reconnect moved files to git repo

venv/lib/python3.11/site-packages/narwhals/_polars/dataframe.py

@@ -0,0 +1,645 @@
+from __future__ import annotations
+
+from collections.abc import Iterator, Mapping, Sequence, Sized
+from typing import TYPE_CHECKING, Any, Generic, Literal, TypeVar, cast, overload
+
+import polars as pl
+
+from narwhals._polars.namespace import PolarsNamespace
+from narwhals._polars.series import PolarsSeries
+from narwhals._polars.utils import (
+    catch_polars_exception,
+    extract_args_kwargs,
+    native_to_narwhals_dtype,
+)
+from narwhals._utils import (
+    Implementation,
+    _into_arrow_table,
+    check_columns_exist,
+    convert_str_slice_to_int_slice,
+    is_compliant_series,
+    is_index_selector,
+    is_range,
+    is_sequence_like,
+    is_slice_index,
+    is_slice_none,
+    parse_columns_to_drop,
+    requires,
+)
+from narwhals.dependencies import is_numpy_array_1d
+from narwhals.exceptions import ColumnNotFoundError
+
+if TYPE_CHECKING:
+    from types import ModuleType
+    from typing import Callable
+
+    import pandas as pd
+    import pyarrow as pa
+    from typing_extensions import Self, TypeAlias, TypeIs
+
+    from narwhals._compliant.typing import CompliantDataFrameAny, CompliantLazyFrameAny
+    from narwhals._polars.expr import PolarsExpr
+    from narwhals._polars.group_by import PolarsGroupBy, PolarsLazyGroupBy
+    from narwhals._translate import IntoArrowTable
+    from narwhals._utils import Version, _LimitedContext
+    from narwhals.dataframe import DataFrame, LazyFrame
+    from narwhals.dtypes import DType
+    from narwhals.schema import Schema
+    from narwhals.typing import (
+        JoinStrategy,
+        MultiColSelector,
+        MultiIndexSelector,
+        PivotAgg,
+        SingleIndexSelector,
+        _2DArray,
+    )
+
+    T = TypeVar("T")
+    R = TypeVar("R")
+
+Method: TypeAlias = "Callable[..., R]"
+"""Generic alias representing all methods implemented via `__getattr__`.
+
+Where `R` is the return type.
+"""
+
+# DataFrame methods where PolarsDataFrame just defers to Polars.DataFrame directly.
+INHERITED_METHODS = frozenset(
+    [
+        "clone",
+        "drop_nulls",
+        "estimated_size",
+        "explode",
+        "filter",
+        "gather_every",
+        "head",
+        "is_unique",
+        "item",
+        "iter_rows",
+        "join_asof",
+        "rename",
+        "row",
+        "rows",
+        "sample",
+        "select",
+        "sink_parquet",
+        "sort",
+        "tail",
+        "to_arrow",
+        "to_pandas",
+        "unique",
+        "with_columns",
+        "write_csv",
+        "write_parquet",
+    ]
+)
+
+NativePolarsFrame = TypeVar("NativePolarsFrame", pl.DataFrame, pl.LazyFrame)
+
+
+class PolarsBaseFrame(Generic[NativePolarsFrame]):
+    drop_nulls: Method[Self]
+    explode: Method[Self]
+    filter: Method[Self]
+    gather_every: Method[Self]
+    head: Method[Self]
+    join_asof: Method[Self]
+    rename: Method[Self]
+    select: Method[Self]
+    sort: Method[Self]
+    tail: Method[Self]
+    unique: Method[Self]
+    with_columns: Method[Self]
+
+    _implementation = Implementation.POLARS
+
+    def __init__(
+        self,
+        df: NativePolarsFrame,
+        *,
+        version: Version,
+        validate_backend_version: bool = False,
+    ) -> None:
+        self._native_frame: NativePolarsFrame = df
+        self._version = version
+        if validate_backend_version:
+            self._validate_backend_version()
+
+    def _validate_backend_version(self) -> None:
+        """Raise if installed version below `nw._utils.MIN_VERSIONS`.
+
+        **Only use this when moving between backends.**
+        Otherwise, the validation will have taken place already.
+        """
+        _ = self._implementation._backend_version()
+
+    @property
+    def _backend_version(self) -> tuple[int, ...]:
+        return self._implementation._backend_version()
+
+    @property
+    def native(self) -> NativePolarsFrame:
+        return self._native_frame
+
+    @property
+    def columns(self) -> list[str]:
+        return self.native.columns
+
+    def __narwhals_namespace__(self) -> PolarsNamespace:
+        return PolarsNamespace(version=self._version)
+
+    def __native_namespace__(self) -> ModuleType:
+        if self._implementation is Implementation.POLARS:
+            return self._implementation.to_native_namespace()
+
+        msg = f"Expected polars, got: {type(self._implementation)}"  # pragma: no cover
+        raise AssertionError(msg)
+
+    def _with_native(self, df: NativePolarsFrame) -> Self:
+        return self.__class__(df, version=self._version)
+
+    def _with_version(self, version: Version) -> Self:
+        return self.__class__(self.native, version=version)
+
+    @classmethod
+    def from_native(cls, data: NativePolarsFrame, /, *, context: _LimitedContext) -> Self:
+        return cls(data, version=context._version)
+
+    def _check_columns_exist(self, subset: Sequence[str]) -> ColumnNotFoundError | None:
+        return check_columns_exist(  # pragma: no cover
+            subset, available=self.columns
+        )
+
+    def simple_select(self, *column_names: str) -> Self:
+        return self._with_native(self.native.select(*column_names))
+
+    def aggregate(self, *exprs: Any) -> Self:
+        return self.select(*exprs)
+
+    @property
+    def schema(self) -> dict[str, DType]:
+        return self.collect_schema()
+
+    def join(
+        self,
+        other: Self,
+        *,
+        how: JoinStrategy,
+        left_on: Sequence[str] | None,
+        right_on: Sequence[str] | None,
+        suffix: str,
+    ) -> Self:
+        how_native = (
+            "outer" if (self._backend_version < (0, 20, 29) and how == "full") else how
+        )
+        return self._with_native(
+            self.native.join(
+                other=other.native,
+                how=how_native,  # type: ignore[arg-type]
+                left_on=left_on,
+                right_on=right_on,
+                suffix=suffix,
+            )
+        )
+
+    def unpivot(
+        self,
+        on: Sequence[str] | None,
+        index: Sequence[str] | None,
+        variable_name: str,
+        value_name: str,
+    ) -> Self:
+        if self._backend_version < (1, 0, 0):
+            return self._with_native(
+                self.native.melt(
+                    id_vars=index,
+                    value_vars=on,
+                    variable_name=variable_name,
+                    value_name=value_name,
+                )
+            )
+        return self._with_native(
+            self.native.unpivot(
+                on=on, index=index, variable_name=variable_name, value_name=value_name
+            )
+        )
+
+    def collect_schema(self) -> dict[str, DType]:
+        df = self.native
+        schema = df.schema if self._backend_version < (1,) else df.collect_schema()
+        return {
+            name: native_to_narwhals_dtype(dtype, self._version)
+            for name, dtype in schema.items()
+        }
+
+    def with_row_index(self, name: str, order_by: Sequence[str] | None) -> Self:
+        frame = self.native
+        if order_by is None:
+            result = frame.with_row_index(name)
+        else:
+            end = pl.count() if self._backend_version < (0, 20, 5) else pl.len()
+            result = frame.select(
+                pl.int_range(start=0, end=end).sort_by(order_by).alias(name), pl.all()
+            )
+
+        return self._with_native(result)
+
+
+class PolarsDataFrame(PolarsBaseFrame[pl.DataFrame]):
+    clone: Method[Self]
+    collect: Method[CompliantDataFrameAny]
+    estimated_size: Method[int | float]
+    gather_every: Method[Self]
+    item: Method[Any]
+    iter_rows: Method[Iterator[tuple[Any, ...]] | Iterator[Mapping[str, Any]]]
+    is_unique: Method[PolarsSeries]
+    row: Method[tuple[Any, ...]]
+    rows: Method[Sequence[tuple[Any, ...]] | Sequence[Mapping[str, Any]]]
+    sample: Method[Self]
+    to_arrow: Method[pa.Table]
+    to_pandas: Method[pd.DataFrame]
+    # NOTE: `write_csv` requires an `@overload` for `str | None`
+    # Can't do that here 😟
+    write_csv: Method[Any]
+    write_parquet: Method[None]
+
+    # CompliantDataFrame
+    _evaluate_aliases: Any
+
+    @classmethod
+    def from_arrow(cls, data: IntoArrowTable, /, *, context: _LimitedContext) -> Self:
+        if context._implementation._backend_version() >= (1, 3):
+            native = pl.DataFrame(data)
+        else:  # pragma: no cover
+            native = cast("pl.DataFrame", pl.from_arrow(_into_arrow_table(data, context)))
+        return cls.from_native(native, context=context)
+
+    @classmethod
+    def from_dict(
+        cls,
+        data: Mapping[str, Any],
+        /,
+        *,
+        context: _LimitedContext,
+        schema: Mapping[str, DType] | Schema | None,
+    ) -> Self:
+        from narwhals.schema import Schema
+
+        pl_schema = Schema(schema).to_polars() if schema is not None else schema
+        return cls.from_native(pl.from_dict(data, pl_schema), context=context)
+
+    @staticmethod
+    def _is_native(obj: pl.DataFrame | Any) -> TypeIs[pl.DataFrame]:
+        return isinstance(obj, pl.DataFrame)
+
+    @classmethod
+    def from_numpy(
+        cls,
+        data: _2DArray,
+        /,
+        *,
+        context: _LimitedContext,  # NOTE: Maybe only `Implementation`?
+        schema: Mapping[str, DType] | Schema | Sequence[str] | None,
+    ) -> Self:
+        from narwhals.schema import Schema
+
+        pl_schema = (
+            Schema(schema).to_polars()
+            if isinstance(schema, (Mapping, Schema))
+            else schema
+        )
+        return cls.from_native(pl.from_numpy(data, pl_schema), context=context)
+
+    def to_narwhals(self) -> DataFrame[pl.DataFrame]:
+        return self._version.dataframe(self, level="full")
+
+    def __repr__(self) -> str:  # pragma: no cover
+        return "PolarsDataFrame"
+
+    def __narwhals_dataframe__(self) -> Self:
+        return self
+
+    @overload
+    def _from_native_object(self, obj: pl.Series) -> PolarsSeries: ...
+
+    @overload
+    def _from_native_object(self, obj: pl.DataFrame) -> Self: ...
+
+    @overload
+    def _from_native_object(self, obj: T) -> T: ...
+
+    def _from_native_object(
+        self, obj: pl.Series | pl.DataFrame | T
+    ) -> Self | PolarsSeries | T:
+        if isinstance(obj, pl.Series):
+            return PolarsSeries.from_native(obj, context=self)
+        if self._is_native(obj):
+            return self._with_native(obj)
+        # scalar
+        return obj
+
+    def __len__(self) -> int:
+        return len(self.native)
+
+    def __getattr__(self, attr: str) -> Any:
+        if attr not in INHERITED_METHODS:  # pragma: no cover
+            msg = f"{self.__class__.__name__} has not attribute '{attr}'."
