from __future__ import annotations from functools import partial from operator import methodcaller from typing import ( TYPE_CHECKING, Any, Callable, Generic, Literal, Mapping, Protocol, Sequence, ) from narwhals._compliant.any_namespace import ( CatNamespace, DateTimeNamespace, ListNamespace, NameNamespace, StringNamespace, StructNamespace, ) from narwhals._compliant.namespace import CompliantNamespace from narwhals._compliant.typing import ( AliasName, AliasNames, CompliantExprT_co, CompliantFrameT, CompliantLazyFrameT, CompliantSeriesOrNativeExprT_co, EagerDataFrameT, EagerExprT, EagerSeriesT, LazyExprT, NativeExprT, ) from narwhals._typing_compat import Protocol38, deprecated from narwhals._utils import _StoresCompliant, not_implemented from narwhals.dependencies import get_numpy, is_numpy_array if TYPE_CHECKING: from typing import Mapping from typing_extensions import Self, TypeIs from narwhals._compliant.namespace import CompliantNamespace, EagerNamespace from narwhals._compliant.series import CompliantSeries from narwhals._compliant.typing import ( AliasNames, EvalNames, EvalSeries, ScalarKwargs, WindowFunction, ) from narwhals._expression_parsing import ExprKind, ExprMetadata from narwhals._utils import Implementation, Version, _FullContext from narwhals.typing import ( FillNullStrategy, IntoDType, NonNestedLiteral, NumericLiteral, RankMethod, RollingInterpolationMethod, TemporalLiteral, TimeUnit, ) __all__ = ["CompliantExpr", "EagerExpr", "LazyExpr", "NativeExpr"] class NativeExpr(Protocol): """An `Expr`-like object from a package with [Lazy-only support](https://narwhals-dev.github.io/narwhals/extending/#levels-of-support). Protocol members are chosen *purely* for matching statically - as they are common to all currently supported packages. """ def between(self, *args: Any, **kwds: Any) -> Any: ... def isin(self, *args: Any, **kwds: Any) -> Any: ... class CompliantExpr(Protocol38[CompliantFrameT, CompliantSeriesOrNativeExprT_co]): _implementation: Implementation _backend_version: tuple[int, ...] _version: Version _evaluate_output_names: EvalNames[CompliantFrameT] _alias_output_names: AliasNames | None _metadata: ExprMetadata | None def __call__( self, df: CompliantFrameT ) -> Sequence[CompliantSeriesOrNativeExprT_co]: ... def __narwhals_expr__(self) -> None: ... def __narwhals_namespace__(self) -> CompliantNamespace[CompliantFrameT, Self]: ... @classmethod def from_column_names( cls, evaluate_column_names: EvalNames[CompliantFrameT], /, *, context: _FullContext, ) -> Self: ... @classmethod def from_column_indices(cls, *column_indices: int, context: _FullContext) -> Self: ... @staticmethod def _eval_names_indices(indices: Sequence[int], /) -> EvalNames[CompliantFrameT]: def fn(df: CompliantFrameT) -> Sequence[str]: column_names = df.columns return [column_names[i] for i in indices] return fn def is_null(self) -> Self: ... def abs(self) -> Self: ... def all(self) -> Self: ... def any(self) -> Self: ... def alias(self, name: str) -> Self: ... def cast(self, dtype: IntoDType) -> Self: ... def count(self) -> Self: ... def min(self) -> Self: ... def max(self) -> Self: ... def arg_min(self) -> Self: ... def arg_max(self) -> Self: ... def arg_true(self) -> Self: ... def mean(self) -> Self: ... def sum(self) -> Self: ... def median(self) -> Self: ... def skew(self) -> Self: ... def std(self, *, ddof: int) -> Self: ... def var(self, *, ddof: int) -> Self: ... def n_unique(self) -> Self: ... def null_count(self) -> Self: ... def drop_nulls(self) -> Self: ... def fill_null( self, value: Self | NonNestedLiteral, strategy: FillNullStrategy | None, limit: int | None, ) -> Self: ... def diff(self) -> Self: ... def exp(self) -> Self: ... def unique(self) -> Self: ... def len(self) -> Self: ... def log(self, base: float) -> Self: ... def round(self, decimals: int) -> Self: ... def mode(self) -> Self: ... def head(self, n: int) -> Self: ... def tail(self, n: