Unexpected output of table pivoting when there is little data; a TimeGrouper object is in the index.
``` python
# coding: utf-8
import pandas as pd
import numpy as np

frame = pd.read_csv("table.csv", engine="python", parse_dates=['since'])
print frame
d = pd.pivot_table(frame, index=pd.TimeGrouper(key='since', freq='1d'), values=["value"], columns=['id'], aggfunc=np.sum, fill_value=0)
print d
print "^that is not what I expected"
frame = pd.read_csv("table2.csv", engine="python", parse_dates=['since'])  # add some values to a day
print frame
d = pd.pivot_table(frame, index=pd.TimeGrouper(key='since', freq='1d'), values=["value"], columns=['id'], aggfunc=np.sum, fill_value=0)
print d
```

The following data is the contents of `table.csv`

```
"id","since","value"
"81","2015-01-31 07:00:00+00:00","2200.0000"
"81","2015-02-01 07:00:00+00:00","2200.0000"
```

This is `table2.csv`:

```
"id","since","value"
"81","2015-01-31 07:00:00+00:00","2200.0000"
"81","2015-01-31 08:00:00+00:00","2200.0000"
"81","2015-01-31 09:00:00+00:00","2200.0000"
"81","2015-02-01 07:00:00+00:00","2200.0000"
```

The output of print after pivoting `table.csv`

```
                                                                id  value
<pandas.tseries.resample.TimeGrouper object at 0x7fc595f96c10>  81   2200
id                                                              81   2200
```

I would expect something like this:

```
            value
id             81
since            
2015-01-31   2200
2015-02-01   2200
```

I can trace the problem to here:

https://github.com/pydata/pandas/blob/62529cca28e9c8652ddf7cca3aa6d41d4e30bc0e/pandas/tools/pivot.py#L114 the index created by groupby already has the object there.

I can't figure anything else. What is the problem, any fixes?

Thanks.
