review comments

Author: jreback

doc/source/whatsnew/v0.25.0.rst

@@ -273,7 +273,7 @@ of ``object`` dtype. :attr:`Series.str` will now infer the dtype data *within* t
 ufuncs on Extension Dtype
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Operations with ``numpy`` ufuncs on Extension Arrays, including Sparse Dtypes will now preserve the
+Operations with ``numpy`` ufuncs on DataFrames with Extension Arrays, including Sparse Dtypes will now preserve the
 resulting dtypes to same as the input dtype; previously this would coerce to a dense dtype. (:issue:`23743`)
 
 .. ipython:: python

pandas/core/groupby/groupby.py

@@ -1298,7 +1298,7 @@ def _add_numeric_operations(cls):
         """
 
         def groupby_function(name, alias, npfunc,
-                             numeric_only=True, _convert=False,
+                             numeric_only=True,
                              min_count=-1):
 
             _local_template = """
@@ -1320,27 +1320,30 @@ def f(self, **kwargs):
                     kwargs['min_count'] = min_count
 
                 self._set_group_selection()
+
+                # try a cython aggregation if we can
                 try:
                     return self._cython_agg_general(
                         alias, alt=npfunc, **kwargs)
                 except AssertionError as e:
                     raise SpecificationError(str(e))
                 except Exception:
-                    result = self.aggregate(
-                        lambda x: npfunc(x, axis=self.axis))
-
-                    # coerce the columns if we can
-                    if isinstance(result, DataFrame):
-                        for col in result.columns:
-                            result[col] = self._try_cast(
-                                result[col], self.obj[col])
-                    else:
-                        result = self._try_cast(
-                            result, self.obj)
-
-                    if _convert:
-                        result = result._convert(datetime=True)
-                    return result
+                    pass
+
+                # apply a non-cython aggregation
+                result = self.aggregate(
+                    lambda x: npfunc(x, axis=self.axis))
+
+                # coerce the resulting columns if we can
+                if isinstance(result, DataFrame):
+                    for col in result.columns:
+                        result[col] = self._try_cast(
+                            result[col], self.obj[col])
+                else:
+                    result = self._try_cast(
+                        result, self.obj)
+
+                return result
 
             set_function_name(f, name, cls)
 

pandas/core/internals/blocks.py

@@ -1776,8 +1776,16 @@ def _try_cast_result(self, result, dtype=None):
         """
         if we have an operation that operates on for example floats
         we want to try to cast back to our EA here if possible
+
+        result could be a 2-D numpy array, e.g. the result of
+        a numeric operation; but it must be shape (1, X) because
+        we by-definition operate on the ExtensionBlocks one-by-one
+
+        result could also be an EA Array itself, in which case it
+        is already a 1-D array
         """
         try:
+
             result = self._holder._from_sequence(
                 np.asarray(result).ravel(), dtype=dtype)
         except Exception:

pandas/tests/groupby/test_function.py

@@ -479,8 +479,7 @@ def scipy_sem(*args, **kwargs):
      ('last', lambda x: x.iloc[-1]),
      ('count', np.size),
      pytest.param(
-         'sem', scipy_sem, marks=[pytest.mark.skipif(
-             td._skip_if_no_scipy(), reason='scipy not installed')])])
+         'sem', scipy_sem, marks=td.skip_if_no_scipy)])
 def test_ops_general(op, targop):
     df = DataFrame(np.random.randn(1000))
     labels = np.random.randint(0, 50, size=1000).astype(float)