pd.groupby(pd.TimeGrouper('W')).groups returns Timestamps instead of Periods
Lets consider the following DataFrame:

```
date_range = pd.date_range(dt(2010,1,1), dt(2010,1,31), freq='1D')
df = pd.DataFrame(data = np.random.rand(len(date_range),2), index = date_range)
```
If I group the datapoints by periods of 1 week and visualize the groups definition, I get:

```
In: [1]:df.groupby(pd.TimeGrouper('W')).groups
Out:[1]: 
     {Timestamp('2010-01-03 00:00:00', freq='W-SUN'): 3,
     Timestamp('2010-01-10 00:00:00', freq='W-SUN'): 10,
     Timestamp('2010-01-17 00:00:00', freq='W-SUN'): 17,
     Timestamp('2010-01-24 00:00:00', freq='W-SUN'): 24,
     Timestamp('2010-01-31 00:00:00', freq='W-SUN'): 31}
I retrieve the keys of that dictionary:

In: [2]: list(df.groupby(pd.TimeGrouper('W')).keys())
Out:[2]: 
    [Timestamp('2010-01-03 00:00:00', freq='W-SUN'),
     Timestamp('2010-01-10 00:00:00', freq='W-SUN'),
     Timestamp('2010-01-31 00:00:00', freq='W-SUN'),
     Timestamp('2010-01-17 00:00:00', freq='W-SUN'),
     Timestamp('2010-01-24 00:00:00', freq='W-SUN')]
```
However I am left with those funny variables such as Timestamp('2010-01-24 00:00:00', freq='W-SUN') that have the prefix Timestamp but are structured like Periods. I  think this is not correct, and those variables should be Periods?

