ENH: No numeric_only argument for pandas.core.groupby.GroupBy.rank()
### pandas.core.groupby.GroupBy.rank does not have a numeric_only argument like DataFrame.rank()

I have a DataFrame with several statistics from baseball teams across different years. I want to rank each team in each statistic, grouped by season. Every column is numeric, except for the teamID column which is an object type containing the names of each team as a string. My code looks something like this 
```python
stats = pd.read_csv('stats.csv')
ranked = stats.groupby('yearID').rank(method='average', ascending=False)
ranked['teamID'] = stats['teamID']
``` 
Since it is GroupBy.rank() I can't pass the `numeric_only` argument and that means I have to reassign `ranked['teamID']` to the original column. I also cannot do 
```python 
ranked = stats.groupby(['yearID', 'teamID']).rank(...)
```
because that would give everybody a rank of 1.  
  
Is there a reason that `numeric_only` is included in DataFrame.rank() but not GroupBy.rank(). Could it be added to GroupBy.rank()?

Then I could code it like this, which would be easier.
```python
```python
stats = pd.read_csv('stats.csv')
ranked = stats.groupby('yearID').rank(method='average', ascending=False, numeric_only=False)
``` 

I am just a hobbyist and so I don't know much about the implementation of these methods which means there may be something I am completely ignoring, or another more efficient way to do it. If so I would appreciate some enlightenment about what I am missing. Thanks!
