"Backport PR #31358 on branch 1.0.x" (#31376)

* BUG/REF: multiindex nested tuples with duplicates (#31358)

Author: jbrockmendel

pandas/core/indexes/multi.py

@@ -2546,7 +2546,7 @@ def _partial_tup_index(self, tup, side="left"):
         for k, (lab, lev, labs) in enumerate(zipped):
             section = labs[start:end]
 
-            if lab not in lev and not isna(lab):
+            if lab not in lev and np.ndim(lab) == 0 and not isna(lab):
                 if not lev.is_type_compatible(lib.infer_dtype([lab], skipna=False)):
                     raise TypeError(f"Level type mismatch: {lab}")
 
@@ -2648,7 +2648,8 @@ def _maybe_to_slice(loc):
             mask[loc] = True
             return mask
 
-        if not isinstance(key, tuple):
+        if not isinstance(key, (tuple, list)):
+            # not including list here breaks some indexing, xref #30892
             loc = self._get_level_indexer(key, level=0)
             return _maybe_to_slice(loc)
 

pandas/core/indexing.py

@@ -639,7 +639,7 @@ def _get_setitem_indexer(self, key):
         if isinstance(ax, ABCMultiIndex) and self.name != "iloc":
             try:
                 return ax.get_loc(key)
-            except (TypeError, KeyError):
+            except (TypeError, KeyError, InvalidIndexError):
                 # TypeError e.g. passed a bool
                 pass
 

pandas/tests/indexes/multi/test_indexing.py

@@ -396,7 +396,8 @@ def test_get_loc_missing_nan():
         idx.get_loc(3)
     with pytest.raises(KeyError, match=r"^nan$"):
         idx.get_loc(np.nan)
-    with pytest.raises(KeyError, match=r"^\[nan\]$"):
+    with pytest.raises(TypeError, match="unhashable type: 'list'"):
+        # listlike/non-hashable raises TypeError
         idx.get_loc([np.nan])
 
 

pandas/tests/indexing/multiindex/test_multiindex.py

@@ -92,3 +92,22 @@ def test_contains(self):
         assert tx[0] in idx
         assert "element_not_exit" not in idx
         assert "0 day 09:30:00" in idx
+
+    def test_nested_tuples_duplicates(self):
+        # GH#30892
+
+        dti = pd.to_datetime(["20190101", "20190101", "20190102"])
+        idx = pd.Index(["a", "a", "c"])
+        mi = pd.MultiIndex.from_arrays([dti, idx], names=["index1", "index2"])
+
+        df = pd.DataFrame({"c1": [1, 2, 3], "c2": [np.nan, np.nan, np.nan]}, index=mi)
+
+        expected = pd.DataFrame({"c1": df["c1"], "c2": [1.0, 1.0, np.nan]}, index=mi)
+
+        df2 = df.copy(deep=True)
+        df2.loc[(dti[0], "a"), "c2"] = 1.0
+        tm.assert_frame_equal(df2, expected)
+
+        df3 = df.copy(deep=True)
+        df3.loc[[(dti[0], "a")], "c2"] = 1.0
+        tm.assert_frame_equal(df3, expected)