Change approach to pandas-dev#28773 fix

In new solution, existing machinery is used to apply the function
column-wise, and to recreate final result.

Author: Mateusz Górski

pandas/core/apply.py

@@ -274,13 +274,16 @@ def apply_standard(self):
 
         # we cannot reduce using non-numpy dtypes,
         # as demonstrated in gh-12244
-        if (
+        flag = (
             self.result_type in ["reduce", None]
             and not self.dtypes.apply(is_extension_array_dtype).any()
             # Disallow complex_internals since libreduction shortcut
             #  cannot handle MultiIndex
             and not isinstance(self.agg_axis, ABCMultiIndex)
-        ):
+        )
+        return_result = None
+
+        if flag:
 
             values = self.values
             index = self.obj._get_axis(self.axis)
@@ -308,11 +311,19 @@ def apply_standard(self):
                 # reached via numexpr; fall back to python implementation
                 pass
             else:
-                return self.obj._constructor_sliced(result, index=labels)
+                return_result = self.obj._constructor_sliced(result, index=labels)
+                if self.axis != 0 and self.axis != "index":
+                    return return_result
 
         # compute the result using the series generator
         results, res_index = self.apply_series_generator()
 
+        if flag and return_result is not None:
+            results = np.array([v for v in results.values()])
+            return self.obj._constructor_sliced(
+                results, index=res_index, dtype=return_result.dtype
+            )
+
         # wrap results
         return self.wrap_results(results, res_index)
 

pandas/core/frame.py

@@ -6564,63 +6564,16 @@ def apply(self, func, axis=0, raw=False, result_type=None, args=(), **kwds):
         """
         from pandas.core.apply import frame_apply
 
-        # Old apply function, which will be used for each part of DataFrame
-        def partial_apply(dataframe):
-            op = frame_apply(
-                dataframe,
-                func=func,
-                axis=axis,
-                raw=raw,
-                result_type=result_type,
-                args=args,
-                kwds=kwds,
-            )
-            return op.get_result()
-
-        def get_dtype(dataframe, column):
-            return dataframe.dtypes.values[column]
-
-        if axis == 0 or axis == "index":
-            if self.shape[1] == 0:
-                return partial_apply(self)
-
-            frame = self.iloc[:, [0]]
-            result = partial_apply(frame)
-            if isinstance(result, Series):
-                results = result.values
-            else:
-                results = result
-
-            i = 1
-            while i < self.shape[1]:
-                type = get_dtype(self, i)
-                j = i + 1
-
-                # While the dtype of column is the same as previous ones,
-                # they are handled together
-                while j < self.shape[1] and pandas.core.dtypes.common.is_dtype_equal(
-                    type, get_dtype(self, j)
-                ):
-                    j += 1
-                frame = self.iloc[:, i:j]
-                i = j
-                result = partial_apply(frame)
-
-                if isinstance(result, Series):
-                    results = np.append(results, result.values)
-                else:
-                    for k in range(result.shape[0], results.shape[0]):
-                        result.loc[k, :] = np.nan
-                    for k in range(results.shape[0], result.shape[0]):
-                        results.loc[k, :] = np.nan
-                    results = pandas.concat([results, result], axis=1)
-
-            if isinstance(result, Series):
-                return Series(results, index=self.columns)
-            else:
-                return results
-        else:
-            return partial_apply(self)
+        op = frame_apply(
+            self,
+            func=func,
+            axis=axis,
+            raw=raw,
+            result_type=result_type,
+            args=args,
+            kwds=kwds,
+        )
+        return op.get_result()
 
     def applymap(self, func):
         """