Sum of all-NaN row/column returns 0
#### Code Sample, a copy-pastable example if possible

```python
df = pd.DataFrame({'A':[math.nan,1,math.nan], 'B':[math.nan,math.nan,1], 'C': [math.nan,math.nan,math.nan]})
print(df)
print(df.sum(axis=0, skipna=True))
print(df.sum(axis=1, skipna=True))
```

#### Problem description

Currently, when summing over columns/rows containing all NaNs, a result of 0.0 is returned. This contradicts the [documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sum.html) which states, when skipna is True (the default), "Exclude NA/null values. If an entire row/column is NA, the result will be NA."

I don't have a strong opinion on whether the documented or actual behaviour is better; if existing code depends on this behaviour, it may best to update the documentation.

#### Actual output

```
     A    B   C
0  NaN  NaN NaN
1  1.0  NaN NaN
2  NaN  1.0 NaN

A    1.0
B    1.0
C    0.0
dtype: float64

0    0.0
1    1.0
2    1.0
dtype: float64
```

#### Expected Output

```
     A    B   C
0  NaN  NaN NaN
1  1.0  NaN NaN
2  NaN  1.0 NaN

A    1.0
B    1.0
C    NaN
dtype: float64

0    NaN
1    1.0
2    1.0
dtype: float64
```

#### Output of ``pd.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.1.3-101.fc21.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.12.0
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.8.2
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.1.0
tables: None
numexpr: 2.6.1
matplotlib: 1.5.1
openpyxl: 2.2.0-b1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: None
lxml: 3.6.4
bs4: 4.5.1
html5lib: 0.999999999
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>