pandas timedelta列上的split-apply-combine

import pandas as pd

import numpy as np

pd.__version__
Out[3]: '0.13.0rc1'

np.__version__
Out[4]: '1.8.0'

data = pd.DataFrame(np.random.rand(10, 3), columns=['f1', 'f2', 'td'])

data['td'] *= 10000000

data['td'] = pd.Series(data['td'], dtype='

或者,强制pandas尝试对'td'列进行操作:

data.groupby(data.index < 5)['td'].mean()
---------------------------------------------------------------------------
DataError                                 Traceback (most recent call last)
 in ()
----> 1 data.groupby(data.index < 5)['td'].mean()

/path/to/lib/python3.3/site-packages/pandas-0.13.0rc1-py3.3-linux-x86_64.egg/pandas/core/groupby.py in mean(self)
    417         """
    418         try:
--> 419             return self._cython_agg_general('mean')
    420         except GroupByError:
    421             raise

/path/to/lib/python3.3/site-packages/pandas-0.13.0rc1-py3.3-linux-x86_64.egg/pandas/core/groupby.py in _cython_agg_general(self, how, numeric_only)
    669 
    670         if len(output) == 0:
--> 671             raise DataError('No numeric types to aggregate')
    672 
    673         return self._wrap_aggregated_output(output, names)

DataError: No numeric types to aggregate


但是,取列的平均值可以正常工作,因此应该可以进行数值运算:

data['td'].mean()
Out[11]: 
0   00:00:00.003734
dtype: timedelta64[ns]


显然,在进行组合之前强制浮动是很容易的,但我想我也可以尝试理解我遇到的问题.

编辑:请参阅https://github.com/pydata/pandas/issues/5724