int) -> Self: ... def shift(self, n: int) -> Self: ... def is_finite(self) -> Self: ... def is_nan(self) -> Self: ... def is_unique(self) -> Self: ... def is_first_distinct(self) -> Self: ... def is_last_distinct(self) -> Self: ... def cum_sum(self, *, reverse: bool) -> Self: ... def cum_count(self, *, reverse: bool) -> Self: ... def cum_min(self, *, reverse: bool) -> Self: ... def cum_max(self, *, reverse: bool) -> Self: ... def cum_prod(self, *, reverse: bool) -> Self: ... def is_in(self, other: Any) -> Self: ... def sort(self, *, descending: bool, nulls_last: bool) -> 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 over(self, partition_by: Sequence[str], order_by: Sequence[str]) -> Self: ... def sample( self, n: int | None, *, fraction: float | None, with_replacement: bool, seed: int | None, ) -> Self: ... def quantile( self, quantile: float, interpolation: RollingInterpolationMethod ) -> Self: ... def map_batches( self, function: Callable[[CompliantSeries[Any]], CompliantExpr[Any, Any]], return_dtype: IntoDType | None, ) -> Self: ... def clip( self, lower_bound: Self | NumericLiteral | TemporalLiteral | None, upper_bound: Self | NumericLiteral | TemporalLiteral | None, ) -> 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 rolling_sum( self, window_size: int, *, min_samples: int, center: bool ) -> Self: ... def rolling_mean( 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 rolling_std( self, window_size: int, *, min_samples: int, center: bool, ddof: int ) -> Self: ... @deprecated("Since `1.22.0`") def gather_every(self, n: int, offset: int) -> Self: ... def __and__(self, other: Any) -> Self: ... def __or__(self, other: Any) -> Self: ... def __add__(self, other: Any) -> Self: ... def __sub__(self, other: Any) -> Self: ... def __mul__(self, other: Any) -> Self: ... def __floordiv__(self, other: Any) -> Self: ... def __truediv__(self, other: Any) -> Self: ... def __mod__(self, other: Any) -> Self: ... def __pow__(self, other: Any) -> Self: ... def __gt__(self, other: Any) -> Self: ... def __ge__(self, other: Any) -> Self: ... def __lt__(self, other: Any) -> Self: ... def __le__(self, other: Any) -> Self: ... def __invert__(self) -> Self: ... def broadcast( self, kind: Literal[ExprKind.AGGREGATION, ExprKind.LITERAL] ) -> Self: ... def _is_multi_output_unnamed(self) -> bool: """Return `True` for multi-output aggregations without names. For example, column `'a'` only appears in the output as a grouping key: df.group_by('a').agg(nw.all().sum()) It does not get included in: nw.all().sum(). """ assert self._metadata is not None # noqa: S101 return self._metadata.expansion_kind.is_multi_unnamed() def _evaluate_aliases( self: CompliantExpr[CompliantFrameT, Any], frame: CompliantFrameT, / ) -> Sequence[str]: names = self._evaluate_output_names(frame) return alias(names) if (alias := self._alias_output_names) else names @property def str(self) -> Any: ... @property def name(self) -> Any: ... @property def dt(self) -> Any: ... @property def cat(self) -> Any: ... @property def list(self) -> Any: ... @property def struct(self) -> Any: ... class DepthTrackingExpr( CompliantExpr[CompliantFrameT, CompliantSeriesOrNativeExprT_co], Protocol38[CompliantFrameT, CompliantSeriesOrNativeExprT_co], ): _depth: int _function_name: str @classmethod def from_column_names( cls: type[Self], evaluate_column_names: EvalNames[CompliantFrameT], /, *, context: _FullContext, function_name: str = "", ) -> Self: ... def _is_elementary(self) -> bool: """Check if expr is elementary. Examples: - nw.col('a').mean() # depth 1 - nw.mean('a') # depth 1 - nw.len() # depth 0 as opposed to, say - nw.col('a').filter(nw.col('b')>nw.col('c')).max() Elementary expressions are the only ones supported properly in pandas, PyArrow, and Dask. """ return self._depth < 2 def __repr__(self) -> str: # pragma: no cover return f"{type(self).