rolling_apply only applicable to numeric columns
Hi everybody,

I discovered that the rolling_apply function is only applicable to numeric columns. I think this should be changed as this seems too limited to me. Let's take the following example, 

```
    import datetime as DT
    df = pd.DataFrame({
    'Buyer': 'Carl Mark Carl Joe Joe Carl'.split(),
    'Quantity': [1,3,5,8,9,3],
    'Date' : [
        DT.datetime(2013,9,1,13,0),
        DT.datetime(2013,9,1,13,5),
        DT.datetime(2013,10,1,20,0),
        DT.datetime(2013,10,3,10,0),
        DT.datetime(2013,12,2,12,0),                                      
        DT.datetime(2013,12,2,14,0),
        ]}).set_index('Date')
```

Now I want to count all new customers each 10 days.

```
buyers = []

def novices(x):
    new = [n for n in x if n not in buyers]

    if (len(new) > 0): buyers.extend(new) 

    return len(new)


pd.rolling_apply(df['Buyer'], 10, novices)
```

throws an exception  "ValueError: could not convert string to float: Carl"

However, although meaningless a call with a numeric column such as: 

```
pd.rolling_apply(df['Quantity'], 2, novices)
```

works.