+            raise AttributeError(msg)
+
+        def func(*args: Any, **kwargs: Any) -> Any:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            try:
+                return self._from_native_object(getattr(self.native, attr)(*pos, **kwds))
+            except pl.exceptions.ColumnNotFoundError as e:  # pragma: no cover
+                msg = f"{e!s}\n\nHint: Did you mean one of these columns: {self.columns}?"
+                raise ColumnNotFoundError(msg) from e
+            except Exception as e:  # noqa: BLE001
+                raise catch_polars_exception(e) from None
+
+        return func
+
+    def __array__(
+        self, dtype: Any | None = None, *, copy: bool | None = None
+    ) -> _2DArray:
+        if self._backend_version < (0, 20, 28) and copy is not None:
+            msg = "`copy` in `__array__` is only supported for 'polars>=0.20.28'"
+            raise NotImplementedError(msg)
+        if self._backend_version < (0, 20, 28):
+            return self.native.__array__(dtype)
+        return self.native.__array__(dtype)
+
+    def to_numpy(self, dtype: Any = None, *, copy: bool | None = None) -> _2DArray:
+        return self.native.to_numpy()
+
+    @property
+    def shape(self) -> tuple[int, int]:
+        return self.native.shape
+
+    def __getitem__(  # noqa: C901, PLR0912
+        self,
+        item: tuple[
+            SingleIndexSelector | MultiIndexSelector[PolarsSeries],
+            MultiColSelector[PolarsSeries],
+        ],
+    ) -> Any:
+        rows, columns = item
+        if self._backend_version > (0, 20, 30):
+            rows_native = rows.native if is_compliant_series(rows) else rows
+            columns_native = columns.native if is_compliant_series(columns) else columns
+            selector = rows_native, columns_native
+            selected = self.native.__getitem__(selector)  # type: ignore[index]
+            return self._from_native_object(selected)
+        else:  # pragma: no cover
+            # TODO(marco): we can delete this branch after Polars==0.20.30 becomes the minimum
+            # Polars version we support
+            # This mostly mirrors the logic in `EagerDataFrame.__getitem__`.
+            rows = list(rows) if isinstance(rows, tuple) else rows
+            columns = list(columns) if isinstance(columns, tuple) else columns
+            if is_numpy_array_1d(columns):
+                columns = columns.tolist()
+
+            native = self.native
+            if not is_slice_none(columns):
+                if isinstance(columns, Sized) and len(columns) == 0:
+                    return self.select()
+                if is_index_selector(columns):
+                    if is_slice_index(columns) or is_range(columns):
+                        native = native.select(
+                            self.columns[slice(columns.start, columns.stop, columns.step)]
+                        )
+                    elif is_compliant_series(columns):
+                        native = native[:, columns.native.to_list()]
+                    else:
+                        native = native[:, columns]
+                elif isinstance(columns, slice):
+                    native = native.select(
+                        self.columns[
+                            slice(*convert_str_slice_to_int_slice(columns, self.columns))
+                        ]
+                    )
+                elif is_compliant_series(columns):
+                    native = native.select(columns.native.to_list())
+                elif is_sequence_like(columns):
+                    native = native.select(columns)
+                else:
+                    msg = f"Unreachable code, got unexpected type: {type(columns)}"
+                    raise AssertionError(msg)
+
+            if not is_slice_none(rows):
+                if isinstance(rows, int):
+                    native = native[[rows], :]
+                elif isinstance(rows, (slice, range)):
+                    native = native[rows, :]
+                elif is_compliant_series(rows):
+                    native = native[rows.native, :]
+                elif is_sequence_like(rows):
+                    native = native[rows, :]
+                else:
+                    msg = f"Unreachable code, got unexpected type: {type(rows)}"
+                    raise AssertionError(msg)
+
+            return self._with_native(native)
+
+    def get_column(self, name: str) -> PolarsSeries:
+        return PolarsSeries.from_native(self.native.get_column(name), context=self)
+
+    def iter_columns(self) -> Iterator[PolarsSeries]:
+        for series in self.native.iter_columns():
+            yield PolarsSeries.from_native(series, context=self)
+
+    def lazy(self, *, backend: Implementation | None = None) -> CompliantLazyFrameAny:
+        if backend is None or backend is Implementation.POLARS:
+            return PolarsLazyFrame.from_native(self.native.lazy(), context=self)
+        elif backend is Implementation.DUCKDB:
+            import duckdb  # ignore-banned-import
+
+            from narwhals._duckdb.dataframe import DuckDBLazyFrame
+
+            # NOTE: (F841) is a false positive
+            df = self.native  # noqa: F841
+            return DuckDBLazyFrame(
+                duckdb.table("df"), validate_backend_version=True, version=self._version
+            )
+        elif backend is Implementation.DASK:
+            import dask.dataframe as dd  # ignore-banned-import
+
+            from narwhals._dask.dataframe import DaskLazyFrame
+
+            return DaskLazyFrame(
+                dd.from_pandas(self.native.to_pandas()),
+                validate_backend_version=True,
+                version=self._version,
+            )
+        elif backend.is_ibis():
+            import ibis  # ignore-banned-import
+
+            from narwhals._ibis.dataframe import IbisLazyFrame
+
+            return IbisLazyFrame(
+                ibis.memtable(self.native, columns=self.columns),
+                validate_backend_version=True,
+                version=self._version,
+            )
+
+        raise AssertionError  # pragma: no cover
+
+    @overload
+    def to_dict(self, *, as_series: Literal[True]) -> dict[str, PolarsSeries]: ...
+
+    @overload
+    def to_dict(self, *, as_series: Literal[False]) -> dict[str, list[Any]]: ...
+
+    def to_dict(
+        self, *, as_series: bool
+    ) -> dict[str, PolarsSeries] | dict[str, list[Any]]:
+        if as_series:
+            return {
+                name: PolarsSeries.from_native(col, context=self)
+                for name, col in self.native.to_dict().items()
+            }
+        else:
+            return self.native.to_dict(as_series=False)
+
+    def group_by(
+        self, keys: Sequence[str] | Sequence[PolarsExpr], *, drop_null_keys: bool
+    ) -> PolarsGroupBy:
+        from narwhals._polars.group_by import PolarsGroupBy
+
+        return PolarsGroupBy(self, keys, drop_null_keys=drop_null_keys)
+
+    def drop(self, columns: Sequence[str], *, strict: bool) -> Self:
+        to_drop = parse_columns_to_drop(self, columns, strict=strict)
+        return self._with_native(self.native.drop(to_drop))
+
+    @requires.backend_version((1,))
+    def pivot(
+        self,
+        on: Sequence[str],
+        *,
+        index: Sequence[str] | None,
+        values: Sequence[str] | None,
+        aggregate_function: PivotAgg | None,
+        sort_columns: bool,
+        separator: str,
+    ) -> Self:
+        try:
+            result = self.native.pivot(
+                on,
+                index=index,
+                values=values,
+                aggregate_function=aggregate_function,
+                sort_columns=sort_columns,
+                separator=separator,
+            )
+        except Exception as e:  # noqa: BLE001
+            raise catch_polars_exception(e) from None
+        return self._from_native_object(result)
+
+    def to_polars(self) -> pl.DataFrame:
+        return self.native
+
+    def join(
+        self,
+        other: Self,
+        *,
+        how: JoinStrategy,
+        left_on: Sequence[str] | None,
+        right_on: Sequence[str] | None,
+        suffix: str,
+    ) -> Self:
+        try:
+            return super().join(
+                other=other, how=how, left_on=left_on, right_on=right_on, suffix=suffix
+            )
+        except Exception as e:  # noqa: BLE001
+            raise catch_polars_exception(e) from None
+
+
+class PolarsLazyFrame(PolarsBaseFrame[pl.LazyFrame]):
+    # CompliantLazyFrame
+    sink_parquet: Method[None]
+    _evaluate_expr: Any
+    _evaluate_aliases: Any
+
+    @staticmethod
+    def _is_native(obj: pl.LazyFrame | Any) -> TypeIs[pl.LazyFrame]:
+        return isinstance(obj, pl.LazyFrame)
+
+    def to_narwhals(self) -> LazyFrame[pl.LazyFrame]:
+        return self._version.lazyframe(self, level="lazy")
+
+    def __repr__(self) -> str:  # pragma: no cover
+        return "PolarsLazyFrame"
+
+    def __narwhals_lazyframe__(self) -> Self:
+        return self
+
+    def __getattr__(self, attr: str) -> Any:
+        if attr not in INHERITED_METHODS:  # pragma: no cover
+            msg = f"{self.__class__.__name__} has not attribute '{attr}'."
+            raise AttributeError(msg)
+
+        def func(*args: Any, **kwargs: Any) -> Any:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            try:
+                return self._with_native(getattr(self.native, attr)(*pos, **kwds))
+            except pl.exceptions.ColumnNotFoundError as e:  # pragma: no cover
+                raise ColumnNotFoundError(str(e)) from e
+
+        return func
+
+    def _iter_columns(self) -> Iterator[PolarsSeries]:  # pragma: no cover
+        yield from self.collect(self._implementation).iter_columns()
+
+    def collect_schema(self) -> dict[str, DType]:
+        try:
+            return super().collect_schema()
+        except Exception as e:  # noqa: BLE001
+            raise catch_polars_exception(e) from None
+
+    def collect(
+        self, backend: Implementation | None, **kwargs: Any
+    ) -> CompliantDataFrameAny:
+        try:
+            result = self.native.collect(**kwargs)
+        except Exception as e:  # noqa: BLE001
+            raise catch_polars_exception(e) from None
+
+        if backend is None or backend is Implementation.POLARS:
+            return PolarsDataFrame.from_native(result, context=self)
+
+        if backend is Implementation.PANDAS:
+            from narwhals._pandas_like.dataframe import PandasLikeDataFrame
+
+            return PandasLikeDataFrame(
+                result.to_pandas(),
+                implementation=Implementation.PANDAS,
+                validate_backend_version=True,
+                version=self._version,
+                validate_column_names=False,
+            )
+
+        if backend is Implementation.PYARROW:
+            from narwhals._arrow.dataframe import ArrowDataFrame
+
+            return ArrowDataFrame(
+                result.to_arrow(),
+                validate_backend_version=True,
+                version=self._version,
+                validate_column_names=False,
+            )
+
+        msg = f"Unsupported `backend` value: {backend}"  # pragma: no cover
+        raise ValueError(msg)  # pragma: no cover
+
+    def group_by(
+        self, keys: Sequence[str] | Sequence[PolarsExpr], *, drop_null_keys: bool
+    ) -> PolarsLazyGroupBy:
+        from narwhals._polars.group_by import PolarsLazyGroupBy
+
+        return PolarsLazyGroupBy(self, keys, drop_null_keys=drop_null_keys)
+
+    def drop(self, columns: Sequence[str], *, strict: bool) -> Self:
+        if self._backend_version < (1, 0, 0):
+            return self._with_native(self.native.drop(columns))
+        return self._with_native(self.native.drop(columns, strict=strict))

venv/lib/python3.11/site-packages/narwhals/_polars/expr.py

@@ -0,0 +1,488 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Callable, Literal
+
+import polars as pl
+
+from narwhals._duration import Interval
+from narwhals._polars.utils import (
+    extract_args_kwargs,
+    extract_native,
+    narwhals_to_native_dtype,
+)
+from narwhals._utils import Implementation, requires
+
+if TYPE_CHECKING:
+    from collections.abc import Mapping, Sequence
+
+    from typing_extensions import Self
+
+    from narwhals._expression_parsing import ExprKind, ExprMetadata
+    from narwhals._polars.dataframe import Method
+    from narwhals._polars.namespace import PolarsNamespace
+    from narwhals._utils import Version
+    from narwhals.typing import IntoDType
+
+
+class PolarsExpr:
+    _implementation = Implementation.POLARS
+
+    def __init__(self, expr: pl.Expr, version: Version) -> None:
+        self._native_expr = expr
+        self._version = version
+        self._metadata: ExprMetadata | None = None
+
+    @property
+    def _backend_version(self) -> tuple[int, ...]:
+        return self._implementation._backend_version()
+
+    @property
+    def native(self) -> pl.Expr:
+        return self._native_expr
+
+    def __repr__(self) -> str:  # pragma: no cover
+        return "PolarsExpr"
+
+    def _with_native(self, expr: pl.Expr) -> Self:
+        return self.__class__(expr, self._version)
+
+    @classmethod
+    def _from_series(cls, series: Any) -> Self:
+        return cls(series.native, series._version)
+
+    def broadcast(self, kind: Literal[ExprKind.AGGREGATION, ExprKind.LITERAL]) -> Self:
+        # Let Polars do its thing.
