Tuple-Retrieving vs. -Setting on non-MultiIndex DataFrame
```
df = pd.DataFrame({'a':[55,66,77],'b':[100,111,112],'ch':['x','y','z']})
df.index = pd.Index([10,11,12])

In [6]: df
Out[6]: 
     a    b ch
10  55  100  x
11  66  111  y
12  77  112  z

In [7]: df.loc[10,('b','ch')]
....
ValueError: No axis named 1 for object type <class 'pandas.core.series.Series'>

In [8]: df.ix[10,('b','ch')]
Out[8]: 100

```

more or less understandable, probably both should not work though.

but then, using the same indexing, the setting operation works, unpacking the tuple into the correct columns, as one would use the correct list selector `df.ix[10,['b','ch']]` or `df.loc[10,['b','ch']]`

```
In [10]: df.ix[10,('b','ch')] = (200,'i')

In [11]: df
Out[11]: 
     a    b ch
10  55  200  i
11  66  111  y
12  77  112  z

In [12]: df.loc[10,('b','ch')] = (300,'j')

In [13]: df
Out[13]: 
     a    b ch
10  55  300  j
11  66  111  y
12  77  112  z
```

For .ix setting should throw the same error as for retrieving?
For .loc setting should throw an error as you should expect to set a tuple to a single column? 

Is this wanted behavior?

P.S.: New to advanced / multi-index slicing, my searches didn't bring up that very issue despite the fact that all issues around this indexing seem somehow related.
