add code for analysis of data

12 files changed, 1917 insertions, 0 deletions

diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/__init__.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/__init__.py
new file mode 100644
index 0000000..e69de29
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/__init__.py
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/dataframe.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/dataframe.py
new file mode 100644
index 0000000..4e18fa6
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/dataframe.py
@@ -0,0 +1,430 @@
+from __future__ import annotations
+
+import operator
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Iterable,
+    Iterator,
+    Literal,
+    Mapping,
+    Sequence,
+    cast,
+)
+
+import ibis
+import ibis.expr.types as ir
+
+from narwhals._ibis.utils import evaluate_exprs, native_to_narwhals_dtype
+from narwhals._utils import (
+    Implementation,
+    Version,
+    not_implemented,
+    parse_columns_to_drop,
+    parse_version,
+    validate_backend_version,
+)
+from narwhals.exceptions import ColumnNotFoundError, InvalidOperationError
+from narwhals.typing import CompliantLazyFrame
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+    import pandas as pd
+    import pyarrow as pa
+    from ibis.expr.operations import Binary
+    from typing_extensions import Self, TypeAlias, TypeIs
+
+    from narwhals._compliant.typing import CompliantDataFrameAny
+    from narwhals._ibis.expr import IbisExpr
+    from narwhals._ibis.group_by import IbisGroupBy
+    from narwhals._ibis.namespace import IbisNamespace
+    from narwhals._ibis.series import IbisInterchangeSeries
+    from narwhals._utils import _FullContext
+    from narwhals.dataframe import LazyFrame
+    from narwhals.dtypes import DType
+    from narwhals.stable.v1 import DataFrame as DataFrameV1
+    from narwhals.typing import AsofJoinStrategy, JoinStrategy, LazyUniqueKeepStrategy
+
+    JoinPredicates: TypeAlias = "Sequence[ir.BooleanColumn] | Sequence[str]"
+
+
+class IbisLazyFrame(
+    CompliantLazyFrame[
+        "IbisExpr", "ir.Table", "LazyFrame[ir.Table] | DataFrameV1[ir.Table]"
+    ]
+):
+    _implementation = Implementation.IBIS
+
+    def __init__(
+        self, df: ir.Table, *, backend_version: tuple[int, ...], version: Version
+    ) -> None:
+        self._native_frame: ir.Table = df
+        self._version = version
+        self._backend_version = backend_version
+        self._cached_schema: dict[str, DType] | None = None
+        self._cached_columns: list[str] | None = None
+        validate_backend_version(self._implementation, self._backend_version)
+
+    @staticmethod
+    def _is_native(obj: ir.Table | Any) -> TypeIs[ir.Table]:
+        return isinstance(obj, ir.Table)
+
+    @classmethod
+    def from_native(cls, data: ir.Table, /, *, context: _FullContext) -> Self:
+        return cls(
+            data, backend_version=context._backend_version, version=context._version
+        )
+
+    def to_narwhals(self) -> LazyFrame[ir.Table] | DataFrameV1[ir.Table]:
+        if self._version is Version.MAIN:
+            return self._version.lazyframe(self, level="lazy")
+
+        from narwhals.stable.v1 import DataFrame as DataFrameV1
+
+        return DataFrameV1(self, level="interchange")
+
+    def __narwhals_dataframe__(self) -> Self:  # pragma: no cover
+        # Keep around for backcompat.
+        if self._version is not Version.V1:
+            msg = "__narwhals_dataframe__ is not implemented for IbisLazyFrame"
+            raise AttributeError(msg)
+        return self
+
+    def __narwhals_lazyframe__(self) -> Self:
+        return self
+
+    def __native_namespace__(self) -> ModuleType:
+        return ibis
+
+    def __narwhals_namespace__(self) -> IbisNamespace:
+        from narwhals._ibis.namespace import IbisNamespace
+
+        return IbisNamespace(backend_version=self._backend_version, version=self._version)
+
+    def get_column(self, name: str) -> IbisInterchangeSeries:
+        from narwhals._ibis.series import IbisInterchangeSeries
+
+        return IbisInterchangeSeries(self.native.select(name), version=self._version)
+
+    def _iter_columns(self) -> Iterator[ir.Expr]:
+        for name in self.columns:
+            yield self.native[name]
+
+    def collect(
+        self, backend: ModuleType | Implementation | str | None, **kwargs: Any
+    ) -> CompliantDataFrameAny:
+        if backend is None or backend is Implementation.PYARROW:
+            import pyarrow as pa  # ignore-banned-import
+
+            from narwhals._arrow.dataframe import ArrowDataFrame
+
+            return ArrowDataFrame(
+                self.native.to_pyarrow(),
+                backend_version=parse_version(pa),
+                version=self._version,
+                validate_column_names=True,
+            )
+
+        if backend is Implementation.PANDAS:
+            import pandas as pd  # ignore-banned-import
+
+            from narwhals._pandas_like.dataframe import PandasLikeDataFrame
+
+            return PandasLikeDataFrame(
+                self.native.to_pandas(),
+                implementation=Implementation.PANDAS,
+                backend_version=parse_version(pd),
+                version=self._version,
+                validate_column_names=True,
+            )
+
+        if backend is Implementation.POLARS:
+            import polars as pl  # ignore-banned-import
+
+            from narwhals._polars.dataframe import PolarsDataFrame
+
+            return PolarsDataFrame(
+                self.native.to_polars(),
+                backend_version=parse_version(pl),
+                version=self._version,
+            )
+
+        msg = f"Unsupported `backend` value: {backend}"  # pragma: no cover
+        raise ValueError(msg)  # pragma: no cover
+
+    def head(self, n: int) -> Self:
+        return self._with_native(self.native.head(n))
+
+    def simple_select(self, *column_names: str) -> Self:
+        return self._with_native(self.native.select(*column_names))
+
+    def aggregate(self, *exprs: IbisExpr) -> Self:
+        selection = [
+            cast("ir.Scalar", val.name(name))
+            for name, val in evaluate_exprs(self, *exprs)
+        ]
+        return self._with_native(self.native.aggregate(selection))
+
+    def select(self, *exprs: IbisExpr) -> Self:
+        selection = [val.name(name) for name, val in evaluate_exprs(self, *exprs)]
+        if not selection:
+            msg = "At least one expression must be provided to `select` with the Ibis backend."
+            raise ValueError(msg)
+
+        t = self.native.select(*selection)
+        return self._with_native(t)
+
+    def drop(self, columns: Sequence[str], *, strict: bool) -> Self:
+        columns_to_drop = parse_columns_to_drop(self, columns, strict=strict)
+        selection = (col for col in self.columns if col not in columns_to_drop)
+        return self._with_native(self.native.select(*selection))
+
+    def lazy(self, *, backend: Implementation | None = None) -> Self:
+        # The `backend`` argument has no effect but we keep it here for
+        # backwards compatibility because in `narwhals.stable.v1`
+        # function `.from_native()` will return a DataFrame for Ibis.
+
+        if backend is not None:  # pragma: no cover
+            msg = "`backend` argument is not supported for Ibis"
+            raise ValueError(msg)
+        return self
+
+    def with_columns(self, *exprs: IbisExpr) -> Self:
+        new_columns_map = dict(evaluate_exprs(self, *exprs))
+        return self._with_native(self.native.mutate(**new_columns_map))
+
+    def filter(self, predicate: IbisExpr) -> Self:
+        # `[0]` is safe as the predicate's expression only returns a single column
+        mask = cast("ir.BooleanValue", predicate(self)[0])
+        return self._with_native(self.native.filter(mask))
+
+    @property
+    def schema(self) -> dict[str, DType]:
+        if self._cached_schema is None:
+            # Note: prefer `self._cached_schema` over `functools.cached_property`
+            # due to Python3.13 failures.
