Setting values on slice of multi-index gives NaNs
Best shown with an example.

``` python
import numpy as np, pandas as pd
timestamps = map(pd.Timestamp, ['2014-01-01', '2014-02-01'])
categories = ['A', 'B', 'C', 'D']
df = pd.DataFrame(index=pd.MultiIndex.from_product([timestamps, categories], names=['ts', 'cat']),
                  columns=['Col1', 'Col2'])

>>> df
                Col1  Col2
ts         cat            
2014-01-01 A     NaN   NaN
           B     NaN   NaN
           C     NaN   NaN
           D     NaN   NaN
2014-02-01 A     NaN   NaN
           B     NaN   NaN
           C     NaN   NaN
           D     NaN   NaN
```

I want to set the values for all categories in a single month. These examples work just fine.

``` python
df.loc['2014-01-01', 'Col1'] = 5
df.loc['2014-01-01', 'Col2'] = [1,2,3,4]

>>> df
               Col1 Col2
ts         cat          
2014-01-01 A      5    1
           B      5    2
           C      5    3
           D      5    4
2014-02-01 A    NaN  NaN
           B    NaN  NaN
           C    NaN  NaN
           D    NaN  NaN
```

These examples don't work.

``` python
df.loc['2014-01-01', 'Col1'] += 1
df.loc['2014-02-01', 'Col2'] = df.loc['2014-01-01', 'Col2']

>>> df
               Col1 Col2
ts         cat          
2014-01-01 A    NaN    1
           B    NaN    2
           C    NaN    3
           D    NaN    4
2014-02-01 A    NaN  NaN
           B    NaN  NaN
           C    NaN  NaN
           D    NaN  NaN
```

It doesn't seem to be a "setting a value on a copy" issue. Instead, Pandas is writing the NaNs.

My current workaround is to unstack each column into a DataFrame with simple indexes. This works, but I have lots of columns to work with. One dataframe is much easier to work with than a pile of dataframes.

The computations for each month depend on the values computed in the previous month, hence why it can't be done fully vectorized on an entire column.
