feat: Allow nested structures in lit

narwhals/_arrow/namespace.py

@@ -25,7 +25,7 @@
 
     from narwhals._arrow.typing import ChunkedArrayAny, Incomplete, ScalarAny
     from narwhals._utils import Version
-    from narwhals.typing import IntoDType, NonNestedLiteral
+    from narwhals.typing import IntoDType, PythonLiteral
 
 
 class ArrowNamespace(
@@ -64,7 +64,7 @@ def len(self) -> ArrowExpr:
             version=self._version,
         )
 
-    def lit(self, value: NonNestedLiteral, dtype: IntoDType | None) -> ArrowExpr:
+    def lit(self, value: PythonLiteral, dtype: IntoDType | None) -> ArrowExpr:
         def _lit_arrow_series(_: ArrowDataFrame) -> ArrowSeries:
             arrow_series = ArrowSeries.from_iterable(
                 data=[value], name="literal", context=self

narwhals/_dask/namespace.py

@@ -22,7 +22,7 @@
     combine_alias_output_names,
     combine_evaluate_output_names,
 )
-from narwhals._utils import Implementation, zip_strict
+from narwhals._utils import Implementation, qualified_type_name, zip_strict
 
 if TYPE_CHECKING:
     from collections.abc import Iterable, Iterator
@@ -55,6 +55,13 @@ def __init__(self, *, version: Version) -> None:
         self._version = version
 
     def lit(self, value: NonNestedLiteral, dtype: IntoDType | None) -> DaskExpr:
+        if isinstance(value, (list, tuple, dict)):
+            msg = (
+                "Nested structures are not support for Dask backend, "
+                f" found {qualified_type_name(value)}"
+            )
+            raise NotImplementedError(msg)
+
         def func(df: DaskLazyFrame) -> list[dx.Series]:
             if dtype is not None:
                 native_dtype = narwhals_to_native_dtype(dtype, self._version)

narwhals/_duckdb/namespace.py

@@ -35,7 +35,7 @@
 
     from narwhals._compliant.window import WindowInputs
     from narwhals._utils import Version
-    from narwhals.typing import ConcatMethod, IntoDType, NonNestedLiteral
+    from narwhals.typing import ConcatMethod, IntoDType, PythonLiteral
 
 VARCHAR = duckdb_dtypes.VARCHAR
 
@@ -130,8 +130,12 @@ def func(cols: Iterable[Expression]) -> Expression:
 
         return self._expr._from_elementwise_horizontal_op(func, *exprs)
 
-    def lit(self, value: NonNestedLiteral, dtype: IntoDType | None) -> DuckDBExpr:
+    def lit(self, value: PythonLiteral, dtype: IntoDType | None) -> DuckDBExpr:
         def func(df: DuckDBLazyFrame) -> list[Expression]:
+            if isinstance(value, dict) and len(value) == 0:
+                msg = "Cannot create an empty struct type for DuckDB backend"
+                raise NotImplementedError(msg)
+
             tz = DeferredTimeZone(df.native)
             if dtype is not None:
                 target = narwhals_to_native_dtype(dtype, self._version, tz)

narwhals/_ibis/namespace.py

@@ -114,9 +114,15 @@ def func(cols: Iterable[ir.Value]) -> ir.Value:
 
         return self._expr._from_elementwise_horizontal_op(func, *exprs)
 
-    def lit(self, value: Any, dtype: IntoDType | None) -> IbisExpr:
+    def lit(self, value: PythonLiteral, dtype: IntoDType | None) -> IbisExpr:
         def func(_df: IbisLazyFrame) -> Sequence[ir.Value]:
+            if isinstance(value, dict) and len(value) == 0:
+                msg = "Cannot create an empty struct type for Ibis backend"
+                raise NotImplementedError(msg)
+
             ibis_dtype = narwhals_to_native_dtype(dtype, self._version) if dtype else None
+            if isinstance(value, dict):
+                return [ibis.struct(value, type=ibis_dtype)]
             return [lit(value, ibis_dtype)]
 