+            self._cached_schema = {
+                name: native_to_narwhals_dtype(dtype, self._version)
+                for name, dtype in self.native.schema().fields.items()
+            }
+        return self._cached_schema
+
+    @property
+    def columns(self) -> list[str]:
+        if self._cached_columns is None:
+            self._cached_columns = (
+                list(self.schema)
+                if self._cached_schema is not None
+                else list(self.native.columns)
+            )
+        return self._cached_columns
+
+    def to_pandas(self) -> pd.DataFrame:
+        # only if version is v1, keep around for backcompat
+        import pandas as pd  # ignore-banned-import()
+
+        if parse_version(pd) >= (1, 0, 0):
+            return self.native.to_pandas()
+        else:  # pragma: no cover
+            msg = f"Conversion to pandas requires pandas>=1.0.0, found {pd.__version__}"
+            raise NotImplementedError(msg)
+
+    def to_arrow(self) -> pa.Table:
+        # only if version is v1, keep around for backcompat
+        return self.native.to_pyarrow()
+
+    def _with_version(self, version: Version) -> Self:
+        return self.__class__(
+            self.native, version=version, backend_version=self._backend_version
+        )
+
+    def _with_native(self, df: ir.Table) -> Self:
+        return self.__class__(
+            df, backend_version=self._backend_version, version=self._version
+        )
+
+    def group_by(
+        self, keys: Sequence[str] | Sequence[IbisExpr], *, drop_null_keys: bool
+    ) -> IbisGroupBy:
+        from narwhals._ibis.group_by import IbisGroupBy
+
+        return IbisGroupBy(self, keys, drop_null_keys=drop_null_keys)
+
+    def rename(self, mapping: Mapping[str, str]) -> Self:
+        def _rename(col: str) -> str:
+            return mapping.get(col, col)
+
+        return self._with_native(self.native.rename(_rename))
+
+    @staticmethod
+    def _join_drop_duplicate_columns(df: ir.Table, columns: Iterable[str], /) -> ir.Table:
+        """Ibis adds a suffix to the right table col, even when it matches the left during a join."""
+        duplicates = set(df.columns).intersection(columns)
+        return df.drop(*duplicates) if duplicates else df
+
+    def join(
+        self,
+        other: Self,
+        *,
+        how: JoinStrategy,
+        left_on: Sequence[str] | None,
+        right_on: Sequence[str] | None,
+        suffix: str,
+    ) -> Self:
+        how_native = "outer" if how == "full" else how
+        rname = "{name}" + suffix
+        if other == self:
+            # Ibis does not support self-references unless created as a view
+            other = self._with_native(other.native.view())
+        if how_native == "cross":
+            joined = self.native.join(other.native, how=how_native, rname=rname)
+            return self._with_native(joined)
+        # help mypy
+        assert left_on is not None  # noqa: S101
+        assert right_on is not None  # noqa: S101
+        predicates = self._convert_predicates(other, left_on, right_on)
+        joined = self.native.join(other.native, predicates, how=how_native, rname=rname)
+        if how_native == "left":
+            right_names = (n + suffix for n in right_on)
+            joined = self._join_drop_duplicate_columns(joined, right_names)
+            it = (cast("Binary", p.op()) for p in predicates if not isinstance(p, str))
+            to_drop = []
+            for pred in it:
+                right = pred.right.name
+                # Mirrors how polars works.
+                if right not in self.columns and pred.left.name != right:
+                    to_drop.append(right)
+            if to_drop:
+                joined = joined.drop(*to_drop)
+        return self._with_native(joined)
+
+    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:
+        rname = "{name}" + suffix
+        strategy_op = {"backward": operator.ge, "forward": operator.le}
+        predicates: JoinPredicates = []
+        if op := strategy_op.get(strategy):
+            on: ir.BooleanColumn = op(self.native[left_on], other.native[right_on])
+        else:
+            msg = "Only `backward` and `forward` strategies are currently supported for Ibis"
+            raise NotImplementedError(msg)
+        if by_left is not None and by_right is not None:
+            predicates = self._convert_predicates(other, by_left, by_right)
+        joined = self.native.asof_join(other.native, on, predicates, rname=rname)
+        joined = self._join_drop_duplicate_columns(joined, [right_on + suffix])
+        if by_right is not None:
+            right_names = (n + suffix for n in by_right)
+            joined = self._join_drop_duplicate_columns(joined, right_names)
+        return self._with_native(joined)
+
+    def _convert_predicates(
+        self, other: Self, left_on: Sequence[str], right_on: Sequence[str]
+    ) -> JoinPredicates:
+        if left_on == right_on:
+            return left_on
+        return [
+            cast("ir.BooleanColumn", (self.native[left] == other.native[right]))
+            for left, right in zip(left_on, right_on)
+        ]
+
+    def collect_schema(self) -> dict[str, DType]:
+        return {
+            name: native_to_narwhals_dtype(dtype, self._version)
+            for name, dtype in self.native.schema().fields.items()
+        }
+
+    def unique(
+        self, subset: Sequence[str] | None, *, keep: LazyUniqueKeepStrategy
+    ) -> Self:
+        if subset_ := subset if keep == "any" else (subset or self.columns):
+            # Sanitise input
+            if any(x not in self.columns for x in subset_):
+                msg = f"Columns {set(subset_).difference(self.columns)} not found in {self.columns}."
+                raise ColumnNotFoundError(msg)
+
+            mapped_keep: dict[str, Literal["first"] | None] = {
+                "any": "first",
+                "none": None,
+            }
+            to_keep = mapped_keep[keep]
+            return self._with_native(self.native.distinct(on=subset_, keep=to_keep))
+        return self._with_native(self.native.distinct(on=subset))
+
+    def sort(self, *by: str, descending: bool | Sequence[bool], nulls_last: bool) -> Self:
+        if isinstance(descending, bool):
+            descending = [descending for _ in range(len(by))]
+
+        sort_cols = []
+
+        for i in range(len(by)):
+            direction_fn = ibis.desc if descending[i] else ibis.asc
+            col = direction_fn(by[i], nulls_first=not nulls_last)
+            sort_cols.append(cast("ir.Column", col))
+
+        return self._with_native(self.native.order_by(*sort_cols))
+
+    def drop_nulls(self, subset: Sequence[str] | None) -> Self:
+        subset_ = subset if subset is not None else self.columns
+        return self._with_native(self.native.drop_null(subset_))
+
+    def explode(self, columns: Sequence[str]) -> Self:
+        dtypes = self._version.dtypes
+        schema = self.collect_schema()
+        for col in columns:
+            dtype = schema[col]
+
+            if dtype != dtypes.List:
+                msg = (
+                    f"`explode` operation not supported for dtype `{dtype}`, "
+                    "expected List type"
+                )
+                raise InvalidOperationError(msg)
+
+        if len(columns) != 1:
+            msg = (
+                "Exploding on multiple columns is not supported with Ibis backend since "
+                "we cannot guarantee that the exploded columns have matching element counts."
+            )
+            raise NotImplementedError(msg)
+
+        return self._with_native(self.native.unnest(columns[0], keep_empty=True))
+
+    def unpivot(
+        self,
+        on: Sequence[str] | None,
+        index: Sequence[str] | None,
+        variable_name: str,
+        value_name: str,
+    ) -> Self:
+        import ibis.selectors as s
+
+        index_: Sequence[str] = [] if index is None else index
+        on_: Sequence[str] = (
+            [c for c in self.columns if c not in index_] if on is None else on
+        )
+
+        # Discard columns not in the index
+        final_columns = list(dict.fromkeys([*index_, variable_name, value_name]))
+
+        unpivoted = self.native.pivot_longer(
+            s.cols(*on_), names_to=variable_name, values_to=value_name
+        )
+        return self._with_native(unpivoted.select(*final_columns))
+
+    gather_every = not_implemented.deprecated(
+        "`LazyFrame.gather_every` is deprecated and will be removed in a future version."
+    )
+    tail = not_implemented.deprecated(
+        "`LazyFrame.tail` is deprecated and will be removed in a future version."
