Remove cudf._libs.types.pyx

python/cudf/cudf/_lib/CMakeLists.txt

@@ -1,5 +1,5 @@
 # =============================================================================
-# Copyright (c) 2022-2024, NVIDIA CORPORATION.
+# Copyright (c) 2022-2025, NVIDIA CORPORATION.
 #
 # Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except
 # in compliance with the License. You may obtain a copy of the License at
@@ -12,7 +12,7 @@
 # the License.
 # =============================================================================
 
-set(cython_sources column.pyx scalar.pyx strings_udf.pyx types.pyx)
+set(cython_sources column.pyx scalar.pyx strings_udf.pyx)
 set(linked_libraries cudf::cudf)
 
 rapids_cython_create_modules(

python/cudf/cudf/_lib/column.pxd

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2024, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 
 from typing import Literal
 
@@ -13,6 +13,8 @@ from pylibcudf.libcudf.column.column_view cimport (
 from pylibcudf.libcudf.types cimport size_type
 from rmm.librmm.device_buffer cimport device_buffer
 
+cdef dtype_from_lists_column_view(column_view cv)
+cdef dtype_from_column_view(column_view cv)
 
 cdef class Column:
     cdef public:

python/cudf/cudf/_lib/column.pyx

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2024, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 
 
 from typing import Literal
@@ -19,23 +19,20 @@ from cudf.core.buffer import (
     as_buffer,
     cuda_array_interface_wrapper,
 )
-from cudf.utils.dtypes import _get_base_dtype
+from cudf.utils.dtypes import (
+    _get_base_dtype,
+    dtype_to_pylibcudf_type,
+    PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES,
+)
 
 from cpython.buffer cimport PyObject_CheckBuffer
-from libc.stdint cimport uintptr_t
-from libcpp.memory cimport make_unique, unique_ptr
+from libc.stdint cimport uintptr_t, int32_t
+from libcpp.memory cimport make_shared, make_unique, shared_ptr, unique_ptr
 from libcpp.utility cimport move
 from libcpp.vector cimport vector
 
 from rmm.pylibrmm.device_buffer cimport DeviceBuffer
 
-from cudf._lib.types cimport (
-    dtype_from_column_view,
-    dtype_to_pylibcudf_type,
-)
-
-from cudf._lib.types import dtype_from_pylibcudf_column
-
 from pylibcudf cimport DataType as plc_DataType
 cimport pylibcudf.libcudf.copying as cpp_copying
 cimport pylibcudf.libcudf.types as libcudf_types
@@ -45,6 +42,7 @@ from pylibcudf.libcudf.column.column_factories cimport (
     make_numeric_column
 )
 from pylibcudf.libcudf.column.column_view cimport column_view
+from pylibcudf.libcudf.lists.lists_column_view cimport lists_column_view
 from pylibcudf.libcudf.null_mask cimport null_count as cpp_null_count
 from pylibcudf.libcudf.scalar.scalar cimport scalar
 
@@ -64,6 +62,80 @@ cdef get_element(column_view col_view, size_type index):
     )
 
 
+def dtype_from_pylibcudf_column(col):
+    type_ = col.type()
+    tid = type_.id()
+
+    if tid == pylibcudf.TypeId.LIST:
+        child = col.list_view().child()
+        return cudf.ListDtype(dtype_from_pylibcudf_column(child))
+    elif tid == pylibcudf.TypeId.STRUCT:
+        fields = {
+            str(i): dtype_from_pylibcudf_column(col.child(i))
+            for i in range(col.num_children())
+        }
+        return cudf.StructDtype(fields)
+    elif tid == pylibcudf.TypeId.DECIMAL64:
+        return cudf.Decimal64Dtype(
+            precision=cudf.Decimal64Dtype.MAX_PRECISION,
+            scale=-type_.scale()
+        )
+    elif tid == pylibcudf.TypeId.DECIMAL32:
+        return cudf.Decimal32Dtype(
+            precision=cudf.Decimal32Dtype.MAX_PRECISION,
+            scale=-type_.scale()
+        )
+    elif tid == pylibcudf.TypeId.DECIMAL128:
+        return cudf.Decimal128Dtype(
+            precision=cudf.Decimal128Dtype.MAX_PRECISION,
+            scale=-type_.scale()
+        )
+    else:
+        return PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES[tid]
+
+
+cdef dtype_from_lists_column_view(column_view cv):
+    # lists_column_view have no default constructor, so we heap
+    # allocate it to get around Cython's limitation of requiring
+    # default constructors for stack allocated objects
+    cdef shared_ptr[lists_column_view] lv = make_shared[lists_column_view](cv)
+    cdef column_view child = lv.get()[0].child()
+
+    if child.type().id() == libcudf_types.type_id.LIST:
+        return cudf.ListDtype(dtype_from_lists_column_view(child))
+    else:
+        return cudf.ListDtype(dtype_from_column_view(child))
+
+
+cdef dtype_from_column_view(column_view cv):
+    cdef libcudf_types.type_id tid = cv.type().id()
+    if tid == libcudf_types.type_id.LIST:
+        return dtype_from_lists_column_view(cv)
+    elif tid == libcudf_types.type_id.STRUCT:
+        fields = {
+            str(i): dtype_from_column_view(cv.child(i))
+            for i in range(cv.num_children())
+        }
+        return cudf.StructDtype(fields)
+    elif tid == libcudf_types.type_id.DECIMAL64:
+        return cudf.Decimal64Dtype(
+            precision=cudf.Decimal64Dtype.MAX_PRECISION,
+            scale=-cv.type().scale()
+        )
+    elif tid == libcudf_types.type_id.DECIMAL32:
+        return cudf.Decimal32Dtype(
+            precision=cudf.Decimal32Dtype.MAX_PRECISION,
+            scale=-cv.type().scale()
+        )
+    elif tid == libcudf_types.type_id.DECIMAL128:
+        return cudf.Decimal128Dtype(
+            precision=cudf.Decimal128Dtype.MAX_PRECISION,
+            scale=-cv.type().scale()
+        )
+    else:
+        return PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES[<int32_t>(tid)]
+
+
 cdef class Column:
     """
     A Column stores columnar data in device memory.
@@ -424,7 +496,7 @@ cdef class Column:
             col = self
             data_dtype = col.dtype
 
