pivot_table changes data type of unrelated columns
When using the pivot_table command, the data type of a float64 column changes to object when I include a boolean column in the pivot table. This is unrelated to issue #713.

I'm using    

```
python: 2.7.3.final.0
pandas: 0.13.1
```

For example, I've created the table below with two float64 columns a,b and one boolean column c

``` python
import pandas as pd
import numpy as np

np.random.seed(2)
x = pd.DataFrame(np.random.random((10,2)), columns=list('ab'))
x['pivot_column'] = x.a > 0.5
x['c'] = x.b > 0.5
```

I wish to create a pivot table, splitting on the 'pivot_column'. When using the columns a and b (both float64), the resulting table is as expected:

``` python
p2 = x.reset_index().pivot_table(rows='index',
                                     cols='pivot_column',
                                     values=list('ab'))
p2.info()
```

with output

```
Int64Index: 10 entries, 0 to 9
Data columns (total 4 columns):
(a, False)    6 non-null float64
(a, True)     4 non-null float64
(b, False)    6 non-null float64
(b, True)     4 non-null float64
dtypes: float64(4)<class 'pandas.core.frame.DataFrame'>
```

But, when I use the columns a,b,c the data type of a and b changes from float64 to object

``` python
p1 = x.reset_index().pivot_table(rows='index',
                                     cols='pivot_column',
                                     values=list('abc'))
p1.info()
```

with output

```
Int64Index: 10 entries, 0 to 9
Data columns (total 6 columns):
(a, False)    6 non-null object
(a, True)     4 non-null object
(b, False)    6 non-null object
(b, True)     4 non-null object
(c, False)    6 non-null object
(c, True)     4 non-null object
dtypes: object(6)<class 'pandas.core.frame.DataFrame'>
```

Is this a bug or is there some internal logic I don't know about (or both)?

Using unstack works as expected. 

P.s. Pandas is great. Thanks for creating it! D.s.