+    )
+    with_row_index = not_implemented()
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/expr.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/expr.py
new file mode 100644
index 0000000..4fc8c79
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/expr.py
@@ -0,0 +1,698 @@
+from __future__ import annotations
+
+import operator
+from functools import partial
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Iterator,
+    Literal,
+    Sequence,
+    TypeVar,
+    cast,
+)
+
+import ibis
+
+from narwhals._compliant import LazyExpr
+from narwhals._compliant.window import WindowInputs
+from narwhals._ibis.expr_dt import IbisExprDateTimeNamespace
+from narwhals._ibis.expr_list import IbisExprListNamespace
+from narwhals._ibis.expr_str import IbisExprStringNamespace
+from narwhals._ibis.expr_struct import IbisExprStructNamespace
+from narwhals._ibis.utils import is_floating, lit, narwhals_to_native_dtype
+from narwhals._utils import Implementation, not_implemented
+
+if TYPE_CHECKING:
+    import ibis.expr.types as ir
+    from typing_extensions import Self
+
+    from narwhals._compliant.typing import (
+        AliasNames,
+        EvalNames,
+        EvalSeries,
+        WindowFunction,
+    )
+    from narwhals._expression_parsing import ExprKind, ExprMetadata
+    from narwhals._ibis.dataframe import IbisLazyFrame
+    from narwhals._ibis.namespace import IbisNamespace
+    from narwhals._utils import Version, _FullContext
+    from narwhals.typing import IntoDType, RankMethod, RollingInterpolationMethod
+
+    ExprT = TypeVar("ExprT", bound=ir.Value)
+    IbisWindowFunction = WindowFunction[IbisLazyFrame, ir.Value]
+    IbisWindowInputs = WindowInputs[ir.Value]
+
+
+class IbisExpr(LazyExpr["IbisLazyFrame", "ir.Column"]):
+    _implementation = Implementation.IBIS
+
+    def __init__(
+        self,
+        call: EvalSeries[IbisLazyFrame, ir.Value],
+        window_function: IbisWindowFunction | None = None,
+        *,
+        evaluate_output_names: EvalNames[IbisLazyFrame],
+        alias_output_names: AliasNames | None,
+        backend_version: tuple[int, ...],
+        version: Version,
+    ) -> None:
+        self._call = call
+        self._evaluate_output_names = evaluate_output_names
+        self._alias_output_names = alias_output_names
+        self._backend_version = backend_version
+        self._version = version
+        self._metadata: ExprMetadata | None = None
+        self._window_function: IbisWindowFunction | None = window_function
+
+    @property
+    def window_function(self) -> IbisWindowFunction:
+        def default_window_func(
+            df: IbisLazyFrame, window_inputs: IbisWindowInputs
+        ) -> list[ir.Value]:
+            assert not window_inputs.order_by  # noqa: S101
+            return [
+                expr.over(ibis.window(group_by=window_inputs.partition_by))
+                for expr in self(df)
+            ]
+
+        return self._window_function or default_window_func
+
+    def __call__(self, df: IbisLazyFrame) -> Sequence[ir.Value]:
+        return self._call(df)
+
+    def __narwhals_expr__(self) -> None: ...
+
+    def __narwhals_namespace__(self) -> IbisNamespace:  # pragma: no cover
+        # Unused, just for compatibility with PandasLikeExpr
+        from narwhals._ibis.namespace import IbisNamespace
+
+        return IbisNamespace(backend_version=self._backend_version, version=self._version)
+
+    def _cum_window_func(
+        self, *, reverse: bool, func_name: Literal["sum", "max", "min", "count"]
+    ) -> IbisWindowFunction:
+        def func(df: IbisLazyFrame, inputs: IbisWindowInputs) -> Sequence[ir.Value]:
+            window = ibis.window(
+                group_by=list(inputs.partition_by),
+                order_by=self._sort(
+                    *inputs.order_by, descending=reverse, nulls_last=reverse
+                ),
+                preceding=None,  # unbounded
+                following=0,
+            )
+
+            return [getattr(expr, func_name)().over(window) for expr in self(df)]
+
+        return func
+
+    def _rolling_window_func(
+        self,
+        *,
+        func_name: Literal["sum", "mean", "std", "var"],
+        center: bool,
+        window_size: int,
+        min_samples: int,
+        ddof: int | None = None,
+    ) -> IbisWindowFunction:
+        supported_funcs = ["sum", "mean", "std", "var"]
+
+        if center:
+            preceding = window_size // 2
+            following = window_size - preceding - 1
+        else:
+            preceding = window_size - 1
+            following = 0
+
+        def func(df: IbisLazyFrame, inputs: IbisWindowInputs) -> Sequence[ir.Value]:
+            window = ibis.window(
+                group_by=list(inputs.partition_by),
+                order_by=self._sort(*inputs.order_by),
+                preceding=preceding,
+                following=following,
+            )
+
+            def inner_f(expr: ir.NumericColumn) -> ir.Value:
+                if func_name in {"sum", "mean"}:
+                    func_ = getattr(expr, func_name)()
+                elif func_name == "var" and ddof == 0:
+                    func_ = expr.var(how="pop")
+                elif func_name in "var" and ddof == 1:
+                    func_ = expr.var(how="sample")
+                elif func_name == "std" and ddof == 0:
+                    func_ = expr.std(how="pop")
+                elif func_name == "std" and ddof == 1:
+                    func_ = expr.std(how="sample")
+                elif func_name in {"var", "std"}:  # pragma: no cover
+                    msg = f"Only ddof=0 and ddof=1 are currently supported for rolling_{func_name}."
+                    raise ValueError(msg)
+                else:  # pragma: no cover
+                    msg = f"Only the following functions are supported: {supported_funcs}.\nGot: {func_name}."
+                    raise ValueError(msg)
+
+                rolling_calc = func_.over(window)
+                valid_count = expr.count().over(window)
+                return ibis.cases(
+                    (valid_count >= ibis.literal(min_samples), rolling_calc),
+                    else_=ibis.null(),
+                )
+
+            return [inner_f(cast("ir.NumericColumn", expr)) for expr in self(df)]
+
+        return func
+
+    def broadcast(self, kind: Literal[ExprKind.AGGREGATION, ExprKind.LITERAL]) -> Self:
+        # Ibis does its own broadcasting.
+        return self
+
+    def _sort(
+        self, *cols: ir.Column | str, descending: bool = False, nulls_last: bool = False
+    ) -> Iterator[ir.Column]:
+        mapping = {
+            (False, False): partial(ibis.asc, nulls_first=True),
+            (False, True): partial(ibis.asc, nulls_first=False),
+            (True, False): partial(ibis.desc, nulls_first=True),
+            (True, True): partial(ibis.desc, nulls_first=False),
+        }
+        sort = mapping[(descending, nulls_last)]
+        yield from (cast("ir.Column", sort(col)) for col in cols)
+
+    @classmethod
+    def from_column_names(
+        cls: type[Self],
+        evaluate_column_names: EvalNames[IbisLazyFrame],
+        /,
+        *,
+        context: _FullContext,
+    ) -> Self:
+        def func(df: IbisLazyFrame) -> list[ir.Column]:
+            return [df.native[name] for name in evaluate_column_names(df)]
+
+        return cls(
+            func,
+            evaluate_output_names=evaluate_column_names,
+            alias_output_names=None,
+            backend_version=context._backend_version,
+            version=context._version,
+        )
+
+    @classmethod
+    def from_column_indices(cls, *column_indices: int, context: _FullContext) -> Self:
+        def func(df: IbisLazyFrame) -> list[ir.Column]:
+            return [df.native[i] for i in column_indices]
+
+        return cls(
+            func,
+            evaluate_output_names=cls._eval_names_indices(column_indices),
+            alias_output_names=None,
+            backend_version=context._backend_version,
+            version=context._version,
+        )
+
+    def _with_callable(
+        self, call: Callable[..., ir.Value], /, **expressifiable_args: Self | Any
+    ) -> Self:
+        """Create expression from callable.
+
+        Arguments:
+            call: Callable from compliant DataFrame to native Expression
+            expr_name: Expression name
+            expressifiable_args: arguments pass to expression which should be parsed
+                as expressions (e.g. in `nw.col('a').is_between('b', 'c')`)
+        """
+
+        def func(df: IbisLazyFrame) -> list[ir.Value]:
+            native_series_list = self(df)
+            other_native_series = {
+                key: df._evaluate_expr(value) if self._is_expr(value) else value
+                for key, value in expressifiable_args.items()
+            }
+            return [
+                call(native_series, **other_native_series)
+                for native_series in native_series_list
+            ]
+
+        return self.__class__(
+            func,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=self._alias_output_names,
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def _with_alias_output_names(self, func: AliasNames | None, /) -> Self:
+        return type(self)(
+            self._call,
+            self._window_function,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=func,
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def _with_window_function(self, window_function: IbisWindowFunction) -> Self:
+        return self.__class__(
+            self._call,
+            window_function,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=self._alias_output_names,
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    @classmethod
+    def _alias_native(cls, expr: ExprT, name: str, /) -> ExprT:
+        return cast("ExprT", expr.name(name))
+
+    def __and__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr & other, other=other)
+
+    def __or__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr | other, other=other)
+
+    def __add__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr + other, other=other)
+
+    def __truediv__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr / other, other=other)
+
+    def __rtruediv__(self, other: IbisExpr) -> Self:
+        return self._with_callable(
+            lambda expr, other: expr.__rtruediv__(other), other=other
+        ).alias("literal")
+
+    def __floordiv__(self, other: IbisExpr) -> Self:
+        return self._with_callable(
+            lambda expr, other: expr.__floordiv__(other), other=other
+        )
+
+    def __rfloordiv__(self, other: IbisExpr) -> Self:
+        return self._with_callable(
+            lambda expr, other: expr.__rfloordiv__(other), other=other
+        ).alias("literal")
+
+    def __mod__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr.__mod__(other), other=other)
+
+    def __rmod__(self, other: IbisExpr) -> Self:
+        return self._with_callable(
+            lambda expr, other: expr.__rmod__(other), other=other
+        ).alias("literal")
+
+    def __sub__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr - other, other=other)
+
+    def __rsub__(self, other: IbisExpr) -> Self:
+        return self._with_callable(
+            lambda expr, other: expr.__rsub__(other), other=other
+        ).alias("literal")
+
+    def __mul__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr * other, other=other)
+
+    def __pow__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr**other, other=other)
+
+    def __rpow__(self, other: IbisExpr) -> Self:
+        return self._with_callable(
+            lambda expr, other: expr.__rpow__(other), other=other
+        ).alias("literal")
+
+    def __lt__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr < other, other=other)
+
+    def __gt__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr > other, other=other)
+
+    def __le__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr <= other, other=other)
+
+    def __ge__(self, other: IbisExpr) -> Self:
+        return self._with_callable(lambda expr, other: expr >= other, other=other)
+
+    def __eq__(self, other: IbisExpr) -> Self:  # type: ignore[override]
+        return self._with_callable(lambda expr, other: expr == other, other=other)
+
+    def __ne__(self, other: IbisExpr) -> Self:  # type: ignore[override]
+        return self._with_callable(lambda expr, other: expr != other, other=other)
+
+    def __invert__(self) -> Self:
+        invert = cast("Callable[..., ir.Value]", operator.invert)
+        return self._with_callable(invert)
+
+    def abs(self) -> Self:
+        return self._with_callable(lambda expr: expr.abs())
+
+    def mean(self) -> Self:
+        return self._with_callable(lambda expr: expr.mean())
+
+    def median(self) -> Self:
+        return self._with_callable(lambda expr: expr.median())
+
+    def all(self) -> Self:
+        return self._with_callable(lambda expr: expr.all().fill_null(lit(True)))  # noqa: FBT003
+
+    def any(self) -> Self:
+        return self._with_callable(lambda expr: expr.any().fill_null(lit(False)))  # noqa: FBT003
+
+    def quantile(
+        self, quantile: float, interpolation: RollingInterpolationMethod
+    ) -> Self:
+        if interpolation != "linear":
+            msg = "Only linear interpolation methods are supported for Ibis quantile."
