BUG: GroupBy.ohlc with as_index=False

doc/source/whatsnew/v2.0.0.rst

@@ -1367,6 +1367,7 @@ Groupby/resample/rolling
 - Bug in :meth:`.DataFrameGroupBy.describe` produced incorrect results when data had duplicate columns (:issue:`50806`)
 - Bug in :meth:`.DataFrameGroupBy.agg` with ``engine="numba"`` failing to respect ``as_index=False`` (:issue:`51228`)
 - Bug in :meth:`DataFrameGroupBy.agg`, :meth:`SeriesGroupBy.agg`, and :meth:`Resampler.agg` would ignore arguments when passed a list of functions (:issue:`50863`)
+- Bug in :meth:`DataFrameGroupBy.ohlc` ignoring ``as_index=False`` (:issue:`51413`)
 -
 
 Reshaping

pandas/core/groupby/groupby.py

@@ -2501,10 +2501,13 @@ def ohlc(self) -> DataFrame:
             )
             return self._reindex_output(result)
 
-        # TODO: 2023-02-05 all tests that get here have self.as_index
-        return self._apply_to_column_groupbys(
+        result = self._apply_to_column_groupbys(
             lambda x: x.ohlc(), self._obj_with_exclusions
         )
+        if not self.as_index:
+            result = self._insert_inaxis_grouper(result)
+            result.index = default_index(len(result))
+        return result
 
     @doc(DataFrame.describe)
     def describe(

pandas/tests/groupby/aggregate/test_aggregate.py

@@ -586,7 +586,8 @@ def test_ohlc_ea_dtypes(any_numeric_ea_dtype):
         {"a": [1, 1, 2, 3, 4, 4], "b": [22, 11, pd.NA, 10, 20, pd.NA]},
         dtype=any_numeric_ea_dtype,
     )
-    result = df.groupby("a").ohlc()
+    gb = df.groupby("a")
+    result = gb.ohlc()
     expected = DataFrame(
         [[22, 22, 11, 11], [pd.NA] * 4, [10] * 4, [20] * 4],
         columns=MultiIndex.from_product([["b"], ["open", "high", "low", "close"]]),
@@ -595,6 +596,11 @@ def test_ohlc_ea_dtypes(any_numeric_ea_dtype):
     )
     tm.assert_frame_equal(result, expected)
 
+    gb2 = df.groupby("a", as_index=False)
+    result2 = gb2.ohlc()
+    expected2 = expected.reset_index()
+    tm.assert_frame_equal(result2, expected2)
+
 
 @pytest.mark.parametrize("dtype", [np.int64, np.uint64])
 @pytest.mark.parametrize("how", ["first", "last", "min", "max", "mean", "median"])