how to sort_index when having 2 levels columns
#### Code Sample, a copy-pastable example if possible

import pandas as pd

unames = ['user_id','gender','age','occupation','zip']
users = pd.read_table('D:/ProgramData/movielens/users.dat',sep='::', header=None, names=unames)
rnames = ['user_id','movie_id','rating','timestamp']
ratings = pd.read_table('D:/ProgramData/movielens/ratings.dat',sep='::', header=None, names=rnames)
mnames = ['movie_id','title','genres']
movies = pd.read_table('D:/ProgramData/movielens/movies.dat',sep='::', header=None, names=mnames)

data = pd.merge(pd.merge(ratings,users),movies)
mean_ratings = data.pivot_table(['rating'], index=['title'], columns='gender', aggfunc='mean')
ratings_by_title = data.groupby('title').size()
active_titles = ratings_by_title.index[ratings_by_title >= 250]
mean_ratings = mean_ratings.ix[active_titles]

top_female_ratings = mean_ratings.sort_index(by='F', ascending=False)
print top_female_ratings[:10]

```
#### Problem description

In[54]: mean_ratings[:5]
Out[54]: 
                                 rating          
gender                                F         M
title                                            
$1,000,000 Duck (1971)         3.375000  2.761905
'Night Mother (1986)           3.388889  3.352941
'Til There Was You (1997)      2.675676  2.733333
'burbs, The (1989)             2.793478  2.962085
...And Justice for All (1979)  3.828571  3.689024

In[55]: top_female_ratings = mean_ratings.sort_values('F', ascending=False)

KeyErrorTraceback (most recent call last)
<ipython-input-55-641d7284b025> in <module>()
----> 1 top_female_ratings = mean_ratings.sort_values('F', ascending=False)

C:\ProgramData\Anaconda2\lib\site-packages\pandas\core\frame.pyc in sort_values(self, by, axis, ascending, inplace, kind, na_position)
   4416             by = by[0]
   4417             k = self._get_label_or_level_values(by, axis=axis,
-> 4418                                                 stacklevel=stacklevel)
   4419 
   4420             if isinstance(ascending, (tuple, list)):

C:\ProgramData\Anaconda2\lib\site-packages\pandas\core\generic.pyc in _get_label_or_level_values(self, key, axis, stacklevel)
   1377             values = self.axes[axis].get_level_values(key)._values
   1378         else:
-> 1379             raise KeyError(key)
   1380 
   1381         # Check for duplicates

KeyError: 'F'


#### Expected Output

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

<details>

pd.show_versions()

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.15.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 62 Stepping 4, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.23.0
pytest: 3.5.1
pip: 10.0.1
setuptools: 39.1.0
Cython: 0.28.2
numpy: 1.14.3
scipy: 1.1.0
pyarrow: None
xarray: None
IPython: 5.7.0
sphinx: 1.7.4
patsy: 0.5.0
dateutil: 2.7.3
pytz: 2018.4
blosc: None
bottleneck: 1.2.1
tables: 3.4.3
numexpr: 2.6.5
feather: None
matplotlib: 2.2.2
openpyxl: 2.5.3
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.4
lxml: 4.2.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.7
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
