done

lib/python3.11/site-packages/narwhals/_compliant/__init__.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+from narwhals._compliant.dataframe import (
+    CompliantDataFrame,
+    CompliantFrame,
+    CompliantLazyFrame,
+    EagerDataFrame,
+)
+from narwhals._compliant.expr import (
+    CompliantExpr,
+    DepthTrackingExpr,
+    EagerExpr,
+    LazyExpr,
+    LazyExprNamespace,
+)
+from narwhals._compliant.group_by import (
+    CompliantGroupBy,
+    DepthTrackingGroupBy,
+    EagerGroupBy,
+)
+from narwhals._compliant.namespace import (
+    CompliantNamespace,
+    DepthTrackingNamespace,
+    EagerNamespace,
+    LazyNamespace,
+)
+from narwhals._compliant.selectors import (
+    CompliantSelector,
+    CompliantSelectorNamespace,
+    EagerSelectorNamespace,
+    LazySelectorNamespace,
+)
+from narwhals._compliant.series import (
+    CompliantSeries,
+    EagerSeries,
+    EagerSeriesCatNamespace,
+    EagerSeriesDateTimeNamespace,
+    EagerSeriesHist,
+    EagerSeriesListNamespace,
+    EagerSeriesNamespace,
+    EagerSeriesStringNamespace,
+    EagerSeriesStructNamespace,
+)
+from narwhals._compliant.typing import (
+    CompliantExprT,
+    CompliantFrameT,
+    CompliantSeriesOrNativeExprT_co,
+    CompliantSeriesT,
+    EagerDataFrameT,
+    EagerSeriesT,
+    EvalNames,
+    EvalSeries,
+    NativeFrameT_co,
+    NativeSeriesT_co,
+)
+from narwhals._compliant.when_then import CompliantThen, CompliantWhen, EagerWhen
+from narwhals._compliant.window import WindowInputs
+
+__all__ = [
+    "CompliantDataFrame",
+    "CompliantExpr",
+    "CompliantExprT",
+    "CompliantFrame",
+    "CompliantFrameT",
+    "CompliantGroupBy",
+    "CompliantLazyFrame",
+    "CompliantNamespace",
+    "CompliantSelector",
+    "CompliantSelectorNamespace",
+    "CompliantSeries",
+    "CompliantSeriesOrNativeExprT_co",
+    "CompliantSeriesT",
+    "CompliantThen",
+    "CompliantWhen",
+    "DepthTrackingExpr",
+    "DepthTrackingGroupBy",
+    "DepthTrackingNamespace",
+    "EagerDataFrame",
+    "EagerDataFrameT",
+    "EagerExpr",
+    "EagerGroupBy",
+    "EagerNamespace",
+    "EagerSelectorNamespace",
+    "EagerSeries",
+    "EagerSeriesCatNamespace",
+    "EagerSeriesDateTimeNamespace",
+    "EagerSeriesHist",
+    "EagerSeriesListNamespace",
+    "EagerSeriesNamespace",
+    "EagerSeriesStringNamespace",
+    "EagerSeriesStructNamespace",
+    "EagerSeriesT",
+    "EagerWhen",
+    "EvalNames",
+    "EvalSeries",
+    "LazyExpr",
+    "LazyExprNamespace",
+    "LazyNamespace",
+    "LazySelectorNamespace",
+    "NativeFrameT_co",
+    "NativeSeriesT_co",
+    "WindowInputs",
+]

lib/python3.11/site-packages/narwhals/_compliant/any_namespace.py

@@ -0,0 +1,94 @@
+"""`Expr` and `Series` namespace accessor protocols."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Protocol
+
+from narwhals._utils import CompliantT_co, _StoresCompliant
+
+if TYPE_CHECKING:
+    from typing import Callable
+
+    from narwhals.typing import NonNestedLiteral, TimeUnit
+
+__all__ = [
+    "CatNamespace",
+    "DateTimeNamespace",
+    "ListNamespace",
+    "NameNamespace",
+    "StringNamespace",
+    "StructNamespace",
+]
+
+
+class CatNamespace(_StoresCompliant[CompliantT_co], Protocol[CompliantT_co]):
+    def get_categories(self) -> CompliantT_co: ...
+
+
+class DateTimeNamespace(_StoresCompliant[CompliantT_co], Protocol[CompliantT_co]):
+    def to_string(self, format: str) -> CompliantT_co: ...
+    def replace_time_zone(self, time_zone: str | None) -> CompliantT_co: ...
+    def convert_time_zone(self, time_zone: str) -> CompliantT_co: ...
+    def timestamp(self, time_unit: TimeUnit) -> CompliantT_co: ...
+    def date(self) -> CompliantT_co: ...
+    def year(self) -> CompliantT_co: ...
+    def month(self) -> CompliantT_co: ...
+    def day(self) -> CompliantT_co: ...
+    def hour(self) -> CompliantT_co: ...
+    def minute(self) -> CompliantT_co: ...
+    def second(self) -> CompliantT_co: ...
+    def millisecond(self) -> CompliantT_co: ...
+    def microsecond(self) -> CompliantT_co: ...
+    def nanosecond(self) -> CompliantT_co: ...
+    def ordinal_day(self) -> CompliantT_co: ...
+    def weekday(self) -> CompliantT_co: ...
+    def total_minutes(self) -> CompliantT_co: ...
+    def total_seconds(self) -> CompliantT_co: ...
+    def total_milliseconds(self) -> CompliantT_co: ...
+    def total_microseconds(self) -> CompliantT_co: ...
+    def total_nanoseconds(self) -> CompliantT_co: ...
+    def truncate(self, every: str) -> CompliantT_co: ...
+    def offset_by(self, by: str) -> CompliantT_co: ...
+
+
+class ListNamespace(_StoresCompliant[CompliantT_co], Protocol[CompliantT_co]):
+    def get(self, index: int) -> CompliantT_co: ...
+
+    def len(self) -> CompliantT_co: ...
+
+    def unique(self) -> CompliantT_co: ...
+    def contains(self, item: NonNestedLiteral) -> CompliantT_co: ...
+
+
+class NameNamespace(_StoresCompliant[CompliantT_co], Protocol[CompliantT_co]):
+    def keep(self) -> CompliantT_co: ...
+    def map(self, function: Callable[[str], str]) -> CompliantT_co: ...
+    def prefix(self, prefix: str) -> CompliantT_co: ...
+    def suffix(self, suffix: str) -> CompliantT_co: ...
+    def to_lowercase(self) -> CompliantT_co: ...
+    def to_uppercase(self) -> CompliantT_co: ...
+
+
+class StringNamespace(_StoresCompliant[CompliantT_co], Protocol[CompliantT_co]):
+    def len_chars(self) -> CompliantT_co: ...
+    def replace(
+        self, pattern: str, value: str, *, literal: bool, n: int
+    ) -> CompliantT_co: ...
+    def replace_all(
+        self, pattern: str, value: str, *, literal: bool
+    ) -> CompliantT_co: ...
+    def strip_chars(self, characters: str | None) -> CompliantT_co: ...
+    def starts_with(self, prefix: str) -> CompliantT_co: ...
+    def ends_with(self, suffix: str) -> CompliantT_co: ...
+    def contains(self, pattern: str, *, literal: bool) -> CompliantT_co: ...
+    def slice(self, offset: int, length: int | None) -> CompliantT_co: ...
+    def split(self, by: str) -> CompliantT_co: ...
+    def to_datetime(self, format: str | None) -> CompliantT_co: ...
+    def to_date(self, format: str | None) -> CompliantT_co: ...
+    def to_lowercase(self) -> CompliantT_co: ...
+    def to_uppercase(self) -> CompliantT_co: ...
+    def zfill(self, width: int) -> CompliantT_co: ...
+
+
+class StructNamespace(_StoresCompliant[CompliantT_co], Protocol[CompliantT_co]):
+    def field(self, name: str) -> CompliantT_co: ...

