Index Column duplication when adding rows in a grouped-by DataFrame
#### Here's my code

```python
import numpy as np
import pandas as pd
from numpy.random import randn

az = pd.DataFrame({
    'A' : ['a1', 'a1', 'a2', 'a3']
  , 'B' : ['b1', 'b2', 'b3', 'b4']
  , 'Vals' : randn(4)
}).groupby(['A', 'B']).sum()

def add_zero(row) :
    name = row.name
    row.loc[name,'b0'] = 0
    return row

az['Vals'].groupby(level='A').apply(lambda s : add_zero(s))

>>> A   A   B 
>>> a1  a1  b1    1.238985
>>>         b2    0.686438
>>>         b0    0.000000
>>> a2  a2  b3   -0.503039
>>>         b0    0.000000
>>> a3  a3  b4   -0.083150
>>>         b0    0.000000
>>> Name: Vals, dtype: float64
```
#### Expected Output

```python
>>> A   B 
>>> a1  b1    1.238985
>>>     b2    0.686438
>>>     b0    0.000000
>>> a2  b3   -0.503039
>>>     b0    0.000000
>>> a3  b4   -0.083150
>>>     b0    0.000000
>>> Name: Vals, dtype: float64
```

#### Problem description

I would like to add rows with new index in a grouped by Dataframe, it seems to work, yet it duplicates an index/level column, and I can't figure why it does.
Is there any way around ?

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.13.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 27.2.0
Cython: 0.25.2
numpy: 1.11.3
scipy: 0.18.1
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.5.1
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2016.10
blosc: None
bottleneck: 1.2.0
tables: 3.2.2
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: 2.4.1
xlrd: 1.0.0
xlwt: 1.2.0
xlsxwriter: 0.9.6
lxml: 3.7.2
bs4: 4.5.3
html5lib: None
httplib2: 0.10.3
apiclient: 1.6.2
sqlalchemy: 1.1.5
pymysql: None
psycopg2: None
jinja2: 2.9.4
boto: 2.45.0
pandas_datareader: None
</details>