-        cdef plc_DataType dtype = dtype_to_pylibcudf_type(data_dtype)
+        cdef plc_DataType dtype = <plc_DataType>dtype_to_pylibcudf_type(data_dtype)
         cdef libcudf_types.size_type offset = self.offset
         cdef vector[column_view] children
         cdef void* data

python/cudf/cudf/_lib/scalar.pyx

@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2024, NVIDIA CORPORATION.
+# Copyright (c) 2020-2025, NVIDIA CORPORATION.
 
 import copy
 
@@ -14,17 +14,16 @@ import pylibcudf as plc
 
 import cudf
 from cudf.core.dtypes import ListDtype, StructDtype
-from cudf._lib.types import PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES
-from cudf._lib.types cimport dtype_from_column_view, underlying_type_t_type_id
 from cudf.core.missing import NA, NaT
+from cudf.utils.dtypes import PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES
 
 # We currently need this cimport because some of the implementations here
 # access the c_obj of the scalar, and because we need to be able to call
 # pylibcudf.Scalar.from_libcudf. Both of those are temporarily acceptable until
 # DeviceScalar is phased out entirely from cuDF Cython (at which point
 # cudf.Scalar will be directly backed by pylibcudf.Scalar).
-from pylibcudf cimport Scalar as plc_Scalar, type_id as plc_TypeID
-from pylibcudf.libcudf.scalar.scalar cimport list_scalar, scalar, struct_scalar
+from pylibcudf cimport Scalar as plc_Scalar
+from pylibcudf.libcudf.scalar.scalar cimport scalar
 
 
 def _replace_nested(obj, check, replacement):
@@ -223,40 +222,22 @@ cdef class DeviceScalar:
         return s
 
     cdef void _set_dtype(self, dtype=None):
-        cdef plc_TypeID cdtype_id = self.c_value.type().id()
+        cdtype_id = self.c_value.type().id()
         if dtype is not None:
             self._dtype = dtype
         elif cdtype_id in {
-            plc_TypeID.DECIMAL32,
-            plc_TypeID.DECIMAL64,
-            plc_TypeID.DECIMAL128,
+            plc.TypeID.DECIMAL32,
+            plc.TypeID.DECIMAL64,
+            plc.TypeID.DECIMAL128,
         }:
             raise TypeError(
                 "Must pass a dtype when constructing from a fixed-point scalar"
             )
-        elif cdtype_id == plc_TypeID.STRUCT:
-            struct_table_view = (<struct_scalar*>self.get_raw_ptr())[0].view()
-            self._dtype = StructDtype({
-                str(i): dtype_from_column_view(struct_table_view.column(i))
-                for i in range(struct_table_view.num_columns())
-            })
-        elif cdtype_id == plc_TypeID.LIST:
-            if (
-                <list_scalar*>self.get_raw_ptr()
-            )[0].view().type().id() == plc_TypeID.LIST:
-                self._dtype = dtype_from_column_view(
-                    (<list_scalar*>self.get_raw_ptr())[0].view()
-                )
-            else:
-                self._dtype = ListDtype(
-                    PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES[
-                        <underlying_type_t_type_id>(
-                            (<list_scalar*>self.get_raw_ptr())[0]
-                            .view().type().id()
-                        )
-                    ]
-                )
+        elif cdtype_id == plc.TypeID.STRUCT:
+            self._dtype = StructDtype.from_arrow(
+                plc.interop.to_arrow(self.c_value).type
+            )
+        elif cdtype_id == plc.TypeID.LIST:
+            self._dtype = ListDtype.from_arrow(plc.interop.to_arrow(self.c_value).type)
         else:
-            self._dtype = PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES[
-                <underlying_type_t_type_id>(cdtype_id)
-            ]
+            self._dtype = PYLIBCUDF_TO_SUPPORTED_NUMPY_TYPES[cdtype_id]