lib/python3.11/site-packages/narwhals/_compliant/column.py

@@ -0,0 +1,213 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Protocol
+
+if TYPE_CHECKING:
+    from collections.abc import Mapping, Sequence
+
+    from typing_extensions import Self
+
+    from narwhals._compliant.any_namespace import (
+        CatNamespace,
+        DateTimeNamespace,
+        ListNamespace,
+        StringNamespace,
+        StructNamespace,
+    )
+    from narwhals._compliant.namespace import CompliantNamespace
+    from narwhals._utils import Version
+    from narwhals.typing import (
+        ClosedInterval,
+        FillNullStrategy,
+        IntoDType,
+        ModeKeepStrategy,
+        NonNestedLiteral,
+        NumericLiteral,
+        RankMethod,
+        TemporalLiteral,
+    )
+
+__all__ = ["CompliantColumn"]
+
+
+class CompliantColumn(Protocol):
+    """Common parts of `Expr`, `Series`."""
+
+    _version: Version
+
+    def __add__(self, other: Any) -> Self: ...
+    def __and__(self, other: Any) -> Self: ...
+    def __eq__(self, other: object) -> Self: ...  # type: ignore[override]
+    def __floordiv__(self, other: Any) -> Self: ...
+    def __ge__(self, other: Any) -> Self: ...
+    def __gt__(self, other: Any) -> Self: ...
+    def __invert__(self) -> Self: ...
+    def __le__(self, other: Any) -> Self: ...
+    def __lt__(self, other: Any) -> Self: ...
+    def __mod__(self, other: Any) -> Self: ...
+    def __mul__(self, other: Any) -> Self: ...
+    def __ne__(self, other: object) -> Self: ...  # type: ignore[override]
+    def __or__(self, other: Any) -> Self: ...
+    def __pow__(self, other: Any) -> Self: ...
+    def __rfloordiv__(self, other: Any) -> Self: ...
+    def __rmod__(self, other: Any) -> Self: ...
+    def __rpow__(self, other: Any) -> Self: ...
+    def __rsub__(self, other: Any) -> Self: ...
+    def __rtruediv__(self, other: Any) -> Self: ...
+    def __sub__(self, other: Any) -> Self: ...
+    def __truediv__(self, other: Any) -> Self: ...
+
+    def __narwhals_namespace__(self) -> CompliantNamespace[Any, Any]: ...
+
+    def abs(self) -> Self: ...
+    def alias(self, name: str) -> Self: ...
+    def cast(self, dtype: IntoDType) -> Self: ...
+    def clip(
+        self,
+        lower_bound: Self | NumericLiteral | TemporalLiteral | None,
+        upper_bound: Self | NumericLiteral | TemporalLiteral | None,
+    ) -> Self: ...
+    def cum_count(self, *, reverse: bool) -> Self: ...
+    def cum_max(self, *, reverse: bool) -> Self: ...
+    def cum_min(self, *, reverse: bool) -> Self: ...
+    def cum_prod(self, *, reverse: bool) -> Self: ...
+    def cum_sum(self, *, reverse: bool) -> Self: ...
+    def diff(self) -> Self: ...
+    def drop_nulls(self) -> 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: ...
+    def exp(self) -> Self: ...
+    def sqrt(self) -> Self: ...
+    def fill_nan(self, value: float | None) -> Self: ...
+    def fill_null(
+        self,
+        value: Self | NonNestedLiteral,
+        strategy: FillNullStrategy | None,
+        limit: int | None,
+    ) -> Self: ...
+    def is_between(
+        self, lower_bound: Self, upper_bound: Self, closed: ClosedInterval
+    ) -> Self:
+        if closed == "left":
+            return (self >= lower_bound) & (self < upper_bound)
+        if closed == "right":
+            return (self > lower_bound) & (self <= upper_bound)
+        if closed == "none":
+            return (self > lower_bound) & (self < upper_bound)
+        return (self >= lower_bound) & (self <= upper_bound)
+
+    def is_close(
+        self,
+        other: Self | NumericLiteral,
+        *,
+        abs_tol: float,
+        rel_tol: float,
+        nans_equal: bool,
+    ) -> Self:
+        from decimal import Decimal
+
+        other_abs: Self | NumericLiteral
+        other_is_nan: Self | bool
+        other_is_inf: Self | bool
+        other_is_not_inf: Self | bool
+
+        if isinstance(other, (float, int, Decimal)):
+            from math import isinf, isnan
+
+            # NOTE: See https://discuss.python.org/t/inferred-type-of-function-that-calls-dunder-abs-abs/101447
+            other_abs = other.__abs__()
+            other_is_nan = isnan(other)
+            other_is_inf = isinf(other)
+
+            # Define the other_is_not_inf variable to prevent triggering the following warning:
+            # > DeprecationWarning: Bitwise inversion '~' on bool is deprecated and will be
+            # >     removed in Python 3.16.
+            other_is_not_inf = not other_is_inf
+
+        else:
+            other_abs, other_is_nan = other.abs(), other.is_nan()
+            other_is_not_inf = other.is_finite() | other_is_nan
+            other_is_inf = ~other_is_not_inf
+
+        rel_threshold = self.abs().clip(lower_bound=other_abs, upper_bound=None) * rel_tol
+        tolerance = rel_threshold.clip(lower_bound=abs_tol, upper_bound=None)
+
+        self_is_nan = self.is_nan()
+        self_is_not_inf = self.is_finite() | self_is_nan
+
+        # Values are close if abs_diff <= tolerance, and both finite
+        is_close = (
+            ((self - other).abs() <= tolerance) & self_is_not_inf & other_is_not_inf
+        )
+
+        # Handle infinity cases: infinities are close/equal if they have the same sign
+        self_sign, other_sign = self > 0, other > 0
+        is_same_inf = (~self_is_not_inf) & other_is_inf & (self_sign == other_sign)
+
+        # Handle nan cases:
+        #   * If any value is NaN, then False (via `& ~either_nan`)
+        #   * However, if `nans_equals = True` and if _both_ values are NaN, then True
+        either_nan = self_is_nan | other_is_nan
+        result = (is_close | is_same_inf) & ~either_nan
+
+        if nans_equal:
+            both_nan = self_is_nan & other_is_nan
+            result = result | both_nan
+
+        return result
+
+    def is_duplicated(self) -> Self:
+        return ~self.is_unique()
+
+    def is_finite(self) -> Self: ...
+    def is_first_distinct(self) -> Self: ...
+    def is_in(self, other: Any) -> Self: ...
+    def is_last_distinct(self) -> Self: ...
+    def is_nan(self) -> Self: ...
+    def is_null(self) -> Self: ...
+    def is_unique(self) -> Self: ...
+    def log(self, base: float) -> Self: ...
+    def mode(self, *, keep: ModeKeepStrategy) -> Self: ...
+    def rank(self, method: RankMethod, *, descending: bool) -> Self: ...
+    def replace_strict(
+        self,
+        old: Sequence[Any] | Mapping[Any, Any],
+        new: Sequence[Any],
+        *,
+        return_dtype: IntoDType | None,
+    ) -> Self: ...
+    def rolling_mean(
+        self, window_size: int, *, min_samples: int, center: bool
+    ) -> Self: ...
+    def rolling_std(
+        self, window_size: int, *, min_samples: int, center: bool, ddof: int
+    ) -> Self: ...
+    def rolling_sum(
+        self, window_size: int, *, min_samples: int, center: bool
+    ) -> Self: ...
+    def rolling_var(
+        self, window_size: int, *, min_samples: int, center: bool, ddof: int
+    ) -> Self: ...
+    def round(self, decimals: int) -> Self: ...
+    def shift(self, n: int) -> Self: ...
+    def unique(self) -> Self: ...
+
+    @property
+    def str(self) -> StringNamespace[Self]: ...
+    @property
+    def dt(self) -> DateTimeNamespace[Self]: ...
+    @property
+    def cat(self) -> CatNamespace[Self]: ...
+    @property
+    def list(self) -> ListNamespace[Self]: ...
+    @property
+    def struct(self) -> StructNamespace[Self]: ...

