BUG: Fixed DataFrameGroupBy.transform with numba returning the wrong order with non increasing indexes #57069 (#58030)

* Fix #57069: DataFrameGroupBy.transform with numba returning the wrong order with non monotonically increasing indexes

Fixed a bug that was returning the wrong order unless the index was monotonically increasing while utilizing DataFrameGroupBy.transform with engine='numba'
Fixed the test "pandas/tests/groupby/transform/test_numba.py::test_index_data_correctly_passed" to expect a result in the correct order
Added a test "pandas/tests/groupby/transform/test_numba.py::test_index_order_consistency_preserved" to test DataFrameGroupBy.transform with engine='numba' with a decreasing index
Updated whatsnew to reflect changes

* Apply suggestions from code review

Co-authored-by: Matthew Roeschke <[email protected]>

* Fixed pre-commit requirements

---------

Co-authored-by: Matthew Roeschke <[email protected]>

Author: andremcorreia

Diff for: doc/source/whatsnew/v3.0.0.rst

@@ -327,6 +327,7 @@ Bug fixes
 - Fixed bug in :meth:`DataFrame.cumsum` which was raising ``IndexError`` if dtype is ``timedelta64[ns]`` (:issue:`57956`)
 - Fixed bug in :meth:`DataFrame.join` inconsistently setting result index name (:issue:`55815`)
 - Fixed bug in :meth:`DataFrame.to_string` that raised ``StopIteration`` with nested DataFrames. (:issue:`16098`)
+- Fixed bug in :meth:`DataFrame.transform` that was returning the wrong order unless the index was monotonically increasing. (:issue:`57069`)
 - Fixed bug in :meth:`DataFrame.update` bool dtype being converted to object (:issue:`55509`)
 - Fixed bug in :meth:`DataFrameGroupBy.apply` that was returning a completely empty DataFrame when all return values of ``func`` were ``None`` instead of returning an empty DataFrame with the original columns and dtypes. (:issue:`57775`)
 - Fixed bug in :meth:`Series.diff` allowing non-integer values for the ``periods`` argument. (:issue:`56607`)

Diff for: pandas/core/groupby/groupby.py

@@ -1439,6 +1439,7 @@ def _transform_with_numba(self, func, *args, engine_kwargs=None, **kwargs):
         data and indices into a Numba jitted function.
         """
         data = self._obj_with_exclusions
+        index_sorting = self._grouper.result_ilocs
         df = data if data.ndim == 2 else data.to_frame()
 
         starts, ends, sorted_index, sorted_data = self._numba_prep(df)
@@ -1456,7 +1457,7 @@ def _transform_with_numba(self, func, *args, engine_kwargs=None, **kwargs):
         )
         # result values needs to be resorted to their original positions since we
         # evaluated the data sorted by group
-        result = result.take(np.argsort(sorted_index), axis=0)
+        result = result.take(np.argsort(index_sorting), axis=0)
         index = data.index
         if data.ndim == 1:
             result_kwargs = {"name": data.name}

Diff for: pandas/tests/groupby/transform/test_numba.py

@@ -181,10 +181,25 @@ def f(values, index):
 
     df = DataFrame({"group": ["A", "A", "B"], "v": [4, 5, 6]}, index=[-1, -2, -3])
     result = df.groupby("group").transform(f, engine="numba")
-    expected = DataFrame([-4.0, -3.0, -2.0], columns=["v"], index=[-1, -2, -3])
+    expected = DataFrame([-2.0, -3.0, -4.0], columns=["v"], index=[-1, -2, -3])
     tm.assert_frame_equal(result, expected)
 
 
+def test_index_order_consistency_preserved():
+    # GH 57069
+    pytest.importorskip("numba")
+
+    def f(values, index):
+        return values
+
+    df = DataFrame(
+        {"vals": [0.0, 1.0, 2.0, 3.0], "group": [0, 1, 0, 1]}, index=range(3, -1, -1)
+    )
+    result = df.groupby("group")["vals"].transform(f, engine="numba")
+    expected = Series([0.0, 1.0, 2.0, 3.0], index=range(3, -1, -1), name="vals")
+    tm.assert_series_equal(result, expected)
+
+
 def test_engine_kwargs_not_cached():
     # If the user passes a different set of engine_kwargs don't return the same
     # jitted function