API: dont-special-case datetimelike in setting new column

pandas/core/frame.py

@@ -99,7 +99,6 @@
     maybe_box_native,
     maybe_convert_platform,
     maybe_downcast_to_dtype,
-    maybe_infer_to_datetimelike,
     validate_numeric_casting,
 )
 from pandas.core.dtypes.common import (
@@ -4045,7 +4044,7 @@ def _sanitize_column(self, value) -> ArrayLike:
 
             # possibly infer to datetimelike
             if is_object_dtype(value.dtype):
-                value = maybe_infer_to_datetimelike(value)
+                value = Series(value)._values
 
         else:
             value = construct_1d_arraylike_from_scalar(value, len(self), dtype=None)

pandas/tests/frame/indexing/test_setitem.py

@@ -421,19 +421,26 @@ def test_setitem_intervals(self):
 
         # B & D end up as Categoricals
         # the remainer are converted to in-line objects
-        # contining an IntervalIndex.values
+        # containing an IntervalIndex.values
         df["B"] = ser
         df["C"] = np.array(ser)
         df["D"] = ser.values
         df["E"] = np.array(ser.values)
+        df["F"] = ser.astype(object)
 
         assert is_categorical_dtype(df["B"].dtype)
         assert is_interval_dtype(df["B"].cat.categories)
         assert is_categorical_dtype(df["D"].dtype)
         assert is_interval_dtype(df["D"].cat.categories)
 
-        assert is_object_dtype(df["C"])
-        assert is_object_dtype(df["E"])
+        # Thes goes through the Series constructor and so get inferred back
+        #  to IntervalDtype
+        assert is_interval_dtype(df["C"])
+        assert is_interval_dtype(df["E"])
+
+        # But the Series constructor doesn't do inference on Series objects,
+        #  so setting df["F"] doesnt get cast back to IntervalDtype
+        assert is_object_dtype(df["F"])
 
         # they compare equal as Index
         # when converted to numpy objects