lib/python3.11/site-packages/narwhals/_compliant/dataframe.py

@@ -0,0 +1,426 @@
+from __future__ import annotations
+
+from collections.abc import Iterator, Mapping, Sequence, Sized
+from itertools import chain
+from typing import TYPE_CHECKING, Any, Literal, Protocol, TypeVar, overload
+
+from narwhals._compliant.typing import (
+    CompliantDataFrameAny,
+    CompliantExprT_contra,
+    CompliantLazyFrameAny,
+    CompliantSeriesT,
+    EagerExprT,
+    EagerSeriesT,
+    NativeDataFrameT,
+    NativeLazyFrameT,
+    NativeSeriesT,
+)
+from narwhals._translate import (
+    ArrowConvertible,
+    DictConvertible,
+    FromNative,
+    NumpyConvertible,
+    ToNarwhals,
+    ToNarwhalsT_co,
+)
+from narwhals._typing_compat import assert_never
+from narwhals._utils import (
+    ValidateBackendVersion,
+    Version,
+    _StoresNative,
+    check_columns_exist,
+    is_compliant_series,
+    is_index_selector,
+    is_range,
+    is_sequence_like,
+    is_sized_multi_index_selector,
+    is_slice_index,
+    is_slice_none,
+)
+
+if TYPE_CHECKING:
+    from io import BytesIO
+    from pathlib import Path
+    from types import ModuleType
+
+    import pandas as pd
+    import polars as pl
+    import pyarrow as pa
+    from typing_extensions import Self, TypeAlias
+
+    from narwhals._compliant.group_by import CompliantGroupBy, DataFrameGroupBy
+    from narwhals._compliant.namespace import EagerNamespace
+    from narwhals._spark_like.utils import SparkSession
+    from narwhals._translate import IntoArrowTable
+    from narwhals._typing import _EagerAllowedImpl, _LazyAllowedImpl
+    from narwhals._utils import Implementation, _LimitedContext
+    from narwhals.dataframe import DataFrame
+    from narwhals.dtypes import DType
+    from narwhals.exceptions import ColumnNotFoundError
+    from narwhals.typing import (
+        AsofJoinStrategy,
+        IntoSchema,
+        JoinStrategy,
+        LazyUniqueKeepStrategy,
+        MultiColSelector,
+        MultiIndexSelector,
+        PivotAgg,
+        SingleIndexSelector,
+        SizedMultiIndexSelector,
+        SizedMultiNameSelector,
+        SizeUnit,
+        UniqueKeepStrategy,
+        _2DArray,
+        _SliceIndex,
+        _SliceName,
+    )
+
+    Incomplete: TypeAlias = Any
+
+__all__ = ["CompliantDataFrame", "CompliantFrame", "CompliantLazyFrame", "EagerDataFrame"]
+
+T = TypeVar("T")
+
+_ToDict: TypeAlias = "dict[str, CompliantSeriesT] | dict[str, list[Any]]"  # noqa: PYI047
+
+_NativeFrameT = TypeVar("_NativeFrameT")
+
+
+class CompliantFrame(
+    _StoresNative[_NativeFrameT],
+    FromNative[_NativeFrameT],
+    ToNarwhals[ToNarwhalsT_co],
+    Protocol[CompliantExprT_contra, _NativeFrameT, ToNarwhalsT_co],
+):
+    """Common parts of `DataFrame`, `LazyFrame`."""
+
+    _native_frame: _NativeFrameT
+    _implementation: Implementation
+    _version: Version
+
+    def __native_namespace__(self) -> ModuleType: ...
+    def __narwhals_namespace__(self) -> Any: ...
+    def _with_version(self, version: Version) -> Self: ...
+    @classmethod
+    def from_native(cls, data: _NativeFrameT, /, *, context: _LimitedContext) -> Self: ...
+    @property
+    def columns(self) -> Sequence[str]: ...
+    @property
+    def native(self) -> _NativeFrameT:
+        return self._native_frame
+
+    @property
+    def schema(self) -> Mapping[str, DType]: ...
+
+    def collect_schema(self) -> Mapping[str, DType]: ...
+    def drop(self, columns: Sequence[str], *, strict: bool) -> Self: ...
+    def drop_nulls(self, subset: Sequence[str] | None) -> Self: ...
+    def explode(self, columns: Sequence[str]) -> Self: ...
+    def filter(self, predicate: CompliantExprT_contra | Incomplete) -> Self: ...
+    def group_by(
+        self,
+        keys: Sequence[str] | Sequence[CompliantExprT_contra],
+        *,
+        drop_null_keys: bool,
+    ) -> CompliantGroupBy[Self, CompliantExprT_contra]: ...
+    def head(self, n: int) -> Self: ...
+    def join(
+        self,
+        other: Self,
+        *,
+        how: JoinStrategy,
+        left_on: Sequence[str] | None,
+        right_on: Sequence[str] | None,
+        suffix: str,
+    ) -> Self: ...
+    def join_asof(
+        self,
+        other: Self,
+        *,
+        left_on: str,
+        right_on: str,
+        by_left: Sequence[str] | None,
+        by_right: Sequence[str] | None,
+        strategy: AsofJoinStrategy,
+        suffix: str,
+    ) -> Self: ...
+    def rename(self, mapping: Mapping[str, str]) -> Self: ...
+    def select(self, *exprs: CompliantExprT_contra) -> Self: ...
+    def simple_select(self, *column_names: str) -> Self:
+        """`select` where all args are column names."""
+        ...
+
+    def sort(
+        self, *by: str, descending: bool | Sequence[bool], nulls_last: bool
+    ) -> Self: ...
+    def tail(self, n: int) -> Self: ...
+    def unique(
+        self, subset: Sequence[str] | None, *, keep: LazyUniqueKeepStrategy
+    ) -> Self: ...
+    def unpivot(
+        self,
+        on: Sequence[str] | None,
+        index: Sequence[str] | None,
+        variable_name: str,
+        value_name: str,
+    ) -> Self: ...
+    def with_columns(self, *exprs: CompliantExprT_contra) -> Self: ...
+    def with_row_index(self, name: str, order_by: Sequence[str]) -> Self: ...
+
+
+class CompliantDataFrame(
+    NumpyConvertible["_2DArray", "_2DArray"],
+    DictConvertible["_ToDict[CompliantSeriesT]", Mapping[str, Any]],
+    ArrowConvertible["pa.Table", "IntoArrowTable"],
+    Sized,
+    CompliantFrame[CompliantExprT_contra, NativeDataFrameT, ToNarwhalsT_co],
+    Protocol[CompliantSeriesT, CompliantExprT_contra, NativeDataFrameT, ToNarwhalsT_co],
+):
+    def __narwhals_dataframe__(self) -> Self: ...
+    @classmethod
+    def from_arrow(cls, data: IntoArrowTable, /, *, context: _LimitedContext) -> Self: ...
+    @classmethod
+    def from_dict(
+        cls,
+        data: Mapping[str, Any],
+        /,
+        *,
+        context: _LimitedContext,
+        schema: IntoSchema | None,
+    ) -> Self: ...
+    @classmethod
+    def from_numpy(
+        cls,
+        data: _2DArray,
+        /,
+        *,
+        context: _LimitedContext,
+        schema: IntoSchema | Sequence[str] | None,
+    ) -> Self: ...
+    def __array__(self, dtype: Any, *, copy: bool | None) -> _2DArray: ...
+    def __getitem__(
+        self,
+        item: tuple[
+            SingleIndexSelector | MultiIndexSelector[CompliantSeriesT],
+            MultiColSelector[CompliantSeriesT],
+        ],
+    ) -> Self: ...
+
+    @property
+    def shape(self) -> tuple[int, int]: ...
+    def clone(self) -> Self: ...
+    def estimated_size(self, unit: SizeUnit) -> int | float: ...
+    def gather_every(self, n: int, offset: int) -> Self: ...
+    def get_column(self, name: str) -> CompliantSeriesT: ...
+    def group_by(
+        self,
+        keys: Sequence[str] | Sequence[CompliantExprT_contra],
+        *,
+        drop_null_keys: bool,
+    ) -> DataFrameGroupBy[Self, Any]: ...
+    def item(self, row: int | None, column: int | str | None) -> Any: ...
+    def iter_columns(self) -> Iterator[CompliantSeriesT]: ...
+    def iter_rows(
+        self, *, named: bool, buffer_size: int
+    ) -> Iterator[tuple[Any, ...]] | Iterator[Mapping[str, Any]]: ...
+    def is_unique(self) -> CompliantSeriesT: ...
+    def lazy(
+        self, backend: _LazyAllowedImpl | None, *, session: SparkSession | None
+    ) -> CompliantLazyFrameAny: ...
+    def pivot(
+        self,
+        on: Sequence[str],
+        *,
+        index: Sequence[str] | None,
+        values: Sequence[str] | None,
+        aggregate_function: PivotAgg | None,
+        sort_columns: bool,
+        separator: str,
+    ) -> Self: ...
+    def row(self, index: int) -> tuple[Any, ...]: ...
+    def rows(
+        self, *, named: bool
