refactor: Add CompliantSeries.from_numpy

narwhals/_arrow/expr.py

@@ -13,8 +13,6 @@
 from narwhals._expression_parsing import ExprKind
 from narwhals._expression_parsing import evaluate_output_names_and_aliases
 from narwhals._expression_parsing import is_scalar_like
-from narwhals.dependencies import get_numpy
-from narwhals.dependencies import is_numpy_array
 from narwhals.exceptions import ColumnNotFoundError
 from narwhals.utils import Implementation
 from narwhals.utils import generate_temporary_column_name
@@ -25,7 +23,6 @@
 
     from narwhals._arrow.dataframe import ArrowDataFrame
     from narwhals._arrow.namespace import ArrowNamespace
-    from narwhals.dtypes import DType
     from narwhals.utils import Version
     from narwhals.utils import _FullContext
 
@@ -203,44 +200,6 @@ def func(df: ArrowDataFrame) -> Sequence[ArrowSeries]:
             version=self._version,
         )
 
-    def map_batches(
-        self: Self,
-        function: Callable[[Any], Any],
-        return_dtype: DType | type[DType] | None,
-    ) -> Self:
-        def func(df: ArrowDataFrame) -> list[ArrowSeries]:
-            input_series_list = self._call(df)
-            output_names = [input_series.name for input_series in input_series_list]
-            result = [function(series) for series in input_series_list]
-
-            if is_numpy_array(result[0]):
-                result = [
-                    df.__narwhals_namespace__()
-                    ._create_compliant_series(array)
-                    .alias(output_name)
-                    for array, output_name in zip(result, output_names)
-                ]
-            elif (np := get_numpy()) is not None and np.isscalar(result[0]):
-                result = [
-                    df.__narwhals_namespace__()
-                    ._create_compliant_series([array])
-                    .alias(output_name)
-                    for array, output_name in zip(result, output_names)
-                ]
-            if return_dtype is not None:
-                result = [series.cast(return_dtype) for series in result]
-            return result
-
-        return self.__class__(
-            func,
-            depth=self._depth + 1,
-            function_name=self._function_name + "->map_batches",
-            evaluate_output_names=self._evaluate_output_names,
-            alias_output_names=self._alias_output_names,
-            backend_version=self._backend_version,
-            version=self._version,
-        )
-
     def cum_count(self: Self, *, reverse: bool) -> Self:
         return self._reuse_series("cum_count", reverse=reverse)
 

narwhals/_arrow/namespace.py

@@ -51,9 +51,6 @@ def _expr(self) -> type[ArrowExpr]:
     def _series(self) -> type[ArrowSeries]:
         return ArrowSeries
 
-    def _create_compliant_series(self: Self, value: Any) -> ArrowSeries:
-        return self._series._from_iterable(value, name="", context=self)
-
     # --- not in spec ---
     def __init__(
         self: Self, *, backend_version: tuple[int, ...], version: Version

narwhals/_arrow/series.py

@@ -27,6 +27,7 @@
 from narwhals._arrow.utils import nulls_like
 from narwhals._arrow.utils import pad_series
 from narwhals._compliant import EagerSeries
+from narwhals.dependencies import is_numpy_array_1d
 from narwhals.exceptions import InvalidOperationError
 from narwhals.utils import Implementation
 from narwhals.utils import generate_temporary_column_name
@@ -52,6 +53,7 @@
     from narwhals._arrow.typing import _AsPyType
     from narwhals._arrow.typing import _BasicDataType
     from narwhals.dtypes import DType
+    from narwhals.typing import Into1DArray
     from narwhals.typing import _1DArray
     from narwhals.typing import _2DArray
     from narwhals.utils import Version
@@ -156,6 +158,12 @@ def _from_scalar(self, value: Any) -> Self:
             value = value.as_py()
         return super()._from_scalar(value)
 
+    @classmethod
+    def from_numpy(cls, data: Into1DArray, /, *, context: _FullContext) -> Self:
+        return cls._from_iterable(
+            data if is_numpy_array_1d(data) else [data], name="", context=context
+        )
+
     def __narwhals_namespace__(self: Self) -> ArrowNamespace:
         from narwhals._arrow.namespace import ArrowNamespace
 