         return self._expr(

narwhals/_pandas_like/dataframe.py

@@ -10,6 +10,7 @@
 from narwhals._pandas_like.series import PANDAS_TO_NUMPY_DTYPE_MISSING, PandasLikeSeries
 from narwhals._pandas_like.utils import (
     align_and_extract_native,
+    broadcast_series_to_index,
     get_dtype_backend,
     import_array_module,
     iter_dtype_backends,
@@ -307,8 +308,12 @@ def _with_native(self, df: Any, *, validate_column_names: bool = True) -> Self:
     def _extract_comparand(self, other: PandasLikeSeries) -> pd.Series[Any]:
         index = self.native.index
         if other._broadcast:
-            s = other.native
-            return type(s)(s.iloc[0], index=index, dtype=s.dtype, name=s.name)
+            native = other.native
+            is_nested = other.dtype.is_nested()
+            return broadcast_series_to_index(
+                native, index, is_nested=is_nested, series_class=type(native)
+            )
+
         if (len_other := len(other)) != (len_idx := len(index)):
             msg = f"Expected object of length {len_idx}, got: {len_other}."
             raise ShapeError(msg)

narwhals/_pandas_like/namespace.py

@@ -25,7 +25,7 @@
     from typing_extensions import TypeAlias
 
     from narwhals._utils import Implementation, Version
-    from narwhals.typing import IntoDType, NonNestedLiteral
+    from narwhals.typing import IntoDType, PythonLiteral
 
 
 Incomplete: TypeAlias = Any
@@ -83,17 +83,46 @@ def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
             context=self,
         )
 
-    def lit(self, value: NonNestedLiteral, dtype: IntoDType | None) -> PandasLikeExpr:
+    def lit(self, value: PythonLiteral, dtype: IntoDType | None) -> PandasLikeExpr:
         def _lit_pandas_series(df: PandasLikeDataFrame) -> PandasLikeSeries:
-            pandas_series = self._series.from_iterable(
+            if isinstance(value, (list, tuple, dict)):
+                try:
+                    import pandas as pd  # ignore-banned-import
+                    import pyarrow as pa  # ignore-banned-import
+                except ImportError as exc:  # pragma: no cover
+                    msg = (
+                        "Nested structures require pyarrow to be installed for pandas backend. "
+                        "Please install pyarrow: pip install pyarrow"
+                    )
+                    raise ImportError(msg) from exc
+
+                from narwhals._arrow.utils import (
+                    narwhals_to_native_dtype as _to_arrow_dtype,
+                )
+
+                array_value = list(value) if isinstance(value, tuple) else value
+                pa_dtype = _to_arrow_dtype(dtype, self._version) if dtype else None
+                pa_array = pa.array([array_value], type=pa_dtype)  # type: ignore[arg-type, list-item]
+
+                # Use ArrowExtensionArray to avoid pandas unpacking the nested structure
+                ns = self._implementation.to_native_namespace()
+                pandas_series_native = ns.Series(
+                    pd.arrays.ArrowExtensionArray(pa_array),  # type: ignore[attr-defined]
+                    name="literal",
+                    index=df._native_frame.index[0:1],
+                )
+
+                return self._series.from_native(pandas_series_native, context=self)
+
+            pandas_like_series = self._series.from_iterable(
                 data=[value],
                 name="literal",
                 index=df._native_frame.index[0:1],
                 context=self,
             )
             if dtype:
-                return pandas_series.cast(dtype)
-            return pandas_series
+                return pandas_like_series.cast(dtype)
+            return pandas_like_series
 
         return PandasLikeExpr(
             lambda df: [_lit_pandas_series(df)],

narwhals/_pandas_like/series.py

@@ -14,6 +14,7 @@
 from narwhals._pandas_like.series_struct import PandasLikeSeriesStructNamespace
 from narwhals._pandas_like.utils import (
     align_and_extract_native,
+    broadcast_series_to_index,
     get_dtype_backend,
     import_array_module,
     narwhals_to_native_dtype,
@@ -211,8 +212,8 @@ def _align_full_broadcast(cls, *series: Self) -> Sequence[Self]:
         reindexed = []
         for s in series:
             if s._broadcast:
-                native = Series(
-                    s.native.iloc[0], index=idx, name=s.name, dtype=s.native.dtype
+                native = broadcast_series_to_index(
+                    s.native, idx, is_nested=s.dtype.is_nested(), series_class=Series
                 )
                 compliant = s._with_native(native)
             elif s.native.index is not idx:

narwhals/_pandas_like/utils.py

@@ -3,7 +3,7 @@
 import functools
 import operator
 import re
-from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar, cast
+from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar, cast, no_type_check
 
 import pandas as pd
 
@@ -663,3 +663,38 @@ class PandasLikeSeriesNamespace(EagerSeriesNamespace["PandasLikeSeries", Any]):
 