+        return self
+
+    def __getattr__(self, attr: str) -> Any:
+        def func(*args: Any, **kwargs: Any) -> Any:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self._with_native(getattr(self.native, attr)(*pos, **kwds))
+
+        return func
+
+    def _renamed_min_periods(self, min_samples: int, /) -> dict[str, Any]:
+        name = "min_periods" if self._backend_version < (1, 21, 0) else "min_samples"
+        return {name: min_samples}
+
+    def cast(self, dtype: IntoDType) -> Self:
+        dtype_pl = narwhals_to_native_dtype(dtype, self._version)
+        return self._with_native(self.native.cast(dtype_pl))
+
+    def ewm_mean(
+        self,
+        *,
+        com: float | None,
+        span: float | None,
+        half_life: float | None,
+        alpha: float | None,
+        adjust: bool,
+        min_samples: int,
+        ignore_nulls: bool,
+    ) -> Self:
+        native = self.native.ewm_mean(
+            com=com,
+            span=span,
+            half_life=half_life,
+            alpha=alpha,
+            adjust=adjust,
+            ignore_nulls=ignore_nulls,
+            **self._renamed_min_periods(min_samples),
+        )
+        if self._backend_version < (1,):  # pragma: no cover
+            native = pl.when(~self.native.is_null()).then(native).otherwise(None)
+        return self._with_native(native)
+
+    def is_nan(self) -> Self:
+        if self._backend_version >= (1, 18):
+            native = self.native.is_nan()
+        else:  # pragma: no cover
+            native = pl.when(self.native.is_not_null()).then(self.native.is_nan())
+        return self._with_native(native)
+
+    def over(self, partition_by: Sequence[str], order_by: Sequence[str]) -> Self:
+        if self._backend_version < (1, 9):
+            if order_by:
+                msg = "`order_by` in Polars requires version 1.10 or greater"
+                raise NotImplementedError(msg)
+            native = self.native.over(partition_by or pl.lit(1))
+        else:
+            native = self.native.over(
+                partition_by or pl.lit(1), order_by=order_by or None
+            )
+        return self._with_native(native)
+
+    @requires.backend_version((1,))
+    def rolling_var(
+        self, window_size: int, *, min_samples: int, center: bool, ddof: int
+    ) -> Self:
+        kwds = self._renamed_min_periods(min_samples)
+        native = self.native.rolling_var(
+            window_size=window_size, center=center, ddof=ddof, **kwds
+        )
+        return self._with_native(native)
+
+    @requires.backend_version((1,))
+    def rolling_std(
+        self, window_size: int, *, min_samples: int, center: bool, ddof: int
+    ) -> Self:
+        kwds = self._renamed_min_periods(min_samples)
+        native = self.native.rolling_std(
+            window_size=window_size, center=center, ddof=ddof, **kwds
+        )
+        return self._with_native(native)
+
+    def rolling_sum(self, window_size: int, *, min_samples: int, center: bool) -> Self:
+        kwds = self._renamed_min_periods(min_samples)
+        native = self.native.rolling_sum(window_size=window_size, center=center, **kwds)
+        return self._with_native(native)
+
+    def rolling_mean(self, window_size: int, *, min_samples: int, center: bool) -> Self:
+        kwds = self._renamed_min_periods(min_samples)
+        native = self.native.rolling_mean(window_size=window_size, center=center, **kwds)
+        return self._with_native(native)
+
+    def map_batches(
+        self, function: Callable[[Any], Any], return_dtype: IntoDType | None
+    ) -> Self:
+        return_dtype_pl = (
+            narwhals_to_native_dtype(return_dtype, self._version)
+            if return_dtype
+            else None
+        )
+        native = self.native.map_batches(function, return_dtype_pl)
+        return self._with_native(native)
+
+    @requires.backend_version((1,))
+    def replace_strict(
+        self,
+        old: Sequence[Any] | Mapping[Any, Any],
+        new: Sequence[Any],
+        *,
+        return_dtype: IntoDType | None,
+    ) -> Self:
+        return_dtype_pl = (
+            narwhals_to_native_dtype(return_dtype, self._version)
+            if return_dtype
+            else None
+        )
+        native = self.native.replace_strict(old, new, return_dtype=return_dtype_pl)
+        return self._with_native(native)
+
+    def __eq__(self, other: object) -> Self:  # type: ignore[override]
+        return self._with_native(self.native.__eq__(extract_native(other)))  # type: ignore[operator]
+
+    def __ne__(self, other: object) -> Self:  # type: ignore[override]
+        return self._with_native(self.native.__ne__(extract_native(other)))  # type: ignore[operator]
+
+    def __ge__(self, other: Any) -> Self:
+        return self._with_native(self.native.__ge__(extract_native(other)))
+
+    def __gt__(self, other: Any) -> Self:
+        return self._with_native(self.native.__gt__(extract_native(other)))
+
+    def __le__(self, other: Any) -> Self:
+        return self._with_native(self.native.__le__(extract_native(other)))
+
+    def __lt__(self, other: Any) -> Self:
+        return self._with_native(self.native.__lt__(extract_native(other)))
+
+    def __and__(self, other: PolarsExpr | bool | Any) -> Self:
+        return self._with_native(self.native.__and__(extract_native(other)))  # type: ignore[operator]
+
+    def __or__(self, other: PolarsExpr | bool | Any) -> Self:
+        return self._with_native(self.native.__or__(extract_native(other)))  # type: ignore[operator]
+
+    def __add__(self, other: Any) -> Self:
+        return self._with_native(self.native.__add__(extract_native(other)))
+
+    def __sub__(self, other: Any) -> Self:
+        return self._with_native(self.native.__sub__(extract_native(other)))
+
+    def __mul__(self, other: Any) -> Self:
+        return self._with_native(self.native.__mul__(extract_native(other)))
+
+    def __pow__(self, other: Any) -> Self:
+        return self._with_native(self.native.__pow__(extract_native(other)))
+
+    def __truediv__(self, other: Any) -> Self:
+        return self._with_native(self.native.__truediv__(extract_native(other)))
+
+    def __floordiv__(self, other: Any) -> Self:
+        return self._with_native(self.native.__floordiv__(extract_native(other)))
+
+    def __mod__(self, other: Any) -> Self:
+        return self._with_native(self.native.__mod__(extract_native(other)))
+
+    def __invert__(self) -> Self:
+        return self._with_native(self.native.__invert__())
+
+    def cum_count(self, *, reverse: bool) -> Self:
+        return self._with_native(self.native.cum_count(reverse=reverse))
+
+    def __narwhals_expr__(self) -> None: ...
+    def __narwhals_namespace__(self) -> PolarsNamespace:  # pragma: no cover
+        from narwhals._polars.namespace import PolarsNamespace
+
+        return PolarsNamespace(version=self._version)
+
+    @property
+    def dt(self) -> PolarsExprDateTimeNamespace:
+        return PolarsExprDateTimeNamespace(self)
+
+    @property
+    def str(self) -> PolarsExprStringNamespace:
+        return PolarsExprStringNamespace(self)
+
+    @property
+    def cat(self) -> PolarsExprCatNamespace:
+        return PolarsExprCatNamespace(self)
+
+    @property
+    def name(self) -> PolarsExprNameNamespace:
+        return PolarsExprNameNamespace(self)
+
+    @property
+    def list(self) -> PolarsExprListNamespace:
+        return PolarsExprListNamespace(self)
+
+    @property
+    def struct(self) -> PolarsExprStructNamespace:
+        return PolarsExprStructNamespace(self)
+
+    # CompliantExpr
+    _alias_output_names: Any
+    _evaluate_aliases: Any
+    _evaluate_output_names: Any
+    _is_multi_output_unnamed: Any
+    __call__: Any
+    from_column_names: Any
+    from_column_indices: Any
+    _eval_names_indices: Any
+
+    # Polars
+    abs: Method[Self]
+    all: Method[Self]
+    any: Method[Self]
+    alias: Method[Self]
+    arg_max: Method[Self]
+    arg_min: Method[Self]
+    arg_true: Method[Self]
+    clip: Method[Self]
+    count: Method[Self]
+    cum_max: Method[Self]
+    cum_min: Method[Self]
+    cum_prod: Method[Self]
+    cum_sum: Method[Self]
+    diff: Method[Self]
+    drop_nulls: Method[Self]
+    exp: Method[Self]
+    fill_null: Method[Self]
+    gather_every: Method[Self]
+    head: Method[Self]
+    is_finite: Method[Self]
+    is_first_distinct: Method[Self]
+    is_in: Method[Self]
+    is_last_distinct: Method[Self]
+    is_null: Method[Self]
+    is_unique: Method[Self]
+    kurtosis: Method[Self]
+    len: Method[Self]
+    log: Method[Self]
+    max: Method[Self]
+    mean: Method[Self]
+    median: Method[Self]
+    min: Method[Self]
+    mode: Method[Self]
+    n_unique: Method[Self]
+    null_count: Method[Self]
+    quantile: Method[Self]
+    rank: Method[Self]
+    round: Method[Self]
+    sample: Method[Self]
+    shift: Method[Self]
+    skew: Method[Self]
+    sqrt: Method[Self]
+    std: Method[Self]
+    sum: Method[Self]
+    sort: Method[Self]
+    tail: Method[Self]
+    unique: Method[Self]
+    var: Method[Self]
+
+
+class PolarsExprNamespace:
+    def __init__(self, expr: PolarsExpr) -> None:
+        self._expr = expr
+
+    @property
+    def compliant(self) -> PolarsExpr:
+        return self._expr
+
+    @property
+    def native(self) -> pl.Expr:
+        return self._expr.native
+
+
+class PolarsExprDateTimeNamespace(PolarsExprNamespace):
+    def truncate(self, every: str) -> PolarsExpr:
+        Interval.parse(every)  # Ensure consistent error message is raised.
+        return self.compliant._with_native(self.native.dt.truncate(every))
+
+    def offset_by(self, by: str) -> PolarsExpr:
+        # Ensure consistent error message is raised.
