fix: allow any name in expression parsing

narwhals/_arrow/dataframe.py

@@ -294,7 +294,7 @@ def simple_select(self, *column_names: str) -> Self:
         return self._from_native_frame(self._native_frame.select(list(column_names)))
 
     def select(self: Self, *exprs: IntoArrowExpr, **named_exprs: IntoArrowExpr) -> Self:
-        new_series = evaluate_into_exprs(self, *exprs, **named_exprs)
+        new_series: list[ArrowSeries] = evaluate_into_exprs(self)(*exprs, **named_exprs)
         if not new_series:
             # return empty dataframe, like Polars does
             return self._from_native_frame(self._native_frame.__class__.from_arrays([]))
@@ -306,7 +306,7 @@ def with_columns(
         self: Self, *exprs: IntoArrowExpr, **named_exprs: IntoArrowExpr
     ) -> Self:
         native_frame = self._native_frame
-        new_columns = evaluate_into_exprs(self, *exprs, **named_exprs)
+        new_columns: list[ArrowSeries] = evaluate_into_exprs(self)(*exprs, **named_exprs)
 
         length = len(self)
         columns = self.columns

narwhals/_dask/dataframe.py

@@ -75,7 +75,7 @@ def _from_native_frame(self: Self, df: Any) -> Self:
 
     def with_columns(self: Self, *exprs: DaskExpr, **named_exprs: DaskExpr) -> Self:
         df = self._native_frame
-        new_series = parse_exprs_and_named_exprs(self, *exprs, **named_exprs)
+        new_series = parse_exprs_and_named_exprs(self)(*exprs, **named_exprs)
         df = df.assign(**new_series)
         return self._from_native_frame(df)
 
@@ -115,7 +115,7 @@ def simple_select(self: Self, *column_names: str) -> Self:
         )
 
     def select(self: Self, *exprs: DaskExpr, **named_exprs: DaskExpr) -> Self:
-        new_series = parse_exprs_and_named_exprs(self, *exprs, **named_exprs)
+        new_series = parse_exprs_and_named_exprs(self)(*exprs, **named_exprs)
 
         if not new_series:
             # return empty dataframe, like Polars does

narwhals/_dask/utils.py

@@ -2,6 +2,7 @@
 
 from typing import TYPE_CHECKING
 from typing import Any
+from typing import Callable
 
 from narwhals._expression_parsing import evaluate_output_names_and_aliases
 from narwhals._pandas_like.utils import select_columns_by_name
@@ -44,29 +45,32 @@ def maybe_evaluate(df: DaskLazyFrame, obj: Any) -> Any:
     return obj
 
 
-def parse_exprs_and_named_exprs(
-    df: DaskLazyFrame, *exprs: DaskExpr, **named_exprs: DaskExpr
-) -> dict[str, dx.Series]:
-    native_results: dict[str, dx.Series] = {}
-    for expr in exprs:
-        native_series_list = expr._call(df)
-        return_scalar = getattr(expr, "_returns_scalar", False)
-        _, aliases = evaluate_output_names_and_aliases(expr, df, [])
-        if len(aliases) != len(native_series_list):  # pragma: no cover
-            msg = f"Internal error: got aliases {aliases}, but only got {len(native_series_list)} results"
-            raise AssertionError(msg)
-        for native_series, alias in zip(native_series_list, aliases):
-            native_results[alias] = native_series[0] if return_scalar else native_series
-    for name, value in named_exprs.items():
-        native_series_list = value._call(df)
-        if len(native_series_list) != 1:  # pragma: no cover
-            msg = "Named expressions must return a single column"
-            raise AssertionError(msg)
-        return_scalar = getattr(value, "_returns_scalar", False)
-        native_results[name] = (
-            native_series_list[0][0] if return_scalar else native_series_list[0]
-        )
-    return native_results
+def parse_exprs_and_named_exprs(df: DaskLazyFrame) -> Callable[..., dict[str, dx.Series]]:
+    def func(*exprs: DaskExpr, **named_exprs: DaskExpr) -> dict[str, dx.Series]:
+        native_results: dict[str, dx.Series] = {}
+        for expr in exprs:
+            native_series_list = expr._call(df)
+            return_scalar = getattr(expr, "_returns_scalar", False)
+            _, aliases = evaluate_output_names_and_aliases(expr, df, [])
+            if len(aliases) != len(native_series_list):  # pragma: no cover
+                msg = f"Internal error: got aliases {aliases}, but only got {len(native_series_list)} results"
+                raise AssertionError(msg)
+            for native_series, alias in zip(native_series_list, aliases):
+                native_results[alias] = (
+                    native_series[0] if return_scalar else native_series
+                )
+        for name, value in named_exprs.items():
+            native_series_list = value._call(df)
+            if len(native_series_list) != 1:  # pragma: no cover
+                msg = "Named expressions must return a single column"
+                raise AssertionError(msg)
+            return_scalar = getattr(value, "_returns_scalar", False)
+            native_results[name] = (
+                native_series_list[0][0] if return_scalar else native_series_list[0]
+            )
+        return native_results
+
+    return func
 
