BUG: IntervalIndex.unique() only contains the first interval if all interval borders are negative

pandas/core/arrays/interval.py

@@ -1932,16 +1932,16 @@ def isin(self, values: ArrayLike) -> npt.NDArray[np.bool_]:
             if self.dtype == values.dtype:
                 # GH#38353 instead of casting to object, operating on a
                 #  complex128 ndarray is much more performant.
-                left = self._combined.view("complex128")
-                right = values._combined.view("complex128")
+                left = self._combined
+                right = values._combined
                 # error: Argument 1 to "isin" has incompatible type
                 # "Union[ExtensionArray, ndarray[Any, Any],
                 # ndarray[Any, dtype[Any]]]"; expected
                 # "Union[_SupportsArray[dtype[Any]],
                 # _NestedSequence[_SupportsArray[dtype[Any]]], bool,
                 # int, float, complex, str, bytes, _NestedSequence[
                 # Union[bool, int, float, complex, str, bytes]]]"
-                return np.isin(left, right).ravel()  # type: ignore[arg-type]
+                return np.isin(left, right).ravel()
 
             elif needs_i8_conversion(self.left.dtype) ^ needs_i8_conversion(
                 values.left.dtype
@@ -1963,18 +1963,21 @@ def _combined(self) -> IntervalSide:
             comb = left._concat_same_type(  # type: ignore[union-attr]
                 [left, right], axis=1
             )
+            comb = comb.view("complex128")[:, 0]
         else:
-            comb = np.concatenate([left, right], axis=1)
+            comb = (np.array(left.ravel(), dtype=complex)) + (
+                1j * np.array(right.ravel(), dtype=complex)
+            )
         return comb
 
     def _from_combined(self, combined: np.ndarray) -> IntervalArray:
         """
         Create a new IntervalArray with our dtype from a 1D complex128 ndarray.
         """
-        nc = combined.view("i8").reshape(-1, 2)
 
         dtype = self._left.dtype
         if needs_i8_conversion(dtype):
+            nc = combined.view("i8").reshape(-1, 2)
             assert isinstance(self._left, (DatetimeArray, TimedeltaArray))
             new_left: DatetimeArray | TimedeltaArray | np.ndarray = type(
                 self._left
@@ -1985,16 +1988,14 @@ def _from_combined(self, combined: np.ndarray) -> IntervalArray:
             )._from_sequence(nc[:, 1], dtype=dtype)
         else:
             assert isinstance(dtype, np.dtype)
-            new_left = nc[:, 0].view(dtype)
-            new_right = nc[:, 1].view(dtype)
+            new_left = np.real(combined).astype(dtype).ravel()
+            new_right = np.imag(combined).astype(dtype).ravel()
         return self._shallow_copy(left=new_left, right=new_right)
 
     def unique(self) -> IntervalArray:
         # No overload variant of "__getitem__" of "ExtensionArray" matches argument
         # type "Tuple[slice, int]"
-        nc = unique(
-            self._combined.view("complex128")[:, 0]  # type: ignore[call-overload]
-        )
+        nc = unique(self._combined)
         nc = nc[:, None]
         return self._from_combined(nc)
 

pandas/tests/arrays/interval/test_interval.py

@@ -111,6 +111,31 @@ def test_shift_datetime(self):
         with pytest.raises(TypeError, match=msg):
             a.shift(1, fill_value=np.timedelta64("NaT", "ns"))
 
+    def test_unique_with_negatives(self):
+        # GH#61917
+        idx_pos = IntervalIndex.from_tuples(
+            [(3, 4), (3, 4), (2, 3), (2, 3), (1, 2), (1, 2)]
+        )
+        result = idx_pos.unique()
+        expected = IntervalIndex.from_tuples([(3, 4), (2, 3), (1, 2)])
+        tm.assert_index_equal(result, expected)
+
+        idx_neg = IntervalIndex.from_tuples(
+            [(-4, -3), (-4, -3), (-3, -2), (-3, -2), (-2, -1), (-2, -1)]
+        )
+        result = idx_neg.unique()
+        expected = IntervalIndex.from_tuples([(-4, -3), (-3, -2), (-2, -1)])
+        tm.assert_index_equal(result, expected)
+
+        idx_mix = IntervalIndex.from_tuples(
+            [(1, 2), (0, 1), (-1, 0), (-2, -1), (-3, -2), (-3, -2)]
+        )
+        result = idx_mix.unique()
+        expected = IntervalIndex.from_tuples(
+            [(1, 2), (0, 1), (-1, 0), (-2, -1), (-3, -2)]
+        )
+        tm.assert_index_equal(result, expected)
+
 
 class TestSetitem:
     def test_set_na(self, left_right_dtypes):