__name__}(depth={self._depth}, function_name={self._function_name})" class EagerExpr( DepthTrackingExpr[EagerDataFrameT, EagerSeriesT], Protocol38[EagerDataFrameT, EagerSeriesT], ): _call: EvalSeries[EagerDataFrameT, EagerSeriesT] _scalar_kwargs: ScalarKwargs def __init__( self, call: EvalSeries[EagerDataFrameT, EagerSeriesT], *, depth: int, function_name: str, evaluate_output_names: EvalNames[EagerDataFrameT], alias_output_names: AliasNames | None, implementation: Implementation, backend_version: tuple[int, ...], version: Version, scalar_kwargs: ScalarKwargs | None = None, ) -> None: ... def __call__(self, df: EagerDataFrameT) -> Sequence[EagerSeriesT]: return self._call(df) def __narwhals_namespace__( self, ) -> EagerNamespace[EagerDataFrameT, EagerSeriesT, Self, Any]: ... def __narwhals_expr__(self) -> None: ... @classmethod def _from_callable( cls, func: EvalSeries[EagerDataFrameT, EagerSeriesT], *, depth: int, function_name: str, evaluate_output_names: EvalNames[EagerDataFrameT], alias_output_names: AliasNames | None, context: _FullContext, scalar_kwargs: ScalarKwargs | None = None, ) -> Self: return cls( func, depth=depth, function_name=function_name, evaluate_output_names=evaluate_output_names, alias_output_names=alias_output_names, implementation=context._implementation, backend_version=context._backend_version, version=context._version, scalar_kwargs=scalar_kwargs, ) @classmethod def _from_series(cls, series: EagerSeriesT) -> Self: return cls( lambda _df: [series], depth=0, function_name="series", evaluate_output_names=lambda _df: [series.name], alias_output_names=None, implementation=series._implementation, backend_version=series._backend_version, version=series._version, ) def _reuse_series( self, method_name: str, *, returns_scalar: bool = False, scalar_kwargs: ScalarKwargs | None = None, **expressifiable_args: Any, ) -> Self: """Reuse Series implementation for expression. If Series.foo is already defined, and we'd like Expr.foo to be the same, we can leverage this method to do that for us. Arguments: method_name: name of method. returns_scalar: whether the Series version returns a scalar. In this case, the expression version should return a 1-row Series. scalar_kwargs: non-expressifiable args which we may need to reuse in `agg` or `over`, such as `ddof` for `std` and `var`. expressifiable_args: keyword arguments to pass to function, which may be expressifiable (e.g. `nw.col('a').is_between(3, nw.col('b')))`). """ func = partial( self._reuse_series_inner, method_name=method_name, returns_scalar=returns_scalar, scalar_kwargs=scalar_kwargs or {}, expressifiable_args=expressifiable_args, ) return self._from_callable( func, depth=self._depth + 1, function_name=f"{self._function_name}->{method_name}", evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, scalar_kwargs=scalar_kwargs, context=self, ) # For PyArrow.Series, we return Python Scalars (like Polars does) instead of PyArrow Scalars. # However, when working with expressions, we keep everything PyArrow-native. def _reuse_series_extra_kwargs( self, *, returns_scalar: bool = False ) -> dict[str, Any]: return {} @classmethod def _is_expr(cls, obj: Self | Any) -> TypeIs[Self]: return hasattr(obj, "__narwhals_expr__") def _reuse_series_inner( self, df: EagerDataFrameT, *, method_name: str, returns_scalar: bool, scalar_kwargs: ScalarKwargs, expressifiable_args: dict[str, Any], ) -> Sequence[EagerSeriesT]: kwargs = { **scalar_kwargs, **{ name: df._evaluate_expr(value) if self._is_expr(value) else value for name, value in expressifiable_args.items() }, } method = methodcaller( method_name, **self._reuse_series_extra_kwargs(returns_scalar=returns_scalar), **kwargs, ) out: Sequence[EagerSeriesT] = [ series._from_scalar(method(series)) if returns_scalar else method(series) for series in self(df) ] aliases = self._evaluate_aliases(df) if [s.name for s in out] != list(aliases): # pragma: no cover msg = ( f"Safety assertion failed, please report a bug to