API: ensure IntervalIndex.left/right are 64bit if numeric, part II (#…

…50195)

pandas/core/arrays/interval.py

@@ -1787,5 +1787,7 @@ def _maybe_convert_platform_interval(values) -> ArrayLike:
         values = extract_array(values, extract_numpy=True)
 
     if not hasattr(values, "dtype"):
-        return np.asarray(values)
+        values = np.asarray(values)
+        if is_integer_dtype(values) and values.dtype != np.int64:
+            values = values.astype(np.int64)
     return values

pandas/core/dtypes/cast.py

@@ -35,6 +35,7 @@
     ArrayLike,
     Dtype,
     DtypeObj,
+    NumpyIndexT,
     Scalar,
     npt,
 )
@@ -65,6 +66,7 @@
     is_numeric_dtype,
     is_object_dtype,
     is_scalar,
+    is_signed_integer_dtype,
     is_string_dtype,
     is_timedelta64_dtype,
     is_unsigned_integer_dtype,
@@ -412,6 +414,29 @@ def trans(x):
     return result
 
 
+def maybe_upcast_numeric_to_64bit(arr: NumpyIndexT) -> NumpyIndexT:
+    """
+    If array is a int/uint/float bit size lower than 64 bit, upcast it to 64 bit.
+
+    Parameters
+    ----------
+    arr : ndarray or ExtensionArray
+
+    Returns
+    -------
+    ndarray or ExtensionArray
+    """
+    dtype = arr.dtype
+    if is_signed_integer_dtype(dtype) and dtype != np.int64:
+        return arr.astype(np.int64)
+    elif is_unsigned_integer_dtype(dtype) and dtype != np.uint64:
+        return arr.astype(np.uint64)
+    elif is_float_dtype(dtype) and dtype != np.float64:
+        return arr.astype(np.float64)
+    else:
+        return arr
+
+
 def maybe_cast_pointwise_result(
     result: ArrayLike,
     dtype: DtypeObj,

pandas/core/indexes/interval.py

@@ -44,6 +44,7 @@
     infer_dtype_from_scalar,
     maybe_box_datetimelike,
     maybe_downcast_numeric,
+    maybe_upcast_numeric_to_64bit,
 )
 from pandas.core.dtypes.common import (
     ensure_platform_int,
@@ -59,8 +60,6 @@
     is_number,
     is_object_dtype,
     is_scalar,
-    is_signed_integer_dtype,
-    is_unsigned_integer_dtype,
 )
 from pandas.core.dtypes.dtypes import IntervalDtype
 from pandas.core.dtypes.missing import is_valid_na_for_dtype
@@ -342,8 +341,11 @@ def from_tuples(
     # "Union[IndexEngine, ExtensionEngine]" in supertype "Index"
     @cache_readonly
     def _engine(self) -> IntervalTree:  # type: ignore[override]
+        # IntervalTree does not supports numpy array unless they are 64 bit
         left = self._maybe_convert_i8(self.left)
+        left = maybe_upcast_numeric_to_64bit(left)
         right = self._maybe_convert_i8(self.right)
+        right = maybe_upcast_numeric_to_64bit(right)
         return IntervalTree(left, right, closed=self.closed)
 
     def __contains__(self, key: Any) -> bool:
@@ -520,13 +522,12 @@ def _maybe_convert_i8(self, key):
             The original key if no conversion occurred, int if converted scalar,
             Int64Index if converted list-like.
         """
-        original = key
         if is_list_like(key):
             key = ensure_index(key)
-            key = self._maybe_convert_numeric_to_64bit(key)
+            key = maybe_upcast_numeric_to_64bit(key)
 
         if not self._needs_i8_conversion(key):
-            return original
+            return key
 
         scalar = is_scalar(key)
         if is_interval_dtype(key) or isinstance(key, Interval):
@@ -569,20 +570,6 @@ def _maybe_convert_i8(self, key):
 
         return key_i8
 
-    def _maybe_convert_numeric_to_64bit(self, idx: Index) -> Index:
-        # IntervalTree only supports 64 bit numpy array
-        dtype = idx.dtype
-        if np.issubclass_(dtype.type, np.number):
-            return idx
-        elif is_signed_integer_dtype(dtype) and dtype != np.int64:
-            return idx.astype(np.int64)
-        elif is_unsigned_integer_dtype(dtype) and dtype != np.uint64:
-            return idx.astype(np.uint64)
-        elif is_float_dtype(dtype) and dtype != np.float64:
-            return idx.astype(np.float64)
-        else:
-            return idx
-
     def _searchsorted_monotonic(self, label, side: Literal["left", "right"] = "left"):
         if not self.is_non_overlapping_monotonic:
             raise KeyError(

pandas/tests/indexes/interval/test_interval.py

@@ -415,27 +415,36 @@ def test_maybe_convert_i8_nat(self, breaks):
         tm.assert_index_equal(result, expected)
 
     @pytest.mark.parametrize(
-        "breaks",
-        [np.arange(5, dtype="int64"), np.arange(5, dtype="float64")],
-        ids=lambda x: str(x.dtype),
+        "make_key",
+        [lambda breaks: breaks, list],
+        ids=["lambda", "list"],
     )
+    def test_maybe_convert_i8_numeric(self, make_key, any_real_numpy_dtype):
+        # GH 20636
+        breaks = np.arange(5, dtype=any_real_numpy_dtype)
+        index = IntervalIndex.from_breaks(breaks)
+        key = make_key(breaks)
+
+        result = index._maybe_convert_i8(key)
+        expected = Index(key)
+        tm.assert_index_equal(result, expected)
+
     @pytest.mark.parametrize(
         "make_key",
         [
             IntervalIndex.from_breaks,
             lambda breaks: Interval(breaks[0], breaks[1]),
-            lambda breaks: breaks,
             lambda breaks: breaks[0],
-            list,
         ],
-        ids=["IntervalIndex", "Interval", "Index", "scalar", "list"],
+        ids=["IntervalIndex", "Interval", "scalar"],
     )
-    def test_maybe_convert_i8_numeric(self, breaks, make_key):
+    def test_maybe_convert_i8_numeric_identical(self, make_key, any_real_numpy_dtype):
         # GH 20636
+        breaks = np.arange(5, dtype=any_real_numpy_dtype)
         index = IntervalIndex.from_breaks(breaks)
         key = make_key(breaks)
 
-        # no conversion occurs for numeric
+        # test if _maybe_convert_i8 won't change key if an Interval or IntervalIndex
         result = index._maybe_convert_i8(key)
         assert result is key