BUG: 2D ndarray of dtype 'object' is always copied upon construction

doc/source/whatsnew/v1.4.0.rst

@@ -178,6 +178,7 @@ Categorical
 Datetimelike
 ^^^^^^^^^^^^
 - Bug in :func:`to_datetime` returning pd.NaT for inputs that produce duplicated values, when ``cache=True`` (:issue:`42259`)
+- Bug in :class:`DataFrame` constructor unnecessarily copying non-datetimelike 2D object arrays (:issue:`39272`)
 -
 
 Timedelta

pandas/core/internals/construction.py

@@ -348,22 +348,17 @@ def ndarray_to_mgr(
     # on the entire block; this is to convert if we have datetimelike's
     # embedded in an object type
     if dtype is None and is_object_dtype(values.dtype):
-
-        if values.ndim == 2 and values.shape[0] != 1:
-            # transpose and separate blocks
-
-            dtlike_vals = [maybe_infer_to_datetimelike(row) for row in values]
-            dvals_list = [ensure_block_shape(dval, 2) for dval in dtlike_vals]
-
-            # TODO: What about re-joining object columns?
+        obj_columns = list(values)
+        maybe_datetime = [maybe_infer_to_datetimelike(x) for x in obj_columns]
+        # don't convert (and copy) the objects if no type inference occurs
+        if any(x is not y for x, y in zip(obj_columns, maybe_datetime)):
+            dvals_list = [ensure_block_shape(dval, 2) for dval in maybe_datetime]
             block_values = [
                 new_block(dvals_list[n], placement=n, ndim=2)
                 for n in range(len(dvals_list))
             ]
-
         else:
-            datelike_vals = maybe_infer_to_datetimelike(values)
-            nb = new_block(datelike_vals, placement=slice(len(columns)), ndim=2)
+            nb = new_block(values, placement=slice(len(columns)), ndim=2)
             block_values = [nb]
     else:
         nb = new_block(values, placement=slice(len(columns)), ndim=2)

pandas/tests/frame/test_constructors.py

@@ -253,6 +253,20 @@ def test_constructor_dtype_nocast_view_2d_array(self):
         should_be_view[0][0] = 97
         assert df.values[0, 0] == 97
 
+    @td.skip_array_manager_invalid_test
+    def test_1d_object_array_does_not_copy(self):
+        # https://github.com/pandas-dev/pandas/issues/39272
+        arr = np.array(["a", "b"], dtype="object")
+        df = DataFrame(arr)
+        assert np.shares_memory(df.values, arr)
+
+    @td.skip_array_manager_invalid_test
+    def test_2d_object_array_does_not_copy(self):
+        # https://github.com/pandas-dev/pandas/issues/39272
+        arr = np.array([["a", "b"], ["c", "d"]], dtype="object")
+        df = DataFrame(arr)
+        assert np.shares_memory(df.values, arr)
+
     def test_constructor_dtype_list_data(self):
         df = DataFrame([[1, "2"], [None, "a"]], dtype=object)
         assert df.loc[1, 0] is None