@@ -437,7 +445,7 @@ def to_list(self: Self) -> list[Any]:
     def __array__(self: Self, dtype: Any = None, *, copy: bool | None = None) -> _1DArray:
         return self.native.__array__(dtype=dtype, copy=copy)
 
-    def to_numpy(self: Self) -> _1DArray:
+    def to_numpy(self: Self, dtype: Any = None, *, copy: bool | None = None) -> _1DArray:
         return self.native.to_numpy()
 
     def alias(self: Self, name: str) -> Self:

narwhals/_compliant/expr.py

@@ -29,6 +29,8 @@
 from narwhals._compliant.typing import EagerSeriesT
 from narwhals._compliant.typing import NativeExprT_co
 from narwhals._expression_parsing import evaluate_output_names_and_aliases
+from narwhals.dependencies import get_numpy
+from narwhals.dependencies import is_numpy_array
 from narwhals.dtypes import DType
 from narwhals.utils import _ExprNamespace
 from narwhals.utils import deprecated
@@ -760,6 +762,38 @@ def rolling_var(
             ddof=ddof,
         )
 
+    def map_batches(
+        self: Self,
+        function: Callable[[Any], Any],
+        return_dtype: DType | type[DType] | None,
+    ) -> Self:
+        def func(df: EagerDataFrameT) -> Sequence[EagerSeriesT]:
+            input_series_list = self(df)
+            output_names = [input_series.name for input_series in input_series_list]
+            result = [function(series) for series in input_series_list]
+            if is_numpy_array(result[0]) or (
+                (np := get_numpy()) is not None and np.isscalar(result[0])
+            ):
+                from_numpy = partial(
+                    self.__narwhals_namespace__()._series.from_numpy, context=self
+                )
+                result = [
+                    from_numpy(array).alias(output_name)
+                    for array, output_name in zip(result, output_names)
+                ]
+            if return_dtype is not None:
+                result = [series.cast(return_dtype) for series in result]
+            return result
+
+        return self._from_callable(
+            func,
+            depth=self._depth + 1,
+            function_name=self._function_name + "->map_batches",
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=self._alias_output_names,
+            context=self,
+        )
+
     @property
     def cat(self) -> EagerExprCatNamespace[Self]:
         return EagerExprCatNamespace(self)

narwhals/_compliant/namespace.py

@@ -13,7 +13,6 @@
 from narwhals._compliant.typing import EagerDataFrameT
 from narwhals._compliant.typing import EagerExprT
 from narwhals._compliant.typing import EagerSeriesT_co
-from narwhals.utils import deprecated
 from narwhals.utils import exclude_column_names
 from narwhals.utils import get_column_names
 from narwhals.utils import passthrough_column_names
@@ -85,13 +84,3 @@ class EagerNamespace(
 ):
     @property
     def _series(self) -> type[EagerSeriesT_co]: ...
-
-    @deprecated(
-        "Internally used for `numpy.ndarray` -> `CompliantSeries`\n"
-        "Also referenced in untyped `nw.dataframe.DataFrame._extract_compliant`\n"
-        "See Also:\n"
-        "  - https://github.com/narwhals-dev/narwhals/pull/2149#discussion_r1986283345\n"
-        "  - https://github.com/narwhals-dev/narwhals/issues/2116\n"
-        "  - https://github.com/narwhals-dev/narwhals/pull/2169"
-    )
-    def _create_compliant_series(self, value: Any) -> EagerSeriesT_co: ...

narwhals/_compliant/series.py

@@ -6,6 +6,8 @@
 from typing import Protocol
 from typing import TypeVar
 
+from narwhals._translate import NumpyConvertible
+
 if TYPE_CHECKING:
     from typing_extensions import Self
 
@@ -14,7 +16,9 @@
     from narwhals._compliant.namespace import CompliantNamespace  # noqa: F401
     from narwhals._compliant.namespace import EagerNamespace
     from narwhals.dtypes import DType
+    from narwhals.typing import Into1DArray
     from narwhals.typing import NativeSeries
+    from narwhals.typing import _1DArray  # noqa: F401
     from narwhals.utils import Implementation
     from narwhals.utils import Version
     from narwhals.utils import _FullContext
@@ -24,7 +28,7 @@
 NativeSeriesT_co = TypeVar("NativeSeriesT_co", bound="NativeSeries", covariant=True)
 
