Inconsistent output when using integer labels in multiindex on both column and index
#### Description of problem
Forgive me if I'm missing a sublety when using integers for multiindexing, but I seem to be getting inconsistent behavior when using multiindexing. Using loc to index both column and index simultaneously doesn't always give the same result. This seems to depend on the datatype of the innermost index.

#### Example of the expected behavior
The following example works as I'd expect, giving me a dataframe representing the (0,0) label for the outermost index level:
```python
>>>ind = pd.MultiIndex.from_product([[0,1],['A','B','C','D','E']])
>>>df = pd.DataFrame(np.random.rand(10,10), index=ind, columns=ind)
>>>print(df.loc[0,0])

          A         B         C         D         E
A  0.392093  0.167340  0.292854  0.138955  0.575715
B  0.495728  0.062870  0.733270  0.889761  0.141171
C  0.973444  0.518498  0.648546  0.448096  0.383729
D  0.987809  0.697177  0.601228  0.094184  0.986927
E  0.950939  0.109866  0.151390  0.173802  0.855105
```
#### Example of the unexpected behavior
However, if I change the second index level dataype to, for example, floats or ints, loc uses positional indexing rather than label based indexing for the second label. Thus, the same syntax returns a series of a single column, rather than a dataframe.
```python
>>>ind = pd.MultiIndex.from_product([[0,1],np.linspace(0,1,5)])
>>>df = pd.DataFrame(np.random.rand(10,10), index=ind, columns=ind)
>>>print(df.loc[0,0])
0  0.00    0.666874
   0.25    0.023773
   0.50    0.799715
   0.75    0.752675
   1.00    0.935531
1  0.00    0.510080
   0.25    0.845125
   0.50    0.410635
   0.75    0.067144
   1.00    0.658522
```


#### Problem description
The problem is that the output is inconsistent. My code breaks depending upon the datatypes used for the indices in a non-obvious way. I would expect things to work as in my first example, with the str dtype used for the second index level. At a minimum, I'd prefer it if the behavior was consistent, regardless of the datatype of the second index level.



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

<details>
# Paste the output here pd.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: None.None

pandas: 0.19.1
nose: 1.3.7
pip: 9.0.1
setuptools: 23.1.0
Cython: 0.24
numpy: 1.10.4
scipy: 0.18.1
statsmodels: 0.6.1
xarray: 0.7.2
IPython: 4.1.2
sphinx: 1.4
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.7
blosc: None
bottleneck: None
tables: 3.2.2
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: None
bs4: 4.4.1
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None
</details>