+    ) -> Sequence[tuple[Any, ...]] | Sequence[Mapping[str, Any]]: ...
+    def sample(
+        self,
+        n: int | None,
+        *,
+        fraction: float | None,
+        with_replacement: bool,
+        seed: int | None,
+    ) -> Self: ...
+    def to_arrow(self) -> pa.Table: ...
+    def to_pandas(self) -> pd.DataFrame: ...
+    def to_polars(self) -> pl.DataFrame: ...
+    @overload
+    def to_dict(self, *, as_series: Literal[True]) -> dict[str, CompliantSeriesT]: ...
+    @overload
+    def to_dict(self, *, as_series: Literal[False]) -> dict[str, list[Any]]: ...
+    def to_dict(
+        self, *, as_series: bool
+    ) -> dict[str, CompliantSeriesT] | dict[str, list[Any]]: ...
+    def unique(
+        self,
+        subset: Sequence[str] | None,
+        *,
+        keep: UniqueKeepStrategy,
+        maintain_order: bool | None = None,
+    ) -> Self: ...
+    def with_row_index(self, name: str, order_by: Sequence[str] | None) -> Self: ...
+    @overload
+    def write_csv(self, file: None) -> str: ...
+    @overload
+    def write_csv(self, file: str | Path | BytesIO) -> None: ...
+    def write_csv(self, file: str | Path | BytesIO | None) -> str | None: ...
+    def write_parquet(self, file: str | Path | BytesIO) -> None: ...
+
+
+class CompliantLazyFrame(
+    CompliantFrame[CompliantExprT_contra, NativeLazyFrameT, ToNarwhalsT_co],
+    Protocol[CompliantExprT_contra, NativeLazyFrameT, ToNarwhalsT_co],
+):
+    def __narwhals_lazyframe__(self) -> Self: ...
+    # `LazySelectorNamespace._iter_columns` depends
+    def _iter_columns(self) -> Iterator[Any]: ...
+    def aggregate(self, *exprs: CompliantExprT_contra) -> Self:
+        """`select` where all args are aggregations or literals.
+
+        (so, no broadcasting is necessary).
+        """
+        ...
+
+    def collect(
+        self, backend: _EagerAllowedImpl | None, **kwargs: Any
+    ) -> CompliantDataFrameAny: ...
+    def sink_parquet(self, file: str | Path | BytesIO) -> None: ...
+
+
+class EagerDataFrame(
+    CompliantDataFrame[
+        EagerSeriesT, EagerExprT, NativeDataFrameT, "DataFrame[NativeDataFrameT]"
+    ],
+    CompliantLazyFrame[EagerExprT, "Incomplete", "DataFrame[NativeDataFrameT]"],
+    ValidateBackendVersion,
+    Protocol[EagerSeriesT, EagerExprT, NativeDataFrameT, NativeSeriesT],
+):
+    @property
+    def _backend_version(self) -> tuple[int, ...]:
+        return self._implementation._backend_version()
+
+    def __narwhals_namespace__(
+        self,
+    ) -> EagerNamespace[
+        Self, EagerSeriesT, EagerExprT, NativeDataFrameT, NativeSeriesT
+    ]: ...
+
+    def to_narwhals(self) -> DataFrame[NativeDataFrameT]:
+        return self._version.dataframe(self, level="full")
+
+    def aggregate(self, *exprs: EagerExprT) -> Self:
+        # NOTE: Ignore intermittent [False Negative]
+        # Argument of type "EagerExprT@EagerDataFrame" cannot be assigned to parameter "exprs" of type "EagerExprT@EagerDataFrame" in function "select"
+        #  Type "EagerExprT@EagerDataFrame" is not assignable to type "EagerExprT@EagerDataFrame"
+        return self.select(*exprs)  # pyright: ignore[reportArgumentType]
+
+    def _with_native(
+        self, df: NativeDataFrameT, *, validate_column_names: bool = True
+    ) -> Self: ...
+
+    def _check_columns_exist(self, subset: Sequence[str]) -> ColumnNotFoundError | None:
+        return check_columns_exist(subset, available=self.columns)
+
+    def _evaluate_expr(self, expr: EagerExprT, /) -> EagerSeriesT:
+        """Evaluate `expr` and ensure it has a **single** output."""
+        result: Sequence[EagerSeriesT] = expr(self)
+        assert len(result) == 1  # debug assertion  # noqa: S101
+        return result[0]
+
+    def _evaluate_into_exprs(self, *exprs: EagerExprT) -> Sequence[EagerSeriesT]:
+        # NOTE: Ignore intermittent [False Negative]
+        # Argument of type "EagerExprT@EagerDataFrame" cannot be assigned to parameter "expr" of type "EagerExprT@EagerDataFrame" in function "_evaluate_into_expr"
+        #  Type "EagerExprT@EagerDataFrame" is not assignable to type "EagerExprT@EagerDataFrame"
+        return list(chain.from_iterable(self._evaluate_into_expr(expr) for expr in exprs))  # pyright: ignore[reportArgumentType]
+
+    def _evaluate_into_expr(self, expr: EagerExprT, /) -> Sequence[EagerSeriesT]:
+        """Return list of raw columns.
+
+        For eager backends we alias operations at each step.
+
+        As a safety precaution, here we can check that the expected result names match those
+        we were expecting from the various `evaluate_output_names` / `alias_output_names` calls.
+
+        Note that for PySpark / DuckDB, we are less free to liberally set aliases whenever we want.
+        """
+        aliases = expr._evaluate_aliases(self)
+        result = expr(self)
+        if list(aliases) != (
+            result_aliases := [s.name for s in result]
+        ):  # pragma: no cover
+            msg = f"Safety assertion failed, expected {aliases}, got {result_aliases}"
+            raise AssertionError(msg)
+        return result
+
+    def _extract_comparand(self, other: EagerSeriesT, /) -> Any:
+        """Extract native Series, broadcasting to `len(self)` if necessary."""
+        ...
+
+    @staticmethod
+    def _numpy_column_names(
+        data: _2DArray, columns: Sequence[str] | None, /
+    ) -> list[str]:
+        return list(columns or (f"column_{x}" for x in range(data.shape[1])))
+
+    def _gather(self, rows: SizedMultiIndexSelector[NativeSeriesT]) -> Self: ...
+    def _gather_slice(self, rows: _SliceIndex | range) -> Self: ...
+    def _select_multi_index(
+        self, columns: SizedMultiIndexSelector[NativeSeriesT]
+    ) -> Self: ...
+    def _select_multi_name(
+        self, columns: SizedMultiNameSelector[NativeSeriesT]
+    ) -> Self: ...
+    def _select_slice_index(self, columns: _SliceIndex | range) -> Self: ...
+    def _select_slice_name(self, columns: _SliceName) -> Self: ...
+    def __getitem__(  # noqa: C901, PLR0912
+        self,
+        item: tuple[
+            SingleIndexSelector | MultiIndexSelector[EagerSeriesT],
+            MultiColSelector[EagerSeriesT],
+        ],
+    ) -> Self:
+        rows, columns = item
+        compliant = self
+        if not is_slice_none(columns):
+            if isinstance(columns, Sized) and len(columns) == 0:
+                return compliant.select()
+            if is_index_selector(columns):
+                if is_slice_index(columns) or is_range(columns):
+                    compliant = compliant._select_slice_index(columns)
+                elif is_compliant_series(columns):
+                    compliant = self._select_multi_index(columns.native)
+                else:
+                    compliant = compliant._select_multi_index(columns)
+            elif isinstance(columns, slice):
+                compliant = compliant._select_slice_name(columns)
+            elif is_compliant_series(columns):
+                compliant = self._select_multi_name(columns.native)
+            elif is_sequence_like(columns):
+                compliant = self._select_multi_name(columns)
+            else:
+                assert_never(columns)
+
+        if not is_slice_none(rows):
+            if isinstance(rows, int):
+                compliant = compliant._gather([rows])
+            elif isinstance(rows, (slice, range)):
+                compliant = compliant._gather_slice(rows)
+            elif is_compliant_series(rows):
+                compliant = compliant._gather(rows.native)
+            elif is_sized_multi_index_selector(rows):
+                compliant = compliant._gather(rows)
+            else:
+                assert_never(rows)
+
+        return compliant
+
+    def sink_parquet(self, file: str | Path | BytesIO) -> None:
+        return self.write_parquet(file)

