Added cast blacklist for certain transform agg funcs

doc/source/whatsnew/v0.23.0.txt

@@ -506,6 +506,7 @@ Groupby/Resample/Rolling
 - Fixed regression in :func:`DataFrame.groupby` which would not emit an error when called with a tuple key not in the index (:issue:`18798`)
 - Bug in :func:`DataFrame.resample` which silently ignored unsupported (or mistyped) options for ``label``, ``closed`` and ``convention`` (:issue:`19303`)
 - Bug in :func:`DataFrame.groupby` where tuples were interpreted as lists of keys rather than as keys (:issue:`17979`, :issue:`18249`)
+- Bug in ``transform`` where particular aggregation functions were being incorrectly cast to match the dtype(s) of the grouped data (:issue:`19200`)
 -
 
 Sparse

pandas/core/groupby.py

@@ -345,6 +345,8 @@
 _cython_transforms = frozenset(['cumprod', 'cumsum', 'shift',
                                 'cummin', 'cummax'])
 
+_cython_cast_blacklist = frozenset(['rank', 'count', 'size'])
+
 
 class Grouper(object):
     """
@@ -965,6 +967,21 @@ def _try_cast(self, result, obj, numeric_only=False):
 
         return result
 
+    def _transform_should_cast(self, func_nm):
+        """
+        Parameters:
+        -----------
+        func_nm: str
+            The name of the aggregation function being performed
+
+        Returns:
+        --------
+        bool
+            Whether transform should attempt to cast the result of aggregation
+        """
+        return (self.size().fillna(0) > 0).any() and (func_nm not in
+                                                      _cython_cast_blacklist)
+
     def _cython_transform(self, how, numeric_only=True):
         output = collections.OrderedDict()
         for name, obj in self._iterate_slices():
@@ -3333,7 +3350,7 @@ def transform(self, func, *args, **kwargs):
             else:
                 # cythonized aggregation and merge
                 return self._transform_fast(
-                    lambda: getattr(self, func)(*args, **kwargs))
+                    lambda: getattr(self, func)(*args, **kwargs), func)
 
         # reg transform
         klass = self._selected_obj.__class__
@@ -3364,7 +3381,7 @@ def transform(self, func, *args, **kwargs):
         result.index = self._selected_obj.index
         return result
 
-    def _transform_fast(self, func):
+    def _transform_fast(self, func, func_nm):
         """
         fast version of transform, only applicable to
         builtin/cythonizable functions
@@ -3373,7 +3390,7 @@ def _transform_fast(self, func):
             func = getattr(self, func)
 
         ids, _, ngroup = self.grouper.group_info
-        cast = (self.size().fillna(0) > 0).any()
+        cast = self._transform_should_cast(func_nm)
         out = algorithms.take_1d(func().values, ids)
         if cast:
             out = self._try_cast(out, self.obj)
@@ -4127,15 +4144,15 @@ def transform(self, func, *args, **kwargs):
         if not result.columns.equals(obj.columns):
             return self._transform_general(func, *args, **kwargs)
 
-        return self._transform_fast(result, obj)
+        return self._transform_fast(result, obj, func)
 
-    def _transform_fast(self, result, obj):
+    def _transform_fast(self, result, obj, func_nm):
         """
         Fast transform path for aggregations
         """
         # if there were groups with no observations (Categorical only?)
         # try casting data to original dtype
-        cast = (self.size().fillna(0) > 0).any()
+        cast = self._transform_should_cast(func_nm)
 
         # for each col, reshape to to size of original frame
         # by take operation

pandas/tests/groupby/test_transform.py

@@ -582,3 +582,28 @@ def test_transform_with_non_scalar_group(self):
                                'group.*',
                                df.groupby(axis=1, level=1).transform,
                                lambda z: z.div(z.sum(axis=1), axis=0))
+
+    @pytest.mark.parametrize('cols,exp,comp_func', [
+        ('a', pd.Series([1, 1, 1], name='a'), tm.assert_series_equal),
+        (['a', 'c'], pd.DataFrame({'a': [1, 1, 1], 'c': [1, 1, 1]}),
+         tm.assert_frame_equal)
+    ])
+    @pytest.mark.parametrize('agg_func', [
+        'count', 'rank', 'size'])
+    def test_transform_numeric_ret(self, cols, exp, comp_func, agg_func):
+        if agg_func == 'size' and isinstance(cols, list):
+            pytest.xfail("'size' transformation not supported with "
+                         "NDFrameGroupy")
+
+        # GH 19200
+        df = pd.DataFrame(
+            {'a': pd.date_range('2018-01-01', periods=3),
+             'b': range(3),
+             'c': range(7, 10)})
+
+        result = df.groupby('b')[cols].transform(agg_func)
+
+        if agg_func == 'rank':
+            exp = exp.astype('float')
+
+        comp_func(result, exp)