Preserve EA dtype in DataFrame.stack

doc/source/whatsnew/v0.24.0.txt

@@ -853,6 +853,7 @@ update the ``ExtensionDtype._metadata`` tuple to match the signature of your
 - Updated the ``.type`` attribute for ``PeriodDtype``, ``DatetimeTZDtype``, and ``IntervalDtype`` to be instances of the dtype (``Period``, ``Timestamp``, and ``Interval`` respectively) (:issue:`22938`)
 - :func:`ExtensionArray.isna` is allowed to return an ``ExtensionArray`` (:issue:`22325`).
 - Support for reduction operations such as ``sum``, ``mean`` via opt-in base class method override (:issue:`22762`)
+- :meth:`DataFrame.stack` no longer converts to object dtype for DataFrames where each column has the same extension dtype. The output Series will have the same dtype as the columns (:issue:`23077`).
 - :meth:`Series.unstack` and :meth:`DataFrame.unstack` no longer convert extension arrays to object-dtype ndarrays. Each column in the output ``DataFrame`` will now have the same dtype as the input (:issue:`23077`).
 - Bug when grouping :meth:`Dataframe.groupby()` and aggregating on ``ExtensionArray`` it was not returning the actual ``ExtensionArray`` dtype (:issue:`23227`).
 

pandas/core/internals/blocks.py

@@ -35,9 +35,9 @@
     is_numeric_v_string_like, is_extension_type,
     is_extension_array_dtype,
     is_list_like,
-    is_sparse,
     is_re,
     is_re_compilable,
+    is_sparse,
     pandas_dtype)
 from pandas.core.dtypes.cast import (
     maybe_downcast_to_dtype,

pandas/core/reshape/reshape.py

@@ -494,8 +494,9 @@ def factorize(index):
         if is_extension_array_dtype(dtype):
             arr = dtype.construct_array_type()
             new_values = arr._concat_same_type([
-                col for _, col in frame.iteritems()
+                col._values for _, col in frame.iteritems()
             ])
+            new_values = _reorder_for_extension_array_stack(new_values, N, K)
         else:
             # homogeneous, non-EA
             new_values = frame.values.ravel()
@@ -624,16 +625,32 @@ def _convert_level_number(level_num, columns):
             slice_len = loc.stop - loc.start
 
         if slice_len != levsize:
-            chunk = this.loc[:, this.columns[loc]]
+            chunk = this[this.columns[loc]]
             chunk.columns = level_vals.take(chunk.columns.labels[-1])
             value_slice = chunk.reindex(columns=level_vals_used).values
         else:
-            if frame._is_mixed_type:
-                value_slice = this.loc[:, this.columns[loc]].values
+            if (frame._is_homogeneous_type and
+                    is_extension_array_dtype(frame.dtypes.iloc[0])):
+                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.iteritems()]
+                )
+                N, K = this.shape
+                idx = np.arange(N * K).reshape(K, N).T.ravel()
+                value_slice = value_slice.take(idx)
+
+            elif frame._is_mixed_type:
+                value_slice = this[this.columns[loc]].values
             else:
                 value_slice = this.values[:, loc]
 
-        new_data[key] = value_slice.ravel()
+        if value_slice.ndim > 1:
+            # i.e. not extension
+            value_slice = value_slice.ravel()
+
+        new_data[key] = value_slice
 
     if len(drop_cols) > 0:
         new_columns = new_columns.difference(drop_cols)
@@ -971,3 +988,38 @@ def make_axis_dummies(frame, axis='minor', transform=None):
     values = values.take(labels, axis=0)
 
     return DataFrame(values, columns=items, index=frame.index)
+
+
+def _reorder_for_extension_array_stack(arr, n_rows, n_columns):
+    """
+    Re-orders the values when stacking multiple extension-arrays.
+
+    The indirect stacking method used for EAs requires a followup
+    take to get the order correct.
+
+    Parameters
+    ----------
+    arr : ExtensionArray
+    n_rows, n_columns : int
+        The number of rows and columns in the original DataFrame.
+
+    Returns
+    -------
+    taken : ExtensionArray
+        The original `arr` with elements re-ordered appropriately
+
+    Examples
+    --------
+    >>> arr = np.array(['a', 'b', 'c', 'd', 'e', 'f'])
+    >>> _reorder_for_extension_array_stack(arr, 2, 3)
+    array(['a', 'c', 'e', 'b', 'd', 'f'], dtype='<U1')
+
+    >>> _reorder_for_extension_array_stack(arr, 3, 2)
+    array(['a', 'd', 'b', 'e', 'c', 'f'], dtype='<U1')
+    """
+    # final take to get the order correct.
+    # idx is an indexer like
+    # [c0r0, c1r0, c2r0, ...,
+    #  c0r1, c1r1, c2r1, ...]
+    idx = np.arange(n_rows * n_columns).reshape(n_columns, n_rows).T.ravel()
+    return arr.take(idx)

