Swapping levels does not seem to work as advertised (nevermind, one needs to sortlevels as well)
Consider this dataframe:
```
                    spend           impressions            actions            CPA         
item_type               A      Foo            P        Foo       P   Foo        P      Foo
date                                                                                       
2016-10-21         3926.88  1228.58  14121218.00  266168.00    2.00  7.00  1963.44   175.51
2016-10-22         4455.42  1633.45  16243562.00  341964.00    2.00  1.00  2227.71  1633.45
2016-10-23         1606.02   678.07   4846722.00  198431.00    1.00  3.00  1606.02   226.02
2016-10-24         1586.00   494.29   4493480.00  158689.00    4.00  2.00   396.50   247.14
2016-10-25         1583.77   481.33   3021006.00  127491.00    4.00  0.00   395.94      inf
2016-10-26         3145.96   528.68   5077469.00  130744.00    2.00  3.00  1572.98   176.23
2016-10-27         3163.02  1061.60   4577752.00  188609.00    4.00  2.00   790.76   530.80
2016-10-28         4091.34  1097.26   5834854.00  179609.00    0.00  2.00      inf   548.63
2016-10-29         3661.87   866.31   5269912.00  145719.00    2.00  1.00  1830.93   866.31
2016-10-30         3260.80   945.99   4681901.00  161267.00    1.00  2.00  3260.80   472.99
2016-10-31         2466.62  1165.83   3693572.00  204602.00    4.00  1.00   616.66  1165.83
2016-11-01         1184.63  1084.31   2509393.00  196103.00    1.00  1.00  1184.63  1084.31
2016-11-02         1118.49   949.56   2262660.00  165717.00    2.00  1.00   559.24   949.56
2016-11-03         1002.56  1004.88   1678139.00  169733.00    2.00  2.00   501.28   502.44
```
We have:
```
> df.columns

MultiIndex(levels=[['spend', 'impressions', 'actions', 'CPA'], ['A', 'Foo']],
           labels=[[0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 0, 1, 0, 1, 0, 1]],
           names=[None, 'item_type'])
```
when I do:
```python
fsa_all.swaplevel(axis=1)
```
I expect to get the columns organized as follows:

```
                        A                                 Foo
                    spend   impressions  actions  CPA   spend   impressions  actions  CPA
item_type             ...

```

but instead I get:

```
item_type                A    Foo            A         Foo       A     Foo        A      Foo
                     spend  spend  impressions impressions actions actions      CPA      CPA
                      ...                                                                                
```

Is this expected? 
#### Output of ``pd.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Darwin
OS-release: 16.1.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.0
nose: 1.3.7
pip: 9.0.0
setuptools: 28.7.1
Cython: 0.25.1
numpy: 1.11.2
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.4.8
patsy: 0.4.1
dateutil: 2.2
pytz: 2016.7
blosc: None
bottleneck: 1.2.0
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 1.5.3
openpyxl: 2.2.0-b1
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.3
lxml: 3.6.4
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.1.3
pymysql: None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.8
boto: 2.43.0
pandas_datareader: None
</details>