lib/python3.11/site-packages/narwhals/_compliant/group_by.py

@@ -0,0 +1,180 @@
+from __future__ import annotations
+
+import re
+from itertools import chain
+from typing import TYPE_CHECKING, Any, Callable, ClassVar, Protocol, TypeVar
+
+from narwhals._compliant.typing import (
+    CompliantDataFrameT,
+    CompliantDataFrameT_co,
+    CompliantExprT_contra,
+    CompliantFrameT,
+    CompliantFrameT_co,
+    DepthTrackingExprAny,
+    DepthTrackingExprT_contra,
+    EagerExprT_contra,
+    ImplExprT_contra,
+    NarwhalsAggregation,
+)
+from narwhals._utils import is_sequence_of, zip_strict
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Iterator, Mapping, Sequence
+
+    from narwhals._compliant.expr import ImplExpr
+
+
+__all__ = ["CompliantGroupBy", "DepthTrackingGroupBy", "EagerGroupBy"]
+
+NativeAggregationT_co = TypeVar(
+    "NativeAggregationT_co", bound="str | Callable[..., Any]", covariant=True
+)
+
+
+_RE_LEAF_NAME: re.Pattern[str] = re.compile(r"(\w+->)")
+
+
+def _evaluate_aliases(
+    frame: CompliantFrameT, exprs: Iterable[ImplExpr[CompliantFrameT, Any]], /
+) -> list[str]:
+    it = (expr._evaluate_aliases(frame) for expr in exprs)
+    return list(chain.from_iterable(it))
+
+
+class CompliantGroupBy(Protocol[CompliantFrameT_co, CompliantExprT_contra]):
+    _compliant_frame: Any
+
+    @property
+    def compliant(self) -> CompliantFrameT_co:
+        return self._compliant_frame  # type: ignore[no-any-return]
+
+    def __init__(
+        self,
+        compliant_frame: CompliantFrameT_co,
+        keys: Sequence[CompliantExprT_contra] | Sequence[str],
+        /,
+        *,
+        drop_null_keys: bool,
+    ) -> None: ...
+
+    def agg(self, *exprs: CompliantExprT_contra) -> CompliantFrameT_co: ...
+
+
+class DataFrameGroupBy(
+    CompliantGroupBy[CompliantDataFrameT_co, CompliantExprT_contra],
+    Protocol[CompliantDataFrameT_co, CompliantExprT_contra],
+):
+    def __iter__(self) -> Iterator[tuple[Any, CompliantDataFrameT_co]]: ...
+
+
+class ParseKeysGroupBy(
+    CompliantGroupBy[CompliantFrameT, ImplExprT_contra],
+    Protocol[CompliantFrameT, ImplExprT_contra],
+):
+    def _parse_keys(
+        self,
+        compliant_frame: CompliantFrameT,
+        keys: Sequence[ImplExprT_contra] | Sequence[str],
+    ) -> tuple[CompliantFrameT, list[str], list[str]]:
+        if is_sequence_of(keys, str):
+            keys_str = list(keys)
+            return compliant_frame, keys_str, keys_str.copy()
+        return self._parse_expr_keys(compliant_frame, keys=keys)
+
+    @staticmethod
+    def _parse_expr_keys(
+        compliant_frame: CompliantFrameT, keys: Sequence[ImplExprT_contra]
+    ) -> tuple[CompliantFrameT, list[str], list[str]]:
+        """Parses key expressions to set up `.agg` operation with correct information.
+
+        Since keys are expressions, it's possible to alias any such key to match
+        other dataframe column names.
+
+        In order to match polars behavior and not overwrite columns when evaluating keys:
+
+        - We evaluate what the output key names should be, in order to remap temporary column
+            names to the expected ones, and to exclude those from unnamed expressions in
+            `.agg(...)` context (see https://github.com/narwhals-dev/narwhals/pull/2325#issuecomment-2800004520)
+        - Create temporary names for evaluated key expressions that are guaranteed to have
+            no overlap with any existing column name.
+        - Add these temporary columns to the compliant dataframe.
+        """
+        tmp_name_length = max(len(str(c)) for c in compliant_frame.columns) + 1
+
+        def _temporary_name(key: str) -> str:
+            # 5 is the length of `__tmp`
+            key_str = str(key)  # pandas allows non-string column names :sob:
+            return f"_{key_str}_tmp{'_' * (tmp_name_length - len(key_str) - 5)}"
+
+        keys_aliases = [expr._evaluate_aliases(compliant_frame) for expr in keys]
+        safe_keys = [
+            # multi-output expression cannot have duplicate names, hence it's safe to suffix
+            key.name.map(_temporary_name)
+            if (metadata := key._metadata) and metadata.expansion_kind.is_multi_output()
+            # otherwise it's single named and we can use Expr.alias
+            else key.alias(_temporary_name(new_names[0]))
+            for key, new_names in zip_strict(keys, keys_aliases)
+        ]
+        return (
+            compliant_frame.with_columns(*safe_keys),
+            _evaluate_aliases(compliant_frame, safe_keys),
+            list(chain.from_iterable(keys_aliases)),
+        )
+
+
+class DepthTrackingGroupBy(
+    ParseKeysGroupBy[CompliantFrameT, DepthTrackingExprT_contra],
+    Protocol[CompliantFrameT, DepthTrackingExprT_contra, NativeAggregationT_co],
+):
+    """`CompliantGroupBy` variant, deals with `Eager` and other backends that utilize `CompliantExpr._depth`."""
+
+    _REMAP_AGGS: ClassVar[Mapping[NarwhalsAggregation, Any]]
+    """Mapping from `narwhals` to native representation.
+
+    Note:
+    - `Dask` *may* return a `Callable` instead of a `str` referring to one.
+    """
+
+    def _ensure_all_simple(self, exprs: Sequence[DepthTrackingExprT_contra]) -> None:
+        for expr in exprs:
+            if not self._is_simple(expr):
+                name = self.compliant._implementation.name.lower()
+                msg = (
+                    f"Non-trivial complex aggregation found.\n\n"
+                    f"Hint: you were probably trying to apply a non-elementary aggregation with a"
+                    f"{name!r} table.\n"
+                    "Please rewrite your query such that group-by aggregations "
+                    "are elementary. For example, instead of:\n\n"
+                    "    df.group_by('a').agg(nw.col('b').round(2).mean())\n\n"
+                    "use:\n\n"
+                    "    df.with_columns(nw.col('b').round(2)).group_by('a').agg(nw.col('b').mean())\n\n"
+                )
+                raise ValueError(msg)
+
+    @classmethod
+    def _is_simple(cls, expr: DepthTrackingExprAny, /) -> bool:
+        """Return `True` is we can efficiently use `expr` in a native `group_by` context."""
+        return expr._is_elementary() and cls._leaf_name(expr) in cls._REMAP_AGGS
+
+    @classmethod
+    def _remap_expr_name(
+        cls, name: NarwhalsAggregation | Any, /
+    ) -> NativeAggregationT_co:
+        """Replace `name`, with some native representation.
+
+        Arguments:
+            name: Name of a `nw.Expr` aggregation method.
+        """
+        return cls._REMAP_AGGS.get(name, name)
+
+    @classmethod
+    def _leaf_name(cls, expr: DepthTrackingExprAny, /) -> NarwhalsAggregation | Any:
+        """Return the last function name in the chain defined by `expr`."""
+        return _RE_LEAF_NAME.sub("", expr._function_name)
+
+
+class EagerGroupBy(
+    DepthTrackingGroupBy[CompliantDataFrameT, EagerExprT_contra, NativeAggregationT_co],
+    DataFrameGroupBy[CompliantDataFrameT, EagerExprT_contra],
+    Protocol[CompliantDataFrameT, EagerExprT_contra, NativeAggregationT_co],
+): ...

lib/python3.11/site-packages/narwhals/_compliant/namespace.py

@@ -0,0 +1,238 @@
+from __future__ import annotations
+
+from functools import partial
+from typing import TYPE_CHECKING, Any, Protocol, overload
+
+from narwhals._compliant.typing import (
+    CompliantExprT,
+    CompliantFrameT,
+    CompliantLazyFrameT,
+    DepthTrackingExprT,
+    EagerDataFrameT,
+    EagerExprT,
+    EagerSeriesT,
+    LazyExprT,
+    NativeFrameT,
+    NativeFrameT_co,
+    NativeSeriesT,
+)
+from narwhals._expression_parsing import is_expr, is_series
+from narwhals._utils import (
+    exclude_column_names,
+    get_column_names,
+    passthrough_column_names,
+)
+from narwhals.dependencies import is_numpy_array, is_numpy_array_2d
+
+if TYPE_CHECKING:
+    from collections.abc import Container, Iterable, Sequence
+
+    from typing_extensions import TypeAlias
+
+    from narwhals._compliant.selectors import CompliantSelectorNamespace
+    from narwhals._compliant.when_then import CompliantWhen, EagerWhen
+    from narwhals._utils import Implementation, Version
+    from narwhals.expr import Expr
+    from narwhals.series import Series
+    from narwhals.typing import (
+        ConcatMethod,
+        Into1DArray,
+        IntoDType,
+        IntoSchema,
+        NonNestedLiteral,
+        _1DArray,
+        _2DArray,
+    )
+
+    Incomplete: TypeAlias = Any
+
+__all__ = [
+    "CompliantNamespace",
+    "DepthTrackingNamespace",
+    "EagerNamespace",
+    "LazyNamespace",
+]
+
+
+class CompliantNamespace(Protocol[CompliantFrameT, CompliantExprT]):
+    # NOTE: `narwhals`
+    _implementation: Implementation
+    _version: Version
+
+    @property
+    def _expr(self) -> type[CompliantExprT]: ...
+    def parse_into_expr(
+        self, data: Expr | NonNestedLiteral | Any, /, *, str_as_lit: bool
+    ) -> CompliantExprT | NonNestedLiteral:
+        if is_expr(data):
+            expr = data._to_compliant_expr(self)
+            assert isinstance(expr, self._expr)  # noqa: S101
+            return expr
+        if isinstance(data, str) and not str_as_lit:
+            return self.col(data)
+        return data
+
+    # NOTE: `polars`
+    def all(self) -> CompliantExprT:
+        return self._expr.from_column_names(get_column_names, context=self)
+
+    def col(self, *column_names: str) -> CompliantExprT:
+        return self._expr.from_column_names(
+            passthrough_column_names(column_names), context=self
+        )
+
+    def exclude(self, excluded_names: Container[str]) -> CompliantExprT:
+        return self._expr.from_column_names(
+            partial(exclude_column_names, names=excluded_names), context=self
+        )
+
+    def nth(self, *column_indices: int) -> CompliantExprT:
+        return self._expr.from_column_indices(*column_indices, context=self)
+
+    def len(self) -> CompliantExprT: ...
+    def lit(self, value: NonNestedLiteral, dtype: IntoDType | None) -> CompliantExprT: ...
+    def all_horizontal(
+        self, *exprs: CompliantExprT, ignore_nulls: bool
+    ) -> CompliantExprT: ...
+    def any_horizontal(
+        self, *exprs: CompliantExprT, ignore_nulls: bool
+    ) -> CompliantExprT: ...
+    def sum_horizontal(self, *exprs: CompliantExprT) -> CompliantExprT: ...
+    def mean_horizontal(self, *exprs: CompliantExprT) -> CompliantExprT: ...
+    def min_horizontal(self, *exprs: CompliantExprT) -> CompliantExprT: ...
+    def max_horizontal(self, *exprs: CompliantExprT) -> CompliantExprT: ...
+    def concat(
+        self, items: Iterable[CompliantFrameT], *, how: ConcatMethod
+    ) -> CompliantFrameT: ...
+    def when(
+        self, predicate: CompliantExprT
+    ) -> CompliantWhen[CompliantFrameT, Incomplete, CompliantExprT]: ...
+    def concat_str(
+        self, *exprs: CompliantExprT, separator: str, ignore_nulls: bool
+    ) -> CompliantExprT: ...
+    @property
+    def selectors(self) -> CompliantSelectorNamespace[Any, Any]: ...
+    def coalesce(self, *exprs: CompliantExprT) -> CompliantExprT: ...
+
+
+class DepthTrackingNamespace(
+    CompliantNamespace[CompliantFrameT, DepthTrackingExprT],
+    Protocol[CompliantFrameT, DepthTrackingExprT],
+):
+    def all(self) -> DepthTrackingExprT:
+        return self._expr.from_column_names(
+            get_column_names, function_name="all", context=self
+        )
+
+    def col(self, *column_names: str) -> DepthTrackingExprT:
+        return self._expr.from_column_names(
+            passthrough_column_names(column_names), function_name="col", context=self
+        )
+
+    def exclude(self, excluded_names: Container[str]) -> DepthTrackingExprT:
+        return self._expr.from_column_names(
+            partial(exclude_column_names, names=excluded_names),
+            function_name="exclude",
+            context=self,
+        )
+
+
+class LazyNamespace(
+    CompliantNamespace[CompliantLazyFrameT, LazyExprT],
+    Protocol[CompliantLazyFrameT, LazyExprT, NativeFrameT_co],
+):
+    @property
+    def _backend_version(self) -> tuple[int, ...]:
+        return self._implementation._backend_version()
+
+    @property
+    def _lazyframe(self) -> type[CompliantLazyFrameT]: ...
+
+    def from_native(self, data: NativeFrameT_co | Any, /) -> CompliantLazyFrameT:
+        if self._lazyframe._is_native(data):
+            return self._lazyframe.from_native(data, context=self)
+        msg = f"Unsupported type: {type(data).__name__!r}"  # pragma: no cover
+        raise TypeError(msg)
+
+
+class EagerNamespace(
+    DepthTrackingNamespace[EagerDataFrameT, EagerExprT],
+    Protocol[EagerDataFrameT, EagerSeriesT, EagerExprT, NativeFrameT, NativeSeriesT],
+):
+    @property
+    def _backend_version(self) -> tuple[int, ...]:
+        return self._implementation._backend_version()
+
+    @property
+    def _dataframe(self) -> type[EagerDataFrameT]: ...
+    @property
+    def _series(self) -> type[EagerSeriesT]: ...
+    def when(
+        self, predicate: EagerExprT
+    ) -> EagerWhen[EagerDataFrameT, EagerSeriesT, EagerExprT, NativeSeriesT]: ...
+
+    @overload
+    def from_native(self, data: NativeFrameT, /) -> EagerDataFrameT: ...
+    @overload
+    def from_native(self, data: NativeSeriesT, /) -> EagerSeriesT: ...
+    def from_native(
+        self, data: NativeFrameT | NativeSeriesT | Any, /
+    ) -> EagerDataFrameT | EagerSeriesT:
+        if self._dataframe._is_native(data):
+            return self._dataframe.from_native(data, context=self)
+        if self._series._is_native(data):
+            return self._series.from_native(data, context=self)
+        msg = f"Unsupported type: {type(data).__name__!r}"
+        raise TypeError(msg)
+
+    def parse_into_expr(
+        self,
+        data: Expr | Series[NativeSeriesT] | _1DArray | NonNestedLiteral,
+        /,
+        *,
+        str_as_lit: bool,
+    ) -> EagerExprT | NonNestedLiteral:
+        if not (is_series(data) or is_numpy_array(data)):
+            return super().parse_into_expr(data, str_as_lit=str_as_lit)
+        return self._expr._from_series(
+            data._compliant_series
+            if is_series(data)
+            else self._series.from_numpy(data, context=self)
+        )
+
+    @overload
+    def from_numpy(self, data: Into1DArray, /, schema: None = ...) -> EagerSeriesT: ...
+
+    @overload
+    def from_numpy(
+        self, data: _2DArray, /, schema: IntoSchema | Sequence[str] | None
+    ) -> EagerDataFrameT: ...
+
+    def from_numpy(
+        self,
+        data: Into1DArray | _2DArray,
+        /,
+        schema: IntoSchema | Sequence[str] | None = None,
+    ) -> EagerDataFrameT | EagerSeriesT:
+        if is_numpy_array_2d(data):
+            return self._dataframe.from_numpy(data, schema=schema, context=self)
+        return self._series.from_numpy(data, context=self)
+
+    def _concat_diagonal(self, dfs: Sequence[NativeFrameT], /) -> NativeFrameT: ...
+    def _concat_horizontal(
+        self, dfs: Sequence[NativeFrameT | Any], /
+    ) -> NativeFrameT: ...
+    def _concat_vertical(self, dfs: Sequence[NativeFrameT], /) -> NativeFrameT: ...
+    def concat(
+        self, items: Iterable[EagerDataFrameT], *, how: ConcatMethod
+    ) -> EagerDataFrameT:
+        dfs = [item.native for item in items]
+        if how == "horizontal":
+            native = self._concat_horizontal(dfs)
+        elif how == "vertical":
+            native = self._concat_vertical(dfs)
+        elif how == "diagonal":
+            native = self._concat_diagonal(dfs)
+        else:  # pragma: no cover
+            raise NotImplementedError
+        return self._dataframe.from_native(native, context=self)

