BUG/PERF: use lexsort_indexer in MultiIndex.argsort (#48495)

doc/source/whatsnew/v1.6.0.rst

@@ -220,6 +220,7 @@ Missing
 
 MultiIndex
 ^^^^^^^^^^
+- Bug in :meth:`MultiIndex.argsort` raising ``TypeError`` when index contains :attr:`NA` (:issue:`48495`)
 - Bug in :meth:`MultiIndex.difference` losing extension array dtype (:issue:`48606`)
 - Bug in :class:`MultiIndex.set_levels` raising ``IndexError`` when setting empty level (:issue:`48636`)
 - Bug in :meth:`MultiIndex.unique` losing extension array dtype (:issue:`48335`)

pandas/core/indexes/multi.py

@@ -1952,7 +1952,7 @@ def _lexsort_depth(self) -> int:
             return self.sortorder
         return _lexsort_depth(self.codes, self.nlevels)
 
-    def _sort_levels_monotonic(self) -> MultiIndex:
+    def _sort_levels_monotonic(self, raise_if_incomparable: bool = False) -> MultiIndex:
         """
         This is an *internal* function.
 
@@ -1999,7 +1999,8 @@ def _sort_levels_monotonic(self) -> MultiIndex:
                     # indexer to reorder the levels
                     indexer = lev.argsort()
                 except TypeError:
-                    pass
+                    if raise_if_incomparable:
+                        raise
                 else:
                     lev = lev.take(indexer)
 
@@ -2245,9 +2246,9 @@ def append(self, other):
 
     def argsort(self, *args, **kwargs) -> npt.NDArray[np.intp]:
         if len(args) == 0 and len(kwargs) == 0:
-            # np.lexsort is significantly faster than self._values.argsort()
-            values = [self._get_level_values(i) for i in reversed(range(self.nlevels))]
-            return np.lexsort(values)
+            # lexsort is significantly faster than self._values.argsort()
+            target = self._sort_levels_monotonic(raise_if_incomparable=True)
+            return lexsort_indexer(target._get_codes_for_sorting())
         return self._values.argsort(*args, **kwargs)
 
     @Appender(_index_shared_docs["repeat"] % _index_doc_kwargs)

pandas/tests/indexes/multi/test_sorting.py

@@ -14,6 +14,7 @@
     Index,
     MultiIndex,
     RangeIndex,
+    Timestamp,
 )
 import pandas._testing as tm
 from pandas.core.indexes.frozen import FrozenList
@@ -280,3 +281,26 @@ def test_remove_unused_levels_with_nan():
     result = idx.levels
     expected = FrozenList([["a", np.nan], [4]])
     assert str(result) == str(expected)
+
+
+def test_sort_values_nan():
+    # GH48495, GH48626
+    midx = MultiIndex(levels=[["A", "B", "C"], ["D"]], codes=[[1, 0, 2], [-1, -1, 0]])
+    result = midx.sort_values()
+    expected = MultiIndex(
+        levels=[["A", "B", "C"], ["D"]], codes=[[0, 1, 2], [-1, -1, 0]]
+    )
+    tm.assert_index_equal(result, expected)
+
+
+def test_sort_values_incomparable():
+    # GH48495
+    mi = MultiIndex.from_arrays(
+        [
+            [1, Timestamp("2000-01-01")],
+            [3, 4],
+        ]
+    )
+    match = "'<' not supported between instances of 'Timestamp' and 'int'"
+    with pytest.raises(TypeError, match=match):
+        mi.sort_values()

pandas/tests/indexing/multiindex/test_sorted.py

@@ -2,9 +2,11 @@
 import pytest
 
 from pandas import (
+    NA,
     DataFrame,
     MultiIndex,
     Series,
+    array,
 )
 import pandas._testing as tm
 
@@ -86,6 +88,36 @@ def test_sort_values_key(self):
 
         tm.assert_frame_equal(result, expected)
 
+    def test_argsort_with_na(self):
+        # GH48495
+        arrays = [
+            array([2, NA, 1], dtype="Int64"),
+            array([1, 2, 3], dtype="Int64"),
+        ]
+        index = MultiIndex.from_arrays(arrays)
+        result = index.argsort()
+        expected = np.array([2, 0, 1], dtype=np.intp)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_sort_values_with_na(self):
+        # GH48495
+        arrays = [
+            array([2, NA, 1], dtype="Int64"),
+            array([1, 2, 3], dtype="Int64"),
+        ]
+        index = MultiIndex.from_arrays(arrays)
+        index = index.sort_values()
+        result = DataFrame(range(3), index=index)
+
+        arrays = [
+            array([1, 2, NA], dtype="Int64"),
+            array([3, 1, 2], dtype="Int64"),
+        ]
+        index = MultiIndex.from_arrays(arrays)
+        expected = DataFrame(range(3), index=index)
+
+        tm.assert_frame_equal(result, expected)
+
     def test_frame_getitem_not_sorted(self, multiindex_dataframe_random_data):
         frame = multiindex_dataframe_random_data
         df = frame.T