BUG: Python operator 'in' behaves in two different ways on Series object
The python operator 'in' acting on a pandas.Series object behaves in two different ways.
When iterating over the series it acts on the list of values. But for membership testing it acts on the index. Is this the desired behavior?

```
import pandas as pd
s = pd.Series([1, 2, 3])
for v in s: print v
    # prints 1 2 3, i.e. 'in' is acting on the values
for v in [1, 2, 3]: print v in s
    # prints True True False, i.e. the latter 'in' is acting on the index
```

This behavior also leads to peculiar behavior when working with DataFrames.

```
df = pd.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]})
df

#      a  b
#  0  1  4
#  1  2  5
#  2  3  6

for value in df['a']:
    print value,
    if value in df['b']:
        print 'also in column b'

#  1 also in column b
#  2 also in column b
#  3
```

This is not what one would naively expect from that code snippet and it can potentially lead to errors that might be difficult to spot.
This is the behavior that is also mentioned in the docs (in a pretty hidden way -- one has to actively search for it), but I suggest that this should not be the desired behavior. Instead, I'd find it much more natural, if 'in' worked always on the values and never on the index. If needed one can always use 'in s.index', but the standard way should be acting on the values always.

pandas version 0.18.1
