multiindexed DataFrame excel export and import doesn't yield original
I am using a multiindexed dataframe and save and load it to and from .xlsx files. This is part of my unit testing procedure, and after upgrading to pandas 0.20.1, the multiindex is not recovered any more.


#### Code Sample, a copy-pastable example if possible

```python
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
                ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(6, 6), index=index[:6], columns=index[:6])
print(df)
df.to_excel('test.xlsx')
dfin = pd.read_excel('test.xlsx')
print(dfin)
```
#### Problem description

the re-loaded dataframe has "unknown#" labels at level0 and Nan at higher levels in index as well as columns, instead of correctly recreating the multiindex from the original DataFrame. 

#### Expected Output
df:

first              bar                 baz                 foo
second             one       two       one       two       one       two
first second
bar   one    -0.841135 -1.173882  0.206777 -0.805181  0.027380  0.326886
      two    -0.139861 -0.749935 -1.965764  0.236397  0.607762  0.016523
baz   one    -0.438246  0.315781  0.253052 -0.117903  0.546020  0.834073
      two    -1.772478  0.509509  0.003263  1.801822 -0.297373  0.342030
foo   one     0.661079  0.078506  0.572583 -0.459106 -0.584597  1.988036
      two     0.007465  0.811799 -1.606569  0.463014 -1.242725  1.490809

dfin:

        first       bar Unnamed: 2        baz Unnamed: 4        foo Unnamed: 6
NaN    second       one        two        one        two        one        two
first  second       NaN        NaN        NaN        NaN        NaN        NaN
bar       one   0.55971  0.0495165   -2.48716  -0.608794   0.171518    1.61321
NaN       two  0.167981   0.947228  -0.129498  -0.475977  0.0289783     1.9852
baz       one -0.827946   0.225728 -0.0272926   -1.39887  -0.147073   0.201533
NaN       two   1.55474  -0.406972   -1.85484   0.409694   -1.82329    1.03537
foo       one  0.721785   -1.61517   -0.69211   0.330506   0.796323  -0.537428
NaN       two   1.46575    1.55758    1.73001   -0.47926   0.162746   0.783132


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

<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.0.final.0
python-bits: 64
OS: Darwin
OS-release: 16.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 21.2.1
Cython: 0.23.4
numpy: 1.11.2
scipy: 0.18.0
statsmodels: 0.6.1
xarray: None
IPython: 4.0.1
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.4.2
pytz: 2015.7
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 1.5.0
openpyxl: 2.4.0
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.7
lxml: 3.4.4
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.9
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.38.0
pandas_datareader: None
</details>