 def make_group_by_kwargs(*, drop_null_keys: bool) -> dict[str, bool]:
     return {"sort": False, "as_index": True, "dropna": drop_null_keys, "observed": True}
+
+
+@no_type_check
+def broadcast_series_to_index(
+    native: NativeSeriesT,
+    index: Any,
+    *,
+    is_nested: bool,
+    series_class: type[NativeSeriesT],
+) -> NativeSeriesT:
+    """Broadcast a scalar value from a (one element) Series to match a target index.
+
+    For nested (arrow-backed) types, we rely on
+    [pandas.arrays.ArrowExtensionArray](https://pandas.pydata.org/docs/reference/api/pandas.arrays.ArrowExtensionArray.html).
+
+    Arguments:
+        native: The native pandas-like Series containing the scalar value to broadcast.
+        index: The target index to broadcast to.
+        is_nested: Whether the Series has a nested (arrow-backed) dtype.
+        series_class: Series class to use for constructing the result.
+
+    Returns:
+        A new Series with the scalar value broadcast to match the target index.
+    """
+    value = native.iloc[0]
+    if is_nested:
+        import pyarrow as pa  # ignore-banned-import
+
+        pa_type = native.dtype.pyarrow_dtype
+        pa_array = pd.arrays.ArrowExtensionArray(
+            pa.repeat(pa.scalar(value, pa_type), len(index))
+        )
+        return series_class(pa_array, index=index, name=native.name)
+
+    return series_class(value, index=index, dtype=native.dtype, name=native.name)

narwhals/_spark_like/namespace.py

@@ -28,7 +28,7 @@
     from narwhals._compliant.window import WindowInputs
     from narwhals._spark_like.dataframe import SQLFrameDataFrame  # noqa: F401
     from narwhals._utils import Implementation, Version
-    from narwhals.typing import ConcatMethod, IntoDType, NonNestedLiteral, PythonLiteral
+    from narwhals.typing import ConcatMethod, IntoDType, PythonLiteral
 
 # Adjust slight SQL vs PySpark differences
 FUNCTION_REMAPPINGS = {
@@ -91,9 +91,22 @@ def _when(
     def _coalesce(self, *exprs: Column) -> Column:
         return self._F.coalesce(*exprs)
 
-    def lit(self, value: NonNestedLiteral, dtype: IntoDType | None) -> SparkLikeExpr:
+    def lit(self, value: PythonLiteral, dtype: IntoDType | None) -> SparkLikeExpr:
         def func(df: SparkLikeLazyFrame) -> list[Column]:
-            column = df._F.lit(value)
+            F = df._F
+
+            if isinstance(value, (list, tuple)):
+                lit_values = [F.lit(v) for v in value]
+                column = F.lit(F.array(lit_values))
+            elif isinstance(value, dict):
+                if (not self._implementation.is_pyspark()) and (len(value) == 0):
+                    msg = f"Cannot create an empty struct type for {self._implementation} backend"
+                    raise NotImplementedError(msg)
+                lit_values = [F.lit(v).alias(k) for k, v in value.items()]
+                column = F.struct(*lit_values)
+            else:
+                column = F.lit(value)
+
             if dtype:
                 native_dtype = narwhals_to_native_dtype(
                     dtype, self._version, df._native_dtypes, df.native.sparkSession

narwhals/functions.py

@@ -46,6 +46,7 @@
         IntoExpr,
         IntoSchema,
         NonNestedLiteral,
+        PythonLiteral,
         _2DArray,
     )
 
@@ -1422,27 +1423,70 @@ def all_horizontal(*exprs: IntoExpr | Iterable[IntoExpr], ignore_nulls: bool) ->
     )
 
 
-def lit(value: NonNestedLiteral, dtype: IntoDType | None = None) -> Expr:
+def lit(value: PythonLiteral, dtype: IntoDType | None = None) -> Expr:
     """Return an expression representing a literal value.
 
     Arguments:
-        value: The value to use as literal.
+        value: The value to use as literal. Can be a scalar value, list, tuple, or dict.
+            Lists and tuples are converted to `List` dtype, dicts to `Struct` dtype.
         dtype: The data type of the literal value. If not provided, the data type will
-            be inferred by the native library.
+            be inferred by the native library. For empty lists/dicts, dtype must be
+            specified explicitly.
 