+        Interval.parse_no_constraints(by)
+        return self.compliant._with_native(self.native.dt.offset_by(by))
+
+    def __getattr__(self, attr: str) -> Callable[[Any], PolarsExpr]:
+        def func(*args: Any, **kwargs: Any) -> PolarsExpr:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self.compliant._with_native(
+                getattr(self.native.dt, attr)(*pos, **kwds)
+            )
+
+        return func
+
+    to_string: Method[PolarsExpr]
+    replace_time_zone: Method[PolarsExpr]
+    convert_time_zone: Method[PolarsExpr]
+    timestamp: Method[PolarsExpr]
+    date: Method[PolarsExpr]
+    year: Method[PolarsExpr]
+    month: Method[PolarsExpr]
+    day: Method[PolarsExpr]
+    hour: Method[PolarsExpr]
+    minute: Method[PolarsExpr]
+    second: Method[PolarsExpr]
+    millisecond: Method[PolarsExpr]
+    microsecond: Method[PolarsExpr]
+    nanosecond: Method[PolarsExpr]
+    ordinal_day: Method[PolarsExpr]
+    weekday: Method[PolarsExpr]
+    total_minutes: Method[PolarsExpr]
+    total_seconds: Method[PolarsExpr]
+    total_milliseconds: Method[PolarsExpr]
+    total_microseconds: Method[PolarsExpr]
+    total_nanoseconds: Method[PolarsExpr]
+
+
+class PolarsExprStringNamespace(PolarsExprNamespace):
+    def zfill(self, width: int) -> PolarsExpr:
+        backend_version = self.compliant._backend_version
+        native_result = self.native.str.zfill(width)
+
+        if backend_version < (0, 20, 5):  # pragma: no cover
+            # Reason:
+            # `TypeError: argument 'length': 'Expr' object cannot be interpreted as an integer`
+            # in `native_expr.str.slice(1, length)`
+            msg = "`zfill` is only available in 'polars>=0.20.5', found version '0.20.4'."
+            raise NotImplementedError(msg)
+
+        if backend_version <= (1, 30, 0):
+            length = self.native.str.len_chars()
+            less_than_width = length < width
+            plus = "+"
+            starts_with_plus = self.native.str.starts_with(plus)
+            native_result = (
+                pl.when(starts_with_plus & less_than_width)
+                .then(
+                    self.native.str.slice(1, length)
+                    .str.zfill(width - 1)
+                    .str.pad_start(width, plus)
+                )
+                .otherwise(native_result)
+            )
+
+        return self.compliant._with_native(native_result)
+
+    def __getattr__(self, attr: str) -> Callable[[Any], PolarsExpr]:
+        def func(*args: Any, **kwargs: Any) -> PolarsExpr:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self.compliant._with_native(
+                getattr(self.native.str, attr)(*pos, **kwds)
+            )
+
+        return func
+
+    len_chars: Method[PolarsExpr]
+    replace: Method[PolarsExpr]
+    replace_all: Method[PolarsExpr]
+    strip_chars: Method[PolarsExpr]
+    starts_with: Method[PolarsExpr]
+    ends_with: Method[PolarsExpr]
+    contains: Method[PolarsExpr]
+    slice: Method[PolarsExpr]
+    split: Method[PolarsExpr]
+    to_date: Method[PolarsExpr]
+    to_datetime: Method[PolarsExpr]
+    to_lowercase: Method[PolarsExpr]
+    to_uppercase: Method[PolarsExpr]
+
+
+class PolarsExprCatNamespace(PolarsExprNamespace):
+    def __getattr__(self, attr: str) -> Callable[[Any], PolarsExpr]:
+        def func(*args: Any, **kwargs: Any) -> PolarsExpr:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self.compliant._with_native(
+                getattr(self.native.cat, attr)(*pos, **kwds)
+            )
+
+        return func
+
+    get_categories: Method[PolarsExpr]
+
+
+class PolarsExprNameNamespace(PolarsExprNamespace):
+    def __getattr__(self, attr: str) -> Callable[[Any], PolarsExpr]:
+        def func(*args: Any, **kwargs: Any) -> PolarsExpr:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self.compliant._with_native(
+                getattr(self.native.name, attr)(*pos, **kwds)
+            )
+
+        return func
+
+    keep: Method[PolarsExpr]
+    map: Method[PolarsExpr]
+    prefix: Method[PolarsExpr]
+    suffix: Method[PolarsExpr]
+    to_lowercase: Method[PolarsExpr]
+    to_uppercase: Method[PolarsExpr]
+
+
+class PolarsExprListNamespace(PolarsExprNamespace):
+    def len(self) -> PolarsExpr:
+        native_expr = self.compliant._native_expr
+        native_result = native_expr.list.len()
+
+        if self.compliant._backend_version < (1, 16):  # pragma: no cover
+            native_result = (
+                pl.when(~native_expr.is_null()).then(native_result).cast(pl.UInt32())
+            )
+        elif self.compliant._backend_version < (1, 17):  # pragma: no cover
+            native_result = native_result.cast(pl.UInt32())
+
+        return self.compliant._with_native(native_result)
+
+    # TODO(FBruzzesi): Remove `pragma: no cover` once other namespace methods are added
+    def __getattr__(self, attr: str) -> Callable[[Any], PolarsExpr]:  # pragma: no cover
+        def func(*args: Any, **kwargs: Any) -> PolarsExpr:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self.compliant._with_native(
+                getattr(self.native.list, attr)(*pos, **kwds)
+            )
+
+        return func
+
+
+class PolarsExprStructNamespace(PolarsExprNamespace):
+    def __getattr__(self, attr: str) -> Callable[[Any], PolarsExpr]:  # pragma: no cover
+        def func(*args: Any, **kwargs: Any) -> PolarsExpr:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self.compliant._with_native(
+                getattr(self.native.struct, attr)(*pos, **kwds)
+            )
+
+        return func
+
+    field: Method[PolarsExpr]

venv/lib/python3.11/site-packages/narwhals/_polars/group_by.py

@@ -0,0 +1,80 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, cast
+
+from narwhals._utils import is_sequence_of
+
+if TYPE_CHECKING:
+    from collections.abc import Iterator, Sequence
+
+    from polars.dataframe.group_by import GroupBy as NativeGroupBy
+    from polars.lazyframe.group_by import LazyGroupBy as NativeLazyGroupBy
+
+    from narwhals._polars.dataframe import PolarsDataFrame, PolarsLazyFrame
+    from narwhals._polars.expr import PolarsExpr
+
+
+class PolarsGroupBy:
+    _compliant_frame: PolarsDataFrame
+    _grouped: NativeGroupBy
+    _drop_null_keys: bool
+    _output_names: Sequence[str]
+
+    @property
+    def compliant(self) -> PolarsDataFrame:
+        return self._compliant_frame
+
+    def __init__(
+        self,
+        df: PolarsDataFrame,
+        keys: Sequence[PolarsExpr] | Sequence[str],
+        /,
+        *,
+        drop_null_keys: bool,
+    ) -> None:
+        self._keys = list(keys)
+        self._compliant_frame = df.drop_nulls(keys) if drop_null_keys else df
+        self._grouped = (
+            self.compliant.native.group_by(keys)
+            if is_sequence_of(keys, str)
+            else self.compliant.native.group_by(arg.native for arg in keys)
+        )
+
+    def agg(self, *aggs: PolarsExpr) -> PolarsDataFrame:
+        agg_result = self._grouped.agg(arg.native for arg in aggs)
+        return self.compliant._with_native(agg_result)
+
+    def __iter__(self) -> Iterator[tuple[tuple[str, ...], PolarsDataFrame]]:
+        for key, df in self._grouped:
+            yield tuple(cast("str", key)), self.compliant._with_native(df)
+
+
+class PolarsLazyGroupBy:
+    _compliant_frame: PolarsLazyFrame
+    _grouped: NativeLazyGroupBy
+    _drop_null_keys: bool
+    _output_names: Sequence[str]
+
+    @property
+    def compliant(self) -> PolarsLazyFrame:
+        return self._compliant_frame
+
+    def __init__(
+        self,
+        df: PolarsLazyFrame,
+        keys: Sequence[PolarsExpr] | Sequence[str],
+        /,
+        *,
+        drop_null_keys: bool,
+    ) -> None:
+        self._keys = list(keys)
+        self._compliant_frame = df.drop_nulls(keys) if drop_null_keys else df
+        self._grouped = (
+            self.compliant.native.group_by(keys)
+            if is_sequence_of(keys, str)
+            else self.compliant.native.group_by(arg.native for arg in keys)
+        )
+
+    def agg(self, *aggs: PolarsExpr) -> PolarsLazyFrame:
+        agg_result = self._grouped.agg(arg.native for arg in aggs)
+        return self.compliant._with_native(agg_result)

venv/lib/python3.11/site-packages/narwhals/_polars/namespace.py

@@ -0,0 +1,257 @@
+from __future__ import annotations
+
+import operator
+from typing import TYPE_CHECKING, Any, Literal, cast, overload
+
+import polars as pl
+
+from narwhals._polars.expr import PolarsExpr
+from narwhals._polars.series import PolarsSeries
+from narwhals._polars.utils import extract_args_kwargs, narwhals_to_native_dtype
+from narwhals._utils import Implementation, requires
+from narwhals.dependencies import is_numpy_array_2d
+from narwhals.dtypes import DType
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Mapping, Sequence
+    from datetime import timezone
+
+    from narwhals._compliant import CompliantSelectorNamespace, CompliantWhen
+    from narwhals._polars.dataframe import Method, PolarsDataFrame, PolarsLazyFrame
+    from narwhals._polars.typing import FrameT
+    from narwhals._utils import Version, _LimitedContext
+    from narwhals.schema import Schema
+    from narwhals.typing import Into1DArray, IntoDType, TimeUnit, _2DArray
+
+
+class PolarsNamespace:
+    all: Method[PolarsExpr]
+    coalesce: Method[PolarsExpr]
+    col: Method[PolarsExpr]
+    exclude: Method[PolarsExpr]
+    sum_horizontal: Method[PolarsExpr]
+    min_horizontal: Method[PolarsExpr]
+    max_horizontal: Method[PolarsExpr]
+
+    # NOTE: `pyright` accepts, `mypy` doesn't highlight the issue
+    #   error: Type argument "PolarsExpr" of "CompliantWhen" must be a subtype of "CompliantExpr[Any, Any]"
+    when: Method[CompliantWhen[PolarsDataFrame, PolarsSeries, PolarsExpr]]  # type: ignore[type-var]
+
+    _implementation = Implementation.POLARS
+
+    @property
+    def _backend_version(self) -> tuple[int, ...]:
+        return self._implementation._backend_version()
+
+    def __init__(self, *, version: Version) -> None:
+        self._version = version
+
+    def __getattr__(self, attr: str) -> Any:
+        def func(*args: Any, **kwargs: Any) -> Any:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self._expr(getattr(pl, attr)(*pos, **kwds), version=self._version)
+
+        return func
+
+    @property
+    def _dataframe(self) -> type[PolarsDataFrame]:
+        from narwhals._polars.dataframe import PolarsDataFrame
+
+        return PolarsDataFrame
+
+    @property
+    def _lazyframe(self) -> type[PolarsLazyFrame]:
+        from narwhals._polars.dataframe import PolarsLazyFrame
+
+        return PolarsLazyFrame
+
+    @property
+    def _expr(self) -> type[PolarsExpr]:
+        return PolarsExpr
+
+    @property
+    def _series(self) -> type[PolarsSeries]:
+        return PolarsSeries
+
+    @overload
+    def from_native(self, data: pl.DataFrame, /) -> PolarsDataFrame: ...
