BUG: pass 2D ndarray and EA-dtype to DataFrame, closes #12513 (#30507)

doc/source/whatsnew/v1.0.0.rst

@@ -983,7 +983,8 @@ Other
 - Fixed ``pow`` operations for :class:`IntegerArray` when the other value is ``0`` or ``1`` (:issue:`29997`)
 - Bug in :meth:`Series.count` raises if use_inf_as_na is enabled (:issue:`29478`)
 - Bug in :class:`Index` where a non-hashable name could be set without raising ``TypeError`` (:issue:`29069`)
-
+- Bug in :class:`DataFrame` constructor when passing a 2D ``ndarray`` and an extension dtype (:issue:`12513`)
+-
 
 .. _whatsnew_1000.contributors:
 

pandas/core/internals/construction.py

@@ -152,9 +152,17 @@ def init_ndarray(values, index, columns, dtype=None, copy=False):
         return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
     elif is_extension_array_dtype(values) or is_extension_array_dtype(dtype):
         # GH#19157
+
+        if isinstance(values, np.ndarray) and values.ndim > 1:
+            # GH#12513 a EA dtype passed with a 2D array, split into
+            #  multiple EAs that view the values
+            values = [values[:, n] for n in range(values.shape[1])]
+        else:
+            values = [values]
+
         if columns is None:
-            columns = [0]
-        return arrays_to_mgr([values], columns, index, columns, dtype=dtype)
+            columns = list(range(len(values)))
+        return arrays_to_mgr(values, columns, index, columns, dtype=dtype)
 
     # by definition an array here
     # the dtypes will be coerced to a single dtype

pandas/tests/frame/test_constructors.py

@@ -2551,3 +2551,11 @@ def test_from_tzaware_mixed_object_array(self):
             "datetime64[ns, CET]",
         ]
         assert (res.dtypes == expected_dtypes).all()
+
+    def test_from_2d_ndarray_with_dtype(self):
+        # GH#12513
+        array_dim2 = np.arange(10).reshape((5, 2))
+        df = pd.DataFrame(array_dim2, dtype="datetime64[ns, UTC]")
+
+        expected = pd.DataFrame(array_dim2).astype("datetime64[ns, UTC]")
+        tm.assert_frame_equal(df, expected)