 
-class CompliantSeries(Protocol):
+class CompliantSeries(NumpyConvertible["_1DArray", "Into1DArray"], Protocol):
     @property
     def dtype(self) -> DType: ...
     @property
@@ -36,6 +40,8 @@ def alias(self, name: str) -> Self: ...
     def __narwhals_namespace__(self) -> Any: ...  # CompliantNamespace[Any, Self]: ...
     def _from_native_series(self, series: Any) -> Self: ...
     def _to_expr(self) -> Any: ...  # CompliantExpr[Any, Self]: ...
+    @classmethod
+    def from_numpy(cls, data: Into1DArray, /, *, context: _FullContext) -> Self: ...
 
 
 class EagerSeries(CompliantSeries, Protocol[NativeSeriesT_co]):
@@ -60,3 +66,5 @@ def __narwhals_namespace__(self) -> EagerNamespace[Any, Self, Any]: ...
 
     def _to_expr(self) -> EagerExpr[Any, Any]:
         return self.__narwhals_namespace__()._expr._from_series(self)  # type: ignore[no-any-return]
+
+    def cast(self, dtype: DType | type[DType]) -> Self: ...

narwhals/_compliant/typing.py

@@ -15,6 +15,7 @@
     from narwhals._compliant.expr import CompliantExpr
     from narwhals._compliant.expr import EagerExpr
     from narwhals._compliant.expr import NativeExpr
+    from narwhals._compliant.namespace import EagerNamespace
     from narwhals._compliant.series import CompliantSeries
     from narwhals._compliant.series import EagerSeries
 
@@ -48,5 +49,9 @@
 EagerSeriesT = TypeVar("EagerSeriesT", bound="EagerSeries[Any]")
 EagerSeriesT_co = TypeVar("EagerSeriesT_co", bound="EagerSeries[Any]", covariant=True)
 EagerExprT = TypeVar("EagerExprT", bound="EagerExpr[Any, Any]")
+EagerNamespaceAny: TypeAlias = (
+    "EagerNamespace[EagerDataFrame[Any], EagerSeries[Any], EagerExpr[Any, Any]]"
+)
+
 AliasNames: TypeAlias = Callable[[Sequence[str]], Sequence[str]]
 AliasName: TypeAlias = Callable[[str], str]

narwhals/_expression_parsing.py

@@ -12,6 +12,7 @@
 from typing import Literal
 from typing import Sequence
 from typing import TypeVar
+from typing import cast
 
 from narwhals.dependencies import is_narwhals_series
 from narwhals.dependencies import is_numpy_array
@@ -27,6 +28,7 @@
     from narwhals._compliant import CompliantExprT
     from narwhals._compliant import CompliantFrameT
     from narwhals._compliant import CompliantNamespace
+    from narwhals._compliant.typing import EagerNamespaceAny
     from narwhals.expr import Expr
     from narwhals.typing import CompliantDataFrame
     from narwhals.typing import CompliantLazyFrame
@@ -100,7 +102,8 @@ def extract_compliant(
     if is_narwhals_series(other):
         return other._compliant_series._to_expr()
     if is_numpy_array(other):
-        return plx._create_compliant_series(other)._to_expr()  # type: ignore[attr-defined]
+        ns = cast("EagerNamespaceAny", plx)
+        return ns._series.from_numpy(other, context=ns)._to_expr()
     return other
 
 

narwhals/_pandas_like/dataframe.py

@@ -16,7 +16,6 @@
 from narwhals._pandas_like.utils import align_series_full_broadcast
 from narwhals._pandas_like.utils import check_column_names_are_unique
 from narwhals._pandas_like.utils import convert_str_slice_to_int_slice
-from narwhals._pandas_like.utils import create_compliant_series
 from narwhals._pandas_like.utils import extract_dataframe_comparand
 from narwhals._pandas_like.utils import horizontal_concat
 from narwhals._pandas_like.utils import native_to_narwhals_dtype
@@ -433,16 +432,14 @@ def estimated_size(self: Self, unit: SizeUnit) -> int | float:
         return scale_bytes(sz, unit=unit)
 
