📝 add whatsnew entry to v1.0.1

Author: Marco Gorelli

doc/source/whatsnew/v1.0.0.rst

@@ -1246,7 +1246,6 @@ Reshaping
 - Bug in :func:`merge_asof` merging on a tz-aware ``left_index`` and ``right_on`` a tz-aware column (:issue:`29864`)
 - Improved error message and docstring in :func:`cut` and :func:`qcut` when `labels=True` (:issue:`13318`)
 - Bug in missing `fill_na` parameter to :meth:`DataFrame.unstack` with list of levels (:issue:`30740`)
-- :meth:`DataFrame.merge` now preserves right frame's row order when executing a right merge (:issue:`27453`)
 
 Sparse
 ^^^^^^

pandas/tests/reshape/merge/test_merge.py

@@ -2172,25 +2172,25 @@ def test_merge_datetime_upcast_dtype():
 @pytest.mark.parametrize("how", ["left", "right"])
 def test_merge_preserves_row_order(how):
     # GH 27453
-    population = [
-        ("Jenn", "Jamaica", 3),
-        ("Beth", "Bulgaria", 7),
-        ("Carl", "Canada", 30),
-    ]
-    columns = ["name", "country", "population"]
-    population_df = DataFrame(population, columns=columns)
+    population_df = DataFrame(
+        {
+            "name": ["Jenn", "Beth", "Carl"],
+            "country": ["Jamaica", "Bulgaria", "Canada"],
+            "population": [3, 7, 30],
+        }
+    )
 
-    people = [("Abe", "America"), ("Beth", "Bulgaria"), ("Carl", "Canada")]
-    columns = ["name", "country"]
-    people_df = DataFrame(people, columns=columns)
+    people_df = DataFrame(
+        {"name": ["Abe", "Beth", "Carl"], "country": ["America", "Bulgaria", "Canada"]}
+    )
 
-    expected_data = [
-        ("Abe", "America", np.nan),
-        ("Beth", "Bulgaria", 7),
-        ("Carl", "Canada", 30),
-    ]
-    expected_cols = ["name", "country", "population"]
-    expected = DataFrame(expected_data, columns=expected_cols)
+    expected = DataFrame(
+        {
+            "name": ["Abe", "Beth", "Carl"],
+            "country": ["America", "Bulgaria", "Canada"],
+            "population": [np.nan, 7, 30],
+        }
+    )
 
     if how == "right":
         left_df, right_df = population_df, people_df