 
 def add_row_index(

narwhals/_duckdb/dataframe.py

@@ -104,7 +104,7 @@ def select(
         *exprs: DuckDBExpr,
         **named_exprs: DuckDBExpr,
     ) -> Self:
-        new_columns_map = parse_exprs_and_named_exprs(self, *exprs, **named_exprs)
+        new_columns_map = parse_exprs_and_named_exprs(self)(*exprs, **named_exprs)
         if not new_columns_map:
             # TODO(marco): return empty relation with 0 columns?
             return self._from_native_frame(self._native_frame.limit(0))
@@ -139,7 +139,7 @@ def with_columns(
         *exprs: DuckDBExpr,
         **named_exprs: DuckDBExpr,
     ) -> Self:
-        new_columns_map = parse_exprs_and_named_exprs(self, *exprs, **named_exprs)
+        new_columns_map = parse_exprs_and_named_exprs(self)(*exprs, **named_exprs)
         result = []
         for col in self._native_frame.columns:
             if col in new_columns_map:

narwhals/_duckdb/utils.py

@@ -4,6 +4,7 @@
 from functools import lru_cache
 from typing import TYPE_CHECKING
 from typing import Any
+from typing import Callable
 
 import duckdb
 
@@ -36,25 +37,30 @@ def maybe_evaluate(df: DuckDBLazyFrame, obj: Any) -> Any:
 
 
 def parse_exprs_and_named_exprs(
-    df: DuckDBLazyFrame, *exprs: DuckDBExpr, **named_exprs: DuckDBExpr
-) -> dict[str, duckdb.Expression]:
-    native_results: dict[str, list[duckdb.Expression]] = {}
-    for expr in exprs:
-        native_series_list = expr._call(df)
-        output_names = expr._evaluate_output_names(df)
-        if expr._alias_output_names is not None:
-            output_names = expr._alias_output_names(output_names)
-        if len(output_names) != len(native_series_list):  # pragma: no cover
-            msg = f"Internal error: got output names {output_names}, but only got {len(native_series_list)} results"
-            raise AssertionError(msg)
-        native_results.update(zip(output_names, native_series_list))
-    for col_alias, expr in named_exprs.items():
-        native_series_list = expr._call(df)
-        if len(native_series_list) != 1:  # pragma: no cover
-            msg = "Named expressions must return a single column"
-            raise ValueError(msg)
-        native_results[col_alias] = native_series_list[0]
-    return native_results
+    df: DuckDBLazyFrame,
+) -> Callable[..., dict[str, duckdb.Expression]]:
+    def func(
+        *exprs: DuckDBExpr, **named_exprs: DuckDBExpr
+    ) -> dict[str, duckdb.Expression]:
+        native_results: dict[str, list[duckdb.Expression]] = {}
+        for expr in exprs:
+            native_series_list = expr._call(df)
+            output_names = expr._evaluate_output_names(df)
+            if expr._alias_output_names is not None:
+                output_names = expr._alias_output_names(output_names)
+            if len(output_names) != len(native_series_list):  # pragma: no cover
+                msg = f"Internal error: got output names {output_names}, but only got {len(native_series_list)} results"
+                raise AssertionError(msg)
+            native_results.update(zip(output_names, native_series_list))
+        for col_alias, expr in named_exprs.items():
+            native_series_list = expr._call(df)
+            if len(native_series_list) != 1:  # pragma: no cover
+                msg = "Named expressions must return a single column"
+                raise ValueError(msg)
+            native_results[col_alias] = native_series_list[0]
+        return native_results
+
+    return func
 
 
 @lru_cache(maxsize=16)

narwhals/_expression_parsing.py

@@ -62,23 +62,28 @@ def evaluate_into_expr(
 
 def evaluate_into_exprs(
     df: CompliantDataFrame,
-    *exprs: IntoCompliantExpr[CompliantSeriesT_co],
-    **named_exprs: IntoCompliantExpr[CompliantSeriesT_co],
-) -> Sequence[CompliantSeriesT_co]:
+) -> Callable[..., list[CompliantSeriesT_co]]:
     """Evaluate each expr into Series."""
-    series = [
-        item
-        for sublist in (evaluate_into_expr(df, into_expr) for into_expr in exprs)
-        for item in sublist
-    ]
-    for name, expr in named_exprs.items():
-        evaluated_expr = evaluate_into_expr(df, expr)
-        if len(evaluated_expr) > 1:
-            msg = "Named expressions must return a single column"  # pragma: no cover
-            raise AssertionError(msg)
-        to_append = evaluated_expr[0].alias(name)
-        series.append(to_append)
-    return series
+
+    def func(
+        *exprs: IntoCompliantExpr[CompliantSeriesT_co],
+        **named_exprs: IntoCompliantExpr[CompliantSeriesT_co],
+    ) -> list[CompliantSeriesT_co]:
+        series = [
+            item
+            for sublist in (evaluate_into_expr(df, into_expr) for into_expr in exprs)
+            for item in sublist
+        ]
+        for name, expr in named_exprs.items():
+            evaluated_expr = evaluate_into_expr(df, expr)
+            if len(evaluated_expr) > 1:
+                msg = "Named expressions must return a single column"  # pragma: no cover
+                raise AssertionError(msg)
+            to_append = evaluated_expr[0].alias(name)
+            series.append(to_append)
+        return series
+
+    return func
 