https://github.com/narwhals-dev/narwhals/issues\n" f"Expression aliases: {aliases}\n" f"Series names: {[s.name for s in out]}" ) raise AssertionError(msg) return out def _reuse_series_namespace( self, series_namespace: Literal["cat", "dt", "list", "name", "str", "struct"], method_name: str, **kwargs: Any, ) -> Self: """Reuse Series implementation for expression. Just like `_reuse_series`, but for e.g. `Expr.dt.foo` instead of `Expr.foo`. Arguments: series_namespace: The Series namespace. method_name: name of method, within `series_namespace`. kwargs: keyword arguments to pass to function. """ return self._from_callable( lambda df: [ getattr(getattr(series, series_namespace), method_name)(**kwargs) for series in self(df) ], depth=self._depth + 1, function_name=f"{self._function_name}->{series_namespace}.{method_name}", evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, scalar_kwargs=self._scalar_kwargs, context=self, ) def broadcast(self, kind: Literal[ExprKind.AGGREGATION, ExprKind.LITERAL]) -> Self: # Mark the resulting Series with `_broadcast = True`. # Then, when extracting native objects, `extract_native` will # know what to do. def func(df: EagerDataFrameT) -> list[EagerSeriesT]: results = [] for result in self(df): result._broadcast = True results.append(result) return results return type(self)( func, depth=self._depth, function_name=self._function_name, evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, backend_version=self._backend_version, implementation=self._implementation, version=self._version, scalar_kwargs=self._scalar_kwargs, ) def cast(self, dtype: IntoDType) -> Self: return self._reuse_series("cast", dtype=dtype) def __eq__(self, other: Self | Any) -> Self: # type: ignore[override] return self._reuse_series("__eq__", other=other) def __ne__(self, other: Self | Any) -> Self: # type: ignore[override] return self._reuse_series("__ne__", other=other) def __ge__(self, other: Self | Any) -> Self: return self._reuse_series("__ge__", other=other) def __gt__(self, other: Self | Any) -> Self: return self._reuse_series("__gt__", other=other) def __le__(self, other: Self | Any) -> Self: return self._reuse_series("__le__", other=other) def __lt__(self, other: Self | Any) -> Self: return self._reuse_series("__lt__", other=other) def __and__(self, other: Self | bool | Any) -> Self: return self._reuse_series("__and__", other=other) def __or__(self, other: Self | bool | Any) -> Self: return self._reuse_series("__or__", other=other) def __add__(self, other: Self | Any) -> Self: return self._reuse_series("__add__", other=other) def __sub__(self, other: Self | Any) -> Self: return self._reuse_series("__sub__", other=other) def __rsub__(self, other: Self | Any) -> Self: return self.alias("literal")._reuse_series("__rsub__", other=other) def __mul__(self, other: Self | Any) -> Self: return self._reuse_series("__mul__", other=other) def __truediv__(self, other: Self | Any) -> Self: return self._reuse_series("__truediv__", other=other) def __rtruediv__(self, other: Self | Any) -> Self: return self.alias("literal")._reuse_series("__rtruediv__", other=other) def __floordiv__(self, other: Self | Any) -> Self: return self._reuse_series("__floordiv__", other=other) def __rfloordiv__(self, other: Self | Any) -> Self: return self.alias("literal")._reuse_series("__rfloordiv__", other=other) def __pow__(self, other: Self | Any) -> Self: return self._reuse_series("__pow__", other=other) def __rpow__(self, other: Self | Any) -> Self: return self.alias("literal")._reuse_series("__rpow__", other=other) def __mod__(self, other: Self | Any) -> Self: return self._reuse_series("__mod__", other=other) def __rmod__(self, other: Self | Any) -> Self: return self.alias("literal")._reuse_series("__rmod__", other=other) # Unary def __invert__(self) -> Self: return self._reuse_series("__invert__") # Reductions def null_count(self) -> Self: return self._reuse_series("null_count", returns_scalar=True) def