+            raise NotImplementedError(msg)
+        return self._with_callable(lambda expr: expr.quantile(quantile))
+
+    def clip(self, lower_bound: Any, upper_bound: Any) -> Self:
+        def _clip(expr: ir.NumericValue, lower: Any, upper: Any) -> ir.NumericValue:
+            return expr.clip(lower=lower, upper=upper)
+
+        return self._with_callable(_clip, lower=lower_bound, upper=upper_bound)
+
+    def sum(self) -> Self:
+        return self._with_callable(lambda expr: expr.sum().fill_null(lit(0)))
+
+    def n_unique(self) -> Self:
+        return self._with_callable(
+            lambda expr: expr.nunique() + expr.isnull().any().cast("int8")
+        )
+
+    def count(self) -> Self:
+        return self._with_callable(lambda expr: expr.count())
+
+    def len(self) -> Self:
+        def func(df: IbisLazyFrame) -> list[ir.IntegerScalar]:
+            return [df.native.count()]
+
+        return self.__class__(
+            func,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=self._alias_output_names,
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def std(self, ddof: int) -> Self:
+        def _std(expr: ir.NumericColumn, ddof: int) -> ir.Value:
+            if ddof == 0:
+                return expr.std(how="pop")
+            elif ddof == 1:
+                return expr.std(how="sample")
+            else:
+                n_samples = expr.count()
+                std_pop = expr.std(how="pop")
+                ddof_lit = cast("ir.IntegerScalar", ibis.literal(ddof))
+                return std_pop * n_samples.sqrt() / (n_samples - ddof_lit).sqrt()
+
+        return self._with_callable(lambda expr: _std(expr, ddof))
+
+    def var(self, ddof: int) -> Self:
+        def _var(expr: ir.NumericColumn, ddof: int) -> ir.Value:
+            if ddof == 0:
+                return expr.var(how="pop")
+            elif ddof == 1:
+                return expr.var(how="sample")
+            else:
+                n_samples = expr.count()
+                var_pop = expr.var(how="pop")
+                ddof_lit = cast("ir.IntegerScalar", ibis.literal(ddof))
+                return var_pop * n_samples / (n_samples - ddof_lit)
+
+        return self._with_callable(lambda expr: _var(expr, ddof))
+
+    def max(self) -> Self:
+        return self._with_callable(lambda expr: expr.max())
+
+    def min(self) -> Self:
+        return self._with_callable(lambda expr: expr.min())
+
+    def null_count(self) -> Self:
+        return self._with_callable(lambda expr: expr.isnull().sum())
+
+    def over(self, partition_by: Sequence[str], order_by: Sequence[str]) -> Self:
+        def func(df: IbisLazyFrame) -> Sequence[ir.Value]:
+            return self.window_function(df, WindowInputs(partition_by, order_by))
+
+        return self.__class__(
+            func,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=self._alias_output_names,
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def is_null(self) -> Self:
+        return self._with_callable(lambda expr: expr.isnull())
+
+    def is_nan(self) -> Self:
+        def func(expr: ir.FloatingValue | Any) -> ir.Value:
+            otherwise = expr.isnan() if is_floating(expr.type()) else False
+            return ibis.ifelse(expr.isnull(), None, otherwise)
+
+        return self._with_callable(func)
+
+    def is_finite(self) -> Self:
+        return self._with_callable(lambda expr: ~(expr.isinf() | expr.isnan()))
+
+    def is_in(self, other: Sequence[Any]) -> Self:
+        return self._with_callable(lambda expr: expr.isin(other))
+
+    def round(self, decimals: int) -> Self:
+        return self._with_callable(lambda expr: expr.round(decimals))
+
+    def shift(self, n: int) -> Self:
+        def _func(df: IbisLazyFrame, inputs: IbisWindowInputs) -> Sequence[ir.Value]:
+            return [
+                expr.lag(n).over(  # type: ignore[attr-defined, unused-ignore]
+                    ibis.window(
+                        group_by=inputs.partition_by,
+                        order_by=self._sort(*inputs.order_by),
+                    )
+                )
+                for expr in self(df)
+            ]
+
+        return self._with_window_function(_func)
+
+    def is_first_distinct(self) -> Self:
+        def func(
+            df: IbisLazyFrame, inputs: IbisWindowInputs
+        ) -> Sequence[ir.BooleanValue]:
+            # ibis row_number starts at 0, so need to compare with 0 instead of the usual `1`
+            return [
+                ibis.row_number().over(
+                    ibis.window(
+                        group_by=[*inputs.partition_by, expr],
+                        order_by=self._sort(*inputs.order_by),
+                    )
+                )
+                == lit(0)
+                for expr in self(df)
+            ]
+
+        return self._with_window_function(func)
+
+    def is_last_distinct(self) -> Self:
+        def func(
+            df: IbisLazyFrame, inputs: IbisWindowInputs
+        ) -> Sequence[ir.BooleanValue]:
+            # ibis row_number starts at 0, so need to compare with 0 instead of the usual `1`
+            return [
+                ibis.row_number().over(
+                    ibis.window(
+                        group_by=[*inputs.partition_by, expr],
+                        order_by=self._sort(
+                            *inputs.order_by, descending=True, nulls_last=True
+                        ),
+                    )
+                )
+                == lit(0)
+                for expr in self(df)
+            ]
+
+        return self._with_window_function(func)
+
+    def diff(self) -> Self:
+        def _func(df: IbisLazyFrame, inputs: IbisWindowInputs) -> Sequence[ir.Value]:
+            return [
+                expr
+                - expr.lag().over(  # type: ignore[attr-defined, unused-ignore]
+                    ibis.window(
+                        following=0,
+                        group_by=inputs.partition_by,
+                        order_by=self._sort(*inputs.order_by),
+                    )
+                )
+                for expr in self(df)
+            ]
+
+        return self._with_window_function(_func)
+
+    def cum_sum(self, *, reverse: bool) -> Self:
+        return self._with_window_function(
+            self._cum_window_func(reverse=reverse, func_name="sum")
+        )
+
+    def cum_max(self, *, reverse: bool) -> Self:
+        return self._with_window_function(
+            self._cum_window_func(reverse=reverse, func_name="max")
+        )
+
+    def cum_min(self, *, reverse: bool) -> Self:
+        return self._with_window_function(
+            self._cum_window_func(reverse=reverse, func_name="min")
+        )
+
+    def cum_count(self, *, reverse: bool) -> Self:
+        return self._with_window_function(
+            self._cum_window_func(reverse=reverse, func_name="count")
+        )
+
+    def rolling_sum(self, window_size: int, *, min_samples: int, center: bool) -> Self:
+        return self._with_window_function(
+            self._rolling_window_func(
+                func_name="sum",
+                center=center,
+                window_size=window_size,
+                min_samples=min_samples,
+            )
+        )
+
+    def rolling_mean(self, window_size: int, *, min_samples: int, center: bool) -> Self:
+        return self._with_window_function(
+            self._rolling_window_func(
+                func_name="mean",
+                center=center,
+                window_size=window_size,
+                min_samples=min_samples,
+            )
+        )
+
+    def rolling_var(
+        self, window_size: int, *, min_samples: int, center: bool, ddof: int
