BUG: DataFrame with mismatched NA value and dtype (#44428)

Author: jbrockmendel

doc/source/whatsnew/v1.4.0.rst

@@ -608,6 +608,7 @@ Missing
 ^^^^^^^
 - Bug in :meth:`DataFrame.fillna` with limit and no method ignores axis='columns' or ``axis = 1`` (:issue:`40989`)
 - Bug in :meth:`DataFrame.fillna` not replacing missing values when using a dict-like ``value`` and duplicate column names (:issue:`43476`)
+- Bug in constructing a :class:`DataFrame` with a dictionary ``np.datetime64`` as a value and ``dtype='timedelta64[ns]'``, or vice-versa, incorrectly casting instead of raising (:issue:`??`)
 -
 
 MultiIndex

pandas/core/internals/construction.py

@@ -443,15 +443,18 @@ def dict_to_mgr(
         if missing.any() and not is_integer_dtype(dtype):
             nan_dtype: DtypeObj
 
-            if dtype is None or (
-                isinstance(dtype, np.dtype) and np.issubdtype(dtype, np.flexible)
-            ):
+            if dtype is not None:
+                # calling sanitize_array ensures we don't mix-and-match
+                #  NA dtypes
+                midxs = missing.values.nonzero()[0]
+                for i in midxs:
+                    arr = sanitize_array(arrays.iat[i], index, dtype=dtype)
+                    arrays.iat[i] = arr
+            else:
                 # GH#1783
                 nan_dtype = np.dtype("object")
-            else:
-                nan_dtype = dtype
-            val = construct_1d_arraylike_from_scalar(np.nan, len(index), nan_dtype)
-            arrays.loc[missing] = [val] * missing.sum()
+                val = construct_1d_arraylike_from_scalar(np.nan, len(index), nan_dtype)
+                arrays.loc[missing] = [val] * missing.sum()
 
         arrays = list(arrays)
         columns = ensure_index(columns)

pandas/tests/frame/test_constructors.py

@@ -2903,14 +2903,7 @@ def test_from_timedelta64_scalar_object(self, constructor):
         assert isinstance(get1(obj), np.timedelta64)
 
     @pytest.mark.parametrize("cls", [np.datetime64, np.timedelta64])
-    def test_from_scalar_datetimelike_mismatched(self, constructor, cls, request):
-        node = request.node
-        params = node.callspec.params
-        if params["frame_or_series"] is DataFrame and params["constructor"] is dict:
-            mark = pytest.mark.xfail(
-                reason="DataFrame incorrectly allows mismatched datetimelike"
-            )
-            node.add_marker(mark)
+    def test_from_scalar_datetimelike_mismatched(self, constructor, cls):
         scalar = cls("NaT", "ns")
         dtype = {np.datetime64: "m8[ns]", np.timedelta64: "M8[ns]"}[cls]