Revert "BUG: inconsistent concat casting EA vs non-EA (pandas-dev#38843)"

This reverts commit 2362df9.

Author: jorisvandenbossche

doc/source/whatsnew/v1.3.0.rst

@@ -312,12 +312,10 @@ Reshaping
 - Bug in :func:`merge` raising error when performing an inner join with partial index and ``right_index`` when no overlap between indices (:issue:`33814`)
 - Bug in :meth:`DataFrame.unstack` with missing levels led to incorrect index names (:issue:`37510`)
 - Bug in :func:`join` over :class:`MultiIndex` returned wrong result, when one of both indexes had only one level (:issue:`36909`)
-- Bug in :func:`concat` incorrectly casting to ``object`` dtype in some cases when one or more of the operands is empty (:issue:`38843`, :issue:`38907`)
 - :meth:`merge_asof` raises ``ValueError`` instead of cryptic ``TypeError`` in case of non-numerical merge columns (:issue:`29130`)
-- Bug in :func:`concat` incorrectly casting to ``object`` dtype in some cases when one or more of the operands is empty (:issue:`38843`)
+- Bug in :meth:`DataFrame.unstack` with missing levels led to incorrect index names (:issue:`37510`)
 -
 
-
 Sparse
 ^^^^^^
 

pandas/core/dtypes/concat.py

@@ -127,7 +127,7 @@ def is_nonempty(x) -> bool:
     # marginal given that it would still require shape & dtype calculation and
     # np.concatenate which has them both implemented is compiled.
     non_empties = [x for x in to_concat if is_nonempty(x)]
-    if non_empties:
+    if non_empties and axis == 0:
         to_concat = non_empties
 
     typs = _get_dtype_kinds(to_concat)

pandas/tests/indexing/test_partial.py

@@ -170,21 +170,11 @@ def test_partial_setting_mixed_dtype(self):
         with pytest.raises(ValueError, match=msg):
             df.loc[0] = [1, 2, 3]
 
-    @pytest.mark.parametrize("dtype", [None, "int64", "Int64"])
-    def test_loc_setitem_expanding_empty(self, dtype):
+        # TODO: #15657, these are left as object and not coerced
         df = DataFrame(columns=["A", "B"])
+        df.loc[3] = [6, 7]
 
-        value = [6, 7]
-        if dtype == "int64":
-            value = np.array(value, dtype=dtype)
-        elif dtype == "Int64":
-            value = pd.array(value, dtype=dtype)
-
-        df.loc[3] = value
-
-        exp = DataFrame([[6, 7]], index=[3], columns=["A", "B"], dtype=dtype)
-        if dtype is not None:
-            exp = exp.astype(dtype)
+        exp = DataFrame([[6, 7]], index=[3], columns=["A", "B"], dtype="object")
         tm.assert_frame_equal(df, exp)
 
     def test_series_partial_set(self):

pandas/tests/reshape/concat/test_concat.py

@@ -474,12 +474,11 @@ def test_concat_will_upcast(dt, pdt):
         assert x.values.dtype == "float64"
 
 
-@pytest.mark.parametrize("dtype", ["int64", "Int64"])
-def test_concat_empty_and_non_empty_frame_regression(dtype):
+def test_concat_empty_and_non_empty_frame_regression():
     # GH 18178 regression test
-    df1 = DataFrame({"foo": [1]}).astype(dtype)
+    df1 = DataFrame({"foo": [1]})
     df2 = DataFrame({"foo": []})
-    expected = df1
+    expected = DataFrame({"foo": [1.0]})
     result = pd.concat([df1, df2])
     tm.assert_frame_equal(result, expected)
 

pandas/tests/reshape/concat/test_empty.py

@@ -202,15 +202,12 @@ def test_concat_empty_series_dtypes_sparse(self):
         expected = pd.SparseDtype("object")
         assert result.dtype == expected
 
-    @pytest.mark.parametrize("dtype", ["int64", "Int64"])
-    def test_concat_empty_df_object_dtype(self, dtype):
+    def test_concat_empty_df_object_dtype(self):
         # GH 9149
         df_1 = DataFrame({"Row": [0, 1, 1], "EmptyCol": np.nan, "NumberCol": [1, 2, 3]})
-        df_1["Row"] = df_1["Row"].astype(dtype)
         df_2 = DataFrame(columns=df_1.columns)
         result = pd.concat([df_1, df_2], axis=0)
-        expected = df_1.copy()
-        expected["EmptyCol"] = expected["EmptyCol"].astype(object)  # TODO: why?
+        expected = df_1.astype(object)
         tm.assert_frame_equal(result, expected)
 
     def test_concat_empty_dataframe_dtypes(self):