+    ) -> Self:
+        return self._with_window_function(
+            self._rolling_window_func(
+                func_name="var",
+                center=center,
+                window_size=window_size,
+                min_samples=min_samples,
+                ddof=ddof,
+            )
+        )
+
+    def rolling_std(
+        self, window_size: int, *, min_samples: int, center: bool, ddof: int
+    ) -> Self:
+        return self._with_window_function(
+            self._rolling_window_func(
+                func_name="std",
+                center=center,
+                window_size=window_size,
+                min_samples=min_samples,
+                ddof=ddof,
+            )
+        )
+
+    def fill_null(self, value: Self | Any, strategy: Any, limit: int | None) -> Self:
+        # Ibis doesn't yet allow ignoring nulls in first/last with window functions, which makes forward/backward
+        # strategies inconsistent when there are nulls present: https://github.com/ibis-project/ibis/issues/9539
+        if strategy is not None:
+            msg = "`strategy` is not supported for the Ibis backend"
+            raise NotImplementedError(msg)
+        if limit is not None:
+            msg = "`limit` is not supported for the Ibis backend"  # pragma: no cover
+            raise NotImplementedError(msg)
+
+        def _fill_null(expr: ir.Value, value: ir.Scalar) -> ir.Value:
+            return expr.fill_null(value)
+
+        return self._with_callable(_fill_null, value=value)
+
+    def cast(self, dtype: IntoDType) -> Self:
+        def _func(expr: ir.Column) -> ir.Value:
+            native_dtype = narwhals_to_native_dtype(dtype, self._version)
+            # ibis `cast` overloads do not include DataType, only literals
+            return expr.cast(native_dtype)  # type: ignore[unused-ignore]
+
+        return self._with_callable(_func)
+
+    def is_unique(self) -> Self:
+        return self._with_callable(
+            lambda expr: expr.isnull().count().over(ibis.window(group_by=(expr))) == 1
+        )
+
+    def rank(self, method: RankMethod, *, descending: bool) -> Self:
+        def _rank(expr: ir.Column) -> ir.Column:
+            order_by = next(self._sort(expr, descending=descending, nulls_last=True))
+            window = ibis.window(order_by=order_by)
+
+            if method == "dense":
+                rank_ = order_by.dense_rank()
+            elif method == "ordinal":
+                rank_ = cast("ir.IntegerColumn", ibis.row_number().over(window))
+            else:
+                rank_ = order_by.rank()
+
+            # Ibis uses 0-based ranking. Add 1 to match polars 1-based rank.
+            rank_ = rank_ + cast("ir.IntegerValue", lit(1))
+
+            # For "max" and "average", adjust using the count of rows in the partition.
+            if method == "max":
+                # Define a window partitioned by expr (i.e. each distinct value)
+                partition = ibis.window(group_by=[expr])
+                cnt = cast("ir.IntegerValue", expr.count().over(partition))
+                rank_ = rank_ + cnt - cast("ir.IntegerValue", lit(1))
+            elif method == "average":
+                partition = ibis.window(group_by=[expr])
+                cnt = cast("ir.IntegerValue", expr.count().over(partition))
+                avg = cast(
+                    "ir.NumericValue", (cnt - cast("ir.IntegerScalar", lit(1))) / lit(2.0)
+                )
+                rank_ = rank_ + avg
+
+            return cast("ir.Column", ibis.cases((expr.notnull(), rank_)))
+
+        return self._with_callable(_rank)
+
+    def log(self, base: float) -> Self:
+        def _log(expr: ir.NumericColumn) -> ir.Value:
+            otherwise = expr.log(cast("ir.NumericValue", lit(base)))
+            return ibis.cases(
+                (expr < lit(0), lit(float("nan"))),
+                (expr == lit(0), lit(float("-inf"))),
+                else_=otherwise,
+            )
+
+        return self._with_callable(_log)
+
+    def exp(self) -> Self:
+        def _exp(expr: ir.NumericColumn) -> ir.Value:
+            return expr.exp()
+
+        return self._with_callable(_exp)
+
+    @property
+    def str(self) -> IbisExprStringNamespace:
+        return IbisExprStringNamespace(self)
+
+    @property
+    def dt(self) -> IbisExprDateTimeNamespace:
+        return IbisExprDateTimeNamespace(self)
+
+    @property
+    def list(self) -> IbisExprListNamespace:
+        return IbisExprListNamespace(self)
+
+    @property
+    def struct(self) -> IbisExprStructNamespace:
+        return IbisExprStructNamespace(self)
+
+    # NOTE: https://github.com/ibis-project/ibis/issues/10542
+    cum_prod = not_implemented()
+    drop_nulls = not_implemented()
+
+    # NOTE: https://github.com/ibis-project/ibis/issues/11176
+    skew = not_implemented()
+    unique = not_implemented()
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_dt.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_dt.py
new file mode 100644
index 0000000..14d9d06
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_dt.py
@@ -0,0 +1,98 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Callable
+
+from narwhals._duration import parse_interval_string
+from narwhals._ibis.utils import UNITS_DICT_BUCKET, UNITS_DICT_TRUNCATE
+from narwhals._utils import not_implemented
+
+if TYPE_CHECKING:
+    import ibis.expr.types as ir
+
+    from narwhals._ibis.expr import IbisExpr
+    from narwhals._ibis.utils import BucketUnit, TruncateUnit
+
+
+class IbisExprDateTimeNamespace:
+    def __init__(self, expr: IbisExpr) -> None:
+        self._compliant_expr = expr
+
+    def year(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.year())
+
+    def month(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.month())
+
+    def day(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.day())
+
+    def hour(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.hour())
+
+    def minute(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.minute())
+
+    def second(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.second())
+
+    def millisecond(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.millisecond())
+
+    def microsecond(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.microsecond())
+
+    def to_string(self, format: str) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.strftime(format))
+
+    def weekday(self) -> IbisExpr:
+        # Ibis uses 0-6 for Monday-Sunday. Add 1 to match polars.
+        return self._compliant_expr._with_callable(
+            lambda expr: expr.day_of_week.index() + 1
+        )
+
+    def ordinal_day(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.day_of_year())
+
+    def date(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.date())
+
+    def _bucket(self, kwds: dict[BucketUnit, Any], /) -> Callable[..., ir.TimestampValue]:
+        def fn(expr: ir.TimestampValue) -> ir.TimestampValue:
+            return expr.bucket(**kwds)
+
+        return fn
+
+    def _truncate(self, unit: TruncateUnit, /) -> Callable[..., ir.TimestampValue]:
+        def fn(expr: ir.TimestampValue) -> ir.TimestampValue:
+            return expr.truncate(unit)
+
+        return fn
+
+    def truncate(self, every: str) -> IbisExpr:
+        multiple, unit = parse_interval_string(every)
+        if unit == "q":
+            multiple, unit = 3 * multiple, "mo"
+        if multiple != 1:
+            if self._compliant_expr._backend_version < (7, 1):  # pragma: no cover
+                msg = "Truncating datetimes with multiples of the unit is only supported in Ibis >= 7.1."