 
 def maybe_evaluate_expr(

narwhals/_pandas_like/dataframe.py

@@ -363,7 +363,9 @@ def select(
         *exprs: IntoPandasLikeExpr,
         **named_exprs: IntoPandasLikeExpr,
     ) -> Self:
-        new_series = evaluate_into_exprs(self, *exprs, **named_exprs)
+        new_series: list[PandasLikeSeries] = evaluate_into_exprs(self)(
+            *exprs, **named_exprs
+        )
         if not new_series:
             # return empty dataframe, like Polars does
             return self._from_native_frame(self._native_frame.__class__())
@@ -433,7 +435,9 @@ def with_columns(
         **named_exprs: IntoPandasLikeExpr,
     ) -> Self:
         index = self._native_frame.index
-        new_columns = evaluate_into_exprs(self, *exprs, **named_exprs)
+        new_columns: list[PandasLikeSeries] = evaluate_into_exprs(self)(
+            *exprs, **named_exprs
+        )
         if not new_columns and len(self) == 0:
             return self
 

narwhals/_spark_like/dataframe.py

@@ -94,7 +94,7 @@ def select(
         *exprs: SparkLikeExpr,
         **named_exprs: SparkLikeExpr,
     ) -> Self:
-        new_columns = parse_exprs_and_named_exprs(self, *exprs, **named_exprs)
+        new_columns = parse_exprs_and_named_exprs(self)(*exprs, **named_exprs)
 
         if not new_columns:
             # return empty dataframe, like Polars does
@@ -135,7 +135,7 @@ def with_columns(
         *exprs: SparkLikeExpr,
         **named_exprs: SparkLikeExpr,
     ) -> Self:
-        new_columns_map = parse_exprs_and_named_exprs(self, *exprs, **named_exprs)
+        new_columns_map = parse_exprs_and_named_exprs(self)(*exprs, **named_exprs)
         return self._from_native_frame(self._native_frame.withColumns(new_columns_map))
 
     def drop(self: Self, columns: list[str], strict: bool) -> Self:  # noqa: FBT001

narwhals/_spark_like/utils.py

@@ -3,6 +3,7 @@
 from functools import lru_cache
 from typing import TYPE_CHECKING
 from typing import Any
+from typing import Callable
 
 from pyspark.sql import functions as F  # noqa: N812
 
@@ -110,25 +111,28 @@ def narwhals_to_native_dtype(
 
 
 def parse_exprs_and_named_exprs(
-    df: SparkLikeLazyFrame, *exprs: SparkLikeExpr, **named_exprs: SparkLikeExpr
-) -> dict[str, Column]:
-    native_results: dict[str, list[Column]] = {}
-    for expr in exprs:
-        native_series_list = expr._call(df)
-        output_names = expr._evaluate_output_names(df)
-        if expr._alias_output_names is not None:
-            output_names = expr._alias_output_names(output_names)
-        if len(output_names) != len(native_series_list):  # pragma: no cover
-            msg = f"Internal error: got output names {output_names}, but only got {len(native_series_list)} results"
-            raise AssertionError(msg)
-        native_results.update(zip(output_names, native_series_list))
-    for col_alias, expr in named_exprs.items():
-        native_series_list = expr._call(df)
-        if len(native_series_list) != 1:  # pragma: no cover
-            msg = "Named expressions must return a single column"
-            raise ValueError(msg)
-        native_results[col_alias] = native_series_list[0]
-    return native_results
+    df: SparkLikeLazyFrame,
+) -> Callable[..., dict[str, Column]]:
+    def func(*exprs: SparkLikeExpr, **named_exprs: SparkLikeExpr) -> dict[str, Column]:
+        native_results: dict[str, list[Column]] = {}
+        for expr in exprs:
+            native_series_list = expr._call(df)
+            output_names = expr._evaluate_output_names(df)
+            if expr._alias_output_names is not None:
+                output_names = expr._alias_output_names(output_names)
+            if len(output_names) != len(native_series_list):  # pragma: no cover
+                msg = f"Internal error: got output names {output_names}, but only got {len(native_series_list)} results"
+                raise AssertionError(msg)
+            native_results.update(zip(output_names, native_series_list))
+        for col_alias, expr in named_exprs.items():
+            native_series_list = expr._call(df)
+            if len(native_series_list) != 1:  # pragma: no cover
+                msg = "Named expressions must return a single column"
+                raise ValueError(msg)
+            native_results[col_alias] = native_series_list[0]
+        return native_results
+
+    return func
 
 
 def maybe_evaluate(df: SparkLikeLazyFrame, obj: Any) -> Any: