fill_value in pandas.DataFrame.add: Why must be float?
In pandas.DataFrame.add, the description of fill_value is:

```
fill_value : None or float value, default None
Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing
```

So its type is None or float, which causes that all dtypes in results are floats.
In practise, I find that it impacts memory usage and performance.
For example, after being converted to float64, the size of an int32 DF will be doubled.
#### Code Sample, a copy-pastable example if possible

``` python
import pandas as pd
t1 = pd.DataFrame([(1,2),(3,4),(5,6)], columns=["A","B"],dtype='uint32')
t2 = pd.DataFrame([(1,2),(3,4),(5,6)], columns=["D","C"],dtype='uint32')
t3 = t1.add(t2,axis='index',fill_value=0)
print t3.dtypes
```

```
A    float64
B    float64
C    float64
D    float64
dtype: object
```
#### output of `pd.show_versions()`

```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Darwin
OS-release: 15.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8

pandas: 0.18.0
nose: 1.3.7
pip: 8.1.1
setuptools: 20.0
Cython: None
numpy: 1.11.0
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: 1.5.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
```