+    @overload
+    def from_native(self, data: pl.LazyFrame, /) -> PolarsLazyFrame: ...
+    @overload
+    def from_native(self, data: pl.Series, /) -> PolarsSeries: ...
+    def from_native(
+        self, data: pl.DataFrame | pl.LazyFrame | pl.Series | Any, /
+    ) -> PolarsDataFrame | PolarsLazyFrame | PolarsSeries:
+        if self._dataframe._is_native(data):
+            return self._dataframe.from_native(data, context=self)
+        elif self._series._is_native(data):
+            return self._series.from_native(data, context=self)
+        elif self._lazyframe._is_native(data):
+            return self._lazyframe.from_native(data, context=self)
+        else:  # pragma: no cover
+            msg = f"Unsupported type: {type(data).__name__!r}"
+            raise TypeError(msg)
+
+    @overload
+    def from_numpy(self, data: Into1DArray, /, schema: None = ...) -> PolarsSeries: ...
+
+    @overload
+    def from_numpy(
+        self,
+        data: _2DArray,
+        /,
+        schema: Mapping[str, DType] | Schema | Sequence[str] | None,
+    ) -> PolarsDataFrame: ...
+
+    def from_numpy(
+        self,
+        data: Into1DArray | _2DArray,
+        /,
+        schema: Mapping[str, DType] | Schema | Sequence[str] | None = None,
+    ) -> PolarsDataFrame | PolarsSeries:
+        if is_numpy_array_2d(data):
+            return self._dataframe.from_numpy(data, schema=schema, context=self)
+        return self._series.from_numpy(data, context=self)  # pragma: no cover
+
+    @requires.backend_version(
+        (1, 0, 0), "Please use `col` for columns selection instead."
+    )
+    def nth(self, *indices: int) -> PolarsExpr:
+        return self._expr(pl.nth(*indices), version=self._version)
+
+    def len(self) -> PolarsExpr:
+        if self._backend_version < (0, 20, 5):
+            return self._expr(pl.count().alias("len"), self._version)
+        return self._expr(pl.len(), self._version)
+
+    def all_horizontal(self, *exprs: PolarsExpr, ignore_nulls: bool) -> PolarsExpr:
+        it = (expr.fill_null(True) for expr in exprs) if ignore_nulls else iter(exprs)  # noqa: FBT003
+        return self._expr(pl.all_horizontal(*(expr.native for expr in it)), self._version)
+
+    def any_horizontal(self, *exprs: PolarsExpr, ignore_nulls: bool) -> PolarsExpr:
+        it = (expr.fill_null(False) for expr in exprs) if ignore_nulls else iter(exprs)  # noqa: FBT003
+        return self._expr(pl.any_horizontal(*(expr.native for expr in it)), self._version)
+
+    def concat(
+        self,
+        items: Iterable[FrameT],
+        *,
+        how: Literal["vertical", "horizontal", "diagonal"],
+    ) -> PolarsDataFrame | PolarsLazyFrame:
+        result = pl.concat((item.native for item in items), how=how)
+        if isinstance(result, pl.DataFrame):
+            return self._dataframe(result, version=self._version)
+        return self._lazyframe.from_native(result, context=self)
+
+    def lit(self, value: Any, dtype: IntoDType | None) -> PolarsExpr:
+        if dtype is not None:
+            return self._expr(
+                pl.lit(value, dtype=narwhals_to_native_dtype(dtype, self._version)),
+                version=self._version,
+            )
+        return self._expr(pl.lit(value), version=self._version)
+
+    def mean_horizontal(self, *exprs: PolarsExpr) -> PolarsExpr:
+        if self._backend_version < (0, 20, 8):
+            return self._expr(
+                pl.sum_horizontal(e._native_expr for e in exprs)
+                / pl.sum_horizontal(1 - e.is_null()._native_expr for e in exprs),
+                version=self._version,
+            )
+
+        return self._expr(
+            pl.mean_horizontal(e._native_expr for e in exprs), version=self._version
+        )
+
+    def concat_str(
+        self, *exprs: PolarsExpr, separator: str, ignore_nulls: bool
+    ) -> PolarsExpr:
+        pl_exprs: list[pl.Expr] = [expr._native_expr for expr in exprs]
+
+        if self._backend_version < (0, 20, 6):
+            null_mask = [expr.is_null() for expr in pl_exprs]
+            sep = pl.lit(separator)
+
+            if not ignore_nulls:
+                null_mask_result = pl.any_horizontal(*null_mask)
+                output_expr = pl.reduce(
+                    lambda x, y: x.cast(pl.String()) + sep + y.cast(pl.String()),  # type: ignore[arg-type,return-value]
+                    pl_exprs,
+                )
+                result = pl.when(~null_mask_result).then(output_expr)
+            else:
+                init_value, *values = [
+                    pl.when(nm).then(pl.lit("")).otherwise(expr.cast(pl.String()))
+                    for expr, nm in zip(pl_exprs, null_mask)
+                ]
+                separators = [
+                    pl.when(~nm).then(sep).otherwise(pl.lit("")) for nm in null_mask[:-1]
+                ]
+
+                result = pl.fold(  # type: ignore[assignment]
+                    acc=init_value,
+                    function=operator.add,
+                    exprs=[s + v for s, v in zip(separators, values)],
+                )
+
+            return self._expr(result, version=self._version)
+
+        return self._expr(
+            pl.concat_str(pl_exprs, separator=separator, ignore_nulls=ignore_nulls),
+            version=self._version,
+        )
+
+    # NOTE: Implementation is too different to annotate correctly (vs other `*SelectorNamespace`)
+    # 1. Others have lots of private stuff for code reuse
+    #    i. None of that is useful here
+    # 2. We don't have a `PolarsSelector` abstraction, and just use `PolarsExpr`
+    @property
+    def selectors(self) -> CompliantSelectorNamespace[PolarsDataFrame, PolarsSeries]:
+        return cast(
+            "CompliantSelectorNamespace[PolarsDataFrame, PolarsSeries]",
+            PolarsSelectorNamespace(self),
+        )
+
+
+class PolarsSelectorNamespace:
+    _implementation = Implementation.POLARS
+
+    def __init__(self, context: _LimitedContext, /) -> None:
+        self._version = context._version
+
+    def by_dtype(self, dtypes: Iterable[DType]) -> PolarsExpr:
+        native_dtypes = [
+            narwhals_to_native_dtype(dtype, self._version).__class__
+            if isinstance(dtype, type) and issubclass(dtype, DType)
+            else narwhals_to_native_dtype(dtype, self._version)
+            for dtype in dtypes
+        ]
+        return PolarsExpr(pl.selectors.by_dtype(native_dtypes), version=self._version)
+
+    def matches(self, pattern: str) -> PolarsExpr:
+        return PolarsExpr(pl.selectors.matches(pattern=pattern), version=self._version)
+
+    def numeric(self) -> PolarsExpr:
+        return PolarsExpr(pl.selectors.numeric(), version=self._version)
+
+    def boolean(self) -> PolarsExpr:
+        return PolarsExpr(pl.selectors.boolean(), version=self._version)
+
+    def string(self) -> PolarsExpr:
+        return PolarsExpr(pl.selectors.string(), version=self._version)
+
+    def categorical(self) -> PolarsExpr:
+        return PolarsExpr(pl.selectors.categorical(), version=self._version)
+
+    def all(self) -> PolarsExpr:
+        return PolarsExpr(pl.selectors.all(), version=self._version)
+
+    def datetime(
+        self,
+        time_unit: TimeUnit | Iterable[TimeUnit] | None,
+        time_zone: str | timezone | Iterable[str | timezone | None] | None,
+    ) -> PolarsExpr:
+        return PolarsExpr(
+            pl.selectors.datetime(time_unit=time_unit, time_zone=time_zone),  # type: ignore[arg-type]
+            version=self._version,
+        )

venv/lib/python3.11/site-packages/narwhals/_polars/series.py

@@ -0,0 +1,772 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, ClassVar, cast, overload
+
+import polars as pl
+
+from narwhals._polars.utils import (
+    BACKEND_VERSION,
+    catch_polars_exception,
+    extract_args_kwargs,
+    extract_native,
+    narwhals_to_native_dtype,
+    native_to_narwhals_dtype,
+)
+from narwhals._utils import Implementation, requires
+from narwhals.dependencies import is_numpy_array_1d
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Iterator, Mapping, Sequence
+    from types import ModuleType
+    from typing import Literal, TypeVar
+
+    import pandas as pd
+    import pyarrow as pa
+    from typing_extensions import Self, TypeAlias, TypeIs
+
+    from narwhals._polars.dataframe import Method, PolarsDataFrame
+    from narwhals._polars.expr import PolarsExpr
+    from narwhals._polars.namespace import PolarsNamespace
+    from narwhals._utils import Version, _LimitedContext
+    from narwhals.dtypes import DType
+    from narwhals.series import Series
+    from narwhals.typing import Into1DArray, IntoDType, MultiIndexSelector, _1DArray
+
+    T = TypeVar("T")
+    IncludeBreakpoint: TypeAlias = Literal[False, True]
+
+
+# Series methods where PolarsSeries just defers to Polars.Series directly.
