[python] No numeric types to aggregate - change in groupby() behaviour?

I have a problem with some groupy code which I'm quite sure once ran (on an older pandas version). On 0.9, I get No numeric types to aggregate errors. Any ideas?

In [31]: data
Out[31]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2557 entries, 2004-01-01 00:00:00 to 2010-12-31 00:00:00
Freq: <1 DateOffset>
Columns: 360 entries, -89.75 to 89.75
dtypes: object(360)

In [32]: latedges = linspace(-90., 90., 73)

In [33]: lats_new = linspace(-87.5, 87.5, 72)

In [34]: def _get_gridbox_label(x, bins, labels):
   ....:             return labels[searchsorted(bins, x) - 1]
   ....: 

In [35]: lat_bucket = lambda x: _get_gridbox_label(x, latedges, lats_new)

In [36]: data.T.groupby(lat_bucket).mean()
---------------------------------------------------------------------------
DataError                                 Traceback (most recent call last)
<ipython-input-36-ed9c538ac526> in <module>()
----> 1 data.T.groupby(lat_bucket).mean()

/usr/lib/python2.7/site-packages/pandas/core/groupby.py in mean(self)
    295         """
    296         try:
--> 297             return self._cython_agg_general('mean')
    298         except DataError:
    299             raise

/usr/lib/python2.7/site-packages/pandas/core/groupby.py in _cython_agg_general(self, how, numeric_only)
   1415 
   1416     def _cython_agg_general(self, how, numeric_only=True):
-> 1417         new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
   1418         return self._wrap_agged_blocks(new_blocks)
   1419 

/usr/lib/python2.7/site-packages/pandas/core/groupby.py in _cython_agg_blocks(self, how, numeric_only)
   1455 
   1456         if len(new_blocks) == 0:
-> 1457             raise DataError('No numeric types to aggregate')
   1458 
   1459         return new_blocks

DataError: No numeric types to aggregate

I got this error generating a data frame consisting of timestamps and data:

df = pd.DataFrame({'data':value}, index=pd.DatetimeIndex(timestamp))

Adding the suggested solution works for me:

df = pd.DataFrame({'data':value}, index=pd.DatetimeIndex(timestamp), dtype=float))

Thanks Chang She!

Example:

                     data
2005-01-01 00:10:00  7.53
2005-01-01 00:20:00  7.54
2005-01-01 00:30:00  7.62
2005-01-01 00:40:00  7.68
2005-01-01 00:50:00  7.81
2005-01-01 01:00:00  7.95
2005-01-01 01:10:00  7.96
2005-01-01 01:20:00  7.95
2005-01-01 01:30:00  7.98
2005-01-01 01:40:00  8.06
2005-01-01 01:50:00  8.04
2005-01-01 02:00:00  8.06
2005-01-01 02:10:00  8.12
2005-01-01 02:20:00  8.12
2005-01-01 02:30:00  8.25
2005-01-01 02:40:00  8.27
2005-01-01 02:50:00  8.17
2005-01-01 03:00:00  8.21
2005-01-01 03:10:00  8.29
2005-01-01 03:20:00  8.31
2005-01-01 03:30:00  8.25
2005-01-01 03:40:00  8.19
2005-01-01 03:50:00  8.17
2005-01-01 04:00:00  8.18
                     data
2005-01-01 00:00:00  7.636000
2005-01-01 01:00:00  7.990000
2005-01-01 02:00:00  8.165000
2005-01-01 03:00:00  8.236667
2005-01-01 04:00:00  8.180000