REF: simplify ohlc (pandas-dev#41091)

Author: jbrockmendel

pandas/core/groupby/generic.py

@@ -363,20 +363,10 @@ def _cython_agg_general(
             result = self.grouper._cython_operation(
                 "aggregate", obj._values, how, axis=0, min_count=min_count
             )
-
-            if how == "ohlc":
-                # e.g. ohlc
-                agg_names = ["open", "high", "low", "close"]
-                assert len(agg_names) == result.shape[1]
-                for result_column, result_name in zip(result.T, agg_names):
-                    key = base.OutputKey(label=result_name, position=idx)
-                    output[key] = result_column
-                    idx += 1
-            else:
-                assert result.ndim == 1
-                key = base.OutputKey(label=name, position=idx)
-                output[key] = result
-                idx += 1
+            assert result.ndim == 1
+            key = base.OutputKey(label=name, position=idx)
+            output[key] = result
+            idx += 1
 
         if not output:
             raise DataError("No numeric types to aggregate")
@@ -942,10 +932,6 @@ def count(self) -> Series:
         )
         return self._reindex_output(result, fill_value=0)
 
-    def _apply_to_column_groupbys(self, func):
-        """ return a pass thru """
-        return func(self)
-
     def pct_change(self, periods=1, fill_method="pad", limit=None, freq=None):
         """Calculate pct_change of each value to previous entry in group"""
         # TODO: Remove this conditional when #23918 is fixed
@@ -1137,6 +1123,7 @@ def _cython_agg_general(
     def _cython_agg_manager(
         self, how: str, alt=None, numeric_only: bool = True, min_count: int = -1
     ) -> Manager2D:
+        # Note: we never get here with how="ohlc"; that goes through SeriesGroupBy
 
         data: Manager2D = self._get_data_to_aggregate()
 
@@ -1227,21 +1214,13 @@ def array_func(values: ArrayLike) -> ArrayLike:
                 # generally if we have numeric_only=False
                 # and non-applicable functions
                 # try to python agg
-
-                if alt is None:
-                    # we cannot perform the operation
-                    # in an alternate way, exclude the block
-                    assert how == "ohlc"
-                    raise
-
                 result = py_fallback(values)
 
                 return cast_agg_result(result, values, how)
             return result
 
         # TypeError -> we may have an exception in trying to aggregate
         #  continue and exclude the block
-        # NotImplementedError -> "ohlc" with wrong dtype
         new_mgr = data.grouped_reduce(array_func, ignore_failures=True)
 
         if not len(new_mgr):

pandas/core/groupby/groupby.py

@@ -1791,7 +1791,25 @@ def ohlc(self) -> DataFrame:
         DataFrame
             Open, high, low and close values within each group.
         """
-        return self._apply_to_column_groupbys(lambda x: x._cython_agg_general("ohlc"))
+        if self.obj.ndim == 1:
+            # self._iterate_slices() yields only self._selected_obj
+            obj = self._selected_obj
+
+            is_numeric = is_numeric_dtype(obj.dtype)
+            if not is_numeric:
+                raise DataError("No numeric types to aggregate")
+
+            res_values = self.grouper._cython_operation(
+                "aggregate", obj._values, "ohlc", axis=0, min_count=-1
+            )
+
+            agg_names = ["open", "high", "low", "close"]
+            result = self.obj._constructor_expanddim(
+                res_values, index=self.grouper.result_index, columns=agg_names
+            )
+            return self._reindex_output(result)
+
+        return self._apply_to_column_groupbys(lambda x: x.ohlc())
 
     @final
     @doc(DataFrame.describe)

pandas/tests/resample/test_datetime_index.py

@@ -58,14 +58,16 @@ def test_custom_grouper(index):
     g = s.groupby(b)
 
     # check all cython functions work
-    funcs = ["add", "mean", "prod", "ohlc", "min", "max", "var"]
+    g.ohlc()  # doesn't use _cython_agg_general
+    funcs = ["add", "mean", "prod", "min", "max", "var"]
     for f in funcs:
         g._cython_agg_general(f)
 
     b = Grouper(freq=Minute(5), closed="right", label="right")
     g = s.groupby(b)
     # check all cython functions work
-    funcs = ["add", "mean", "prod", "ohlc", "min", "max", "var"]
+    g.ohlc()  # doesn't use _cython_agg_general
+    funcs = ["add", "mean", "prod", "min", "max", "var"]
     for f in funcs:
         g._cython_agg_general(f)
 
@@ -79,7 +81,7 @@ def test_custom_grouper(index):
     idx = DatetimeIndex(idx, freq="5T")
     expect = Series(arr, index=idx)
 
-    # GH2763 - return in put dtype if we can
+    # GH2763 - return input dtype if we can
     result = g.agg(np.sum)
     tm.assert_series_equal(result, expect)