+INHERITED_METHODS = frozenset(
+    [
+        "__add__",
+        "__and__",
+        "__floordiv__",
+        "__invert__",
+        "__iter__",
+        "__mod__",
+        "__mul__",
+        "__or__",
+        "__pow__",
+        "__radd__",
+        "__rand__",
+        "__rfloordiv__",
+        "__rmod__",
+        "__rmul__",
+        "__ror__",
+        "__rsub__",
+        "__rtruediv__",
+        "__sub__",
+        "__truediv__",
+        "abs",
+        "all",
+        "any",
+        "arg_max",
+        "arg_min",
+        "arg_true",
+        "clip",
+        "count",
+        "cum_max",
+        "cum_min",
+        "cum_prod",
+        "cum_sum",
+        "diff",
+        "drop_nulls",
+        "exp",
+        "fill_null",
+        "filter",
+        "gather_every",
+        "head",
+        "is_between",
+        "is_finite",
+        "is_first_distinct",
+        "is_in",
+        "is_last_distinct",
+        "is_null",
+        "is_sorted",
+        "is_unique",
+        "item",
+        "kurtosis",
+        "len",
+        "log",
+        "max",
+        "mean",
+        "min",
+        "mode",
+        "n_unique",
+        "null_count",
+        "quantile",
+        "rank",
+        "round",
+        "sample",
+        "shift",
+        "skew",
+        "sqrt",
+        "std",
+        "sum",
+        "tail",
+        "to_arrow",
+        "to_frame",
+        "to_list",
+        "to_pandas",
+        "unique",
+        "var",
+        "zip_with",
+    ]
+)
+
+
+class PolarsSeries:
+    _implementation = Implementation.POLARS
+
+    def __init__(self, series: pl.Series, *, version: Version) -> None:
+        self._native_series: pl.Series = series
+        self._version = version
+
+    @property
+    def _backend_version(self) -> tuple[int, ...]:
+        return self._implementation._backend_version()
+
+    def __repr__(self) -> str:  # pragma: no cover
+        return "PolarsSeries"
+
+    def __narwhals_namespace__(self) -> PolarsNamespace:
+        from narwhals._polars.namespace import PolarsNamespace
+
+        return PolarsNamespace(version=self._version)
+
+    def __narwhals_series__(self) -> Self:
+        return self
+
+    def __native_namespace__(self) -> ModuleType:
+        if self._implementation is Implementation.POLARS:
+            return self._implementation.to_native_namespace()
+
+        msg = f"Expected polars, got: {type(self._implementation)}"  # pragma: no cover
+        raise AssertionError(msg)
+
+    def _with_version(self, version: Version) -> Self:
+        return self.__class__(self.native, version=version)
+
+    @classmethod
+    def from_iterable(
+        cls,
+        data: Iterable[Any],
+        *,
+        context: _LimitedContext,
+        name: str = "",
+        dtype: IntoDType | None = None,
+    ) -> Self:
+        version = context._version
+        dtype_pl = narwhals_to_native_dtype(dtype, 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
+        native = pl.Series(name=name, values=cast("Sequence[Any]", data), dtype=dtype_pl)
+        return cls.from_native(native, context=context)
+
+    @staticmethod
+    def _is_native(obj: pl.Series | Any) -> TypeIs[pl.Series]:
+        return isinstance(obj, pl.Series)
+
+    @classmethod
+    def from_native(cls, data: pl.Series, /, *, context: _LimitedContext) -> Self:
+        return cls(data, version=context._version)
+
+    @classmethod
+    def from_numpy(cls, data: Into1DArray, /, *, context: _LimitedContext) -> Self:
+        native = pl.Series(data if is_numpy_array_1d(data) else [data])
+        return cls.from_native(native, context=context)
+
+    def to_narwhals(self) -> Series[pl.Series]:
+        return self._version.series(self, level="full")
+
+    def _with_native(self, series: pl.Series) -> Self:
+        return self.__class__(series, version=self._version)
+
+    @overload
+    def _from_native_object(self, series: pl.Series) -> Self: ...
+
+    @overload
+    def _from_native_object(self, series: pl.DataFrame) -> PolarsDataFrame: ...
+
+    @overload
+    def _from_native_object(self, series: T) -> T: ...
+
+    def _from_native_object(
+        self, series: pl.Series | pl.DataFrame | T
+    ) -> Self | PolarsDataFrame | T:
+        if self._is_native(series):
+            return self._with_native(series)
+        if isinstance(series, pl.DataFrame):
+            from narwhals._polars.dataframe import PolarsDataFrame
+
+            return PolarsDataFrame.from_native(series, context=self)
+        # scalar
+        return series
+
+    def _to_expr(self) -> PolarsExpr:
+        return self.__narwhals_namespace__()._expr._from_series(self)
+
+    def __getattr__(self, attr: str) -> Any:
+        if attr not in INHERITED_METHODS:
+            msg = f"{self.__class__.__name__} has not attribute '{attr}'."
+            raise AttributeError(msg)
+
+        def func(*args: Any, **kwargs: Any) -> Any:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self._from_native_object(getattr(self.native, attr)(*pos, **kwds))
+
+        return func
+
+    def __len__(self) -> int:
+        return len(self.native)
+
+    @property
+    def name(self) -> str:
+        return self.native.name
+
+    @property
+    def dtype(self) -> DType:
+        return native_to_narwhals_dtype(self.native.dtype, self._version)
+
+    @property
+    def native(self) -> pl.Series:
+        return self._native_series
+
+    def alias(self, name: str) -> Self:
+        return self._from_native_object(self.native.alias(name))
+
+    def __getitem__(self, item: MultiIndexSelector[Self]) -> Any | Self:
+        if isinstance(item, PolarsSeries):
+            return self._from_native_object(self.native.__getitem__(item.native))
+        return self._from_native_object(self.native.__getitem__(item))
+
+    def cast(self, dtype: IntoDType) -> Self:
+        dtype_pl = narwhals_to_native_dtype(dtype, self._version)
+        return self._with_native(self.native.cast(dtype_pl))
+
+    @requires.backend_version((1,))
+    def replace_strict(
+        self,
+        old: Sequence[Any] | Mapping[Any, Any],
+        new: Sequence[Any],
+        *,
+        return_dtype: IntoDType | None,
+    ) -> Self:
+        ser = self.native
+        dtype = (
+            narwhals_to_native_dtype(return_dtype, self._version)
+            if return_dtype
+            else None
+        )
+        return self._with_native(ser.replace_strict(old, new, return_dtype=dtype))
+
+    def to_numpy(self, dtype: Any = None, *, copy: bool | None = None) -> _1DArray:
+        return self.__array__(dtype, copy=copy)
+
+    def __array__(self, dtype: Any, *, copy: bool | None) -> _1DArray:
+        if self._backend_version < (0, 20, 29):
+            return self.native.__array__(dtype=dtype)
+        return self.native.__array__(dtype=dtype, copy=copy)
+
+    def __eq__(self, other: object) -> Self:  # type: ignore[override]
+        return self._with_native(self.native.__eq__(extract_native(other)))
+
+    def __ne__(self, other: object) -> Self:  # type: ignore[override]
+        return self._with_native(self.native.__ne__(extract_native(other)))
+
+    # NOTE: `pyright` is being reasonable here
+    def __ge__(self, other: Any) -> Self:
+        return self._with_native(self.native.__ge__(extract_native(other)))  # pyright: ignore[reportArgumentType]
+
+    def __gt__(self, other: Any) -> Self:
+        return self._with_native(self.native.__gt__(extract_native(other)))  # pyright: ignore[reportArgumentType]
+
+    def __le__(self, other: Any) -> Self:
+        return self._with_native(self.native.__le__(extract_native(other)))  # pyright: ignore[reportArgumentType]
+
+    def __lt__(self, other: Any) -> Self:
+        return self._with_native(self.native.__lt__(extract_native(other)))  # pyright: ignore[reportArgumentType]
+
+    def __rpow__(self, other: PolarsSeries | Any) -> Self:
+        result = self.native.__rpow__(extract_native(other))
+        if self._backend_version < (1, 16, 1):
+            # Explicitly set alias to work around https://github.com/pola-rs/polars/issues/20071
+            result = result.alias(self.name)
+        return self._with_native(result)
+
+    def is_nan(self) -> Self:
+        try:
+            native_is_nan = self.native.is_nan()
+        except Exception as e:  # noqa: BLE001
+            raise catch_polars_exception(e) from None
+        if self._backend_version < (1, 18):  # pragma: no cover
+            select = pl.when(self.native.is_not_null()).then(native_is_nan)
+            return self._with_native(pl.select(select)[self.name])
+        return self._with_native(native_is_nan)
+
+    def median(self) -> Any:
+        from narwhals.exceptions import InvalidOperationError
+
+        if not self.dtype.is_numeric():
+            msg = "`median` operation not supported for non-numeric input type."
+            raise InvalidOperationError(msg)
+
+        return self.native.median()
+
+    def to_dummies(self, *, separator: str, drop_first: bool) -> PolarsDataFrame:
+        from narwhals._polars.dataframe import PolarsDataFrame
+
+        if self._backend_version < (0, 20, 15):
+            has_nulls = self.native.is_null().any()
+            result = self.native.to_dummies(separator=separator)
+            output_columns = result.columns
+            if drop_first:
+                _ = output_columns.pop(int(has_nulls))
+
+            result = result.select(output_columns)
+        else:
+            result = self.native.to_dummies(separator=separator, drop_first=drop_first)
+        result = result.with_columns(pl.all().cast(pl.Int8))
+        return PolarsDataFrame.from_native(result, context=self)
+
+    def ewm_mean(
+        self,
+        *,
+        com: float | None,
+        span: float | None,
+        half_life: float | None,
+        alpha: float | None,
+        adjust: bool,
+        min_samples: int,
+        ignore_nulls: bool,
+    ) -> Self:
+        extra_kwargs = (
+            {"min_periods": min_samples}
+            if self._backend_version < (1, 21, 0)
+            else {"min_samples": min_samples}
+        )
+
+        native_result = self.native.ewm_mean(
+            com=com,
+            span=span,
+            half_life=half_life,
+            alpha=alpha,
+            adjust=adjust,
+            ignore_nulls=ignore_nulls,
+            **extra_kwargs,
+        )
+        if self._backend_version < (1,):  # pragma: no cover
+            return self._with_native(
+                pl.select(
+                    pl.when(~self.native.is_null()).then(native_result).otherwise(None)
+                )[self.native.name]
+            )
+
+        return self._with_native(native_result)
+
+    @requires.backend_version((1,))
+    def rolling_var(
+        self, window_size: int, *, min_samples: int, center: bool, ddof: int
+    ) -> Self:
+        extra_kwargs: dict[str, Any] = (
+            {"min_periods": min_samples}
+            if self._backend_version < (1, 21, 0)
+            else {"min_samples": min_samples}
+        )
+        return self._with_native(
+            self.native.rolling_var(
+                window_size=window_size, center=center, ddof=ddof, **extra_kwargs
+            )
+        )
+
+    @requires.backend_version((1,))
+    def rolling_std(
+        self, window_size: int, *, min_samples: int, center: bool, ddof: int
+    ) -> Self:
+        extra_kwargs: dict[str, Any] = (
+            {"min_periods": min_samples}
+            if self._backend_version < (1, 21, 0)
+            else {"min_samples": min_samples}
+        )
+        return self._with_native(
+            self.native.rolling_std(
+                window_size=window_size, center=center, ddof=ddof, **extra_kwargs
+            )
+        )
+
+    def rolling_sum(self, window_size: int, *, min_samples: int, center: bool) -> Self:
+        extra_kwargs: dict[str, Any] = (
+            {"min_periods": min_samples}
+            if self._backend_version < (1, 21, 0)
+            else {"min_samples": min_samples}
+        )
+        return self._with_native(
+            self.native.rolling_sum(
+                window_size=window_size, center=center, **extra_kwargs
+            )
+        )
+
+    def rolling_mean(self, window_size: int, *, min_samples: int, center: bool) -> Self:
+        extra_kwargs: dict[str, Any] = (
+            {"min_periods": min_samples}
+            if self._backend_version < (1, 21, 0)
+            else {"min_samples": min_samples}
+        )
+        return self._with_native(
+            self.native.rolling_mean(
+                window_size=window_size, center=center, **extra_kwargs
+            )
+        )
+
+    def sort(self, *, descending: bool, nulls_last: bool) -> Self:
+        if self._backend_version < (0, 20, 6):
+            result = self.native.sort(descending=descending)
+
+            if nulls_last:
+                is_null = result.is_null()
+                result = pl.concat([result.filter(~is_null), result.filter(is_null)])
+        else:
+            result = self.native.sort(descending=descending, nulls_last=nulls_last)
+
+        return self._with_native(result)
+
+    def scatter(self, indices: int | Sequence[int], values: Any) -> Self:
+        s = self.native.clone().scatter(indices, extract_native(values))
+        return self._with_native(s)
+
+    def value_counts(
+        self, *, sort: bool, parallel: bool, name: str | None, normalize: bool
+    ) -> PolarsDataFrame:
+        from narwhals._polars.dataframe import PolarsDataFrame
+
+        if self._backend_version < (1, 0, 0):
+            value_name_ = name or ("proportion" if normalize else "count")
+
+            result = self.native.value_counts(sort=sort, parallel=parallel).select(
+                **{
+                    (self.native.name): pl.col(self.native.name),
+                    value_name_: pl.col("count") / pl.sum("count")
+                    if normalize
+                    else pl.col("count"),
+                }
+            )
+        else:
+            result = self.native.value_counts(
+                sort=sort, parallel=parallel, name=name, normalize=normalize
+            )
+        return PolarsDataFrame.from_native(result, context=self)
+
+    def cum_count(self, *, reverse: bool) -> Self:
+        return self._with_native(self.native.cum_count(reverse=reverse))
+
+    def __contains__(self, other: Any) -> bool:
+        try:
+            return self.native.__contains__(other)
+        except Exception as e:  # noqa: BLE001
+            raise catch_polars_exception(e) from None
+
+    _HIST_EMPTY_SCHEMA: ClassVar[Mapping[IncludeBreakpoint, Sequence[str]]] = {
+        True: ["breakpoint", "count"],
+        False: ["count"],
+    }
+
+    def hist_from_bins(
+        self, bins: list[float], *, include_breakpoint: bool
+    ) -> PolarsDataFrame:
+        if len(bins) <= 1:
+            native = pl.DataFrame(schema=self._HIST_EMPTY_SCHEMA[include_breakpoint])
+        elif self.native.is_empty():
+            if include_breakpoint:
+                native = (
+                    pl.Series(bins[1:])
+                    .to_frame("breakpoint")
+                    .with_columns(count=pl.lit(0, pl.Int64))
+                )
+            else:
+                native = pl.select(count=pl.zeros(len(bins) - 1, pl.Int64))
+        else:
+            return self._hist_from_data(
+                bins=bins, bin_count=None, include_breakpoint=include_breakpoint
+            )
+        return self.__narwhals_namespace__()._dataframe.from_native(native, context=self)
+
+    def hist_from_bin_count(
+        self, bin_count: int, *, include_breakpoint: bool
+    ) -> PolarsDataFrame:
+        if bin_count == 0:
+            native = pl.DataFrame(schema=self._HIST_EMPTY_SCHEMA[include_breakpoint])
+        elif self.native.is_empty():
+            if include_breakpoint:
+                native = pl.select(
+                    breakpoint=pl.int_range(1, bin_count + 1) / bin_count,
+                    count=pl.lit(0, pl.Int64),
+                )
+            else:
+                native = pl.select(count=pl.zeros(bin_count, pl.Int64))
+        else:
+            count: int | None
+            if BACKEND_VERSION < (1, 15):  # pragma: no cover
+                count = None
+                bins = self._bins_from_bin_count(bin_count=bin_count)
+            else:
+                count = bin_count
+                bins = None
+            return self._hist_from_data(
+                bins=bins,  # type: ignore[arg-type]
+                bin_count=count,
+                include_breakpoint=include_breakpoint,
+            )
+        return self.__narwhals_namespace__()._dataframe.from_native(native, context=self)
+
+    def _bins_from_bin_count(self, bin_count: int) -> pl.Series:  # pragma: no cover
+        """Prepare bins based on backend version compatibility.