n_unique(self) -> Self: return self._reuse_series("n_unique", returns_scalar=True) def sum(self) -> Self: return self._reuse_series("sum", returns_scalar=True) def count(self) -> Self: return self._reuse_series("count", returns_scalar=True) def mean(self) -> Self: return self._reuse_series("mean", returns_scalar=True) def median(self) -> Self: return self._reuse_series("median", returns_scalar=True) def std(self, *, ddof: int) -> Self: return self._reuse_series( "std", returns_scalar=True, scalar_kwargs={"ddof": ddof} ) def var(self, *, ddof: int) -> Self: return self._reuse_series( "var", returns_scalar=True, scalar_kwargs={"ddof": ddof} ) def skew(self) -> Self: return self._reuse_series("skew", returns_scalar=True) def any(self) -> Self: return self._reuse_series("any", returns_scalar=True) def all(self) -> Self: return self._reuse_series("all", returns_scalar=True) def max(self) -> Self: return self._reuse_series("max", returns_scalar=True) def min(self) -> Self: return self._reuse_series("min", returns_scalar=True) def arg_min(self) -> Self: return self._reuse_series("arg_min", returns_scalar=True) def arg_max(self) -> Self: return self._reuse_series("arg_max", returns_scalar=True) # Other def clip( self, lower_bound: Self | NumericLiteral | TemporalLiteral | None, upper_bound: Self | NumericLiteral | TemporalLiteral | None, ) -> Self: return self._reuse_series( "clip", lower_bound=lower_bound, upper_bound=upper_bound ) def is_null(self) -> Self: return self._reuse_series("is_null") def is_nan(self) -> Self: return self._reuse_series("is_nan") def fill_null( self, value: Self | NonNestedLiteral, strategy: FillNullStrategy | None, limit: int | None, ) -> Self: return self._reuse_series( "fill_null", value=value, strategy=strategy, limit=limit ) def is_in(self, other: Any) -> Self: return self._reuse_series("is_in", other=other) def arg_true(self) -> Self: return self._reuse_series("arg_true") def filter(self, *predicates: Self) -> Self: plx = self.__narwhals_namespace__() predicate = plx.all_horizontal(*predicates) return self._reuse_series("filter", predicate=predicate) def drop_nulls(self) -> Self: return self._reuse_series("drop_nulls") def replace_strict( self, old: Sequence[Any] | Mapping[Any, Any], new: Sequence[Any], *, return_dtype: IntoDType | None, ) -> Self: return self._reuse_series( "replace_strict", old=old, new=new, return_dtype=return_dtype ) def sort(self, *, descending: bool, nulls_last: bool) -> Self: return self._reuse_series("sort", descending=descending, nulls_last=nulls_last) def abs(self) -> Self: return self._reuse_series("abs") def unique(self) -> Self: return self._reuse_series("unique", maintain_order=False) def diff(self) -> Self: return self._reuse_series("diff") def sample( self, n: int | None, *, fraction: float | None, with_replacement: bool, seed: int | None, ) -> Self: return self._reuse_series( "sample", n=n, fraction=fraction, with_replacement=with_replacement, seed=seed ) def alias(self, name: str) -> Self: def alias_output_names(names: Sequence[str]) -> Sequence[str]: if len(names) != 1: msg = f"Expected function with single output, found output names: {names}" raise ValueError(msg) return [name] # Define this one manually, so that we can # override `output_names` and not increase depth return type(self)( lambda df: [series.alias(name) for series in self(df)], depth=self._depth, function_name=self._function_name, evaluate_output_names=self._evaluate_output_names, alias_output_names=alias_output_names, backend_version=self._backend_version, implementation=self._implementation, version=self._version, scalar_kwargs=self._scalar_kwargs, ) def is_unique(self) -> Self: return self._reuse_series("is_unique") def is_first_distinct(self) -> Self: return self._reuse_series("is_first_distinct") def is_last_distinct(self) -> Self: return self._reuse_series("is_last_distinct") def quantile( self, quantile: float, interpolation: RollingInterpolationMethod ) -> Self: return self._reuse_series( "quantile", quantile=quantile, interpolation=interpolation, returns_scalar=True, ) def head(self, n: int) -> Self: return self._reuse_series("head", n=n) def tail(self, n: int) -> Self: return self._reuse_series("tail", n=n) def round(self, decimals: int) -> Self: return self._reuse_series("round", decimals=decimals) def len(self) -> Self: return self._reuse_series("len", returns_scalar=True) def gather_every(self, n: int, offset: int) -> Self: return self._reuse_series("gather_every", n=n, offset=offset) def mode(self) -> Self: return self._reuse_series("mode") def is_finite(self) -> Self: return self._reuse_series("is_finite") def rolling_mean(self, window_size: int, *, min_samples: int, center: bool) -> Self: return self._reuse_series( "rolling_mean", window_size=window_size, min_samples=min_samples, center=center, ) def rolling_std( self, window_size: int, *, min_samples: int, center: bool, ddof: int ) -> Self: return self._reuse_series( "rolling_std", window_size=window_size, min_samples=min_samples, center=center, ddof=ddof, ) def rolling_sum(self, window_size: int, *, min_samples: int, center: bool) -> Self: return self._reuse_series( "rolling_sum", window_size=window_size, min_samples=min_samples, center=center ) def rolling_var( self, window_size: int, *, min_samples: int, center: bool, ddof: int ) -> Self: return self._reuse_series( "rolling_var", window_size=window_size, min_samples=min_samples, center=center, ddof=ddof, ) def map_batches( self, function: Callable[[Any], Any], return_dtype: IntoDType | None ) -> Self: def func(df: EagerDataFrameT) -> Sequence[EagerSeriesT]: input_series_list = self(df) output_names = [input_series.name for input_series in input_series_list] result = [function(series) for series in input_series_list] if is_numpy_array(result[0]) or ( (np := get_numpy()) is not None and np.isscalar(result[0]) ): from_numpy = partial( self.__narwhals_namespace__()._series.from_numpy, context=self ) result = [ from_numpy(array).alias(output_name) for array, output_name in zip(result, output_names) ] if return_dtype is not None: result = [series.cast(return_dtype) for series in result] return result return self._from_callable( func, depth=self._depth + 1, function_name=self._function_name + "->map_batches", evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, context=self, ) @property def cat(self) -> EagerExprCatNamespace[Self]: return EagerExprCatNamespace(self) @property def dt(self) -> EagerExprDateTimeNamespace[Self]: return EagerExprDateTimeNamespace(self) @property def list(self) -> EagerExprListNamespace[Self]: return EagerExprListNamespace(self) @property def name(self) -> EagerExprNameNamespace[Self]: return EagerExprNameNamespace(self) @property def str(self) -> EagerExprStringNamespace[Self]: return EagerExprStringNamespace(self) @property def struct(self) -> EagerExprStructNamespace[Self]: return EagerExprStructNamespace(self) class LazyExpr( CompliantExpr[CompliantLazyFrameT, NativeExprT], Protocol38[CompliantLazyFrameT, NativeExprT], ): arg_min: not_implemented = not_implemented() arg_max: not_implemented = not_implemented() arg_true: not_implemented = not_implemented() head: not_implemented = not_implemented() tail: not_implemented = not_implemented() mode: not_implemented = not_implemented() sort: not_implemented = not_implemented() sample: not_implemented = not_implemented() map_batches: not_implemented = not_implemented() ewm_mean: not_implemented = not_implemented() gather_every: not_implemented = not_implemented() replace_strict: not_implemented = not_implemented() cat: not_implemented = not_implemented() # pyright: ignore[reportAssignmentType] @property def window_function(self) -> WindowFunction[CompliantLazyFrameT, NativeExprT]: ... @classmethod def _is_expr(cls, obj: Self | Any) -> TypeIs[Self]: return