BUG: non-64-bit numeric dtypes should raise in IntervalDtype constructor

Author: topper-123

doc/source/whatsnew/v2.0.0.rst

@@ -1237,6 +1237,8 @@ Interval
 - Bug in :meth:`IntervalIndex.is_overlapping` incorrect output if interval has duplicate left boundaries (:issue:`49581`)
 - Bug in :meth:`Series.infer_objects` failing to infer :class:`IntervalDtype` for an object series of :class:`Interval` objects (:issue:`50090`)
 - Bug in :meth:`Series.shift` with :class:`IntervalDtype` and invalid null ``fill_value`` failing to raise ``TypeError`` (:issue:`51258`)
+- Bug in :class:`IntervalDtype` where it accepted non-64-bit numeric subtypes, even though :class:`arrays.InterArray` only can hold numeric data if it is 64-bit.
+  Supplying non-64-bit numeric subtypes to :class:`IntervalDtype` now raises a ``TypeError`` (:issue:`45412`)
 -
 
 Indexing

pandas/core/arrays/interval.py

@@ -270,6 +270,7 @@ def __new__(
                 copy=copy,
                 dtype=dtype,
             )
+            1 / 0
 
         if verify_integrity:
             cls._validate(left, right, dtype=dtype)

pandas/core/dtypes/common.py

@@ -861,6 +861,56 @@ def is_int64_dtype(arr_or_dtype) -> bool:
     return _is_dtype_type(arr_or_dtype, classes(np.int64))
 
 
+def is_64bit_real_numeric_dtype(arr_or_dtype) -> bool:
+    """
+    Check whether the provided array or dtype is of 64-bit dtype.
+
+    Parameters
+    ----------
+    arr_or_dtype : array-like or dtype
+        The array or dtype to check.
+
+    Returns
+    -------
+    boolean
+        Whether or not the array or dtype is of 64-bit dtype.
+
+    Examples
+    --------
+    >>> is_64bit_real_numeric_dtype(str)
+    False
+    >>> is_64bit_real_numeric_dtype(np.int32)
+    False
+    >>> is_64bit_real_numeric_dtype(np.int64)
+    True
+    >>> is_64bit_real_numeric_dtype('int8')
+    False
+    >>> is_64bit_real_numeric_dtype('Int8')
+    True
+    >>> is_64bit_real_numeric_dtype(pd.Int64Dtype)
+    True
+    >>> is_64bit_real_numeric_dtype(float)
+    True
+    >>> is_64bit_real_numeric_dtype(np.uint64)  # unsigned
+    True
+    >>> is_64bit_real_numeric_dtype(np.array(['a', 'b']))
+    False
+    >>> is_64bit_real_numeric_dtype(np.array([1, 2], dtype=np.int64))
+    True
+    >>> is_64bit_real_numeric_dtype(pd.Index([1, 2.]))  # float
+    True
+    >>> is_64bit_real_numeric_dtype(np.array([1, 2], dtype=np.uint32))
+    False
+    """
+    return _is_dtype_type(
+        arr_or_dtype, classes(np.int64, np.uint64, np.float64)
+    ) or _is_dtype(
+        arr_or_dtype,
+        lambda typ: isinstance(typ, ExtensionDtype)
+        and typ.type in (np.int64, np.uint64, np.float64),
+    )
+
+
 def is_datetime64_any_dtype(arr_or_dtype) -> bool:
     """
     Check whether the provided array or dtype is of the datetime64 dtype.

pandas/core/dtypes/dtypes.py

@@ -1082,6 +1082,8 @@ class IntervalDtype(PandasExtensionDtype):
 
     def __new__(cls, subtype=None, closed: str_type | None = None):
         from pandas.core.dtypes.common import (
+            is_64bit_real_numeric_dtype,
+            is_any_real_numeric_dtype,
             is_string_dtype,
             pandas_dtype,
         )
@@ -1132,6 +1134,12 @@ def __new__(cls, subtype=None, closed: str_type | None = None):
                 "for IntervalDtype"
             )
             raise TypeError(msg)
+        elif is_any_real_numeric_dtype(subtype) and not is_64bit_real_numeric_dtype(
+            subtype
+        ):
+            raise TypeError(
+                f"numeric subtype must be 64-bit numeric dtype, was {subtype}"
+            )
 
         key = f"{subtype}{closed}"
         try:

pandas/tests/dtypes/test_dtypes.py

@@ -8,6 +8,7 @@
 
 from pandas.core.dtypes.base import _registry as registry
 from pandas.core.dtypes.common import (
+    is_64bit_real_numeric_dtype,
     is_bool_dtype,
     is_categorical_dtype,
     is_datetime64_any_dtype,
@@ -16,6 +17,7 @@
     is_datetime64tz_dtype,
     is_dtype_equal,
     is_interval_dtype,
+    is_numeric_dtype,
     is_period_dtype,
     is_string_dtype,
 )
@@ -597,6 +599,7 @@ def test_construction_generic(self, subtype):
     @pytest.mark.parametrize(
         "subtype",
         [
+            *[x for x in tm.ALL_REAL_DTYPES if not is_64bit_real_numeric_dtype(x)],
             CategoricalDtype(list("abc"), False),
             CategoricalDtype(list("wxyz"), True),
             object,
@@ -607,11 +610,14 @@ def test_construction_generic(self, subtype):
         ],
     )
     def test_construction_not_supported(self, subtype):
-        # GH 19016
-        msg = (
-            "category, object, and string subtypes are not supported "
-            "for IntervalDtype"
-        )
+        # GH19016, GH45412
+        if is_numeric_dtype(subtype):
+            msg = "numeric subtype must be 64-bit numeric dtype, was"
+        else:
+            msg = (
+                "category, object, and string subtypes are not supported "
+                "for IntervalDtype"
+            )
         with pytest.raises(TypeError, match=msg):
             IntervalDtype(subtype)