pandas/tests/extension/base/reshaping.py

@@ -173,6 +173,28 @@ def test_merge(self, data, na_value):
                  dtype=data.dtype)})
         self.assert_frame_equal(res, exp[['ext', 'int1', 'key', 'int2']])
 
+    @pytest.mark.parametrize("columns", [
+        ["A", "B"],
+        pd.MultiIndex.from_tuples([('A', 'a'), ('A', 'b')],
+                                  names=['outer', 'inner']),
+    ])
+    def test_stack(self, data, columns):
+        df = pd.DataFrame({"A": data[:5], "B": data[:5]})
+        df.columns = columns
+        result = df.stack()
+        expected = df.astype(object).stack()
+        # we need a second astype(object), in case the constructor inferred
+        # object -> specialized, as is done for period.
+        expected = expected.astype(object)
+
+        if isinstance(expected, pd.Series):
+            assert result.dtype == df.iloc[:, 0].dtype
+        else:
+            assert all(result.dtypes == df.iloc[:, 0].dtype)
+
+        result = result.astype(object)
+        self.assert_equal(result, expected)
+
     @pytest.mark.parametrize("index", [
         # Two levels, uniform.
         pd.MultiIndex.from_product(([['A', 'B'], ['a', 'b']]),

pandas/tests/extension/json/test_json.py

@@ -139,6 +139,15 @@ def test_from_dtype(self, data):
 
 
 class TestReshaping(BaseJSON, base.BaseReshapingTests):
+
+    @pytest.mark.skip(reason="Different definitions of NA")
+    def test_stack(self):
+        """
+        The test does .astype(object).stack(). If we happen to have
+        any missing values in `data`, then we'll end up with different
+        rows since we consider `{}` NA, but `.astype(object)` doesn't.
+        """
+
     @pytest.mark.xfail(reason="dict for NA", strict=True)
     def test_unstack(self, data, index):
         # The base test has NaN for the expected NA value.

pandas/tests/frame/test_reshape.py

@@ -874,6 +874,17 @@ def test_stack_preserve_categorical_dtype(self, ordered, labels):
 
         tm.assert_series_equal(result, expected)
 
+    def test_stack_preserve_categorical_dtype_values(self):
+        # GH-23077
+        cat = pd.Categorical(['a', 'a', 'b', 'c'])
+        df = pd.DataFrame({"A": cat, "B": cat})
+        result = df.stack()
+        index = pd.MultiIndex.from_product([[0, 1, 2, 3], ['A', 'B']])
+        expected = pd.Series(pd.Categorical(['a', 'a', 'a', 'a',
+                                             'b', 'b', 'c', 'c']),
+                             index=index)
+        tm.assert_series_equal(result, expected)
+
     @pytest.mark.parametrize('level', [0, 1])
     def test_unstack_mixed_extension_types(self, level):
         index = pd.MultiIndex.from_tuples([('A', 0), ('A', 1), ('B', 1)],

pandas/tests/sparse/frame/test_frame.py

@@ -736,6 +736,16 @@ def test_astype_bool(self):
         assert res['A'].dtype == SparseDtype(np.bool)
         assert res['B'].dtype == SparseDtype(np.bool)
 
+    def test_astype_object(self):
+        # This may change in GH-23125
+        df = pd.DataFrame({"A": SparseArray([0, 1]),
+                           "B": SparseArray([0, 1])})
+        result = df.astype(object)
+        dtype = SparseDtype(object, 0)
+        expected = pd.DataFrame({"A": SparseArray([0, 1], dtype=dtype),
+                                 "B": SparseArray([0, 1], dtype=dtype)})
+        tm.assert_frame_equal(result, expected)
+
     def test_fillna(self, float_frame_fill0, float_frame_fill0_dense):
         df = float_frame_fill0.reindex(lrange(5))
         dense = float_frame_fill0_dense.reindex(lrange(5))