ENH: New short indexer for operating on values
First of all, if I missed a point, please feel free to comment.

Using arithmetic operations on pd.DataFrames is sometimes a mouthful. Take the following example, where columns `a` and `b` should be multiplied by the column `c`:

```python
import numpy as np
import pandas as pd

np.random.seed(0)

df = pd.DataFrame(np.random.randn(3, 3), columns=list('abc'))

df[['a', 'b']] * df['c']
```

Apparently this doesn't work as expected. Instead one has to use either `pd.Dataframe.mul()`, which brings up poor legibility, or `pd.Dataframe.values`, which yields long lines and therefore also results in poor legibility:

```python
# using pd.DataFrame.mul()
df[['a', 'b']].mul(df['c'], axis='index')

# This is quite short, but does not work...
df[['a', 'b']] * df[['c']].values

# .. you have to use numpy arrays instead
df[['a', 'b']].values * df[['c']].values
```

Surely, the last call in this example returns a numpy array, but in my case thats the only thing I'm interested in, since I'm rewrapping my data at a later stage.

I'm proposing a new short indexer for operating on values, sth like:

```python
df.v[['a', 'b']] * df.v[['c']]

# which returns the same as
df[['a', 'b']].values * df[['c']].values
```

Or even more sophisticated:
```python
df[['a', 'b']] * df.v[['c']]

# which returns the same as
df[['a', 'b']].mul(df['c'], axis='index')
```

Btw the same goes for all other arithmetic operators.