hasattr(obj, "__narwhals_expr__") def _with_callable(self, call: Callable[..., Any], /) -> Self: ... def _with_alias_output_names(self, func: AliasNames | None, /) -> Self: ... def alias(self, name: str) -> Self: def fn(names: Sequence[str]) -> Sequence[str]: if len(names) != 1: msg = f"Expected function with single output, found output names: {names}" raise ValueError(msg) return [name] return self._with_alias_output_names(fn) @classmethod def _alias_native(cls, expr: NativeExprT, name: str, /) -> NativeExprT: ... @property def name(self) -> LazyExprNameNamespace[Self]: return LazyExprNameNamespace(self) class _ExprNamespace( # type: ignore[misc] _StoresCompliant[CompliantExprT_co], Protocol[CompliantExprT_co] ): _compliant_expr: CompliantExprT_co @property def compliant(self) -> CompliantExprT_co: return self._compliant_expr class EagerExprNamespace(_ExprNamespace[EagerExprT], Generic[EagerExprT]): def __init__(self, expr: EagerExprT, /) -> None: self._compliant_expr = expr class LazyExprNamespace(_ExprNamespace[LazyExprT], Generic[LazyExprT]): def __init__(self, expr: LazyExprT, /) -> None: self._compliant_expr = expr class EagerExprCatNamespace( EagerExprNamespace[EagerExprT], CatNamespace[EagerExprT], Generic[EagerExprT] ): def get_categories(self) -> EagerExprT: return self.compliant._reuse_series_namespace("cat", "get_categories") class EagerExprDateTimeNamespace( EagerExprNamespace[EagerExprT], DateTimeNamespace[EagerExprT], Generic[EagerExprT] ): def to_string(self, format: str) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "to_string", format=format) def replace_time_zone(self, time_zone: str | None) -> EagerExprT: return self.compliant._reuse_series_namespace( "dt", "replace_time_zone", time_zone=time_zone ) def convert_time_zone(self, time_zone: str) -> EagerExprT: return self.compliant._reuse_series_namespace( "dt", "convert_time_zone", time_zone=time_zone ) def timestamp(self, time_unit: TimeUnit) -> EagerExprT: return self.compliant._reuse_series_namespace( "dt", "timestamp", time_unit=time_unit ) def date(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "date") def year(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "year") def month(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "month") def day(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "day") def hour(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "hour") def minute(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "minute") def second(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "second") def millisecond(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "millisecond") def microsecond(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "microsecond") def nanosecond(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "nanosecond") def ordinal_day(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "ordinal_day") def weekday(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "weekday") def total_minutes(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "total_minutes") def total_seconds(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "total_seconds") def total_milliseconds(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "total_milliseconds") def total_microseconds(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "total_microseconds") def total_nanoseconds(self) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "total_nanoseconds") def truncate(self, every: str) -> EagerExprT: return self.compliant._reuse_series_namespace("dt", "truncate", every=every) class EagerExprListNamespace( EagerExprNamespace[EagerExprT], ListNamespace[EagerExprT], Generic[EagerExprT] ): def len(self) -> EagerExprT: return self.compliant._reuse_series_namespace("list", "len") class CompliantExprNameNamespace( # type: ignore[misc] _ExprNamespace[CompliantExprT_co], NameNamespace[CompliantExprT_co], Protocol[CompliantExprT_co], ): def