BUG(?): Stack dropna being ignored
From my answer to a SO question [here](http://stackoverflow.com/a/17288205/1889400)

`stack` takes an argument `dropna`:

dropna : boolean, default True
    Whether to drop rows in the resulting Frame/Series with no valid
    values

It seems like that argument may be getting ignored.  That or I'm confused and the docstring may need a rewrite.

Here's some code to try it on.

``` python
df = pd.DataFrame({'A': ['loc_a'] * 12 + ['loc_b'],
                   'B': ['group_a'] * 7 + ['group_b'] * 3 + ['group_c'] * 2 + ['group_a'],
                   'Date': ["2013-06-11",
                           "2013-07-02",
                           "2013-07-09",
                           "2013-07-30",
                           "2013-08-06",
                           "2013-09-03",
                           "2013-10-01",
                           "2013-07-09",
                           "2013-08-06",
                           "2013-09-03",
                           "2013-07-09",
                           "2013-09-03",
                           "2013-10-01"],
                    'Value': [22, 35, 14,  9,  4, 40, 18, 4, 2, 5, 1, 2, 3]})

df.Date = pd.to_datetime(df.Date)
df = df.set_index(['A', 'B', 'Date'])

In [75]: df.unstack(['A', 'B']).stack(['A', 'B'], dropna=False)
Out[75]: 
                          Value
Date       A     B             
2013-06-11 loc_a group_a     22
2013-07-02 loc_a group_a     35
2013-07-09 loc_a group_a     14
                 group_b      4
                 group_c      1
2013-07-30 loc_a group_a      9
2013-08-06 loc_a group_a      4
                 group_b      2
2013-09-03 loc_a group_a     40
                 group_b      5
                 group_c      2
2013-10-01 loc_a group_a     18
           loc_b group_a      3

# Same
In [76]: df.unstack(['A', 'B']).stack(['A', 'B'], dropna=True)
Out[76]: 
                          Value
Date       A     B             
2013-06-11 loc_a group_a     22
2013-07-02 loc_a group_a     35
2013-07-09 loc_a group_a     14
                 group_b      4
                 group_c      1
2013-07-30 loc_a group_a      9
2013-08-06 loc_a group_a      4
                 group_b      2
2013-09-03 loc_a group_a     40
                 group_b      5
                 group_c      2
2013-10-01 loc_a group_a     18
           loc_b group_a      3

# Zeros are where NaNs should show up with dropna=False?


In [77]: df.unstack(['A', 'B']).fillna(0).stack(['A', 'B'])
Out[77]: 
                          Value
Date       A     B             
2013-06-11 loc_a group_a     22
                 group_b      0
                 group_c      0
           loc_b group_a      0
2013-07-02 loc_a group_a     35
                 group_b      0
                 group_c      0
           loc_b group_a      0
2013-07-09 loc_a group_a     14
                 group_b      4
                 group_c      1
           loc_b group_a      0
2013-07-30 loc_a group_a      9
                 group_b      0
                 group_c      0
           loc_b group_a      0
2013-08-06 loc_a group_a      4
                 group_b      2
                 group_c      0
           loc_b group_a      0
2013-09-03 loc_a group_a     40
                 group_b      5
                 group_c      2
           loc_b group_a      0
2013-10-01 loc_a group_a     18
                 group_b      0
                 group_c      0
           loc_b group_a      3

In [78]: pd.__version__
Out[78]: '0.11.1.dev-3406a54'
```