+                raise NotImplementedError(msg)
+            fn = self._bucket({UNITS_DICT_BUCKET[unit]: multiple})
+        else:
+            fn = self._truncate(UNITS_DICT_TRUNCATE[unit])
+        return self._compliant_expr._with_callable(fn)
+
+    def replace_time_zone(self, time_zone: str | None) -> IbisExpr:
+        if time_zone is None:
+            return self._compliant_expr._with_callable(
+                lambda _input: _input.cast("timestamp")
+            )
+        else:  # pragma: no cover
+            msg = "`replace_time_zone` with non-null `time_zone` not yet implemented for Ibis"
+            raise NotImplementedError(msg)
+
+    nanosecond = not_implemented()
+    total_minutes = not_implemented()
+    total_seconds = not_implemented()
+    total_milliseconds = not_implemented()
+    total_microseconds = not_implemented()
+    total_nanoseconds = not_implemented()
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_list.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_list.py
new file mode 100644
index 0000000..b29fc83
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_list.py
@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from narwhals._ibis.expr import IbisExpr
+
+
+class IbisExprListNamespace:
+    def __init__(self, expr: IbisExpr) -> None:
+        self._compliant_expr = expr
+
+    def len(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.length())
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_str.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_str.py
new file mode 100644
index 0000000..1c0d6e5
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_str.py
@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Callable
+
+from ibis.expr.datatypes import Timestamp
+
+from narwhals._utils import _is_naive_format, not_implemented
+
+if TYPE_CHECKING:
+    import ibis.expr.types as ir
+
+    from narwhals._ibis.expr import IbisExpr
+
+
+class IbisExprStringNamespace:
+    def __init__(self, expr: IbisExpr) -> None:
+        self._compliant_expr = expr
+
+    def starts_with(self, prefix: str) -> IbisExpr:
+        def fn(expr: ir.StringColumn) -> ir.BooleanValue:
+            return expr.startswith(prefix)
+
+        return self._compliant_expr._with_callable(fn)
+
+    def ends_with(self, suffix: str) -> IbisExpr:
+        def fn(expr: ir.StringColumn) -> ir.BooleanValue:
+            return expr.endswith(suffix)
+
+        return self._compliant_expr._with_callable(fn)
+
+    def contains(self, pattern: str, *, literal: bool) -> IbisExpr:
+        def fn(expr: ir.StringColumn) -> ir.BooleanValue:
+            return expr.contains(pattern) if literal else expr.re_search(pattern)
+
+        return self._compliant_expr._with_callable(fn)
+
+    def slice(self, offset: int, length: int) -> IbisExpr:
+        def fn(expr: ir.StringColumn) -> ir.StringValue:
+            return expr.substr(start=offset, length=length)
+
+        return self._compliant_expr._with_callable(fn)
+
+    def split(self, by: str) -> IbisExpr:
+        def fn(expr: ir.StringColumn) -> ir.ArrayValue:
+            return expr.split(by)
+
+        return self._compliant_expr._with_callable(fn)
+
+    def len_chars(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.length())
+
+    def to_lowercase(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.lower())
+
+    def to_uppercase(self) -> IbisExpr:
+        return self._compliant_expr._with_callable(lambda expr: expr.upper())
+
+    def strip_chars(self, characters: str | None) -> IbisExpr:
+        if characters is not None:
+            msg = "Ibis does not support `characters` argument in `str.strip_chars`"
+            raise NotImplementedError(msg)
+
+        return self._compliant_expr._with_callable(lambda expr: expr.strip())
+
+    def _replace_all(self, pattern: str, value: str) -> Callable[..., ir.StringValue]:
+        def fn(expr: ir.StringColumn) -> ir.StringValue:
+            return expr.re_replace(pattern, value)
+
+        return fn
+
+    def _replace_all_literal(
+        self, pattern: str, value: str
+    ) -> Callable[..., ir.StringValue]:
+        def fn(expr: ir.StringColumn) -> ir.StringValue:
+            return expr.replace(pattern, value)  # pyright: ignore[reportArgumentType]
+
+        return fn
+
+    def replace_all(self, pattern: str, value: str, *, literal: bool) -> IbisExpr:
+        fn = self._replace_all_literal if literal else self._replace_all
+        return self._compliant_expr._with_callable(fn(pattern, value))
+
+    def _to_datetime(self, format: str) -> Callable[..., ir.TimestampValue]:
+        def fn(expr: ir.StringColumn) -> ir.TimestampValue:
+            return expr.as_timestamp(format)
+
+        return fn
+
+    def _to_datetime_naive(self, format: str) -> Callable[..., ir.TimestampValue]:
+        def fn(expr: ir.StringColumn) -> ir.TimestampValue:
+            dtype: Any = Timestamp(timezone=None)
+            return expr.as_timestamp(format).cast(dtype)
+
+        return fn
+
+    def to_datetime(self, format: str | None) -> IbisExpr:
+        if format is None:
+            msg = "Cannot infer format with Ibis backend"
+            raise NotImplementedError(msg)
+        fn = self._to_datetime_naive if _is_naive_format(format) else self._to_datetime
+        return self._compliant_expr._with_callable(fn(format))
+
+    replace = not_implemented()
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_struct.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_struct.py
new file mode 100644
index 0000000..f268281
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/expr_struct.py
@@ -0,0 +1,19 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import ibis.expr.types as ir
+
+    from narwhals._ibis.expr import IbisExpr
+
+
+class IbisExprStructNamespace:
+    def __init__(self, expr: IbisExpr) -> None:
+        self._compliant_expr = expr
+
+    def field(self, name: str) -> IbisExpr:
+        def func(expr: ir.StructColumn) -> ir.Column:
+            return expr[name]
+
+        return self._compliant_expr._with_callable(func).alias(name)
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/group_by.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/group_by.py
new file mode 100644
index 0000000..54fa037
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/group_by.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Sequence
+
+from narwhals._compliant import LazyGroupBy
+
+if TYPE_CHECKING:
+    import ibis.expr.types as ir  # noqa: F401
+
+    from narwhals._ibis.dataframe import IbisLazyFrame
+    from narwhals._ibis.expr import IbisExpr
+
+
+class IbisGroupBy(LazyGroupBy["IbisLazyFrame", "IbisExpr", "ir.Value"]):
+    def __init__(
+        self,
+        df: IbisLazyFrame,
+        keys: Sequence[str] | Sequence[IbisExpr],
+        /,
+        *,
+        drop_null_keys: bool,
+    ) -> None:
+        frame, self._keys, self._output_key_names = self._parse_keys(df, keys=keys)
+        self._compliant_frame = frame.drop_nulls(self._keys) if drop_null_keys else frame
+
+    def agg(self, *exprs: IbisExpr) -> IbisLazyFrame:
+        native = self.compliant.native
+        return self.compliant._with_native(
+            native.group_by(self._keys).aggregate(*self._evaluate_exprs(exprs))
+        ).rename(dict(zip(self._keys, self._output_key_names)))
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/namespace.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/namespace.py
new file mode 100644
index 0000000..25393cf
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/namespace.py
@@ -0,0 +1,227 @@
+from __future__ import annotations
+
+import operator
+from functools import reduce
+from itertools import chain
+from typing import TYPE_CHECKING, Any, Iterable, Sequence, cast
+
+import ibis
+import ibis.expr.types as ir
+
+from narwhals._compliant import LazyNamespace, LazyThen, LazyWhen
+from narwhals._expression_parsing import (
+    combine_alias_output_names,
+    combine_evaluate_output_names,
+)
+from narwhals._ibis.dataframe import IbisLazyFrame
+from narwhals._ibis.expr import IbisExpr
+from narwhals._ibis.selectors import IbisSelectorNamespace
+from narwhals._ibis.utils import lit, narwhals_to_native_dtype
+from narwhals._utils import Implementation, requires
+
+if TYPE_CHECKING:
+    from narwhals._utils import Version
+    from narwhals.typing import ConcatMethod, IntoDType
+
+
+class IbisNamespace(LazyNamespace[IbisLazyFrame, IbisExpr, "ir.Table"]):
+    _implementation: Implementation = Implementation.IBIS
+
+    def __init__(self, *, backend_version: tuple[int, ...], version: Version) -> None:
+        self._backend_version = backend_version
+        self._version = version
+
+    @property
+    def selectors(self) -> IbisSelectorNamespace:
+        return IbisSelectorNamespace.from_namespace(self)
+
+    @property
+    def _expr(self) -> type[IbisExpr]:
+        return IbisExpr
+
+    @property
+    def _lazyframe(self) -> type[IbisLazyFrame]:
+        return IbisLazyFrame
+
+    def concat(
+        self, items: Iterable[IbisLazyFrame], *, how: ConcatMethod
+    ) -> IbisLazyFrame:
+        if how == "diagonal":
+            msg = "diagonal concat not supported for Ibis. Please join instead."