keep(self) -> CompliantExprT_co: return self._from_callable(lambda name: name, alias=False) def map(self, function: AliasName) -> CompliantExprT_co: return self._from_callable(function) def prefix(self, prefix: str) -> CompliantExprT_co: return self._from_callable(lambda name: f"{prefix}{name}") def suffix(self, suffix: str) -> CompliantExprT_co: return self._from_callable(lambda name: f"{name}{suffix}") def to_lowercase(self) -> CompliantExprT_co: return self._from_callable(str.lower) def to_uppercase(self) -> CompliantExprT_co: return self._from_callable(str.upper) @staticmethod def _alias_output_names(func: AliasName, /) -> AliasNames: def fn(output_names: Sequence[str], /) -> Sequence[str]: return [func(name) for name in output_names] return fn def _from_callable( self, func: AliasName, /, *, alias: bool = True ) -> CompliantExprT_co: ... class EagerExprNameNamespace( EagerExprNamespace[EagerExprT], CompliantExprNameNamespace[EagerExprT], Generic[EagerExprT], ): def _from_callable(self, func: AliasName, /, *, alias: bool = True) -> EagerExprT: expr = self.compliant return type(expr)( lambda df: [ series.alias(func(name)) for series, name in zip(expr(df), expr._evaluate_output_names(df)) ], depth=expr._depth, function_name=expr._function_name, evaluate_output_names=expr._evaluate_output_names, alias_output_names=self._alias_output_names(func) if alias else None, backend_version=expr._backend_version, implementation=expr._implementation, version=expr._version, scalar_kwargs=expr._scalar_kwargs, ) class LazyExprNameNamespace( LazyExprNamespace[LazyExprT], CompliantExprNameNamespace[LazyExprT], Generic[LazyExprT], ): def _from_callable(self, func: AliasName, /, *, alias: bool = True) -> LazyExprT: expr = self.compliant output_names = self._alias_output_names(func) if alias else None return expr._with_alias_output_names(output_names) class EagerExprStringNamespace( EagerExprNamespace[EagerExprT], StringNamespace[EagerExprT], Generic[EagerExprT] ): def len_chars(self) -> EagerExprT: return self.compliant._reuse_series_namespace("str", "len_chars") def replace(self, pattern: str, value: str, *, literal: bool, n: int) -> EagerExprT: return self.compliant._reuse_series_namespace( "str", "replace", pattern=pattern, value=value, literal=literal, n=n ) def replace_all(self, pattern: str, value: str, *, literal: bool) -> EagerExprT: return self.compliant._reuse_series_namespace( "str", "replace_all", pattern=pattern, value=value, literal=literal ) def strip_chars(self, characters: str | None) -> EagerExprT: return self.compliant._reuse_series_namespace( "str", "strip_chars", characters=characters ) def starts_with(self, prefix: str) -> EagerExprT: return self.compliant._reuse_series_namespace("str", "starts_with", prefix=prefix) def ends_with(self, suffix: str) -> EagerExprT: return self.compliant._reuse_series_namespace("str", "ends_with", suffix=suffix) def contains(self, pattern: str, *, literal: bool) -> EagerExprT: return self.compliant._reuse_series_namespace( "str", "contains", pattern=pattern, literal=literal ) def slice(self, offset: int, length: int | None) -> EagerExprT: return self.compliant._reuse_series_namespace( "str", "slice", offset=offset, length=length ) def split(self, by: str) -> EagerExprT: return self.compliant._reuse_series_namespace("str", "split", by=by) def to_datetime(self, format: str | None) -> EagerExprT: return self.compliant._reuse_series_namespace("str", "to_datetime", format=format) def to_lowercase(self) -> EagerExprT: return self.compliant._reuse_series_namespace("str", "to_lowercase") def to_uppercase(self) -> EagerExprT: return self.compliant._reuse_series_namespace("str", "to_uppercase") class EagerExprStructNamespace( EagerExprNamespace[EagerExprT], StructNamespace[EagerExprT], Generic[EagerExprT] ): def field(self, name: str) -> EagerExprT: return self.compliant._reuse_series_namespace("struct", "field", name=name).alias( name )