malformed index when reading csv with multiindex
```python
In [23]: pd.read_csv(io.StringIO("a,b,c\n1,,2"), index_col=1).index
Out[23]: Float64Index([nan], dtype='float64', name='b')

In [24]: pd.read_csv(io.StringIO("a,b,c\n1,,2\n3,,4"), index_col=1).index
Out[24]: Float64Index([nan, nan], dtype='float64', name='b')

In [26]: pd.read_csv(io.StringIO("a,b,c\n1,,2\n3,4,5"), index_col=[0,1]).index.levels[1]
Out[26]: Float64Index([4.0], dtype='float64', name='b')

In [27]: pd.read_csv(io.StringIO("a,b,c\n1,,2\n3,4,5"), index_col=1).index
Out[27]: Float64Index([nan, 4.0], dtype='float64', name='b')
```
#### Problem description

I am reading in a csv with `index_col` set to a list of values. Some of the column values are missing leading to nans in the index. If I do `df.index.levels[1]`, the `Float64Index` does not appear to have the nans in the unique value list. This renders nan related functions useless, like `Float64Index.fillna`. However, the nans are still there if you do `df.index.get_level_values(1)`.

#### Expected Output

In [26]: pd.read_csv(io.StringIO("a,b,c\n1,,2\n3,4,5"), index_col=[0,1]).index.levels[1]
Out[26]: Float64Index([nan, 4.0], dtype='float64', name='b')

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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.3
pytest: 3.1.3
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.2
feather: None
matplotlib: None
openpyxl: 2.4.7
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
sqlalchemy: 1.1.11
pymysql: None
psycopg2: 2.7.1 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None
</details>