lib/python3.11/site-packages/narwhals/_compliant/selectors.py

@@ -0,0 +1,318 @@
+"""Almost entirely complete, generic `selectors` implementation."""
+
+from __future__ import annotations
+
+import re
+from functools import partial
+from typing import TYPE_CHECKING, Protocol, TypeVar, overload
+
+from narwhals._compliant.expr import CompliantExpr
+from narwhals._utils import (
+    _parse_time_unit_and_time_zone,
+    dtype_matches_time_unit_and_time_zone,
+    get_column_names,
+    is_compliant_dataframe,
+    zip_strict,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Collection, Iterable, Iterator, Sequence
+    from datetime import timezone
+
+    from typing_extensions import Self, TypeAlias, TypeIs
+
+    from narwhals._compliant.expr import NativeExpr
+    from narwhals._compliant.typing import (
+        CompliantDataFrameAny,
+        CompliantExprAny,
+        CompliantFrameAny,
+        CompliantLazyFrameAny,
+        CompliantSeriesAny,
+        CompliantSeriesOrNativeExprAny,
+        EvalNames,
+        EvalSeries,
+    )
+    from narwhals._utils import Implementation, Version, _LimitedContext
+    from narwhals.dtypes import DType
+    from narwhals.typing import TimeUnit
+
+__all__ = [
+    "CompliantSelector",
+    "CompliantSelectorNamespace",
+    "EagerSelectorNamespace",
+    "LazySelectorNamespace",
+]
+
+
+SeriesOrExprT = TypeVar("SeriesOrExprT", bound="CompliantSeriesOrNativeExprAny")
+SeriesT = TypeVar("SeriesT", bound="CompliantSeriesAny")
+ExprT = TypeVar("ExprT", bound="NativeExpr")
+FrameT = TypeVar("FrameT", bound="CompliantFrameAny")
+DataFrameT = TypeVar("DataFrameT", bound="CompliantDataFrameAny")
+LazyFrameT = TypeVar("LazyFrameT", bound="CompliantLazyFrameAny")
+SelectorOrExpr: TypeAlias = (
+    "CompliantSelector[FrameT, SeriesOrExprT] | CompliantExpr[FrameT, SeriesOrExprT]"
+)
+
+
+class CompliantSelectorNamespace(Protocol[FrameT, SeriesOrExprT]):
+    # NOTE: `narwhals`
+    _implementation: Implementation
+    _version: Version
+
+    @property
+    def _selector(self) -> type[CompliantSelector[FrameT, SeriesOrExprT]]: ...
+    @classmethod
+    def from_namespace(cls, context: _LimitedContext, /) -> Self:
+        obj = cls.__new__(cls)
+        obj._implementation = context._implementation
+        obj._version = context._version
+        return obj
+
+    def _iter_columns(self, df: FrameT, /) -> Iterator[SeriesOrExprT]: ...
+    def _iter_schema(self, df: FrameT, /) -> Iterator[tuple[str, DType]]: ...
+    def _iter_columns_dtypes(
+        self, df: FrameT, /
+    ) -> Iterator[tuple[SeriesOrExprT, DType]]: ...
+    def _iter_columns_names(self, df: FrameT, /) -> Iterator[tuple[SeriesOrExprT, str]]:
+        yield from zip_strict(self._iter_columns(df), df.columns)
+
+    def _is_dtype(
+        self: CompliantSelectorNamespace[FrameT, SeriesOrExprT], dtype: type[DType], /
+    ) -> CompliantSelector[FrameT, SeriesOrExprT]:
+        def series(df: FrameT) -> Sequence[SeriesOrExprT]:
+            return [
+                ser for ser, tp in self._iter_columns_dtypes(df) if isinstance(tp, dtype)
+            ]
+
+        def names(df: FrameT) -> Sequence[str]:
+            return [name for name, tp in self._iter_schema(df) if isinstance(tp, dtype)]
+
+        return self._selector.from_callables(series, names, context=self)
+
+    # NOTE: `polars`
+    def by_dtype(
+        self, dtypes: Collection[DType | type[DType]]
+    ) -> CompliantSelector[FrameT, SeriesOrExprT]:
+        def series(df: FrameT) -> Sequence[SeriesOrExprT]:
+            return [ser for ser, tp in self._iter_columns_dtypes(df) if tp in dtypes]
+
+        def names(df: FrameT) -> Sequence[str]:
+            return [name for name, tp in self._iter_schema(df) if tp in dtypes]
+
+        return self._selector.from_callables(series, names, context=self)
+
+    def matches(self, pattern: str) -> CompliantSelector[FrameT, SeriesOrExprT]:
+        p = re.compile(pattern)
+
+        def series(df: FrameT) -> Sequence[SeriesOrExprT]:
+            if (
+                is_compliant_dataframe(df)
+                and not self._implementation.is_duckdb()
+                and not self._implementation.is_ibis()
+            ):
+                return [df.get_column(col) for col in df.columns if p.search(col)]
+
+            return [ser for ser, name in self._iter_columns_names(df) if p.search(name)]
+
+        def names(df: FrameT) -> Sequence[str]:
+            return [col for col in df.columns if p.search(col)]
+
+        return self._selector.from_callables(series, names, context=self)
+
+    def numeric(self) -> CompliantSelector[FrameT, SeriesOrExprT]:
+        def series(df: FrameT) -> Sequence[SeriesOrExprT]:
+            return [ser for ser, tp in self._iter_columns_dtypes(df) if tp.is_numeric()]
+
+        def names(df: FrameT) -> Sequence[str]:
+            return [name for name, tp in self._iter_schema(df) if tp.is_numeric()]
+
+        return self._selector.from_callables(series, names, context=self)
+
+    def categorical(self) -> CompliantSelector[FrameT, SeriesOrExprT]:
+        return self._is_dtype(self._version.dtypes.Categorical)
+
+    def string(self) -> CompliantSelector[FrameT, SeriesOrExprT]:
+        return self._is_dtype(self._version.dtypes.String)
+
+    def boolean(self) -> CompliantSelector[FrameT, SeriesOrExprT]:
+        return self._is_dtype(self._version.dtypes.Boolean)
+
+    def all(self) -> CompliantSelector[FrameT, SeriesOrExprT]:
+        def series(df: FrameT) -> Sequence[SeriesOrExprT]:
+            return list(self._iter_columns(df))
+
+        return self._selector.from_callables(series, get_column_names, context=self)
+
+    def datetime(
+        self,
+        time_unit: TimeUnit | Iterable[TimeUnit] | None,
+        time_zone: str | timezone | Iterable[str | timezone | None] | None,
+    ) -> CompliantSelector[FrameT, SeriesOrExprT]:
+        time_units, time_zones = _parse_time_unit_and_time_zone(time_unit, time_zone)
+        matches = partial(
+            dtype_matches_time_unit_and_time_zone,
+            dtypes=self._version.dtypes,
+            time_units=time_units,
+            time_zones=time_zones,
+        )
+
+        def series(df: FrameT) -> Sequence[SeriesOrExprT]:
+            return [ser for ser, tp in self._iter_columns_dtypes(df) if matches(tp)]
+
+        def names(df: FrameT) -> Sequence[str]:
+            return [name for name, tp in self._iter_schema(df) if matches(tp)]
+
+        return self._selector.from_callables(series, names, context=self)
+
+
+class EagerSelectorNamespace(
+    CompliantSelectorNamespace[DataFrameT, SeriesT], Protocol[DataFrameT, SeriesT]
+):
+    def _iter_schema(self, df: DataFrameT, /) -> Iterator[tuple[str, DType]]:
+        for ser in self._iter_columns(df):
+            yield ser.name, ser.dtype
+
+    def _iter_columns(self, df: DataFrameT, /) -> Iterator[SeriesT]:
+        yield from df.iter_columns()
+
+    def _iter_columns_dtypes(self, df: DataFrameT, /) -> Iterator[tuple[SeriesT, DType]]:
+        for ser in self._iter_columns(df):
+            yield ser, ser.dtype
+
+
+class LazySelectorNamespace(
+    CompliantSelectorNamespace[LazyFrameT, ExprT], Protocol[LazyFrameT, ExprT]
+):
+    def _iter_schema(self, df: LazyFrameT) -> Iterator[tuple[str, DType]]:
+        yield from df.schema.items()
+
+    def _iter_columns(self, df: LazyFrameT) -> Iterator[ExprT]:
+        yield from df._iter_columns()
+
+    def _iter_columns_dtypes(self, df: LazyFrameT, /) -> Iterator[tuple[ExprT, DType]]:
+        yield from zip_strict(self._iter_columns(df), df.schema.values())
+
+
+class CompliantSelector(
+    CompliantExpr[FrameT, SeriesOrExprT], Protocol[FrameT, SeriesOrExprT]
+):
+    _call: EvalSeries[FrameT, SeriesOrExprT]
+    _function_name: str
+    _implementation: Implementation
+    _version: Version
+
+    @classmethod
+    def from_callables(
+        cls,
+        call: EvalSeries[FrameT, SeriesOrExprT],
+        evaluate_output_names: EvalNames[FrameT],
+        *,
+        context: _LimitedContext,
+    ) -> Self:
+        obj = cls.__new__(cls)
+        obj._call = call
+        obj._evaluate_output_names = evaluate_output_names
+        obj._alias_output_names = None
+        obj._implementation = context._implementation
+        obj._version = context._version
+        return obj
+
+    @property
+    def selectors(self) -> CompliantSelectorNamespace[FrameT, SeriesOrExprT]:
+        return self.__narwhals_namespace__().selectors
+
+    def _to_expr(self) -> CompliantExpr[FrameT, SeriesOrExprT]: ...
+
+    def _is_selector(
+        self, other: Self | CompliantExpr[FrameT, SeriesOrExprT]
+    ) -> TypeIs[CompliantSelector[FrameT, SeriesOrExprT]]:
+        return isinstance(other, type(self))
+
+    @overload
+    def __sub__(self, other: Self) -> Self: ...
+    @overload
+    def __sub__(
+        self, other: CompliantExpr[FrameT, SeriesOrExprT]
+    ) -> CompliantExpr[FrameT, SeriesOrExprT]: ...
+    def __sub__(
+        self, other: SelectorOrExpr[FrameT, SeriesOrExprT]
+    ) -> SelectorOrExpr[FrameT, SeriesOrExprT]:
+        if self._is_selector(other):
+
+            def series(df: FrameT) -> Sequence[SeriesOrExprT]:
+                lhs_names, rhs_names = _eval_lhs_rhs(df, self, other)
+                return [
+                    x
+                    for x, name in zip_strict(self(df), lhs_names)
+                    if name not in rhs_names
+                ]
+
+            def names(df: FrameT) -> Sequence[str]:
+                lhs_names, rhs_names = _eval_lhs_rhs(df, self, other)
+                return [x for x in lhs_names if x not in rhs_names]
+
+            return self.selectors._selector.from_callables(series, names, context=self)
+        return self._to_expr() - other
+
+    @overload
+    def __or__(self, other: Self) -> Self: ...
+    @overload
+    def __or__(
+        self, other: CompliantExpr[FrameT, SeriesOrExprT]
+    ) -> CompliantExpr[FrameT, SeriesOrExprT]: ...
+    def __or__(
+        self, other: SelectorOrExpr[FrameT, SeriesOrExprT]
+    ) -> SelectorOrExpr[FrameT, SeriesOrExprT]:
+        if self._is_selector(other):
+
+            def series(df: FrameT) -> Sequence[SeriesOrExprT]:
+                lhs_names, rhs_names = _eval_lhs_rhs(df, self, other)
+                return [
+                    *(
+                        x
+                        for x, name in zip_strict(self(df), lhs_names)
+                        if name not in rhs_names
+                    ),
+                    *other(df),
+                ]
+
+            def names(df: FrameT) -> Sequence[str]:
+                lhs_names, rhs_names = _eval_lhs_rhs(df, self, other)
+                return [*(x for x in lhs_names if x not in rhs_names), *rhs_names]
+
+            return self.selectors._selector.from_callables(series, names, context=self)
+        return self._to_expr() | other
+
+    @overload
+    def __and__(self, other: Self) -> Self: ...
+    @overload
+    def __and__(
+        self, other: CompliantExpr[FrameT, SeriesOrExprT]
+    ) -> CompliantExpr[FrameT, SeriesOrExprT]: ...
+    def __and__(
+        self, other: SelectorOrExpr[FrameT, SeriesOrExprT]
+    ) -> SelectorOrExpr[FrameT, SeriesOrExprT]:
+        if self._is_selector(other):
+
+            def series(df: FrameT) -> Sequence[SeriesOrExprT]:
+                lhs_names, rhs_names = _eval_lhs_rhs(df, self, other)
+                return [
+                    x for x, name in zip_strict(self(df), lhs_names) if name in rhs_names
+                ]
+
+            def names(df: FrameT) -> Sequence[str]:
+                lhs_names, rhs_names = _eval_lhs_rhs(df, self, other)
+                return [x for x in lhs_names if x in rhs_names]
+
+            return self.selectors._selector.from_callables(series, names, context=self)
+        return self._to_expr() & other
+
+    def __invert__(self) -> CompliantSelector[FrameT, SeriesOrExprT]:
+        return self.selectors.all() - self
+
+
+def _eval_lhs_rhs(
+    df: CompliantFrameAny, lhs: CompliantExprAny, rhs: CompliantExprAny
+) -> tuple[Sequence[str], Sequence[str]]:
+    return lhs._evaluate_output_names(df), rhs._evaluate_output_names(df)

