refactor: Use .from_iterable() in new_series (#2302)

Author: dangotbanned

narwhals/_arrow/series.py

@@ -147,13 +147,20 @@ def _from_native_series(
 
     @classmethod
     def from_iterable(
-        cls, data: Iterable[Any], *, context: _FullContext, name: str = ""
+        cls,
+        data: Iterable[Any],
+        *,
+        context: _FullContext,
+        name: str = "",
+        dtype: DType | type[DType] | None = None,
     ) -> Self:
+        version = context._version
+        dtype_pa = narwhals_to_native_dtype(dtype, version) if dtype else None
         return cls(
-            chunked_array([data]),
+            chunked_array([data], dtype_pa),
             name=name,
             backend_version=context._backend_version,
-            version=context._version,
+            version=version,
         )
 
     def _from_scalar(self, value: Any) -> Self:

narwhals/_arrow/utils.py

@@ -35,6 +35,7 @@
     # NOTE: stubs don't allow for `ChunkedArray[StructArray]`
     # Intended to represent the `.chunks` property storing `list[pa.StructArray]`
     ChunkedArrayStructArray: TypeAlias = ArrowChunkedArray
+    ArrayAny: TypeAlias = "ArrowArray | ArrowChunkedArray"
 
     _T = TypeVar("_T")
 
@@ -74,12 +75,12 @@ def extract_py_scalar(value: Any, /) -> Any:
 
 
 def chunked_array(
-    arr: ArrowArray | list[Iterable[pa.Scalar[Any]]] | ArrowChunkedArray,
+    arr: ArrayAny | list[Iterable[Any]], dtype: pa.DataType | None = None, /
 ) -> ArrowChunkedArray:
     if isinstance(arr, pa.ChunkedArray):
         return arr
     if isinstance(arr, list):
-        return pa.chunked_array(cast("Any", arr))
+        return pa.chunked_array(arr, dtype)
     else:
         return pa.chunked_array([arr], arr.type)
 

narwhals/_compliant/series.py

@@ -70,7 +70,13 @@ def _to_expr(self) -> CompliantExpr[Any, Self]: ...
     def from_numpy(cls, data: Into1DArray, /, *, context: _FullContext) -> Self: ...
     @classmethod
     def from_iterable(
-        cls, data: Iterable[Any], /, *, context: _FullContext, name: str = ""
+        cls,
+        data: Iterable[Any],
+        /,
+        *,
+        context: _FullContext,
+        name: str = "",
+        dtype: DType | type[DType] | None = None,
     ) -> Self: ...
     def _change_version(self, version: Version) -> Self: ...
 

narwhals/_pandas_like/group_by.py

@@ -12,7 +12,6 @@
 from narwhals._compliant import EagerGroupBy
 from narwhals._expression_parsing import evaluate_output_names_and_aliases
 from narwhals._pandas_like.utils import horizontal_concat
-from narwhals._pandas_like.utils import native_series_from_iterable
 from narwhals._pandas_like.utils import select_columns_by_name
 from narwhals._pandas_like.utils import set_columns
 from narwhals.utils import find_stacklevel
@@ -283,12 +282,8 @@ def func(df: Any) -> Any:
                 for result_keys in results_keys:
                     out_group.append(result_keys.native.iloc[0])
                     out_names.append(result_keys.name)
-            return native_series_from_iterable(
-                out_group,
-                index=out_names,
-                name="",
-                implementation=implementation,
-            )
+            ns = self.compliant.__narwhals_namespace__()
+            return ns._series.from_iterable(out_group, index=out_names, context=ns).native
 
         if implementation.is_pandas() and backend_version >= (2, 2):
             result_complex = self._grouped.apply(func, include_groups=False)

narwhals/_pandas_like/series.py

@@ -21,7 +21,6 @@
 from narwhals._pandas_like.utils import align_and_extract_native
 from narwhals._pandas_like.utils import get_dtype_backend
 from narwhals._pandas_like.utils import narwhals_to_native_dtype
-from narwhals._pandas_like.utils import native_series_from_iterable
 from narwhals._pandas_like.utils import native_to_narwhals_dtype
 from narwhals._pandas_like.utils import object_native_to_narwhals_dtype
 from narwhals._pandas_like.utils import rename
@@ -178,18 +177,28 @@ def from_iterable(
         *,
         context: _FullContext,
         name: str = "",
+        dtype: DType | type[DType] | None = None,
         index: Any = None,
     ) -> Self:
+        implementation = context._implementation
+        backend_version = context._backend_version
+        version = context._version
+        ns = implementation.to_native_namespace()
+        kwds: dict[str, Any] = {}
+        if dtype:
+            kwds["dtype"] = narwhals_to_native_dtype(
+                dtype, None, implementation, backend_version, version
+            )
+        else:
+            if implementation.is_pandas():
+                kwds["copy"] = False
+            if index is not None and len(index):
+                kwds["index"] = index
         return cls(
-            native_series_from_iterable(
-                data,
-                name=name,
-                index=[] if index is None else index,
-                implementation=context._implementation,
-            ),
-            implementation=context._implementation,
-            backend_version=context._backend_version,
-            version=context._version,
+            ns.Series(data, name=name, **kwds),
+            implementation=implementation,
+            backend_version=backend_version,
+            version=version,
         )
 
