Getting Average of Pandas with GroupBy- Getting DataError: No numeric types to aggregate -

Question

I know that there are numerous questions about this, like Getting daily averages with pandas and How get monthly mean in pandas using groupby but I'm getting a weird error.

Simple data set, with one index column (type timestamp) and one value column. Would like to get the monthly mean of the data.

In [76]: df.head()
Out[76]: 
                          A
2008-01-02                1
2008-01-03                2
2008-01-04                3
2008-01-07                4
2008-01-08                5

However, when I groupby, I get just the groups of the index and not of the value

In [74]: df.head().groupby(lambda x: x.month).groups
Out[74]: 
{1: [Timestamp('2008-01-02 00:00:00'),
  Timestamp('2008-01-03 00:00:00'),
  Timestamp('2008-01-04 00:00:00'),
  Timestamp('2008-01-07 00:00:00'),
  Timestamp('2008-01-08 00:00:00')]}

Attempts to take means() result in an error:

Have tried both df.head().resample("M", how='mean') and df.head().groupby(lambda x: x.month).mean()

and gets the error: DataError: No numeric types to aggregate

In [75]: df.resample("M", how='mean')
---------------------------------------------------------------------------
DataError                                 Traceback (most recent call last)
<ipython-input-75-79dc1a060ba4> in <module>()
----> 1 df.resample("M", how='mean')

/usr/local/lib/python2.7/site-packages/pandas/core/generic.pyc in resample(self, rule, how, axis, fill_method, closed, label, convention, kind, loffset, limit, base)
   2878                               fill_method=fill_method, convention=convention,
   2879                               limit=limit, base=base)
-> 2880         return sampler.resample(self).__finalize__(self)
   2881 
   2882     def first(self, offset):

/usr/local/lib/python2.7/site-packages/pandas/tseries/resample.pyc in resample(self, obj)
     82 
     83         if isinstance(ax, DatetimeIndex):
---> 84             rs = self._resample_timestamps()
     85         elif isinstance(ax, PeriodIndex):
     86             offset = to_offset(self.freq)

/usr/local/lib/python2.7/site-packages/pandas/tseries/resample.pyc in _resample_timestamps(self)
    286             # Irregular data, have to use groupby
    287             grouped = obj.groupby(grouper, axis=self.axis)
--> 288             result = grouped.aggregate(self._agg_method)
    289 
    290             if self.fill_method is not None:

/usr/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in aggregate(self, arg, *args, **kwargs)
   2436     def aggregate(self, arg, *args, **kwargs):
   2437         if isinstance(arg, compat.string_types):
-> 2438             return getattr(self, arg)(*args, **kwargs)
   2439 
   2440         result = OrderedDict()

/usr/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in mean(self)
    664         """
    665         try:
--> 666             return self._cython_agg_general('mean')
    667         except GroupByError:
    668             raise

/usr/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
   2356 
   2357     def _cython_agg_general(self, how, numeric_only=True):
-> 2358         new_items, new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
   2359         return self._wrap_agged_blocks(new_items, new_blocks)
   2360 

/usr/local/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_blocks(self, how, numeric_only)
   2406 
   2407         if len(new_blocks) == 0:
-> 2408             raise DataError('No numeric types to aggregate')
   2409 
   2410         return data.items, new_blocks

DataError: No numeric types to aggregate

Answer 1

Yeah, you should try coercing A to numeric with something like df['A'] = df['A'].astype(int). Might be worth checking if there's anything in the initial data read-in that caused it to be object instead of numeric as well.