WARN: Don't show FutureWarning when enlarging df with iloc

pandas/core/indexing.py

@@ -2004,11 +2004,16 @@ def _setitem_single_column(self, loc: int, value, plane_indexer):
             if (
                 isinstance(new_values, np.ndarray)
                 and isinstance(orig_values, np.ndarray)
-                and np.shares_memory(new_values, orig_values)
+                and (
+                    np.shares_memory(new_values, orig_values)
+                    or new_values.shape != orig_values.shape
+                )
             ):
                 # TODO: get something like tm.shares_memory working?
                 # The values were set inplace after all, no need to warn,
                 #  e.g. test_rename_nocopy
+                # In case of enlarging we can not set inplace, so need to
+                # warn either
                 pass
             else:
                 warnings.warn(

pandas/tests/frame/indexing/test_indexing.py

@@ -1307,6 +1307,16 @@ def test_loc_setitem_rhs_frame(self, idxr, val):
         expected = DataFrame({"a": [np.nan, val]})
         tm.assert_frame_equal(df, expected)
 
+    @td.skip_array_manager_invalid_test
+    def test_iloc_setitem_enlarge_no_warning(self):
+        # GH#47381
+        df = DataFrame(columns=["a", "b"])
+        expected = df.copy()
+        view = df[:]
+        with tm.assert_produces_warning(None):
+            df.iloc[:, 0] = np.array([1, 2], dtype=np.float64)
+        tm.assert_frame_equal(view, expected)
+
 
 class TestDataFrameIndexingUInt64:
     def test_setitem(self, uint64_frame):

pandas/tests/frame/indexing/test_setitem.py

@@ -806,9 +806,7 @@ def test_setitem_string_column_numpy_dtype_raising(self):
     def test_setitem_empty_df_duplicate_columns(self):
         # GH#38521
         df = DataFrame(columns=["a", "b", "b"], dtype="float64")
-        msg = "will attempt to set the values inplace instead"
-        with tm.assert_produces_warning(FutureWarning, match=msg):
-            df.loc[:, "a"] = list(range(2))
+        df.loc[:, "a"] = list(range(2))
         expected = DataFrame(
             [[0, np.nan, np.nan], [1, np.nan, np.nan]], columns=["a", "b", "b"]
         )