     @classmethod

narwhals/_pandas_like/utils.py

@@ -6,7 +6,6 @@
 from contextlib import suppress
 from typing import TYPE_CHECKING
 from typing import Any
-from typing import Iterable
 from typing import Sized
 from typing import TypeVar
 from typing import cast
@@ -219,26 +218,6 @@ def diagonal_concat(
         raise TypeError(msg)
 
 
-def native_series_from_iterable(
-    data: Iterable[Any],
-    name: str,
-    index: Any,
-    implementation: Implementation,
-) -> Any:
-    """Return native series."""
-    if implementation in PANDAS_LIKE_IMPLEMENTATION:
-        extra_kwargs = {"copy": False} if implementation is Implementation.PANDAS else {}
-        if len(index) == 0:
-            index = None
-        return implementation.to_native_namespace().Series(
-            data, name=name, index=index, **extra_kwargs
-        )
-
-    else:  # pragma: no cover
-        msg = f"Expected pandas-like implementation ({PANDAS_LIKE_IMPLEMENTATION}), found {implementation}"
-        raise TypeError(msg)
-
-
 def set_index(
     obj: T,
     index: Any,

narwhals/_polars/series.py

@@ -2,6 +2,7 @@
 
 from typing import TYPE_CHECKING
 from typing import Any
+from typing import Iterable
 from typing import Sequence
 from typing import cast
 from typing import overload
@@ -74,6 +75,28 @@ def _change_version(self: Self, version: Version) -> Self:
             self.native, backend_version=self._backend_version, version=version
         )
 
+    @classmethod
+    def from_iterable(
+        cls,
+        data: Iterable[Any],
+        *,
+        context: _FullContext,
+        name: str = "",
+        dtype: DType | type[DType] | None = None,
+    ) -> Self:
+        version = context._version
+        backend_version = context._backend_version
+        dtype_pl = (
+            narwhals_to_native_dtype(dtype, version, backend_version) if dtype else None
+        )
+        # NOTE: `Iterable` is fine, annotation is overly narrow
+        # https://github.com/pola-rs/polars/blob/82d57a4ee41f87c11ca1b1af15488459727efdd7/py-polars/polars/series/series.py#L332-L333
+        return cls(
+            pl.Series(name=name, values=cast("Sequence[Any]", data), dtype=dtype_pl),
+            backend_version=backend_version,
+            version=version,
+        )
+
     @classmethod
     def from_numpy(cls, data: Into1DArray, /, *, context: _FullContext) -> Self:
         return cls(

narwhals/functions.py

@@ -44,7 +44,6 @@
 if TYPE_CHECKING:
     from types import ModuleType
 
-    import pyarrow as pa
     from typing_extensions import Self
     from typing_extensions import TypeAlias
     from typing_extensions import TypeIs
@@ -260,58 +259,21 @@ def _new_series_impl(
     version: Version,
 ) -> Series[Any]:
     implementation = Implementation.from_backend(backend)
-    native_namespace = implementation.to_native_namespace()
-
-    if implementation is Implementation.POLARS:
-        if dtype:
-            from narwhals._polars.utils import (
-                narwhals_to_native_dtype as polars_narwhals_to_native_dtype,
-            )
-
-            backend_version = parse_version(native_namespace.__version__)
-            dtype_pl = polars_narwhals_to_native_dtype(
-                dtype, version=version, backend_version=backend_version
-            )
-        else:
-            dtype_pl = None
-
-        native_series = native_namespace.Series(name=name, values=values, dtype=dtype_pl)
-    elif implementation.is_pandas_like():
-        if dtype:
-            from narwhals._pandas_like.utils import (
-                narwhals_to_native_dtype as pandas_like_narwhals_to_native_dtype,
-            )
-
-            backend_version = parse_version(native_namespace)
-            pd_dtype = pandas_like_narwhals_to_native_dtype(
-                dtype, None, implementation, backend_version, version
-            )
-            native_series = native_namespace.Series(values, name=name, dtype=pd_dtype)
-        else:
-            native_series = native_namespace.Series(values, name=name)
-
-    elif implementation is Implementation.PYARROW:
-        pa_dtype: pa.DataType | None = None
-        if dtype:
-            from narwhals._arrow.utils import (
-                narwhals_to_native_dtype as arrow_narwhals_to_native_dtype,
-            )
-
-            pa_dtype = arrow_narwhals_to_native_dtype(dtype, version=version)
-        native_series = native_namespace.chunked_array([values], type=pa_dtype)
-
+    if is_eager_allowed(implementation):
+        ns = _into_compliant_namespace(implementation, version)
+        series = ns._series.from_iterable(values, name=name, context=ns, dtype=dtype)
+        return from_native(series, series_only=True)
     elif implementation is Implementation.DASK:  # pragma: no cover
         msg = "Dask support in Narwhals is lazy-only, so `new_series` is not supported"
         raise NotImplementedError(msg)
     else:  # pragma: no cover
+        native_namespace = implementation.to_native_namespace()
         try:
-            # implementation is UNKNOWN, Narwhals extension using this feature should
-            # implement `from_dict` function in the top-level namespace.
             native_series = native_namespace.new_series(name, values, dtype)
+            return from_native(native_series, series_only=True).alias(name)
         except AttributeError as e:
-            msg = "Unknown namespace is expected to implement `Series` constructor."
+            msg = "Unknown namespace is expected to implement `new_series` constructor."
             raise AttributeError(msg) from e
-    return from_native(native_series, series_only=True).alias(name)
 
 
 @deprecate_native_namespace(warn_version="1.26.0")