DataFrame aggregation methods inconsistent when object columns have missing values
#### Code Sample, a copy-pastable example if possible

```python
>>> df = pd.DataFrame({'A':['a', None], 'B':['a', 'b']})
>>> df.min()
Series([], dtype: float64)

>>> df[['B', 'A']].min()
B    a
dtype: object
```
#### Problem description
Both DataFrames should return the same thing. Order should not matter here. I suppose this is happening based on whether or not the first column has any missing values?

This happens for the other aggregation methods `max` and `sum`

#### Expected Output
For consistency, both should output the same thing.

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.2
pytest: 3.0.7
pip: 9.0.1
setuptools: 35.0.2
Cython: 0.25.2
numpy: 1.13.0
scipy: 0.19.0
xarray: None
IPython: 6.0.0
sphinx: 1.5.5
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: 1.2.0
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.3
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: 0.3.0.post
</details>
