Series.filter returns something different than DataFrame.filter.iloc[0]
I posted this on StackOverflow as a [question](http://stackoverflow.com/questions/25275009/pandas-series-filter-values-returning-different-type-than-numpy-array), but I think it may be a bug in Pandas. 

These two expressions return something slightly different:

```
df.filter(regex='other').iloc[0].values
df.iloc[0].filter(regex='other').values
```

Should they? It seems strange. I found this issue by doing an apply on the dataframe and trying to use a filter on the rows in a `scipy.stats.entropy` calculation. 

The two ways I have seen they are different. First, the bottom throws an error in the entropy calculation:

```
TypeError: ufunc 'xlogy' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''
```

Second, the print representation is slightly different:

```
[12024  9643  7681  8193  8012  7846  7615  7484  5966 11484 13627 17749  9820  5336  4611  3366]
[12024 9643 7681 8193 8012 7846 7615 7484 5966 11484 13627 17749 9820 5336 4611 3366]
```

Is this a bug? Or expected and I am just using filter on the rows incorrectly?

I've also tried using `row[row.filter(regex='other').index].values` with no success.
