BUG: incorrectly output index ordering with an ordered Categorical and pivot
xref #8860, soln might be the same

from [SO](http://stackoverflow.com/questions/26741204/pandas-pivot-table-alphabetically-sorts-categorical-data-incorrectly-when-addi)

```
In [32]: df = DataFrame({'Sales' : [100,120,220], 'Month' : ['January','January','January'], 'Year' : [2013,2014,2013]})

In [33]: df
Out[33]: 
     Month  Sales  Year
0  January    100  2013
1  January    120  2014
2  January    220  2013

In [34]: df['Month'] = df['Month'].astype('category').cat.set_categories(['January', 'February', 'March', 'April', 'May', 'June',      'July', 'August', 'September', 'October', 'November', 'December'])

In [35]: df.dtypes
Out[35]: 
Month    category
Sales       int64
Year        int64
dtype: object

In [36]: df['Month']
Out[36]: 
0    January
1    January
2    January
Name: Month, dtype: category
Categories (12, object): [January < February < March < April ... September < October < November < December]

In [37]: df.pivot_table(values="Sales", index="Month")
Out[37]: 
Month
January      146.666667
February            NaN
March               NaN
April               NaN
May                 NaN
June                NaN
July                NaN
August              NaN
September           NaN
October             NaN
November            NaN
December            NaN
Name: Sales, dtype: float64

In [38]: df.pivot_table(values="Sales", index="Month").index
Out[38]: Index([u'January', u'February', u'March', u'April', u'May', u'June', u'July', u'August', u'September', u'October', u'November', u'December'], dtype='object')

In [39]: result = df.pivot_table(values='Sales', index="Month", columns="Year", aggfunc="sum")

In [40]: result
Out[40]: 
Year       2013  2014
Month                
April       NaN   NaN
August      NaN   NaN
December    NaN   NaN
February    NaN   NaN
January     320   120
July        NaN   NaN
June        NaN   NaN
March       NaN   NaN
May         NaN   NaN
November    NaN   NaN
October     NaN   NaN
September   NaN   NaN
```
