BUG: Strange type conversions when assigning with `df.loc`
From [this StackOverflow question](http://stackoverflow.com/questions/26620647/replace-values-in-a-dataframe-column-based-on-condition), something strange occurs when assigning to a `.loc` subset, when none of the rows match:

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

df = pd.DataFrame({'A': [1, 2, 3],'B': [np.NaN, np.NaN, np.NaN]})
df
Out[6]: 
   A   B
0  1 NaN
1  2 NaN
2  3 NaN

df.dtypes
Out[10]: 
A      int64
B    float64
dtype: object

# This loc shouldn't match any rows
df.loc[df.B > df.A, 'B'] = df.A
df
Out[8]: 
   A                    B
0  1 -9223372036854775808
1  2 -9223372036854775808
2  3 -9223372036854775808

# Why has this become int? Is this expected
# behaviour for this assignment
df.dtypes
Out[12]: 
A    int64
B    int64
dtype: object
```

As long as some of the rows in the `.loc` match, this seems to work okay:

``` python
df = pd.DataFrame({'A': [1, 2, 3],'B': [np.NaN, 4, np.NaN]})
df.loc[df.B > df.A, 'B'] = df.A
df
Out[4]: 
   A   B
0  1 NaN
1  2   2
2  3 NaN
```

I don't know why the `B` column gets converted to int64. Is the weird value in the B column the result of the `np.nan` bit pattern being inserted into an `int64` series?

My `pd.show_versions()`:

```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.6.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 30 Stepping 5, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.15.0
nose: 1.3.4
Cython: 0.21
numpy: 1.9.0
scipy: 0.14.0
statsmodels: 0.5.0
IPython: 2.2.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 1.5
pytz: 2014.7
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.2
openpyxl: 1.8.5
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: 0.999
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
```
