Groupby aggregations could ignore non-numeric columns when axis=1 #3688
Description
Perhaps the following groupby aggregation should work only the numeric columns, as they would when using the dataframe:
In [1]: df = pd.DataFrame({'bar': {0: 1, 1: 1, 2: 1}, 'foo': {0: 0, 1: 1, 2: 2}, 'foo1': {0: 1, 1: 2, 2: 3}, 'hello': {0: 'a', 1: 'a', 2: 'a'}}, columns=['bar', 'foo', 'foo', 'hello'])
In [2]: df
Out[2]:
bar foo foo hello
0 1 0 1 a
1 1 1 2 a
2 1 2 3 a
In [3]: df.mean() # hello is ignored
Out[13]:
bar 1
foo 1
foo 2
dtype: float64
In [4]: df.groupby(level=0, axis=1).mean()
---------------------------------------------------------------------------
DataError Traceback (most recent call last)
<ipython-input-4-7c2612a8fbda> in <module>()
----> 1 df.groupby(level=0, axis=1).mean()
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/groupby.pyc in mean(self)
351 """
352 try:
--> 353 return self._cython_agg_general('mean')
354 except GroupByError:
355 raise
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_general(self, how, numeric_only)
1569
1570 def _cython_agg_general(self, how, numeric_only=True):
-> 1571 new_blocks = self._cython_agg_blocks(how, numeric_only=numeric_only)
1572 return self._wrap_agged_blocks(new_blocks)
1573
/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas/core/groupby.pyc in _cython_agg_blocks(self, how, numeric_only)
1616
1617 if len(new_blocks) == 0:
-> 1618 raise DataError('No numeric types to aggregate')
1619
1620 return new_blocks
DataError: No numeric types to aggregate
From this SO question, where I gave very hacky workaround.
cc #3683 @jreback was this the question you were talking about? This ones related but in the sense of coming up against non unique problems... Thought I should mention it here anyway.