NumPy hid sctypes further away but still use it for now

The alternative is listing them all, but even that is a bit tedious
for integers, because it needs something like:

``(np.byte, np.short, np.intc, getattr(np, "long", np.int_), np.longlong)``

python/cudf/cudf/core/index.py

@@ -60,6 +60,7 @@
 from cudf.core.single_column_frame import SingleColumnFrame
 from cudf.utils.docutils import copy_docstring
 from cudf.utils.dtypes import (
+    _NUMPY_SCTYPES,
     _maybe_convert_to_default_type,
     find_common_type,
     is_mixed_with_object_dtype,
@@ -341,7 +342,10 @@ def _data(self):
     @_cudf_nvtx_annotate
     def __contains__(self, item):
         if isinstance(item, bool) or not isinstance(
-            item, tuple(np.sctypes["int"] + np.sctypes["float"] + [int, float])
+            item,
+            tuple(
+                _NUMPY_SCTYPES["int"] + _NUMPY_SCTYPES["float"] + [int, float]
+            ),
         ):
             return False
         try:

python/cudf/cudf/utils/dtypes.py

@@ -91,6 +91,10 @@
 BOOL_TYPES = {"bool"}
 ALL_TYPES = NUMERIC_TYPES | DATETIME_TYPES | TIMEDELTA_TYPES | OTHER_TYPES
 
+# The NumPy scalar types are a bit of a mess as they align with the C types
+# so for now we use the `sctypes` dict (although it was made private in 2.0)
+_NUMPY_SCTYPES = np.sctypes if hasattr(np, "sctypes") else np._core.sctypes
+
 
 def np_to_pa_dtype(dtype):
     """Util to convert numpy dtype to PyArrow dtype."""
@@ -335,7 +339,7 @@ def min_signed_type(x, min_size=8):
     Return the smallest *signed* integer dtype
     that can represent the integer ``x``
     """
-    for int_dtype in np.sctypes["int"]:
+    for int_dtype in _NUMPY_SCTYPES["int"]:
         if (cudf.dtype(int_dtype).itemsize * 8) >= min_size:
             if np.iinfo(int_dtype).min <= x <= np.iinfo(int_dtype).max:
                 return int_dtype
@@ -348,7 +352,7 @@ def min_unsigned_type(x, min_size=8):
     Return the smallest *unsigned* integer dtype
     that can represent the integer ``x``
     """
-    for int_dtype in np.sctypes["uint"]:
+    for int_dtype in _NUMPY_SCTYPES["uint"]:
         if (cudf.dtype(int_dtype).itemsize * 8) >= min_size:
             if 0 <= x <= np.iinfo(int_dtype).max:
                 return int_dtype