BUG/API: prohibit dtype-changing IntervalArray.__setitem__

doc/source/whatsnew/v1.1.0.rst

@@ -346,7 +346,7 @@ Strings
 
 Interval
 ^^^^^^^^
--
+- Bug in :class:`IntervalArray` incorrectly allowing the underlying data to be changed when setting values (:issue:`32782`)
 -
 
 Indexing

pandas/core/arrays/interval.py

@@ -542,19 +542,19 @@ def __setitem__(self, key, value):
                 msg = f"'value' should be an interval type, got {type(value)} instead."
                 raise TypeError(msg) from err
 
+        if needs_float_conversion:
+            raise ValueError("Cannot set float NaN to integer-backed IntervalArray")
+
         key = check_array_indexer(self, key)
+
         # Need to ensure that left and right are updated atomically, so we're
         # forced to copy, update the copy, and swap in the new values.
         left = self.left.copy(deep=True)
-        if needs_float_conversion:
-            left = left.astype("float")
-        left.values[key] = value_left
+        left._values[key] = value_left
         self._left = left
 
         right = self.right.copy(deep=True)
-        if needs_float_conversion:
-            right = right.astype("float")
-        right.values[key] = value_right
+        right._values[key] = value_right
         self._right = right
 
     def __eq__(self, other):

pandas/tests/arrays/interval/test_interval.py

@@ -104,6 +104,13 @@ class TestSetitem:
     def test_set_na(self, left_right_dtypes):
         left, right = left_right_dtypes
         result = IntervalArray.from_arrays(left, right)
+
+        if result.dtype.subtype.kind in ["i", "u"]:
+            msg = "Cannot set float NaN to integer-backed IntervalArray"
+            with pytest.raises(ValueError, match=msg):
+                result[0] = np.NaN
+            return
+
         result[0] = np.nan
 
         expected_left = Index([left._na_value] + list(left[1:]))
@@ -182,7 +189,7 @@ def test_arrow_array_missing():
     import pyarrow as pa
     from pandas.core.arrays._arrow_utils import ArrowIntervalType
 
-    arr = IntervalArray.from_breaks([0, 1, 2, 3])
+    arr = IntervalArray.from_breaks([0.0, 1.0, 2.0, 3.0])
     arr[1] = None
 
     result = pa.array(arr)
@@ -209,8 +216,8 @@ def test_arrow_array_missing():
 @pyarrow_skip
 @pytest.mark.parametrize(
     "breaks",
-    [[0, 1, 2, 3], pd.date_range("2017", periods=4, freq="D")],
-    ids=["int", "datetime64[ns]"],
+    [[0.0, 1.0, 2.0, 3.0], pd.date_range("2017", periods=4, freq="D")],
+    ids=["float", "datetime64[ns]"],
 )
 def test_arrow_table_roundtrip(breaks):
     import pyarrow as pa

pandas/tests/series/methods/test_convert_dtypes.py

@@ -3,6 +3,8 @@
 import numpy as np
 import pytest
 
+from pandas.core.dtypes.common import is_interval_dtype
+
 import pandas as pd
 import pandas._testing as tm
 
@@ -266,7 +268,12 @@ def test_convert_dtypes(self, data, maindtype, params, answerdict):
 
         # Test that it is a copy
         copy = series.copy(deep=True)
-        ns[ns.notna()] = np.nan
+        if is_interval_dtype(ns.dtype) and ns.dtype.subtype.kind in ["i", "u"]:
+            msg = "Cannot set float NaN to integer-backed IntervalArray"
+            with pytest.raises(ValueError, match=msg):
+                ns[ns.notna()] = np.nan
+        else:
+            ns[ns.notna()] = np.nan
 
         # Make sure original not changed
         tm.assert_series_equal(series, copy)