Indexing semantics vary according to runtime values
Depending on the runtime value used in `[` indexing on a series, the semantics may be array-indexing, or slicing based on a MultiIndex. This is extremely error-prone behavior in a very basic operation.

Is the answer that production code should always use `.loc`/`.ix` instead of `[`? If so, I don't think the [docs](http://pandas.pydata.org/pandas-docs/stable/advanced.html#basic-indexing-on-axis-with-multiindex) make that clear.

``` python
>>> s = pandas.Series({(1, 1): 11, (1, 2): 12, (2, 1): 21, (2, 2): 22})

>>> s

1  1    11
   2    12
2  1    21
   2    22
dtype: int64

#0 isn't recognized as something to do with the index
# so treat it as an array index
>>> s[0]  
    11

# But this could be asking for a slice,
# so return a slice rather than an array-indexed element.
>>> s[1] 

1    11
2    12
dtype: int64

>>> pandas.__version__
    '0.15.2'
```

See also #3390
