BUG: DataFrame.stack with EA columns (#44401)

Author: jbrockmendel

doc/source/whatsnew/v1.4.0.rst

@@ -623,6 +623,8 @@ Reshaping
 - Bug in :func:`crosstab` would fail when inputs are lists or tuples (:issue:`44076`)
 - Bug in :meth:`DataFrame.append` failing to retain ``index.name`` when appending a list of :class:`Series` objects (:issue:`44109`)
 - Fixed metadata propagation in :meth:`Dataframe.apply` method, consequently fixing the same issue for :meth:`Dataframe.transform`, :meth:`Dataframe.nunique` and :meth:`Dataframe.mode` (:issue:`28283`)
+- Bug in :meth:`DataFrame.stack` with ``ExtensionDtype`` columns incorrectly raising (:issue:`43561`)
+-
 
 Sparse
 ^^^^^^

pandas/core/reshape/reshape.py

@@ -745,13 +745,15 @@ def _convert_level_number(level_num, columns):
  if frame._is_homogeneous_type and is_extension_array_dtype(
  frame.dtypes.iloc[0]
  ):
+ # TODO(EA2D): won't need special case, can go through .values
+ # paths below (might change to ._values)
  dtype = this[this.columns[loc]].dtypes.iloc[0]
  subset = this[this.columns[loc]]
 
  value_slice = dtype.construct_array_type()._concat_same_type(
  [x._values for _, x in subset.items()]
  )
- N, K = this.shape
+ N, K = subset.shape
  idx = np.arange(N * K).reshape(K, N).T.ravel()
  value_slice = value_slice.take(idx)
 

pandas/tests/frame/test_stack_unstack.py

@@ -2099,3 +2099,27 @@ def test_stack_unsorted(self):
  result = DF.stack(["VAR", "TYP"]).sort_index()
  expected = DF.sort_index(axis=1).stack(["VAR", "TYP"]).sort_index()
  tm.assert_series_equal(result, expected)
+
+ def test_stack_nullable_dtype(self):
+ # GH#43561
+ columns = MultiIndex.from_product(
+ [["54511", "54515"], ["r", "t_mean"]], names=["station", "element"]
+ )
+ index = Index([1, 2, 3], name="time")
+
+ arr = np.array([[50, 226, 10, 215], [10, 215, 9, 220], [305, 232, 111, 220]])
+ df = DataFrame(arr, columns=columns, index=index, dtype=pd.Int64Dtype())
+
+ result = df.stack("station")
+
+ expected = df.astype(np.int64).stack("station").astype(pd.Int64Dtype())
+ tm.assert_frame_equal(result, expected)
+
+ # non-homogeneous case
+ df[df.columns[0]] = df[df.columns[0]].astype(pd.Float64Dtype())
+ result = df.stack("station")
+
+ # TODO(EA2D): we get object dtype because DataFrame.values can't
+ # be an EA
+ expected = df.astype(object).stack("station")
+ tm.assert_frame_equal(result, expected)