: 1.2.5                                                                   
pymysql: None                                                                       
psycopg2: 2.7.4 (dt dec pq3 ext lo64)                                               
jinja2: 2.10                                                                        
s3fs: None                                                                          
fastparquet: None                                                                   
pandas_gbq: None                                                                    
pandas_datareader: None                                                             

</details>