     def with_row_index(self: Self, name: str) -> Self:
-        row_index = create_compliant_series(
-            range(len(self._native_frame)),
-            index=self._native_frame.index,
-            implementation=self._implementation,
-            backend_version=self._backend_version,
-            version=self._version,
+        frame = self._native_frame
+        namespace = self.__narwhals_namespace__()
+        row_index = namespace._series._from_iterable(
+            range(len(frame)), name="", context=self, index=frame.index
         ).alias(name)
         return self._from_native_frame(
             horizontal_concat(
-                [row_index._native_series, self._native_frame],
+                [row_index.native, frame],
                 implementation=self._implementation,
                 backend_version=self._backend_version,
             )

narwhals/_pandas_like/expr.py

@@ -13,8 +13,6 @@
 from narwhals._expression_parsing import is_elementary_expression
 from narwhals._pandas_like.group_by import AGGREGATIONS_TO_PANDAS_EQUIVALENT
 from narwhals._pandas_like.series import PandasLikeSeries
-from narwhals.dependencies import get_numpy
-from narwhals.dependencies import is_numpy_array
 from narwhals.exceptions import ColumnNotFoundError
 from narwhals.utils import generate_temporary_column_name
 
@@ -23,7 +21,6 @@
 
     from narwhals._pandas_like.dataframe import PandasLikeDataFrame
     from narwhals._pandas_like.namespace import PandasLikeNamespace
-    from narwhals.dtypes import DType
     from narwhals.utils import Implementation
     from narwhals.utils import Version
     from narwhals.utils import _FullContext
@@ -299,39 +296,6 @@ def func(df: PandasLikeDataFrame) -> Sequence[PandasLikeSeries]:
             version=self._version,
         )
 
-    def map_batches(
-        self: Self,
-        function: Callable[[Any], Any],
-        return_dtype: DType | type[DType] | None,
-    ) -> Self:
-        def func(df: PandasLikeDataFrame) -> list[PandasLikeSeries]:
-            input_series_list = self._call(df)
-            output_names = [input_series.name for input_series in input_series_list]
-            result = [function(series) for series in input_series_list]
-            if is_numpy_array(result[0]) or (
-                (np := get_numpy()) is not None and np.isscalar(result[0])
-            ):
-                result = [
-                    df.__narwhals_namespace__()
-                    ._create_compliant_series(array)
-                    .alias(output_name)
-                    for array, output_name in zip(result, output_names)
-                ]
-            if return_dtype is not None:
-                result = [series.cast(return_dtype) for series in result]
-            return result
-
-        return self.__class__(
-            func,
-            depth=self._depth + 1,
-            function_name=self._function_name + "->map_batches",
-            evaluate_output_names=self._evaluate_output_names,
-            alias_output_names=self._alias_output_names,
-            implementation=self._implementation,
-            backend_version=self._backend_version,
-            version=self._version,
-        )
-
     def cum_count(self: Self, *, reverse: bool) -> Self:
         return self._reuse_series("cum_count", call_kwargs={"reverse": reverse})
 

narwhals/_pandas_like/namespace.py

@@ -17,7 +17,6 @@
 from narwhals._pandas_like.selectors import PandasSelectorNamespace
 from narwhals._pandas_like.series import PandasLikeSeries
 from narwhals._pandas_like.utils import align_series_full_broadcast
-from narwhals._pandas_like.utils import create_compliant_series
 from narwhals._pandas_like.utils import diagonal_concat
 from narwhals._pandas_like.utils import extract_dataframe_comparand
 from narwhals._pandas_like.utils import horizontal_concat
@@ -61,14 +60,6 @@ def __init__(
         self._backend_version = backend_version
         self._version = version
 
-    def _create_compliant_series(self: Self, value: Any) -> PandasLikeSeries:
-        return create_compliant_series(
-            value,
-            implementation=self._implementation,
-            backend_version=self._backend_version,
-            version=self._version,
-        )
-
     # --- selection ---
     def lit(self: Self, value: Any, dtype: DType | None) -> PandasLikeExpr:
         def _lit_pandas_series(df: PandasLikeDataFrame) -> PandasLikeSeries: