No numeric types to aggregate - change in groupby behaviour

Question

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     lt class  pandas core frame DataFrame  gt  DatetimeIndex  2557 entries  2004-01-01 00 00 00 to 2010-12-31 00 00 00 Freq   lt 1 DateOffset gt  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   lt ipython-input-36-ed9c538ac526 gt  in  lt module gt    ---- gt  1 data T groupby lat bucket  mean     usr lib python2 7 site-packages pandas core groupby py in mean self      295                 296         try  -- gt  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   - gt  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  - gt  1457             raise DataError  No numeric types to aggregate      1458     1459         return new blocks  DataError  No numeric types to aggregate

User · Answer

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

User · Answer

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     lt class  pandas core frame DataFrame  gt  DatetimeIndex  2557 entries  2004-01-01 00 00 00 to 2010-12-31 00 00 00 Freq   lt 1 DateOffset gt  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

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

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