Applying set to NaN returns list of NaNs, not a set
#### Code Sample, a copy-pastable example if possible

```python
dftest = pd.DataFrame({"a":[np.nan]*5, "b":[1,1,2,3,3]})
dftest.groupby("b").agg(lambda x: set(x))

[Out]
         a
b	
1	{nan, nan}
2	{nan}
3	{nan, nan}
```

```python
dftest.apply(set)

# [Out]
# a    {nan, nan, nan, nan, nan}
# b              {1.0, 2.0, 3.0}
# dtype: object
```

```python
set([np.nan]*5)
```
#### Problem description

 I am trying to count the number of distinct values in a group. When I apply `lambda x: len(set(x))` and a column contains NaN, I am getting unexpected result: each NaN instance is counted as independent value. The expected output is a better solution as it follows the common definition of what SET is, i.e. the abovementioned function to sets:

```python
set([np.nan]*5)
# [Out:]  {np.nan}
```

It might be rooted in a numpy:
```python
set(np.asarray([np.nan]*5))
# [Out] {nan, nan, nan, nan, nan}
```
However:
```python
set(np.asarray([np.nan]*5 + ["a"]))
# [Out] {nan, "a"}
```
#### Expected Output

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-42-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.2
pytest: None
pip: 9.0.1
setuptools: 36.0.1
Cython: None
numpy: 1.13.0
scipy: 0.19.0
xarray: None
IPython: 6.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
