BUG: DataFrame.stack with sort=True and unsorted MultiIndex levels (pandas-dev#53637)

* BUG: DataFrame.stack with sort=True and unsorted MultiIndex levels

* revert ignoring warnings

Author: rhshadrach

doc/source/whatsnew/v2.1.0.rst

@@ -470,6 +470,7 @@ Reshaping
 - Bug in :meth:`DataFrame.idxmin` and :meth:`DataFrame.idxmax`, where the axis dtype would be lost for empty frames (:issue:`53265`)
 - Bug in :meth:`DataFrame.merge` not merging correctly when having ``MultiIndex`` with single level (:issue:`52331`)
 - Bug in :meth:`DataFrame.stack` losing extension dtypes when columns is a :class:`MultiIndex` and frame contains mixed dtypes (:issue:`45740`)
+- Bug in :meth:`DataFrame.stack` would incorrectly order results when ``sort=True`` and the input had :class:`MultiIndex` levels that were not sorted (:issue:`53636`)
 - Bug in :meth:`DataFrame.transpose` inferring dtype for object column (:issue:`51546`)
 - Bug in :meth:`Series.combine_first` converting ``int64`` dtype to ``float64`` and losing precision on very large integers (:issue:`51764`)
 -

pandas/core/reshape/reshape.py

@@ -756,7 +756,16 @@ def _convert_level_number(level_num: int, columns: Index):
     level_vals = mi_cols.levels[-1]
     level_codes = unique(mi_cols.codes[-1])
     if sort:
+        _, index, inverse = np.unique(
+            level_vals, return_index=True, return_inverse=True
+        )
+        sorted_level_vals = np.take(level_vals, index)
         level_codes = np.sort(level_codes)
+        # Take level_codes according to where level_vals get sorted to, while
+        # also allowing for NA (-1) values
+        level_codes = np.where(level_codes == -1, -1, np.take(inverse, level_codes))
+    else:
+        sorted_level_vals = level_vals
     level_vals_nan = level_vals.insert(len(level_vals), None)
 
     level_vals_used = np.take(level_vals_nan, level_codes)
@@ -818,7 +827,7 @@ def _convert_level_number(level_num: int, columns: Index):
         new_codes = [old_codes.repeat(levsize)]
         new_names = [this.index.name]  # something better?
 
-    new_levels.append(level_vals)
+    new_levels.append(sorted_level_vals)
     new_codes.append(np.tile(level_codes, N))
     new_names.append(frame.columns.names[level_num])
 

pandas/tests/frame/test_stack_unstack.py

@@ -1998,18 +1998,20 @@ def __init__(self, *args, **kwargs) -> None:
         ),
     )
     @pytest.mark.parametrize("stack_lev", range(2))
-    def test_stack_order_with_unsorted_levels(self, levels, stack_lev):
+    @pytest.mark.parametrize("sort", [True, False])
+    def test_stack_order_with_unsorted_levels(self, levels, stack_lev, sort):
         # GH#16323
         # deep check for 1-row case
         columns = MultiIndex(levels=levels, codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
         df = DataFrame(columns=columns, data=[range(4)])
-        df_stacked = df.stack(stack_lev)
-        assert all(
-            df.loc[row, col]
-            == df_stacked.loc[(row, col[stack_lev]), col[1 - stack_lev]]
-            for row in df.index
-            for col in df.columns
-        )
+        df_stacked = df.stack(stack_lev, sort=sort)
+        for row in df.index:
+            for col in df.columns:
+                expected = df.loc[row, col]
+                result_row = row, col[stack_lev]
+                result_col = col[1 - stack_lev]
+                result = df_stacked.loc[result_row, result_col]
+                assert result == expected
 
     def test_stack_order_with_unsorted_levels_multi_row(self):
         # GH#16323
@@ -2028,6 +2030,26 @@ def test_stack_order_with_unsorted_levels_multi_row(self):
             for col in df.columns
         )
 
+    def test_stack_order_with_unsorted_levels_multi_row_2(self):
+        # GH#53636
+        levels = ((0, 1), (1, 0))
+        stack_lev = 1
+        columns = MultiIndex(levels=levels, codes=[[0, 0, 1, 1], [0, 1, 0, 1]])
+        df = DataFrame(columns=columns, data=[range(4)], index=[1, 0, 2, 3])
+        result = df.stack(stack_lev, sort=True)
+        expected_index = MultiIndex(
+            levels=[[0, 1, 2, 3], [0, 1]],
+            codes=[[1, 1, 0, 0, 2, 2, 3, 3], [1, 0, 1, 0, 1, 0, 1, 0]],
+        )
+        expected = DataFrame(
+            {
+                0: [0, 1, 0, 1, 0, 1, 0, 1],
+                1: [2, 3, 2, 3, 2, 3, 2, 3],
+            },
+            index=expected_index,
+        )
+        tm.assert_frame_equal(result, expected)
+
     def test_stack_unstack_unordered_multiindex(self):
         # GH# 18265
         values = np.arange(5)