lib/python3.11/site-packages/narwhals/_compliant/series.py

@@ -0,0 +1,411 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Generic, Literal, Protocol
+
+from narwhals._compliant.any_namespace import (
+    CatNamespace,
+    DateTimeNamespace,
+    ListNamespace,
+    StringNamespace,
+    StructNamespace,
+)
+from narwhals._compliant.column import CompliantColumn
+from narwhals._compliant.typing import (
+    CompliantSeriesT_co,
+    EagerDataFrameAny,
+    EagerSeriesT_co,
+    NativeSeriesT,
+    NativeSeriesT_co,
+)
+from narwhals._translate import FromIterable, FromNative, NumpyConvertible, ToNarwhals
+from narwhals._typing_compat import TypeVar, assert_never
+from narwhals._utils import (
+    _StoresCompliant,
+    _StoresNative,
+    is_compliant_series,
+    is_sized_multi_index_selector,
+    unstable,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Iterator, Sequence
+    from types import ModuleType
+
+    import pandas as pd
+    import polars as pl
+    import pyarrow as pa
+    from typing_extensions import NotRequired, Self, TypedDict
+
+    from narwhals._compliant.dataframe import CompliantDataFrame
+    from narwhals._compliant.namespace import EagerNamespace
+    from narwhals._utils import Implementation, Version, _LimitedContext
+    from narwhals.dtypes import DType
+    from narwhals.series import Series
+    from narwhals.typing import (
+        Into1DArray,
+        IntoDType,
+        MultiIndexSelector,
+        RollingInterpolationMethod,
+        SizedMultiIndexSelector,
+        _1DArray,
+        _SliceIndex,
+    )
+
+    class HistData(TypedDict, Generic[NativeSeriesT, "_CountsT_co"]):
+        breakpoint: NotRequired[list[float] | _1DArray | list[Any]]
+        count: NativeSeriesT | _1DArray | _CountsT_co | list[Any]
+
+
+_CountsT_co = TypeVar("_CountsT_co", bound="Iterable[Any]", covariant=True)
+
+__all__ = [
+    "CompliantSeries",
+    "EagerSeries",
+    "EagerSeriesCatNamespace",
+    "EagerSeriesDateTimeNamespace",
+    "EagerSeriesHist",
+    "EagerSeriesListNamespace",
+    "EagerSeriesNamespace",
+    "EagerSeriesStringNamespace",
+    "EagerSeriesStructNamespace",
+]
+
+
+class CompliantSeries(
+    NumpyConvertible["_1DArray", "Into1DArray"],
+    FromIterable,
+    FromNative[NativeSeriesT],
+    ToNarwhals["Series[NativeSeriesT]"],
+    CompliantColumn,
+    Protocol[NativeSeriesT],
+):
+    # NOTE: `narwhals`
+    _implementation: Implementation
+
+    @property
+    def native(self) -> NativeSeriesT: ...
+    def __narwhals_series__(self) -> Self:
+        return self
+
+    def __native_namespace__(self) -> ModuleType: ...
+    @classmethod
+    def from_native(cls, data: NativeSeriesT, /, *, context: _LimitedContext) -> Self: ...
+    def to_narwhals(self) -> Series[NativeSeriesT]:
+        return self._version.series(self, level="full")
+
+    def _with_native(self, series: Any) -> Self: ...
+    def _with_version(self, version: Version) -> Self: ...
+
+    # NOTE: `polars`
+    @property
+    def dtype(self) -> DType: ...
+    @property
+    def name(self) -> str: ...
+    def __array__(self, dtype: Any, *, copy: bool | None) -> _1DArray: ...
+    def __contains__(self, other: Any) -> bool: ...
+    def __getitem__(self, item: MultiIndexSelector[Self]) -> Any: ...
+    def __iter__(self) -> Iterator[Any]: ...
+    def __len__(self) -> int:
+        return len(self.native)
+
+    @classmethod
+    def from_numpy(cls, data: Into1DArray, /, *, context: _LimitedContext) -> Self: ...
+    @classmethod
+    def from_iterable(
+        cls,
+        data: Iterable[Any],
+        /,
+        *,
+        context: _LimitedContext,
+        name: str = "",
+        dtype: IntoDType | None = None,
+    ) -> Self: ...
+    def __radd__(self, other: Any) -> Self: ...
+    def __rand__(self, other: Any) -> Self: ...
+    def __rmul__(self, other: Any) -> Self: ...
+    def __ror__(self, other: Any) -> Self: ...
+    def all(self) -> bool: ...
+    def any(self) -> bool: ...
+    def arg_max(self) -> int: ...
+    def arg_min(self) -> int: ...
+    def arg_true(self) -> Self: ...
+    def count(self) -> int: ...
+    def filter(self, predicate: Any) -> Self: ...
+    def gather_every(self, n: int, offset: int) -> Self: ...
+    def head(self, n: int) -> Self: ...
+    def is_empty(self) -> bool:
+        return self.len() == 0
+
+    def is_sorted(self, *, descending: bool) -> bool: ...
+    def item(self, index: int | None) -> Any: ...
+    def kurtosis(self) -> float | None: ...
+    def len(self) -> int: ...
+    def max(self) -> Any: ...
+    def mean(self) -> float: ...
+    def median(self) -> float: ...
+    def min(self) -> Any: ...
+    def n_unique(self) -> int: ...
+    def null_count(self) -> int: ...
+    def quantile(
+        self, quantile: float, interpolation: RollingInterpolationMethod
+    ) -> float: ...
+    def sample(
+        self,
+        n: int | None,
+        *,
+        fraction: float | None,
+        with_replacement: bool,
+        seed: int | None,
+    ) -> Self: ...
+    def scatter(self, indices: int | Sequence[int], values: Any) -> Self: ...
+    def shift(self, n: int) -> Self: ...
+    def skew(self) -> float | None: ...
+    def sort(self, *, descending: bool, nulls_last: bool) -> Self: ...
+    def std(self, *, ddof: int) -> float: ...
+    def sum(self) -> float: ...
+    def tail(self, n: int) -> Self: ...
+    def to_arrow(self) -> pa.Array[Any]: ...
+    def to_dummies(
+        self, *, separator: str, drop_first: bool
+    ) -> CompliantDataFrame[Self, Any, Any, Any]: ...
+    def to_frame(self) -> CompliantDataFrame[Self, Any, Any, Any]: ...
+    def to_list(self) -> list[Any]: ...
+    def to_pandas(self) -> pd.Series[Any]: ...
+    def to_polars(self) -> pl.Series: ...
+    def unique(self, *, maintain_order: bool = False) -> Self: ...
+    def value_counts(
+        self, *, sort: bool, parallel: bool, name: str | None, normalize: bool
+    ) -> CompliantDataFrame[Self, Any, Any, Any]: ...
+    def var(self, *, ddof: int) -> float: ...
+    def zip_with(self, mask: Any, other: Any) -> Self: ...
+
+    # NOTE: *Technically* `polars`
+    @unstable
+    def hist_from_bins(
+        self, bins: list[float], *, include_breakpoint: bool
+    ) -> CompliantDataFrame[Self, Any, Any, Any]:
+        """`Series.hist(bins=..., bin_count=None)`."""
+        ...
+
+    @unstable
+    def hist_from_bin_count(
+        self, bin_count: int, *, include_breakpoint: bool
+    ) -> CompliantDataFrame[Self, Any, Any, Any]:
+        """`Series.hist(bins=None, bin_count=...)`."""
+        ...
+
+
+class EagerSeries(CompliantSeries[NativeSeriesT], Protocol[NativeSeriesT]):
+    _native_series: Any
+    _implementation: Implementation
+    _version: Version
+    _broadcast: bool
+
+    @property
+    def _backend_version(self) -> tuple[int, ...]:
+        return self._implementation._backend_version()
+
+    @classmethod
+    def _align_full_broadcast(cls, *series: Self) -> Sequence[Self]:
+        """Ensure all of `series` have the same length (and index if `pandas`).
+
+        Scalars get broadcasted to the full length of the longest Series.
+
+        This is useful when you need to construct a full Series anyway, such as:
+
+            DataFrame.select(...)
+
+        It should not be used in binary operations, such as:
+
+            nw.col("a") - nw.col("a").mean()
+
+        because then it's more efficient to extract the right-hand-side's single element as a scalar.
+        """
+        ...
+
+    def _from_scalar(self, value: Any) -> Self:
+        return self.from_iterable([value], name=self.name, context=self)
+
+    def _with_native(
+        self, series: NativeSeriesT, *, preserve_broadcast: bool = False
+    ) -> Self:
+        """Return a new `CompliantSeries`, wrapping the native `series`.
+
+        In cases when operations are known to not affect whether a result should
+        be broadcast, we can pass `preserve_broadcast=True`.
+        Set this with care - it should only be set for unary expressions which don't
+        change length or order, such as `.alias` or `.fill_null`. If in doubt, don't
+        set it, you probably don't need it.
+        """
+        ...
+
+    def __narwhals_namespace__(
+        self,
+    ) -> EagerNamespace[Any, Self, Any, Any, NativeSeriesT]: ...
+
+    def _gather(self, rows: SizedMultiIndexSelector[NativeSeriesT]) -> Self: ...
+    def _gather_slice(self, rows: _SliceIndex | range) -> Self: ...
+    def __getitem__(self, item: MultiIndexSelector[Self]) -> Self:
+        if isinstance(item, (slice, range)):
+            return self._gather_slice(item)
+        if is_compliant_series(item):
+            return self._gather(item.native)
+        elif is_sized_multi_index_selector(item):  # noqa: RET505
+            return self._gather(item)
+        assert_never(item)
+
+    @property
+    def str(self) -> EagerSeriesStringNamespace[Self, NativeSeriesT]: ...
+    @property
+    def dt(self) -> EagerSeriesDateTimeNamespace[Self, NativeSeriesT]: ...
+    @property
+    def cat(self) -> EagerSeriesCatNamespace[Self, NativeSeriesT]: ...
+    @property
+    def list(self) -> EagerSeriesListNamespace[Self, NativeSeriesT]: ...
+    @property
+    def struct(self) -> EagerSeriesStructNamespace[Self, NativeSeriesT]: ...
+
+
+class _SeriesNamespace(  # type: ignore[misc]
+    _StoresCompliant[CompliantSeriesT_co],
+    _StoresNative[NativeSeriesT_co],
+    Protocol[CompliantSeriesT_co, NativeSeriesT_co],
+):
+    _compliant_series: CompliantSeriesT_co
+
+    @property
+    def compliant(self) -> CompliantSeriesT_co:
+        return self._compliant_series
+
+    @property
+    def implementation(self) -> Implementation:
+        return self.compliant._implementation
+
+    @property
+    def backend_version(self) -> tuple[int, ...]:
+        return self.implementation._backend_version()
+
+    @property
+    def version(self) -> Version:
+        return self.compliant._version
+
+    @property
+    def native(self) -> NativeSeriesT_co:
+        return self._compliant_series.native  # type: ignore[no-any-return]
+
+    def with_native(self, series: Any, /) -> CompliantSeriesT_co:
+        return self.compliant._with_native(series)
+
+
+class EagerSeriesNamespace(
+    _SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
+    Generic[EagerSeriesT_co, NativeSeriesT_co],
+):
+    _compliant_series: EagerSeriesT_co
+
+    def __init__(self, series: EagerSeriesT_co, /) -> None:
+        self._compliant_series = series
+
+
+class EagerSeriesCatNamespace(  # type: ignore[misc]
+    _SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
+    CatNamespace[EagerSeriesT_co],
+    Protocol[EagerSeriesT_co, NativeSeriesT_co],
+): ...
+
+
+class EagerSeriesDateTimeNamespace(  # type: ignore[misc]
+    _SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
+    DateTimeNamespace[EagerSeriesT_co],
+    Protocol[EagerSeriesT_co, NativeSeriesT_co],
+): ...
+
+
+class EagerSeriesListNamespace(  # type: ignore[misc]
+    _SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
+    ListNamespace[EagerSeriesT_co],
+    Protocol[EagerSeriesT_co, NativeSeriesT_co],
+): ...
+
+
+class EagerSeriesStringNamespace(  # type: ignore[misc]
+    _SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
+    StringNamespace[EagerSeriesT_co],
+    Protocol[EagerSeriesT_co, NativeSeriesT_co],
+): ...
+
+
+class EagerSeriesStructNamespace(  # type: ignore[misc]
+    _SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
+    StructNamespace[EagerSeriesT_co],
+    Protocol[EagerSeriesT_co, NativeSeriesT_co],
+): ...
+
+
+class EagerSeriesHist(Protocol[NativeSeriesT, _CountsT_co]):
+    _series: EagerSeries[NativeSeriesT]
+    _breakpoint: bool
+    _data: HistData[NativeSeriesT, _CountsT_co]
+
+    @property
+    def native(self) -> NativeSeriesT:
+        return self._series.native
+
+    @classmethod
+    def from_series(
+        cls, series: EagerSeries[NativeSeriesT], *, include_breakpoint: bool
+    ) -> Self:
+        obj = cls.__new__(cls)
+        obj._series = series
+        obj._breakpoint = include_breakpoint
+        return obj
+
+    def to_frame(self) -> EagerDataFrameAny: ...
+    def _linear_space(  # NOTE: Roughly `pl.linear_space`
+        self,
+        start: float,
+        end: float,
+        num_samples: int,
+        *,
+        closed: Literal["both", "none"] = "both",
+    ) -> _1DArray: ...
+
+    # NOTE: *Could* be handled at narwhals-level
+    def is_empty_series(self) -> bool: ...
+
+    # NOTE: **Should** be handled at narwhals-level
+    def data_empty(self) -> HistData[NativeSeriesT, _CountsT_co]:
+        return {"breakpoint": [], "count": []} if self._breakpoint else {"count": []}
+
+    # NOTE: *Could* be handled at narwhals-level, **iff** we add `nw.repeat`, `nw.linear_space`
+    # See https://github.com/narwhals-dev/narwhals/pull/2839#discussion_r2215630696
+    def series_empty(
+        self, arg: int | list[float], /
+    ) -> HistData[NativeSeriesT, _CountsT_co]: ...
+
+    def with_bins(self, bins: list[float], /) -> Self:
+        if len(bins) <= 1:
+            self._data = self.data_empty()
+        elif self.is_empty_series():
+            self._data = self.series_empty(bins)
+        else:
+            self._data = self._calculate_hist(bins)
+        return self
+
+    def with_bin_count(self, bin_count: int, /) -> Self:
+        if bin_count == 0:
+            self._data = self.data_empty()
+        elif self.is_empty_series():
+            self._data = self.series_empty(bin_count)
+        else:
+            self._data = self._calculate_hist(self._calculate_bins(bin_count))
+        return self
+
+    def _calculate_breakpoint(self, arg: int | list[float], /) -> list[float] | _1DArray:
+        bins = self._linear_space(0, 1, arg + 1) if isinstance(arg, int) else arg
+        return bins[1:]
+
+    def _calculate_bins(self, bin_count: int) -> _1DArray: ...
+    def _calculate_hist(
+        self, bins: list[float] | _1DArray
+    ) -> HistData[NativeSeriesT, _CountsT_co]: ...

