The groupby filter output differs if we run an aggregate function before
#### Code Sample, a copy-pastable example if possible

```python
# I create a test DataFrame
d = pd.DataFrame({'data': range(6), 'key': list('ABCABC')})
# I groupby the column 'key'
g = d.groupby('key')
# I filter with always True (not that useful, just for the example)
print(g.filter(lambda x: True))
g.sum()
print(g.filter(lambda x: True))

```
#### Problem description
Here is the output of the above code

```
   data key
0     0   A
1     1   B
2     2   C
3     3   A
4     4   B
5     5   C
   data
0     0
1     1
2     2
3     3
4     4
5     5
```

I don't understand why the column key is in the output in the first run of filter, whereas when running an aggregate function (here g.sum()) before the filter, the key column disappear. If I use the as_index=False for the groupby, then the column is correctly preserved (as expected).

It looks like the aggregate function somehow change the groupby object, whereas my understanding of the groupby object is that each function call return a new object (and do not modify the original groupby object). 

#### Expected Output

```
   data key
0     0   A
1     1   B
2     2   C
3     3   A
4     4   B
5     5   C

   data key
0     0   A
1     1   B
2     2   C
3     3   A
4     4   B
5     5   C

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 69 Stepping 1, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

pandas: 0.20.2
pytest: None
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
sqlalchemy: 1.1.11
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

</details>