+
+        polars <1.15 does not adjust the bins when they have equivalent min/max
+        polars <1.5 with bin_count=...
+        returns bins that range from -inf to +inf and has bin_count + 1 bins.
+          for compat: convert `bin_count=` call to `bins=`
+        """
+        from typing import cast
+
+        lower = cast("float", self.native.min())
+        upper = cast("float", self.native.max())
+
+        if lower == upper:
+            lower -= 0.5
+            upper += 0.5
+
+        width = (upper - lower) / bin_count
+        return pl.int_range(0, bin_count + 1, eager=True) * width + lower
+
+    def _hist_from_data(
+        self, bins: list[float] | None, bin_count: int | None, *, include_breakpoint: bool
+    ) -> PolarsDataFrame:
+        """Calculate histogram from non-empty data and post-process the results based on the backend version."""
+        from narwhals._polars.dataframe import PolarsDataFrame
+
+        series = self.native
+
+        # Polars inconsistently handles NaN values when computing histograms
+        #   against predefined bins: https://github.com/pola-rs/polars/issues/21082
+        if BACKEND_VERSION < (1, 15) or bins is not None:
+            series = series.fill_nan(None)
+
+        df = series.hist(
+            bins,
+            bin_count=bin_count,
+            include_category=False,
+            include_breakpoint=include_breakpoint,
+        )
+
+        # Apply post-processing corrections
+
+        # Handle column naming
+        if not include_breakpoint:
+            col_name = df.columns[0]
+            df = df.select(pl.col(col_name).alias("count"))
+        elif BACKEND_VERSION < (1, 0):  # pragma: no cover
+            df = df.rename({"break_point": "breakpoint"})
+
+        if bins is not None:  # pragma: no cover
+            # polars<1.6 implicitly adds -inf and inf to either end of bins
+            if BACKEND_VERSION < (1, 6):
+                r = pl.int_range(0, len(df))
+                df = df.filter((r > 0) & (r < len(df) - 1))
+            # polars<1.27 makes the lowest bin a left/right closed interval
+            if BACKEND_VERSION < (1, 27):
+                df = (
+                    df.slice(0, 1)
+                    .with_columns(pl.col("count") + ((pl.lit(series) == bins[0]).sum()))
+                    .vstack(df.slice(1))
+                )
+
+        return PolarsDataFrame.from_native(df, context=self)
+
+    def to_polars(self) -> pl.Series:
+        return self.native
+
+    @property
+    def dt(self) -> PolarsSeriesDateTimeNamespace:
+        return PolarsSeriesDateTimeNamespace(self)
+
+    @property
+    def str(self) -> PolarsSeriesStringNamespace:
+        return PolarsSeriesStringNamespace(self)
+
+    @property
+    def cat(self) -> PolarsSeriesCatNamespace:
+        return PolarsSeriesCatNamespace(self)
+
+    @property
+    def struct(self) -> PolarsSeriesStructNamespace:
+        return PolarsSeriesStructNamespace(self)
+
+    __add__: Method[Self]
+    __and__: Method[Self]
+    __floordiv__: Method[Self]
+    __invert__: Method[Self]
+    __iter__: Method[Iterator[Any]]
+    __mod__: Method[Self]
+    __mul__: Method[Self]
+    __or__: Method[Self]
+    __pow__: Method[Self]
+    __radd__: Method[Self]
+    __rand__: Method[Self]
+    __rfloordiv__: Method[Self]
+    __rmod__: Method[Self]
+    __rmul__: Method[Self]
+    __ror__: Method[Self]
+    __rsub__: Method[Self]
+    __rtruediv__: Method[Self]
+    __sub__: Method[Self]
+    __truediv__: Method[Self]
+    abs: Method[Self]
+    all: Method[bool]
+    any: Method[bool]
+    arg_max: Method[int]
+    arg_min: Method[int]
+    arg_true: Method[Self]
+    clip: Method[Self]
+    count: Method[int]
+    cum_max: Method[Self]
+    cum_min: Method[Self]
+    cum_prod: Method[Self]
+    cum_sum: Method[Self]
+    diff: Method[Self]
+    drop_nulls: Method[Self]
+    exp: Method[Self]
+    fill_null: Method[Self]
+    filter: Method[Self]
+    gather_every: Method[Self]
+    head: Method[Self]
+    is_between: Method[Self]
+    is_finite: Method[Self]
+    is_first_distinct: Method[Self]
+    is_in: Method[Self]
+    is_last_distinct: Method[Self]
+    is_null: Method[Self]
+    is_sorted: Method[bool]
+    is_unique: Method[Self]
+    item: Method[Any]
+    kurtosis: Method[float | None]
+    len: Method[int]
+    log: Method[Self]
+    max: Method[Any]
+    mean: Method[float]
+    min: Method[Any]
+    mode: Method[Self]
+    n_unique: Method[int]
+    null_count: Method[int]
+    quantile: Method[float]
+    rank: Method[Self]
+    round: Method[Self]
+    sample: Method[Self]
+    shift: Method[Self]
+    skew: Method[float | None]
+    sqrt: Method[Self]
+    std: Method[float]
+    sum: Method[float]
+    tail: Method[Self]
+    to_arrow: Method[pa.Array[Any]]
+    to_frame: Method[PolarsDataFrame]
+    to_list: Method[list[Any]]
+    to_pandas: Method[pd.Series[Any]]
+    unique: Method[Self]
+    var: Method[float]
+    zip_with: Method[Self]
+
+    @property
+    def list(self) -> PolarsSeriesListNamespace:
+        return PolarsSeriesListNamespace(self)
+
+
+class PolarsSeriesDateTimeNamespace:
+    def __init__(self, series: PolarsSeries) -> None:
+        self._compliant_series = series
+
+    def __getattr__(self, attr: str) -> Any:
+        def func(*args: Any, **kwargs: Any) -> Any:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self._compliant_series._with_native(
+                getattr(self._compliant_series.native.dt, attr)(*pos, **kwds)
+            )
+
+        return func
+
+
+class PolarsSeriesStringNamespace:
+    def __init__(self, series: PolarsSeries) -> None:
+        self._compliant_series = series
+
+    def zfill(self, width: int) -> PolarsSeries:
+        series = self._compliant_series
+        name = series.name
+        ns = series.__narwhals_namespace__()
+        return series.to_frame().select(ns.col(name).str.zfill(width)).get_column(name)
+
+    def __getattr__(self, attr: str) -> Any:
+        def func(*args: Any, **kwargs: Any) -> Any:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self._compliant_series._with_native(
+                getattr(self._compliant_series.native.str, attr)(*pos, **kwds)
+            )
+
+        return func
+
+
+class PolarsSeriesCatNamespace:
+    def __init__(self, series: PolarsSeries) -> None:
+        self._compliant_series = series
+
+    def __getattr__(self, attr: str) -> Any:
+        def func(*args: Any, **kwargs: Any) -> Any:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self._compliant_series._with_native(
+                getattr(self._compliant_series.native.cat, attr)(*pos, **kwds)
+            )
+
+        return func
+
+
+class PolarsSeriesListNamespace:
+    def __init__(self, series: PolarsSeries) -> None:
+        self._series = series
+
+    def len(self) -> PolarsSeries:
+        native_series = self._series.native
+        native_result = native_series.list.len()
+
+        if self._series._backend_version < (1, 16):  # pragma: no cover
+            native_result = pl.select(
+                pl.when(~native_series.is_null()).then(native_result).otherwise(None)
+            )[native_series.name].cast(pl.UInt32())
+
+        elif self._series._backend_version < (1, 17):  # pragma: no cover
+            native_result = native_series.cast(pl.UInt32())
+
+        return self._series._with_native(native_result)
+
+    # TODO(FBruzzesi): Remove `pragma: no cover` once other namespace methods are added
+    def __getattr__(self, attr: str) -> Any:  # pragma: no cover
+        def func(*args: Any, **kwargs: Any) -> Any:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self._series._with_native(
+                getattr(self._series.native.list, attr)(*pos, **kwds)
+            )
+
+        return func
+
+
+class PolarsSeriesStructNamespace:
+    def __init__(self, series: PolarsSeries) -> None:
+        self._compliant_series = series
+
+    def __getattr__(self, attr: str) -> Any:
+        def func(*args: Any, **kwargs: Any) -> Any:
+            pos, kwds = extract_args_kwargs(args, kwargs)
+            return self._compliant_series._with_native(
+                getattr(self._compliant_series.native.struct, attr)(*pos, **kwds)
+            )
+
+        return func

venv/lib/python3.11/site-packages/narwhals/_polars/typing.py

@@ -0,0 +1,22 @@
+from __future__ import annotations  # pragma: no cover
+
+from typing import (
+    TYPE_CHECKING,  # pragma: no cover
+    Union,  # pragma: no cover
+)
+
+if TYPE_CHECKING:
+    import sys
+    from typing import TypeVar
+
+    if sys.version_info >= (3, 10):
+        from typing import TypeAlias
+    else:
+        from typing_extensions import TypeAlias
+
+    from narwhals._polars.dataframe import PolarsDataFrame, PolarsLazyFrame
+    from narwhals._polars.expr import PolarsExpr
+    from narwhals._polars.series import PolarsSeries
+
+    IntoPolarsExpr: TypeAlias = Union[PolarsExpr, PolarsSeries]
+    FrameT = TypeVar("FrameT", PolarsDataFrame, PolarsLazyFrame)

venv/lib/python3.11/site-packages/narwhals/_polars/utils.py

@@ -0,0 +1,242 @@
+from __future__ import annotations
+
+from functools import lru_cache
+from typing import TYPE_CHECKING, Any, TypeVar, overload
+
+import polars as pl
+
+from narwhals._utils import (
+    Implementation,
+    Version,
+    _DeferredIterable,
+    isinstance_or_issubclass,
+)
+from narwhals.exceptions import (
+    ColumnNotFoundError,
+    ComputeError,
+    DuplicateError,
+    InvalidOperationError,
+    NarwhalsError,
+    ShapeError,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Iterator, Mapping
+
+    from typing_extensions import TypeIs
+
+    from narwhals._utils import _StoresNative
+    from narwhals.dtypes import DType
+    from narwhals.typing import IntoDType
+
+    T = TypeVar("T")
+    NativeT = TypeVar(
+        "NativeT", bound="pl.DataFrame | pl.LazyFrame | pl.Series | pl.Expr"
+    )
+
+BACKEND_VERSION = Implementation.POLARS._backend_version()
+"""Static backend version for `polars`."""