lib/python3.11/site-packages/narwhals/_compliant/typing.py

@@ -0,0 +1,206 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import TYPE_CHECKING, Any, Callable, Literal, TypedDict, TypeVar
+
+if TYPE_CHECKING:
+    from typing_extensions import TypeAlias
+
+    from narwhals._compliant.dataframe import (
+        CompliantDataFrame,
+        CompliantFrame,
+        CompliantLazyFrame,
+        EagerDataFrame,
+    )
+    from narwhals._compliant.expr import (
+        CompliantExpr,
+        DepthTrackingExpr,
+        EagerExpr,
+        ImplExpr,
+        LazyExpr,
+        NativeExpr,
+    )
+    from narwhals._compliant.namespace import CompliantNamespace, EagerNamespace
+    from narwhals._compliant.series import CompliantSeries, EagerSeries
+    from narwhals._compliant.window import WindowInputs
+    from narwhals.typing import (
+        FillNullStrategy,
+        IntoLazyFrame,
+        ModeKeepStrategy,
+        NativeDataFrame,
+        NativeFrame,
+        NativeSeries,
+        RankMethod,
+        RollingInterpolationMethod,
+    )
+
+    class ScalarKwargs(TypedDict, total=False):
+        """Non-expressifiable args which we may need to reuse in `agg` or `over`."""
+
+        adjust: bool
+        alpha: float | None
+        center: int
+        com: float | None
+        ddof: int
+        descending: bool
+        half_life: float | None
+        ignore_nulls: bool
+        interpolation: RollingInterpolationMethod
+        keep: ModeKeepStrategy
+        limit: int | None
+        method: RankMethod
+        min_samples: int
+        n: int
+        quantile: float
+        reverse: bool
+        span: float | None
+        strategy: FillNullStrategy | None
+        window_size: int
+
+
+__all__ = [
+    "AliasName",
+    "AliasNames",
+    "CompliantDataFrameT",
+    "CompliantFrameT",
+    "CompliantLazyFrameT",
+    "CompliantSeriesT",
+    "EvalNames",
+    "EvalSeries",
+    "NarwhalsAggregation",
+    "NativeFrameT_co",
+    "NativeSeriesT_co",
+]
+CompliantExprAny: TypeAlias = "CompliantExpr[Any, Any]"
+CompliantSeriesAny: TypeAlias = "CompliantSeries[Any]"
+CompliantSeriesOrNativeExprAny: TypeAlias = "CompliantSeriesAny | NativeExpr"
+CompliantDataFrameAny: TypeAlias = "CompliantDataFrame[Any, Any, Any, Any]"
+CompliantLazyFrameAny: TypeAlias = "CompliantLazyFrame[Any, Any, Any]"
+CompliantFrameAny: TypeAlias = "CompliantFrame[Any, Any, Any]"
+CompliantNamespaceAny: TypeAlias = "CompliantNamespace[Any, Any]"
+
+ImplExprAny: TypeAlias = "ImplExpr[Any, Any]"
+
+DepthTrackingExprAny: TypeAlias = "DepthTrackingExpr[Any, Any]"
+
+EagerDataFrameAny: TypeAlias = "EagerDataFrame[Any, Any, Any, Any]"
+EagerSeriesAny: TypeAlias = "EagerSeries[Any]"
+EagerExprAny: TypeAlias = "EagerExpr[Any, Any]"
+EagerNamespaceAny: TypeAlias = "EagerNamespace[EagerDataFrameAny, EagerSeriesAny, EagerExprAny, NativeFrame, NativeSeries]"
+
+LazyExprAny: TypeAlias = "LazyExpr[Any, Any]"
+
+NativeExprT = TypeVar("NativeExprT", bound="NativeExpr")
+NativeExprT_co = TypeVar("NativeExprT_co", bound="NativeExpr", covariant=True)
+NativeSeriesT = TypeVar("NativeSeriesT", bound="NativeSeries")
+NativeSeriesT_co = TypeVar("NativeSeriesT_co", bound="NativeSeries", covariant=True)
+NativeSeriesT_contra = TypeVar(
+    "NativeSeriesT_contra", bound="NativeSeries", contravariant=True
+)
+NativeDataFrameT = TypeVar("NativeDataFrameT", bound="NativeDataFrame")
+NativeLazyFrameT = TypeVar("NativeLazyFrameT", bound="IntoLazyFrame")
+NativeFrameT = TypeVar("NativeFrameT", bound="NativeFrame")
+NativeFrameT_co = TypeVar("NativeFrameT_co", bound="NativeFrame", covariant=True)
+NativeFrameT_contra = TypeVar(
+    "NativeFrameT_contra", bound="NativeFrame", contravariant=True
+)
+
+CompliantExprT = TypeVar("CompliantExprT", bound=CompliantExprAny)
+CompliantExprT_co = TypeVar("CompliantExprT_co", bound=CompliantExprAny, covariant=True)
+CompliantExprT_contra = TypeVar(
+    "CompliantExprT_contra", bound=CompliantExprAny, contravariant=True
+)
+CompliantSeriesT = TypeVar("CompliantSeriesT", bound=CompliantSeriesAny)
+CompliantSeriesT_co = TypeVar(
+    "CompliantSeriesT_co", bound=CompliantSeriesAny, covariant=True
+)
+CompliantSeriesOrNativeExprT = TypeVar(
+    "CompliantSeriesOrNativeExprT", bound=CompliantSeriesOrNativeExprAny
+)
+CompliantSeriesOrNativeExprT_co = TypeVar(
+    "CompliantSeriesOrNativeExprT_co",
+    bound=CompliantSeriesOrNativeExprAny,
+    covariant=True,
+)
+CompliantFrameT = TypeVar("CompliantFrameT", bound=CompliantFrameAny)
+CompliantFrameT_co = TypeVar(
+    "CompliantFrameT_co", bound=CompliantFrameAny, covariant=True
+)
+CompliantDataFrameT = TypeVar("CompliantDataFrameT", bound=CompliantDataFrameAny)
+CompliantDataFrameT_co = TypeVar(
+    "CompliantDataFrameT_co", bound=CompliantDataFrameAny, covariant=True
+)
+CompliantLazyFrameT = TypeVar("CompliantLazyFrameT", bound=CompliantLazyFrameAny)
+CompliantLazyFrameT_co = TypeVar(
+    "CompliantLazyFrameT_co", bound=CompliantLazyFrameAny, covariant=True
+)
+CompliantNamespaceT = TypeVar("CompliantNamespaceT", bound=CompliantNamespaceAny)
+CompliantNamespaceT_co = TypeVar(
+    "CompliantNamespaceT_co", bound=CompliantNamespaceAny, covariant=True
+)
+
+ImplExprT_contra = TypeVar("ImplExprT_contra", bound=ImplExprAny, contravariant=True)
+
+DepthTrackingExprT = TypeVar("DepthTrackingExprT", bound=DepthTrackingExprAny)
+DepthTrackingExprT_contra = TypeVar(
+    "DepthTrackingExprT_contra", bound=DepthTrackingExprAny, contravariant=True
+)
+
+EagerExprT = TypeVar("EagerExprT", bound=EagerExprAny)
+EagerExprT_contra = TypeVar("EagerExprT_contra", bound=EagerExprAny, contravariant=True)
+EagerSeriesT = TypeVar("EagerSeriesT", bound=EagerSeriesAny)
+EagerSeriesT_co = TypeVar("EagerSeriesT_co", bound=EagerSeriesAny, covariant=True)
+
+# NOTE: `pyright` gives false (8) positives if this uses `EagerDataFrameAny`?
+EagerDataFrameT = TypeVar("EagerDataFrameT", bound="EagerDataFrame[Any, Any, Any, Any]")
+
+LazyExprT = TypeVar("LazyExprT", bound=LazyExprAny)
+LazyExprT_contra = TypeVar("LazyExprT_contra", bound=LazyExprAny, contravariant=True)
+
+AliasNames: TypeAlias = Callable[[Sequence[str]], Sequence[str]]
+"""A function aliasing a *sequence* of column names."""
+
+AliasName: TypeAlias = Callable[[str], str]
+"""A function aliasing a *single* column name."""
+
+EvalSeries: TypeAlias = Callable[
+    [CompliantFrameT], Sequence[CompliantSeriesOrNativeExprT]
+]
+"""A function from a `Frame` to a sequence of `Series`*.
+
+See [underwater unicorn magic](https://narwhals-dev.github.io/narwhals/how_it_works/).
+"""
+
+EvalNames: TypeAlias = Callable[[CompliantFrameT], Sequence[str]]
+"""A function from a `Frame` to a sequence of columns names *before* any aliasing takes place."""
+
+WindowFunction: TypeAlias = (
+    "Callable[[CompliantFrameT, WindowInputs[NativeExprT]], Sequence[NativeExprT]]"
+)
+"""A function evaluated with `over(partition_by=..., order_by=...)`."""
+
+NarwhalsAggregation: TypeAlias = Literal[
+    "sum",
+    "mean",
+    "median",
+    "max",
+    "min",
+    "mode",
+    "std",
+    "var",
+    "len",
+    "n_unique",
+    "count",
+    "quantile",
+    "all",
+    "any",
+]
+"""`Expr` methods we aim to support in `DepthTrackingGroupBy`.
+
+Be sure to update me if you're working on one of these:
+- https://github.com/narwhals-dev/narwhals/issues/981
+- https://github.com/narwhals-dev/narwhals/issues/2385
+- https://github.com/narwhals-dev/narwhals/issues/2484
+- https://github.com/narwhals-dev/narwhals/issues/2526
+- https://github.com/narwhals-dev/narwhals/issues/2660
+"""

lib/python3.11/site-packages/narwhals/_compliant/when_then.py

@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Protocol, TypeVar, cast
+
+from narwhals._compliant.expr import CompliantExpr
+from narwhals._compliant.typing import (
+    CompliantExprAny,
+    CompliantFrameAny,
+    CompliantSeriesOrNativeExprAny,
+    EagerDataFrameT,
+    EagerExprT,
+    EagerSeriesT,
+    LazyExprAny,
+    NativeSeriesT,
+)
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+    from typing_extensions import Self, TypeAlias
+
+    from narwhals._compliant.typing import EvalSeries
+    from narwhals._utils import Implementation, Version, _LimitedContext
+    from narwhals.typing import NonNestedLiteral
+
+
+__all__ = ["CompliantThen", "CompliantWhen", "EagerWhen"]
+
+ExprT = TypeVar("ExprT", bound=CompliantExprAny)
+LazyExprT = TypeVar("LazyExprT", bound=LazyExprAny)
+SeriesT = TypeVar("SeriesT", bound=CompliantSeriesOrNativeExprAny)
+FrameT = TypeVar("FrameT", bound=CompliantFrameAny)
+
+Scalar: TypeAlias = Any
+"""A native literal value."""
+
+IntoExpr: TypeAlias = "SeriesT | ExprT | NonNestedLiteral | Scalar"
+"""Anything that is convertible into a `CompliantExpr`."""
+
+
+class CompliantWhen(Protocol[FrameT, SeriesT, ExprT]):
+    _condition: ExprT
+    _then_value: IntoExpr[SeriesT, ExprT]
+    _otherwise_value: IntoExpr[SeriesT, ExprT] | None
+    _implementation: Implementation
+    _version: Version
+
+    @property
+    def _then(self) -> type[CompliantThen[FrameT, SeriesT, ExprT, Self]]: ...
+    def __call__(self, compliant_frame: FrameT, /) -> Sequence[SeriesT]: ...
+    def then(
+        self, value: IntoExpr[SeriesT, ExprT], /
+    ) -> CompliantThen[FrameT, SeriesT, ExprT, Self]:
+        return self._then.from_when(self, value)
+
+    @classmethod
+    def from_expr(cls, condition: ExprT, /, *, context: _LimitedContext) -> Self:
+        obj = cls.__new__(cls)
+        obj._condition = condition
+        obj._then_value = None
+        obj._otherwise_value = None
+        obj._implementation = context._implementation
+        obj._version = context._version
+        return obj
+
+
+WhenT_contra = TypeVar(
+    "WhenT_contra", bound=CompliantWhen[Any, Any, Any], contravariant=True
+)
+
+
+class CompliantThen(
+    CompliantExpr[FrameT, SeriesT], Protocol[FrameT, SeriesT, ExprT, WhenT_contra]
+):
+    _call: EvalSeries[FrameT, SeriesT]
+    _when_value: CompliantWhen[FrameT, SeriesT, ExprT]
+    _implementation: Implementation
+    _version: Version
+
+    @classmethod
+    def from_when(cls, when: WhenT_contra, then: IntoExpr[SeriesT, ExprT], /) -> Self:
+        when._then_value = then
+        obj = cls.__new__(cls)
+        obj._call = when
+        obj._when_value = when
+        obj._evaluate_output_names = getattr(
+            then, "_evaluate_output_names", lambda _df: ["literal"]
+        )
+        obj._alias_output_names = getattr(then, "_alias_output_names", None)
+        obj._implementation = when._implementation
+        obj._version = when._version
+        return obj
+
+    def otherwise(self, otherwise: IntoExpr[SeriesT, ExprT], /) -> ExprT:
+        self._when_value._otherwise_value = otherwise
+        return cast("ExprT", self)
+
+
+class EagerWhen(
+    CompliantWhen[EagerDataFrameT, EagerSeriesT, EagerExprT],
+    Protocol[EagerDataFrameT, EagerSeriesT, EagerExprT, NativeSeriesT],
+):
+    def _if_then_else(
+        self,
+        when: NativeSeriesT,
+        then: NativeSeriesT,
+        otherwise: NativeSeriesT | NonNestedLiteral | Scalar,
+        /,
+    ) -> NativeSeriesT: ...
+
+    def __call__(self, df: EagerDataFrameT, /) -> Sequence[EagerSeriesT]:
+        is_expr = self._condition._is_expr
+        when: EagerSeriesT = self._condition(df)[0]
+        then: EagerSeriesT
+        align = when._align_full_broadcast
+
+        if is_expr(self._then_value):
+            then = self._then_value(df)[0]
+        else:
+            then = when.alias("literal")._from_scalar(self._then_value)
+            then._broadcast = True
+
+        if is_expr(self._otherwise_value):
+            otherwise = self._otherwise_value(df)[0]
+            when, then, otherwise = align(when, then, otherwise)
+            result = self._if_then_else(when.native, then.native, otherwise.native)
+        else:
+            when, then = align(when, then)
+            result = self._if_then_else(when.native, then.native, self._otherwise_value)
+        return [then._with_native(result)]

lib/python3.11/site-packages/narwhals/_compliant/window.py

@@ -0,0 +1,20 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Generic
+
+from narwhals._compliant.typing import NativeExprT_co
+
+if TYPE_CHECKING:
+    from collections.abc import Sequence
+
+__all__ = ["WindowInputs"]
+
+
+class WindowInputs(Generic[NativeExprT_co]):
+    __slots__ = ("order_by", "partition_by")
+
+    def __init__(
+        self, partition_by: Sequence[str | NativeExprT_co], order_by: Sequence[str]
+    ) -> None:
+        self.partition_by = partition_by
+        self.order_by = order_by