Named aggregations with multiple columns
Since `pandas 0.25.0` we have [named aggregations](https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.25.0.html#groupby-aggregation-with-relabeling). 

Which works fine if you do aggregations on single columns. But what if you want to apply aggregations over multiple columns:

**example**:

```python
# example dataframe
df = pd.DataFrame(np.random.rand(4,4), columns=list('abcd'))
df['group'] = [0, 0, 1, 1]

          a         b         c         d  group
0  0.751462  0.572576  0.192957  0.921723      0
1  0.070777  0.801548  0.601678  0.344633      0
2  0.112964  0.361984  0.416241  0.785764      1
3  0.380045  0.486494  0.000594  0.608759      1

# aggregations on single columns
df.groupby('group').agg(
             a_sum=('a', 'sum'),
             a_mean=('a', 'mean'),
             b_mean=('b', 'mean'),
             c_sum=('c', 'sum'),
             d_range=('d', lambda x: x.max() - x.min())
)

          a_sum    a_mean    b_mean     c_sum   d_range
group                                                  
0      0.947337  0.473668  0.871939  0.838150  0.320543
1      0.604149  0.302074  0.656902  0.542985  0.057681
```
But what if we want to calculate the `a.max() - b.max()` while aggregating. That does not seem to work. For example, something like this would make sense:
```python
df.groupby('group').agg(
    diff_a_b=(['a', 'b'], lambda x: x['a'].max() - x['b'].max())
)
```
---
So is it possible to do named aggregations on multiple columns? If not, is this in the pipeline for future releases?