+
+
+@overload
+def extract_native(obj: _StoresNative[NativeT]) -> NativeT: ...
+@overload
+def extract_native(obj: T) -> T: ...
+def extract_native(obj: _StoresNative[NativeT] | T) -> NativeT | T:
+    return obj.native if _is_compliant_polars(obj) else obj
+
+
+def _is_compliant_polars(
+    obj: _StoresNative[NativeT] | Any,
+) -> TypeIs[_StoresNative[NativeT]]:
+    from narwhals._polars.dataframe import PolarsDataFrame, PolarsLazyFrame
+    from narwhals._polars.expr import PolarsExpr
+    from narwhals._polars.series import PolarsSeries
+
+    return isinstance(obj, (PolarsDataFrame, PolarsLazyFrame, PolarsSeries, PolarsExpr))
+
+
+def extract_args_kwargs(
+    args: Iterable[Any], kwds: Mapping[str, Any], /
+) -> tuple[Iterator[Any], dict[str, Any]]:
+    it_args = (extract_native(arg) for arg in args)
+    return it_args, {k: extract_native(v) for k, v in kwds.items()}
+
+
+@lru_cache(maxsize=16)
+def native_to_narwhals_dtype(  # noqa: C901, PLR0912
+    dtype: pl.DataType, version: Version
+) -> DType:
+    dtypes = version.dtypes
+    if dtype == pl.Float64:
+        return dtypes.Float64()
+    if dtype == pl.Float32:
+        return dtypes.Float32()
+    if hasattr(pl, "Int128") and dtype == pl.Int128:  # pragma: no cover
+        # Not available for Polars pre 1.8.0
+        return dtypes.Int128()
+    if dtype == pl.Int64:
+        return dtypes.Int64()
+    if dtype == pl.Int32:
+        return dtypes.Int32()
+    if dtype == pl.Int16:
+        return dtypes.Int16()
+    if dtype == pl.Int8:
+        return dtypes.Int8()
+    if hasattr(pl, "UInt128") and dtype == pl.UInt128:  # pragma: no cover
+        # Not available for Polars pre 1.8.0
+        return dtypes.UInt128()
+    if dtype == pl.UInt64:
+        return dtypes.UInt64()
+    if dtype == pl.UInt32:
+        return dtypes.UInt32()
+    if dtype == pl.UInt16:
+        return dtypes.UInt16()
+    if dtype == pl.UInt8:
+        return dtypes.UInt8()
+    if dtype == pl.String:
+        return dtypes.String()
+    if dtype == pl.Boolean:
+        return dtypes.Boolean()
+    if dtype == pl.Object:
+        return dtypes.Object()
+    if dtype == pl.Categorical:
+        return dtypes.Categorical()
+    if isinstance_or_issubclass(dtype, pl.Enum):
+        if version is Version.V1:
+            return dtypes.Enum()  # type: ignore[call-arg]
+        categories = _DeferredIterable(dtype.categories.to_list)
+        return dtypes.Enum(categories)
+    if dtype == pl.Date:
+        return dtypes.Date()
+    if isinstance_or_issubclass(dtype, pl.Datetime):
+        return (
+            dtypes.Datetime()
+            if dtype is pl.Datetime
+            else dtypes.Datetime(dtype.time_unit, dtype.time_zone)
+        )
+    if isinstance_or_issubclass(dtype, pl.Duration):
+        return (
+            dtypes.Duration()
+            if dtype is pl.Duration
+            else dtypes.Duration(dtype.time_unit)
+        )
+    if isinstance_or_issubclass(dtype, pl.Struct):
+        fields = [
+            dtypes.Field(name, native_to_narwhals_dtype(tp, version))
+            for name, tp in dtype
+        ]
+        return dtypes.Struct(fields)
+    if isinstance_or_issubclass(dtype, pl.List):
+        return dtypes.List(native_to_narwhals_dtype(dtype.inner, version))
+    if isinstance_or_issubclass(dtype, pl.Array):
+        outer_shape = dtype.width if BACKEND_VERSION < (0, 20, 30) else dtype.size
+        return dtypes.Array(native_to_narwhals_dtype(dtype.inner, version), outer_shape)
+    if dtype == pl.Decimal:
+        return dtypes.Decimal()
+    if dtype == pl.Time:
+        return dtypes.Time()
+    if dtype == pl.Binary:
+        return dtypes.Binary()
+    return dtypes.Unknown()
+
+
+def narwhals_to_native_dtype(  # noqa: C901, PLR0912
+    dtype: IntoDType, version: Version
+) -> pl.DataType:
+    dtypes = version.dtypes
+    if dtype == dtypes.Float64:
+        return pl.Float64()
+    if dtype == dtypes.Float32:
+        return pl.Float32()
+    if dtype == dtypes.Int128 and hasattr(pl, "Int128"):
+        # Not available for Polars pre 1.8.0
+        return pl.Int128()
+    if dtype == dtypes.Int64:
+        return pl.Int64()
+    if dtype == dtypes.Int32:
+        return pl.Int32()
+    if dtype == dtypes.Int16:
+        return pl.Int16()
+    if dtype == dtypes.Int8:
+        return pl.Int8()
+    if dtype == dtypes.UInt64:
+        return pl.UInt64()
+    if dtype == dtypes.UInt32:
+        return pl.UInt32()
+    if dtype == dtypes.UInt16:
+        return pl.UInt16()
+    if dtype == dtypes.UInt8:
+        return pl.UInt8()
+    if dtype == dtypes.String:
+        return pl.String()
+    if dtype == dtypes.Boolean:
+        return pl.Boolean()
+    if dtype == dtypes.Object:  # pragma: no cover
+        return pl.Object()
+    if dtype == dtypes.Categorical:
+        return pl.Categorical()
+    if isinstance_or_issubclass(dtype, dtypes.Enum):
+        if version is Version.V1:
+            msg = "Converting to Enum is not supported in narwhals.stable.v1"
+            raise NotImplementedError(msg)
+        if isinstance(dtype, dtypes.Enum):
+            return pl.Enum(dtype.categories)
+        msg = "Can not cast / initialize Enum without categories present"
+        raise ValueError(msg)
+    if dtype == dtypes.Date:
+        return pl.Date()
+    if dtype == dtypes.Time:
+        return pl.Time()
+    if dtype == dtypes.Binary:
+        return pl.Binary()
+    if dtype == dtypes.Decimal:
+        msg = "Casting to Decimal is not supported yet."
+        raise NotImplementedError(msg)
+    if isinstance_or_issubclass(dtype, dtypes.Datetime):
+        return pl.Datetime(dtype.time_unit, dtype.time_zone)  # type: ignore[arg-type]
+    if isinstance_or_issubclass(dtype, dtypes.Duration):
+        return pl.Duration(dtype.time_unit)  # type: ignore[arg-type]
+    if isinstance_or_issubclass(dtype, dtypes.List):
+        return pl.List(narwhals_to_native_dtype(dtype.inner, version))
+    if isinstance_or_issubclass(dtype, dtypes.Struct):
+        fields = [
+            pl.Field(field.name, narwhals_to_native_dtype(field.dtype, version))
+            for field in dtype.fields
+        ]
+        return pl.Struct(fields)
+    if isinstance_or_issubclass(dtype, dtypes.Array):  # pragma: no cover
+        size = dtype.size
+        kwargs = {"width": size} if BACKEND_VERSION < (0, 20, 30) else {"shape": size}
+        return pl.Array(narwhals_to_native_dtype(dtype.inner, version), **kwargs)
+    return pl.Unknown()  # pragma: no cover
+
+
+def _is_polars_exception(exception: Exception) -> bool:
+    if BACKEND_VERSION >= (1,):
+        # Old versions of Polars didn't have PolarsError.
+        return isinstance(exception, pl.exceptions.PolarsError)
+    # Last attempt, for old Polars versions.
+    return "polars.exceptions" in str(type(exception))  # pragma: no cover
+
+
+def _is_cudf_exception(exception: Exception) -> bool:
+    # These exceptions are raised when running polars on GPUs via cuDF
+    return str(exception).startswith("CUDF failure")
+
+
+def catch_polars_exception(exception: Exception) -> NarwhalsError | Exception:
+    if isinstance(exception, pl.exceptions.ColumnNotFoundError):
+        return ColumnNotFoundError(str(exception))
+    elif isinstance(exception, pl.exceptions.ShapeError):
+        return ShapeError(str(exception))
+    elif isinstance(exception, pl.exceptions.InvalidOperationError):
+        return InvalidOperationError(str(exception))
+    elif isinstance(exception, pl.exceptions.DuplicateError):
+        return DuplicateError(str(exception))
+    elif isinstance(exception, pl.exceptions.ComputeError):
+        return ComputeError(str(exception))
+    if _is_polars_exception(exception) or _is_cudf_exception(exception):
+        return NarwhalsError(str(exception))  # pragma: no cover
+    # Just return exception as-is.
+    return exception