BUG: Pandas groupby datetime and column then apply generates ValueError
#### Code Sample, a copy-pastable example if possible
```
    | SYM_ROOT | TIME_M                     | BEST_BID | BEST_ASK | increment | genjud_incre | 
    |----------|----------------------------|----------|----------|-----------|--------------| 
    | A        | 2017-01-03 09:30:00.004712 | 45.91    | 46.12    | 0         | 4680         | 
    | AA       | 2017-01-03 09:30:00.004014 | 28.55    | 28.57    | 0         | 4680         | 
```
```python
# Your code here
df=pd.read_csv('stack.csv')
df['TIME_M']=pd.to_datetime(df['TIME_M'],format='%Y%m%d %H:%M:%S.%f')
df.groupby(['SYM_ROOT',df['TIME_M'].dt.date]).apply(group_increment_to_end)

def group_increment_to_end(x):
    return x.iloc[0:1]
```
#### Problem description

I am trying to group my dataframe and then apply a function to each row of the dataframe. SYM_ROOT is a category variable, while TIME_M is a datetime variable.

However, I keep getting the following error:

`ValueError: Key 2017-01-03 00:00:00 not in level Index([2017-01-03], dtype='object', name=u'TIME_M')`

I am referring to [this](https://stackoverflow.com/questions/51057324/pandas-groupby-datetime-and-column-then-apply-generates-valueerror/51058220#51058220) stackoverflow post. 

I think the reason is that pandas somehow doesn't recognize the date object in the column correctly when using the group by statement. It falsely think compares `2017-01-03 00:00:00` with `TIME_M` and generates the error. After separating out the date as a single column, the problem fixes itself. 

But I think it will be beneficial if pandas can recognize the date object correctly in the columns ...

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

<details>

[paste the output of ``pd.show_versions()`` here below this line]

INSTALLED VERSIONS
------------------
commit: None
python: 2.7.14.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-693.21.1.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: None.None

pandas: 0.22.0
pytest: 3.3.2
pip: 9.0.1
setuptools: 38.4.0
Cython: 0.27.3
numpy: 1.14.0
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 5.4.1
sphinx: 1.6.6
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.4
feather: None
matplotlib: 2.1.2
openpyxl: 2.4.10
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 1.0.2
lxml: 4.1.1
bs4: 4.6.0
html5lib: 1.0.1
sqlalchemy: 1.2.1
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

</details>
