Add Python bindings for string literal support in AST

python/cudf/cudf/_lib/expressions.pxd

@@ -1,4 +1,4 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
 
 from libc.stdint cimport int32_t, int64_t
 from libcpp.memory cimport unique_ptr
@@ -9,25 +9,15 @@ from cudf._lib.cpp.expressions cimport (
     literal,
     operation,
 )
-from cudf._lib.cpp.scalar.scalar cimport numeric_scalar
-
-ctypedef enum scalar_type_t:
-    INT
-    DOUBLE
-
-
-ctypedef union int_or_double_scalar_ptr:
-    unique_ptr[numeric_scalar[int64_t]] int_ptr
-    unique_ptr[numeric_scalar[double]] double_ptr
+from cudf._lib.cpp.scalar.scalar cimport numeric_scalar, scalar, string_scalar
 
 
 cdef class Expression:
     cdef unique_ptr[expression] c_obj
 
 
 cdef class Literal(Expression):
-    cdef scalar_type_t c_scalar_type
-    cdef int_or_double_scalar_ptr c_scalar
+    cdef unique_ptr[scalar] c_scalar
 
 
 cdef class ColumnReference(Expression):

python/cudf/cudf/_lib/expressions.pyx

@@ -1,4 +1,4 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
 
 from enum import Enum
 
@@ -77,27 +77,20 @@ class TableReference(Enum):
 # restrictive at the moment.
 cdef class Literal(Expression):
     def __cinit__(self, value):
-        # TODO: Would love to find a better solution than unions for literals.
-        cdef int intval
-        cdef double doubleval
-
         if isinstance(value, int):
-            self.c_scalar_type = scalar_type_t.INT
-            intval = value
-            self.c_scalar.int_ptr = make_unique[numeric_scalar[int64_t]](
-                intval, True
-            )
+            self.c_scalar.reset(new numeric_scalar[int64_t](value, True))
             self.c_obj = <expression_ptr> make_unique[libcudf_exp.literal](
-                <numeric_scalar[int64_t] &>dereference(self.c_scalar.int_ptr)
+                <numeric_scalar[int64_t] &>dereference(self.c_scalar)
             )
         elif isinstance(value, float):
-            self.c_scalar_type = scalar_type_t.DOUBLE
-            doubleval = value
-            self.c_scalar.double_ptr = make_unique[numeric_scalar[double]](
-                doubleval, True
+            self.c_scalar.reset(new numeric_scalar[double](value, True))
+            self.c_obj = <expression_ptr> make_unique[libcudf_exp.literal](
+                <numeric_scalar[double] &>dereference(self.c_scalar)
             )
+        elif isinstance(value, str):
+            self.c_scalar.reset(new string_scalar(value.encode(), True))
             self.c_obj = <expression_ptr> make_unique[libcudf_exp.literal](
-                <numeric_scalar[double] &>dereference(self.c_scalar.double_ptr)
+                <string_scalar &>dereference(self.c_scalar)
             )
 
 

python/cudf/cudf/core/_internals/expressions.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
 
 import ast
 import functools
@@ -115,7 +115,7 @@ def visit_Name(self, node):
         self.stack.append(ColumnReference(col_id))
 
     def visit_Constant(self, node):
-        if not isinstance(node, ast.Num):
+        if not isinstance(node, (ast.Num, ast.Str)):
             raise ValueError(
                 f"Unsupported literal {repr(node.value)} of type "
                 "{type(node.value).__name__}"

python/cudf/cudf/core/dataframe.py

@@ -7057,7 +7057,8 @@ def eval(self, expr: str, inplace: bool = False, **kwargs):
               Specifically, `&` must be used for bitwise operators on integers,
               not `and`, which is specifically for the logical and between
               booleans.
-            * Only numerical types are currently supported.
+            * Only numerical types are currently supported on all operators.
+            * String types are supported only on comparison operators.
             * Operators generally will not cast automatically. Users are
               responsible for casting columns to suitable types before
               evaluating a function.

python/cudf/cudf/tests/test_dataframe.py

@@ -9820,6 +9820,9 @@ def df_eval(request):
             float,
         ),
         ("a_b_are_equal = (a == b)", int),
+        ("a > b", str),
+        ("a < '1'", str),
+        ('a == "1"', str),
     ],
 )
 def test_dataframe_eval(df_eval, expr, dtype):