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

How are you generating your data?

See how the output shows that your data is of 'object' type? the groupby operations specifically check whether each column is a numeric dtype first.

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)

look ↑

Did you initialize an empty DataFrame first and then filled it? If so that's probably why it changed with the new version as before 0.9 empty DataFrames were initialized to float type but now they are of object type. If so you can change the initialization to DataFrame(dtype=float).

You can also call frame.astype(float)

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

I got this done by :

data_frame.groupby(COL1).COL2.apply(np.mean).reset_index()