+            raise NotImplementedError(msg)
+
+        items = list(items)
+        native_items = [item.native for item in items]
+        schema = items[0].schema
+        if not all(x.schema == schema for x in items[1:]):
+            msg = "inputs should all have the same schema"
+            raise TypeError(msg)
+        return self._lazyframe.from_native(ibis.union(*native_items), context=self)
+
+    def concat_str(
+        self, *exprs: IbisExpr, separator: str, ignore_nulls: bool
+    ) -> IbisExpr:
+        def func(df: IbisLazyFrame) -> list[ir.Value]:
+            cols = list(chain.from_iterable(expr(df) for expr in exprs))
+            cols_casted = [s.cast("string") for s in cols]
+
+            if not ignore_nulls:
+                result = cols_casted[0]
+                for col in cols_casted[1:]:
+                    result = result + separator + col
+            else:
+                sep = cast("ir.StringValue", lit(separator))
+                result = sep.join(cols_casted)
+
+            return [result]
+
+        return self._expr(
+            call=func,
+            evaluate_output_names=combine_evaluate_output_names(*exprs),
+            alias_output_names=combine_alias_output_names(*exprs),
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def all_horizontal(self, *exprs: IbisExpr) -> IbisExpr:
+        def func(df: IbisLazyFrame) -> list[ir.Value]:
+            cols = chain.from_iterable(expr(df) for expr in exprs)
+            return [reduce(operator.and_, cols)]
+
+        return self._expr(
+            call=func,
+            evaluate_output_names=combine_evaluate_output_names(*exprs),
+            alias_output_names=combine_alias_output_names(*exprs),
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def any_horizontal(self, *exprs: IbisExpr) -> IbisExpr:
+        def func(df: IbisLazyFrame) -> list[ir.Value]:
+            cols = chain.from_iterable(expr(df) for expr in exprs)
+            return [reduce(operator.or_, cols)]
+
+        return self._expr(
+            call=func,
+            evaluate_output_names=combine_evaluate_output_names(*exprs),
+            alias_output_names=combine_alias_output_names(*exprs),
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def max_horizontal(self, *exprs: IbisExpr) -> IbisExpr:
+        def func(df: IbisLazyFrame) -> list[ir.Value]:
+            cols = chain.from_iterable(expr(df) for expr in exprs)
+            return [ibis.greatest(*cols)]
+
+        return self._expr(
+            call=func,
+            evaluate_output_names=combine_evaluate_output_names(*exprs),
+            alias_output_names=combine_alias_output_names(*exprs),
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def min_horizontal(self, *exprs: IbisExpr) -> IbisExpr:
+        def func(df: IbisLazyFrame) -> list[ir.Value]:
+            cols = chain.from_iterable(expr(df) for expr in exprs)
+            return [ibis.least(*cols)]
+
+        return self._expr(
+            call=func,
+            evaluate_output_names=combine_evaluate_output_names(*exprs),
+            alias_output_names=combine_alias_output_names(*exprs),
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def sum_horizontal(self, *exprs: IbisExpr) -> IbisExpr:
+        def func(df: IbisLazyFrame) -> list[ir.Value]:
+            cols = [e.fill_null(lit(0)) for _expr in exprs for e in _expr(df)]
+            return [reduce(operator.add, cols)]
+
+        return self._expr(
+            call=func,
+            evaluate_output_names=combine_evaluate_output_names(*exprs),
+            alias_output_names=combine_alias_output_names(*exprs),
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def mean_horizontal(self, *exprs: IbisExpr) -> IbisExpr:
+        def func(df: IbisLazyFrame) -> list[ir.Value]:
+            expr = (
+                cast("ir.NumericColumn", e.fill_null(lit(0)))
+                for _expr in exprs
+                for e in _expr(df)
+            )
+            non_null = (
+                cast("ir.NumericColumn", e.isnull().ifelse(lit(0), lit(1)))
+                for _expr in exprs
+                for e in _expr(df)
+            )
+
+            return [
+                (reduce(lambda x, y: x + y, expr) / reduce(lambda x, y: x + y, non_null))
+            ]
+
+        return self._expr(
+            call=func,
+            evaluate_output_names=combine_evaluate_output_names(*exprs),
+            alias_output_names=combine_alias_output_names(*exprs),
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    @requires.backend_version((10, 0))
+    def when(self, predicate: IbisExpr) -> IbisWhen:
+        return IbisWhen.from_expr(predicate, context=self)
+
+    def lit(self, value: Any, dtype: IntoDType | None) -> IbisExpr:
+        def func(_df: IbisLazyFrame) -> list[ir.Value]:
+            ibis_dtype = narwhals_to_native_dtype(dtype, self._version) if dtype else None
+            return [lit(value, ibis_dtype)]
+
+        return self._expr(
+            func,
+            evaluate_output_names=lambda _df: ["literal"],
+            alias_output_names=None,
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+    def len(self) -> IbisExpr:
+        def func(_df: IbisLazyFrame) -> list[ir.Value]:
+            return [_df.native.count()]
+
+        return self._expr(
+            call=func,
+            evaluate_output_names=lambda _df: ["len"],
+            alias_output_names=None,
+            backend_version=self._backend_version,
+            version=self._version,
+        )
+
+
+class IbisWhen(LazyWhen["IbisLazyFrame", "ir.Value", IbisExpr]):
+    lit = lit
+
+    @property
+    def _then(self) -> type[IbisThen]:
+        return IbisThen
+
+    def __call__(self, df: IbisLazyFrame) -> Sequence[ir.Value]:
+        is_expr = self._condition._is_expr
+        condition = df._evaluate_expr(self._condition)
+        then_ = self._then_value
+        then = df._evaluate_expr(then_) if is_expr(then_) else lit(then_)
+        other_ = self._otherwise_value
+        if other_ is None:
+            result = ibis.cases((condition, then))
+        else:
+            otherwise = df._evaluate_expr(other_) if is_expr(other_) else lit(other_)
+            result = ibis.cases((condition, then), else_=otherwise)
+        return [result]
+
+
+class IbisThen(LazyThen["IbisLazyFrame", "ir.Value", IbisExpr], IbisExpr): ...
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/selectors.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/selectors.py
new file mode 100644
index 0000000..f96243b
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/selectors.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from narwhals._compliant import CompliantSelector, LazySelectorNamespace
+from narwhals._ibis.expr import IbisExpr
+
+if TYPE_CHECKING:
+    import ibis.expr.types as ir  # noqa: F401
+
+    from narwhals._ibis.dataframe import IbisLazyFrame  # noqa: F401
+
+
+class IbisSelectorNamespace(LazySelectorNamespace["IbisLazyFrame", "ir.Value"]):
+    @property
+    def _selector(self) -> type[IbisSelector]:
+        return IbisSelector
+
+
+class IbisSelector(  # type: ignore[misc]
+    CompliantSelector["IbisLazyFrame", "ir.Value"], IbisExpr
+):
+    def _to_expr(self) -> IbisExpr:
+        return IbisExpr(
+            self._call,
+            evaluate_output_names=self._evaluate_output_names,
+            alias_output_names=self._alias_output_names,
+            backend_version=self._backend_version,
+            version=self._version,
+        )
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/series.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/series.py
new file mode 100644
index 0000000..3c55d3c
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/series.py
@@ -0,0 +1,41 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, NoReturn
+
+from narwhals._ibis.utils import native_to_narwhals_dtype
+from narwhals.dependencies import get_ibis
+
+if TYPE_CHECKING:
+    from types import ModuleType
+
+    from typing_extensions import Self
+
+    from narwhals._utils import Version
+    from narwhals.dtypes import DType
+
+
+class IbisInterchangeSeries:
+    def __init__(self, df: Any, version: Version) -> None:
+        self._native_series = df
+        self._version = version
+
+    def __narwhals_series__(self) -> Self:
+        return self
+
+    def __native_namespace__(self) -> ModuleType:
+        return get_ibis()
+
+    @property
+    def dtype(self) -> DType:
+        return native_to_narwhals_dtype(
+            self._native_series.schema().types[0], self._version
+        )
+
+    def __getattr__(self, attr: str) -> NoReturn:
+        msg = (
+            f"Attribute {attr} is not supported for interchange-level dataframes.\n\n"
+            "If you would like to see this kind of object better supported in "
+            "Narwhals, please open a feature request "
+            "at https://github.com/narwhals-dev/narwhals/issues."
+        )
+        raise NotImplementedError(msg)
diff --git a/venv/lib/python3.8/site-packages/narwhals/_ibis/utils.py b/venv/lib/python3.8/site-packages/narwhals/_ibis/utils.py
new file mode 100644
index 0000000..477781f
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_ibis/utils.py
@@ -0,0 +1,227 @@
+from __future__ import annotations
+
+from functools import lru_cache
+from typing import TYPE_CHECKING, Any, Literal, Mapping
+
+import ibis
+import ibis.expr.datatypes as ibis_dtypes
+
+from narwhals._utils import isinstance_or_issubclass
+
+if TYPE_CHECKING:
+    import ibis.expr.types as ir
+    from ibis.expr.datatypes import DataType as IbisDataType
+    from typing_extensions import TypeAlias, TypeIs
+
+    from narwhals._duration import IntervalUnit
+    from narwhals._ibis.dataframe import IbisLazyFrame
+    from narwhals._ibis.expr import IbisExpr
+    from narwhals._utils import Version
+    from narwhals.dtypes import DType
+    from narwhals.typing import IntoDType
+
+lit = ibis.literal
+"""Alias for `ibis.literal`."""
