simplify

narwhals/_duckdb/expr.py

@@ -538,16 +538,13 @@ def fill_null(self: Self, value: Any, strategy: Any, limit: int | None) -> Self:
         )
 
     def cast(self: Self, dtype: DType | type[DType]) -> Self:
-        def func(
-            _input: duckdb.Expression, dtype: DType | type[DType]
-        ) -> duckdb.Expression:
+        def func(_input: duckdb.Expression) -> duckdb.Expression:
             native_dtype = narwhals_to_native_dtype(dtype, self._version)
             return _input.cast(native_dtype)
 
         return self._from_call(
             func,
             "cast",
-            dtype=dtype,
             returns_scalar=self._returns_scalar,
         )
 

narwhals/_duckdb/utils.py

@@ -8,13 +8,13 @@
 
 import duckdb
 
-from narwhals.dtypes import DType
 from narwhals.utils import import_dtypes_module
 from narwhals.utils import isinstance_or_issubclass
 
 if TYPE_CHECKING:
     from narwhals._duckdb.dataframe import DuckDBLazyFrame
     from narwhals._duckdb.expr import DuckDBExpr
+    from narwhals.dtypes import DType
     from narwhals.utils import Version
 
 
@@ -31,8 +31,6 @@ def maybe_evaluate(df: DuckDBLazyFrame, obj: Any) -> Any:
             msg = "Reductions are not yet supported for DuckDB, at least until they implement duckdb.WindowExpression"
             raise NotImplementedError(msg)
         return column_result
-    if isinstance_or_issubclass(obj, DType):
-        return obj
     return duckdb.ConstantExpression(obj)
 
 

narwhals/_spark_like/utils.py

@@ -9,7 +9,6 @@
 from pyspark.sql import types as pyspark_types
 from pyspark.sql.window import Window
 
-from narwhals.dtypes import DType
 from narwhals.exceptions import UnsupportedDTypeError
 from narwhals.utils import import_dtypes_module
 from narwhals.utils import isinstance_or_issubclass
@@ -19,6 +18,7 @@
 
     from narwhals._spark_like.dataframe import SparkLikeLazyFrame
     from narwhals._spark_like.expr import SparkLikeExpr
+    from narwhals.dtypes import DType
     from narwhals.utils import Version
 
 
@@ -145,8 +145,6 @@ def maybe_evaluate(df: SparkLikeLazyFrame, obj: Any) -> Any:
             # Return scalar, let PySpark do its broadcasting
             return column_result.over(Window.partitionBy(F.lit(1)))
         return column_result
-    if isinstance_or_issubclass(obj, DType):
-        return obj
     return F.lit(obj)