REF: standardize get_indexer/get_indexer_non_unique (#39343)

Author: jbrockmendel

pandas/core/indexes/base.py

@@ -3289,11 +3289,10 @@ def get_indexer(
         if not self._index_as_unique:
             raise InvalidIndexError(self._requires_unique_msg)
 
-        # Treat boolean labels passed to a numeric index as not found. Without
-        # this fix False and True would be treated as 0 and 1 respectively.
-        # (GH #16877)
-        if target.is_boolean() and self.is_numeric():
-            return ensure_platform_int(np.repeat(-1, target.size))
+        if not self._should_compare(target) and not is_interval_dtype(self.dtype):
+            # IntervalIndex get special treatment bc numeric scalars can be
+            #  matched to Interval scalars
+            return self._get_indexer_non_comparable(target, method=method, unique=True)
 
         pself, ptarget = self._maybe_promote(target)
         if pself is not self or ptarget is not target:
@@ -3310,8 +3309,9 @@ def _get_indexer(
             tolerance = self._convert_tolerance(tolerance, target)
 
         if not is_dtype_equal(self.dtype, target.dtype):
-            this = self.astype(object)
-            target = target.astype(object)
+            dtype = find_common_type([self.dtype, target.dtype])
+            this = self.astype(dtype, copy=False)
+            target = target.astype(dtype, copy=False)
             return this.get_indexer(
                 target, method=method, limit=limit, tolerance=tolerance
             )
@@ -5060,19 +5060,15 @@ def set_value(self, arr, key, value):
     def get_indexer_non_unique(self, target):
         target = ensure_index(target)
 
-        if target.is_boolean() and self.is_numeric():
-            # Treat boolean labels passed to a numeric index as not found. Without
-            # this fix False and True would be treated as 0 and 1 respectively.
-            # (GH #16877)
+        if not self._should_compare(target) and not is_interval_dtype(self.dtype):
+            # IntervalIndex get special treatment bc numeric scalars can be
+            #  matched to Interval scalars
             return self._get_indexer_non_comparable(target, method=None, unique=False)
 
         pself, ptarget = self._maybe_promote(target)
         if pself is not self or ptarget is not target:
             return pself.get_indexer_non_unique(ptarget)
 
-        if not self._should_compare(target):
-            return self._get_indexer_non_comparable(target, method=None, unique=False)
-
         if not is_dtype_equal(self.dtype, target.dtype):
             # TODO: if object, could use infer_dtype to preempt costly
             #  conversion if still non-comparable?
@@ -5193,6 +5189,15 @@ def _should_compare(self, other: Index) -> bool:
         """
         Check if `self == other` can ever have non-False entries.
         """
+
+        if (other.is_boolean() and self.is_numeric()) or (
+            self.is_boolean() and other.is_numeric()
+        ):
+            # GH#16877 Treat boolean labels passed to a numeric index as not
+            #  found. Without this fix False and True would be treated as 0 and 1
+            #  respectively.
+            return False
+
         other = unpack_nested_dtype(other)
         dtype = other.dtype
         return self._is_comparable_dtype(dtype) or is_object_dtype(dtype)

pandas/tests/indexes/numeric/test_indexing.py

@@ -82,6 +82,37 @@ def test_get_loc_missing_nan(self):
 
 
 class TestGetIndexer:
+    @pytest.mark.parametrize("method", ["pad", "backfill", "nearest"])
+    def test_get_indexer_with_method_numeric_vs_bool(self, method):
+        left = Index([1, 2, 3])
+        right = Index([True, False])
+
+        with pytest.raises(TypeError, match="Cannot compare"):
+            left.get_indexer(right, method=method)
+
+        with pytest.raises(TypeError, match="Cannot compare"):
+            right.get_indexer(left, method=method)
+
+    def test_get_indexer_numeric_vs_bool(self):
+        left = Index([1, 2, 3])
+        right = Index([True, False])
+
+        res = left.get_indexer(right)
+        expected = -1 * np.ones(len(right), dtype=np.intp)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = right.get_indexer(left)
+        expected = -1 * np.ones(len(left), dtype=np.intp)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = left.get_indexer_non_unique(right)[0]
+        expected = -1 * np.ones(len(right), dtype=np.intp)
+        tm.assert_numpy_array_equal(res, expected)
+
+        res = right.get_indexer_non_unique(left)[0]
+        expected = -1 * np.ones(len(left), dtype=np.intp)
+        tm.assert_numpy_array_equal(res, expected)
+
     def test_get_indexer_float64(self):
         idx = Float64Index([0.0, 1.0, 2.0])
         tm.assert_numpy_array_equal(

pandas/tests/indexes/test_base.py

@@ -556,6 +556,17 @@ def test_asof(self, index):
         d = index[0].to_pydatetime()
         assert isinstance(index.asof(d), Timestamp)
 
+    def test_asof_numeric_vs_bool_raises(self):
+        left = Index([1, 2, 3])
+        right = Index([True, False])
+
+        msg = "'<' not supported between instances"
+        with pytest.raises(TypeError, match=msg):
+            left.asof(right)
+
+        with pytest.raises(TypeError, match=msg):
+            right.asof(left)
+
     def test_asof_datetime_partial(self):
         index = date_range("2010-01-01", periods=2, freq="m")
         expected = Timestamp("2010-02-28")

pandas/tests/series/methods/test_reindex.py

@@ -293,7 +293,10 @@ def test_reindex_datetimeindexes_tz_naive_and_aware():
     idx = date_range("20131101", tz="America/Chicago", periods=7)
     newidx = date_range("20131103", periods=10, freq="H")
     s = Series(range(7), index=idx)
-    msg = "Cannot compare tz-naive and tz-aware timestamps"
+    msg = (
+        r"Cannot compare dtypes datetime64\[ns, America/Chicago\] "
+        r"and datetime64\[ns\]"
+    )
     with pytest.raises(TypeError, match=msg):
         s.reindex(newidx, method="ffill")