Strange results when pivoting, doesnot group by day as expected.

ipython output

In [1]: %load test.py

In [2]: 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
   ...: 
/home/aperalta/.virtualenvs/xapo/local/lib/python2.7/site-packages/pandas/io/excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.
  .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))
   id               since  value
0  81 2015-01-31 07:00:00   2200
1  81 2015-02-01 07:00:00   2200
                                                                id  value
<pandas.tseries.resample.TimeGrouper object at 0x7fc595f96c10>  81   2200
id                                                              81   2200
^that is not what I expected
   id               since  value
0  81 2015-01-31 07:00:00   2200
1  81 2015-01-31 08:00:00   2200
2  81 2015-01-31 09:00:00   2200
3  81 2015-02-01 07:00:00   2200
            value
id             81
since            
2015-01-31   6600
2015-02-01   2200

table.csv

table2.csv

wat.py

# 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