+
+BucketUnit: TypeAlias = Literal[
+    "years",
+    "quarters",
+    "months",
+    "days",
+    "hours",
+    "minutes",
+    "seconds",
+    "milliseconds",
+    "microseconds",
+    "nanoseconds",
+]
+TruncateUnit: TypeAlias = Literal[
+    "Y", "Q", "M", "W", "D", "h", "m", "s", "ms", "us", "ns"
+]
+
+UNITS_DICT_BUCKET: Mapping[IntervalUnit, BucketUnit] = {
+    "y": "years",
+    "q": "quarters",
+    "mo": "months",
+    "d": "days",
+    "h": "hours",
+    "m": "minutes",
+    "s": "seconds",
+    "ms": "milliseconds",
+    "us": "microseconds",
+    "ns": "nanoseconds",
+}
+
+UNITS_DICT_TRUNCATE: Mapping[IntervalUnit, TruncateUnit] = {
+    "y": "Y",
+    "q": "Q",
+    "mo": "M",
+    "d": "D",
+    "h": "h",
+    "m": "m",
+    "s": "s",
+    "ms": "ms",
+    "us": "us",
+    "ns": "ns",
+}
+
+
+def evaluate_exprs(df: IbisLazyFrame, /, *exprs: IbisExpr) -> list[tuple[str, ir.Value]]:
+    native_results: list[tuple[str, ir.Value]] = []
+    for expr in exprs:
+        native_series_list = expr(df)
+        output_names = expr._evaluate_output_names(df)
+        if expr._alias_output_names is not None:
+            output_names = expr._alias_output_names(output_names)
+        if len(output_names) != len(native_series_list):  # pragma: no cover
+            msg = f"Internal error: got output names {output_names}, but only got {len(native_series_list)} results"
+            raise AssertionError(msg)
+        native_results.extend(zip(output_names, native_series_list))
+    return native_results
+
+
+@lru_cache(maxsize=16)
+def native_to_narwhals_dtype(ibis_dtype: IbisDataType, version: Version) -> DType:  # noqa: C901, PLR0912
+    dtypes = version.dtypes
+    if ibis_dtype.is_int64():
+        return dtypes.Int64()
+    if ibis_dtype.is_int32():
+        return dtypes.Int32()
+    if ibis_dtype.is_int16():
+        return dtypes.Int16()
+    if ibis_dtype.is_int8():
+        return dtypes.Int8()
+    if ibis_dtype.is_uint64():
+        return dtypes.UInt64()
+    if ibis_dtype.is_uint32():
+        return dtypes.UInt32()
+    if ibis_dtype.is_uint16():
+        return dtypes.UInt16()
+    if ibis_dtype.is_uint8():
+        return dtypes.UInt8()
+    if ibis_dtype.is_boolean():
+        return dtypes.Boolean()
+    if ibis_dtype.is_float64():
+        return dtypes.Float64()
+    if ibis_dtype.is_float32():
+        return dtypes.Float32()
+    if ibis_dtype.is_string():
+        return dtypes.String()
+    if ibis_dtype.is_date():
+        return dtypes.Date()
+    if ibis_dtype.is_timestamp():
+        return dtypes.Datetime()
+    if is_interval(ibis_dtype):
+        _time_unit = ibis_dtype.unit.value
+        if _time_unit not in {"ns", "us", "ms", "s"}:  # pragma: no cover
+            msg = f"Unsupported interval unit: {_time_unit}"
+            raise NotImplementedError(msg)
+        return dtypes.Duration(_time_unit)
+    if is_array(ibis_dtype):
+        if ibis_dtype.length:
+            return dtypes.Array(
+                native_to_narwhals_dtype(ibis_dtype.value_type, version),
+                ibis_dtype.length,
+            )
+        else:
+            return dtypes.List(native_to_narwhals_dtype(ibis_dtype.value_type, version))
+    if is_struct(ibis_dtype):
+        return dtypes.Struct(
+            [
+                dtypes.Field(name, native_to_narwhals_dtype(dtype, version))
+                for name, dtype in ibis_dtype.items()
+            ]
+        )
+    if ibis_dtype.is_decimal():  # pragma: no cover
+        return dtypes.Decimal()
+    if ibis_dtype.is_time():
+        return dtypes.Time()
+    if ibis_dtype.is_binary():
+        return dtypes.Binary()
+    return dtypes.Unknown()  # pragma: no cover
+
+
+def is_interval(obj: IbisDataType) -> TypeIs[ibis_dtypes.Interval]:
+    return obj.is_interval()
+
+
+def is_array(obj: IbisDataType) -> TypeIs[ibis_dtypes.Array[Any]]:
+    return obj.is_array()
+
+
+def is_struct(obj: IbisDataType) -> TypeIs[ibis_dtypes.Struct]:
+    return obj.is_struct()
+
+
+def is_floating(obj: IbisDataType) -> TypeIs[ibis_dtypes.Floating]:
+    return obj.is_floating()
+
+
+def narwhals_to_native_dtype(  # noqa: C901, PLR0912
+    dtype: IntoDType, version: Version
+) -> IbisDataType:
+    dtypes = version.dtypes
+
+    if isinstance_or_issubclass(dtype, dtypes.Decimal):  # pragma: no cover
+        return ibis_dtypes.Decimal()
+    if isinstance_or_issubclass(dtype, dtypes.Float64):
+        return ibis_dtypes.Float64()
+    if isinstance_or_issubclass(dtype, dtypes.Float32):
+        return ibis_dtypes.Float32()
+    if isinstance_or_issubclass(dtype, dtypes.Int128):  # pragma: no cover
+        msg = "Int128 not supported by Ibis"
+        raise NotImplementedError(msg)
+    if isinstance_or_issubclass(dtype, dtypes.Int64):
+        return ibis_dtypes.Int64()
+    if isinstance_or_issubclass(dtype, dtypes.Int32):
+        return ibis_dtypes.Int32()
+    if isinstance_or_issubclass(dtype, dtypes.Int16):
+        return ibis_dtypes.Int16()
+    if isinstance_or_issubclass(dtype, dtypes.Int8):
+        return ibis_dtypes.Int8()
+    if isinstance_or_issubclass(dtype, dtypes.UInt128):  # pragma: no cover
+        msg = "UInt128 not supported by Ibis"
+        raise NotImplementedError(msg)
+    if isinstance_or_issubclass(dtype, dtypes.UInt64):
+        return ibis_dtypes.UInt64()
+    if isinstance_or_issubclass(dtype, dtypes.UInt32):
+        return ibis_dtypes.UInt32()
+    if isinstance_or_issubclass(dtype, dtypes.UInt16):
+        return ibis_dtypes.UInt16()
+    if isinstance_or_issubclass(dtype, dtypes.UInt8):
+        return ibis_dtypes.UInt8()
+    if isinstance_or_issubclass(dtype, dtypes.String):
+        return ibis_dtypes.String()
+    if isinstance_or_issubclass(dtype, dtypes.Boolean):
+        return ibis_dtypes.Boolean()
+    if isinstance_or_issubclass(dtype, dtypes.Categorical):
+        msg = "Categorical not supported by Ibis"
+        raise NotImplementedError(msg)
+    if isinstance_or_issubclass(dtype, dtypes.Datetime):
+        return ibis_dtypes.Timestamp()
+    if isinstance_or_issubclass(dtype, dtypes.Duration):
+        return ibis_dtypes.Interval(unit=dtype.time_unit)  # pyright: ignore[reportArgumentType]
+    if isinstance_or_issubclass(dtype, dtypes.Date):
+        return ibis_dtypes.Date()
+    if isinstance_or_issubclass(dtype, dtypes.Time):
+        return ibis_dtypes.Time()
+    if isinstance_or_issubclass(dtype, dtypes.List):
+        inner = narwhals_to_native_dtype(dtype.inner, version)
+        return ibis_dtypes.Array(value_type=inner)
+    if isinstance_or_issubclass(dtype, dtypes.Struct):
+        fields = [
+            (field.name, narwhals_to_native_dtype(field.dtype, version))
+            for field in dtype.fields
+        ]
+        return ibis_dtypes.Struct.from_tuples(fields)
+    if isinstance_or_issubclass(dtype, dtypes.Array):
+        inner = narwhals_to_native_dtype(dtype.inner, version)
+        return ibis_dtypes.Array(value_type=inner, length=dtype.size)
+    if isinstance_or_issubclass(dtype, dtypes.Binary):
+        return ibis_dtypes.Binary()
+    if isinstance_or_issubclass(dtype, dtypes.Enum):
+        # Ibis does not support: https://github.com/ibis-project/ibis/issues/10991
+        msg = "Enum not supported by Ibis"
+        raise NotImplementedError(msg)
+    msg = f"Unknown dtype: {dtype}"  # pragma: no cover
+    raise AssertionError(msg)