Concatenation of category value counts mixes up the index order
Let's say we have two `Series` s1 and s2, which can be the output of the `pd.value_counts()` function, and we want to combine them into one `DataFrame`

```python
s1 = pd.Series([39,6,4], index=pd.CategoricalIndex(['female','male','unknown']))
s2 = pd.Series([2,152,2,242,150], index=pd.CategoricalIndex(['f','female','m','male','unknown']))
pd.DataFrame([s1,s2])
```

The result is 

        female   male  unknown      f      m
    0     NaN   39.0      NaN    6.0    4.0
    1     2.0  152.0      2.0  242.0  150.0

where the order of categories in the first row is changed.

And the current workaround is 

    pd.DataFrame([pd.Series(s1.values,index=s1.index.astype(list)),pd.Series(s2.values,index=s2.index.astype(list))])

which gives the correct result

            f  female    m   male  unknown
    0  NaN    39.0  NaN    6.0      4.0
    1  2.0   152.0  2.0  242.0    150.0


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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.19.0-31-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.19.0
nose: None
pip: 8.1.2
setuptools: 27.2.0
Cython: None
numpy: 1.11.2
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.7
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None


</details>
