BUG: Preserve key order when using loc on MultiIndex DataFrame

doc/source/whatsnew/v1.1.0.rst

@@ -161,8 +161,14 @@ Missing
 
 MultiIndex
 ^^^^^^^^^^
+- Bug in :meth:`Dataframe.loc` when used with a :class:`MultiIndex`. The returned values were not in the same order as the given inputs (:issue:`22797`)
 
--
+.. ipython:: python
+
+        df = pd.DataFrame(np.arange(4),
+                          index=[["a", "a", "b", "b"], [1, 2, 1, 2]])
+        # Rows are now ordered as the requested keys
+        df.loc[(['b', 'a'], [2, 1]), :]
 -
 
 I/O

pandas/core/indexes/multi.py

@@ -1,6 +1,6 @@
 import datetime
 from sys import getsizeof
-from typing import Any, Hashable, List, Optional, Sequence, Union
+from typing import Any, Hashable, Iterable, List, Optional, Sequence, Tuple, Union
 import warnings
 
 import numpy as np
@@ -9,6 +9,7 @@
 
 from pandas._libs import Timestamp, algos as libalgos, index as libindex, lib, tslibs
 from pandas._libs.hashtable import duplicated_int64
+from pandas._typing import AnyArrayLike, ArrayLike, Scalar
 from pandas.compat.numpy import function as nv
 from pandas.errors import PerformanceWarning, UnsortedIndexError
 from pandas.util._decorators import Appender, cache_readonly
@@ -3081,9 +3082,69 @@ def _update_indexer(idxr, indexer=indexer):
         # empty indexer
         if indexer is None:
             return Int64Index([])._ndarray_values
+
+        indexer = self._reorder_indexer(seq, indexer)
+
         return indexer._ndarray_values
 
-    # --------------------------------------------------------------------
+    def _reorder_indexer(
+        self, seq: Tuple[Union[Scalar, Iterable, AnyArrayLike], ...], indexer: ArrayLike
+    ) -> ArrayLike:
+        """
+        Reorder an indexer of a MultiIndex (self) so that the label are in the
+        same order as given in seq
+
+        Parameters
+        ----------
+        seq : label/slice/list/mask or a sequence of such
+        indexer: an Int64Index indexer of self
+
+        Returns
+        -------
+        indexer : a sorted Int64Index indexer of self ordered as seq
+        """
+        # If the index is lexsorted and the list_like label in seq are sorted
+        # then we do not need to sort
+        if self.is_lexsorted():
+            need_sort = False
+            for i, k in enumerate(seq):
+                if is_list_like(k):
+                    if not need_sort:
+                        k_codes = self.levels[i].get_indexer(k)
+                        k_codes = k_codes[k_codes >= 0]  # Filter absent keys
+                        # True if the given codes are not ordered
+                        need_sort = (k_codes[:-1] > k_codes[1:]).any()
+            # Bail out if both index and seq are sorted
+            if not need_sort:
+                return indexer
+
+        n = len(self)
+        keys: Tuple[np.ndarray, ...] = tuple()
+        # For each level of the sequence in seq, map the level codes with the
+        # order they appears in a list-like sequence
+        # This mapping is then use to reorder the indexer
+        for i, k in enumerate(seq):
+            if com.is_bool_indexer(k):
+                new_order = np.arange(n)[indexer]
+            elif is_list_like(k):
+                # Generate a map with all level codes as sorted initially
+                key_order_map = np.ones(len(self.levels[i]), dtype=np.uint64) * len(
+                    self.levels[i]
+                )
+                # Set order as given in the indexer list
+                level_indexer = self.levels[i].get_indexer(k)
+                level_indexer = level_indexer[level_indexer >= 0]  # Filter absent keys
+                key_order_map[level_indexer] = np.arange(len(level_indexer))
+
+                new_order = key_order_map[self.codes[i][indexer]]
+            else:
+                # For all other case, use the same order as the level
+                new_order = np.arange(n)[indexer]
+            keys = (new_order,) + keys
+
+        # Find the reordering using lexsort on the keys mapping
+        ind = np.lexsort(keys)
+        return indexer[ind]
 
     def truncate(self, before=None, after=None):
         """

pandas/tests/test_multilevel.py

@@ -2534,3 +2534,29 @@ def test_sort_ascending_list(self):
         result = s.sort_index(level=["third", "first"], ascending=[False, True])
         expected = s.iloc[[0, 4, 1, 5, 2, 6, 3, 7]]
         tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "keys, expected",
+        [
+            (["b", "a"], [["b", "b", "a", "a"], [1, 2, 1, 2]]),
+            (["a", "b"], [["a", "a", "b", "b"], [1, 2, 1, 2]]),
+            ((["a", "b"], [1, 2]), [["a", "a", "b", "b"], [1, 2, 1, 2]]),
+            ((["a", "b"], [2, 1]), [["a", "a", "b", "b"], [2, 1, 2, 1]]),
+            ((["b", "a"], [2, 1]), [["b", "b", "a", "a"], [2, 1, 2, 1]]),
+            ((["b", "a"], [1, 2]), [["b", "b", "a", "a"], [1, 2, 1, 2]]),
+            ((["c", "a"], [2, 1]), [["c", "a", "a"], [1, 2, 1]]),
+        ],
+    )
+    @pytest.mark.parametrize("dim", ["index", "columns"])
+    def test_multilevel_index_loc_order(self, dim, keys, expected):
+        # GH 22797
+        # Try to respect order of keys given for MultiIndex.loc
+        kwargs = {dim: [["c", "a", "a", "b", "b"], [1, 1, 2, 1, 2]]}
+        df = pd.DataFrame(np.arange(25).reshape(5, 5), **kwargs,)
+        exp_index = MultiIndex.from_arrays(expected)
+        if dim == "index":
+            res = df.loc[keys, :]
+            tm.assert_index_equal(res.index, exp_index)
+        elif dim == "columns":
+            res = df.loc[:, keys]
+            tm.assert_index_equal(res.columns, exp_index)