     Examples:
-        >>> import pandas as pd
+        Scalar literals:
+
+        >>> import pyarrow as pa
         >>> import narwhals as nw
         >>>
-        >>> df_native = pd.DataFrame({"a": [1, 2]})
-        >>> nw.from_native(df_native).with_columns(nw.lit(3))
+        >>> df_nw = nw.from_native(pa.table({"a": [1, 2]}))
+        >>> df_nw.with_columns(nw.lit(3))
         ┌──────────────────┐
         |Narwhals DataFrame|
         |------------------|
-        |     a  literal   |
-        |  0  1        3   |
-        |  1  2        3   |
+        | pyarrow.Table    |
+        | a: int64         |
+        | literal: int64   |
+        | ----             |
+        | a: [[1,2]]       |
+        | literal: [[3,3]] |
         └──────────────────┘
+
+        List literals (creates a List column):
+
+        >>> df_nw.with_columns(nw.lit([1, 2, 3]).alias("list_col"))
+        ┌─────────────────────────────┐
+        |     Narwhals DataFrame      |
+        |-----------------------------|
+        |pyarrow.Table                |
+        |a: int64                     |
+        |list_col: list<item: int64>  |
+        |  child 0, item: int64       |
+        |----                         |
+        |a: [[1,2]]                   |
+        |list_col: [[[1,2,3],[1,2,3]]]|
+        └─────────────────────────────┘
+
+        Dict literals (creates a Struct column):
+
+        >>> df_nw.with_columns(nw.lit({"x": 1, "y": 2}).alias("struct_col"))
+        ┌──────────────────────────────────────┐
+        |          Narwhals DataFrame          |
+        |--------------------------------------|
+        |pyarrow.Table                         |
+        |a: int64                              |
+        |struct_col: struct<x: int64, y: int64>|
+        |  child 0, x: int64                   |
+        |  child 1, y: int64                   |
+        |----                                  |
+        |a: [[1,2]]                            |
+        |struct_col: [                         |
+        |  -- is_valid: all not null           |
+        |  -- child 0 type: int64              |
+        |[1,1]                                 |
+        |  -- child 1 type: int64              |
+        |[2,2]]                                |
+        └──────────────────────────────────────┘
     """
     if is_numpy_array(value):
         msg = (
@@ -1451,9 +1495,9 @@ def lit(value: NonNestedLiteral, dtype: IntoDType | None = None) -> Expr:
         )
         raise ValueError(msg)
 
-    if isinstance(value, (list, tuple)):
-        msg = f"Nested datatypes are not supported yet. Got {value}"
-        raise NotImplementedError(msg)
+    if isinstance(value, (list, tuple, dict)) and len(value) == 0 and dtype is None:
+        msg = "Cannot infer dtype for empty nested structure. Please provide an explicit dtype parameter."
+        raise ValueError(msg)
 
     return Expr(ExprNode(ExprKind.LITERAL, "lit", value=value, dtype=dtype))
 

narwhals/stable/v2/__init__.py

@@ -653,9 +653,11 @@ def lit(value: NonNestedLiteral, dtype: IntoDType | None = None) -> Expr:
     """Return an expression representing a literal value.
 
     Arguments:
-        value: The value to use as literal.
+        value: The value to use as literal. Can be a scalar value, list, tuple, or dict.
+            Lists and tuples are converted to `List` dtype, dicts to `Struct` dtype.
         dtype: The data type of the literal value. If not provided, the data type will
-            be inferred by the native library.
+            be inferred by the native library. For empty lists/dicts, dtype must be
+            specified explicitly.
     """
     return _stableify(nw.lit(value, dtype))
 

narwhals/typing.py

@@ -270,7 +270,9 @@ def Binary(self) -> type[dtypes.Binary]: ...
 NonNestedLiteral: TypeAlias = (
     "NumericLiteral | TemporalLiteral | str | bool | bytes | None"
 )
-PythonLiteral: TypeAlias = "NonNestedLiteral | list[Any] | tuple[Any, ...]"
+PythonLiteral: TypeAlias = (
+    "NonNestedLiteral | list[Any] | tuple[Any, ...] | dict[str, Any]"
+)
 
 NonNestedDType: TypeAlias = "dtypes.NumericType | dtypes.TemporalType | dtypes.String | dtypes.Boolean | dtypes.Binary | dtypes.Categorical | dtypes.Unknown | dtypes.Object"
 """Any Narwhals DType that does not have required arguments."""