Feature request: aggregation without MultiIndex columns
Currently the group-by-aggregation in pandas will create MultiIndex columns if there are multiple operation on the same column. However, this introduces some friction to reset the column names for fast filter and join. (If all operations could be chained together, analytics would be smoother)

```python
df = pd.DataFrame([
        ['A', 1],
        ['A', 2],
        ['A', 3],
        ['B', 4],
        ['B', 5],
        ['B', 6]
    ], columns=['Key', 'Value'])

df1 = df.groupby('Key') \
    .agg({
            'Value': {'V1': 'sum', 'V2': 'count'}
        })

df1.columns = df1.columns.droplevel() # This line introduce friction
df1.query('V1 > V2')
```

#### Expected Output
It would be great if there is a simple alias function for columns (like the pyspark's implementation), such as
```python

# Just one approach, there may be others more appropriate
df.groupby('Key') \
    .agg(
            pd.Series.sum('Value').alias('V1'),
            pd.Series.count('Value').alias('V2')
        ) \
    .query('V1 > V2')
```