from __future__ import annotations from typing import TYPE_CHECKING, Any, Sequence import pyarrow.compute as pc from narwhals._arrow.series import ArrowSeries from narwhals._compliant import EagerExpr from narwhals._expression_parsing import evaluate_output_names_and_aliases from narwhals._utils import ( Implementation, generate_temporary_column_name, not_implemented, ) if TYPE_CHECKING: from typing_extensions import Self from narwhals._arrow.dataframe import ArrowDataFrame from narwhals._arrow.namespace import ArrowNamespace from narwhals._compliant.typing import AliasNames, EvalNames, EvalSeries, ScalarKwargs from narwhals._expression_parsing import ExprMetadata from narwhals._utils import Version, _FullContext from narwhals.typing import RankMethod class ArrowExpr(EagerExpr["ArrowDataFrame", ArrowSeries]): _implementation: Implementation = Implementation.PYARROW def __init__( self, call: EvalSeries[ArrowDataFrame, ArrowSeries], *, depth: int, function_name: str, evaluate_output_names: EvalNames[ArrowDataFrame], alias_output_names: AliasNames | None, backend_version: tuple[int, ...], version: Version, scalar_kwargs: ScalarKwargs | None = None, implementation: Implementation | None = None, ) -> None: self._call = call self._depth = depth self._function_name = function_name self._depth = depth self._evaluate_output_names = evaluate_output_names self._alias_output_names = alias_output_names self._backend_version = backend_version self._version = version self._scalar_kwargs = scalar_kwargs or {} self._metadata: ExprMetadata | None = None @classmethod def from_column_names( cls: type[Self], evaluate_column_names: EvalNames[ArrowDataFrame], /, *, context: _FullContext, function_name: str = "", ) -> Self: def func(df: ArrowDataFrame) -> list[ArrowSeries]: try: return [ ArrowSeries( df.native[column_name], name=column_name, backend_version=df._backend_version, version=df._version, ) for column_name in evaluate_column_names(df) ] except KeyError as e: if error := df._check_columns_exist(evaluate_column_names(df)): raise error from e raise return cls( func, depth=0, function_name=function_name, 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: ArrowDataFrame) -> list[ArrowSeries]: tbl = df.native cols = df.columns return [ ArrowSeries.from_native(tbl[i], name=cols[i], context=df) for i in column_indices ] return cls( func, depth=0, function_name="nth", evaluate_output_names=cls._eval_names_indices(column_indices), alias_output_names=None, backend_version=context._backend_version, version=context._version, ) def __narwhals_namespace__(self) -> ArrowNamespace: from narwhals._arrow.namespace import ArrowNamespace return ArrowNamespace( backend_version=self._backend_version, version=self._version ) def __narwhals_expr__(self) -> None: ... def _reuse_series_extra_kwargs( self, *, returns_scalar: bool = False ) -> dict[str, Any]: return {"_return_py_scalar": False} if returns_scalar else {} def cum_sum(self, *, reverse: bool) -> Self: return self._reuse_series("cum_sum", reverse=reverse) def shift(self, n: int) -> Self: return self._reuse_series("shift", n=n) def over(self, partition_by: Sequence[str], order_by: Sequence[str]) -> Self: assert self._metadata is not None # noqa: S101 if partition_by and not self._metadata.is_scalar_like: msg = "Only aggregation or literal operations are supported in grouped `over` context for PyArrow." raise NotImplementedError(msg) if not partition_by: # e.g. `nw.col('a').cum_sum().order_by(key)` # which we can always easily support, as it doesn't require grouping. assert order_by # noqa: S101 def func(df: ArrowDataFrame) -> Sequence[ArrowSeries]: token = generate_temporary_column_name(8, df.columns) df = df.with_row_index(token).sort( *order_by, descending=False, nulls_last=False ) result = self(df.drop([token], strict=True)) # TODO(marco): is there a way to do this efficiently without # doing 2 sorts? Here we're sorting the dataframe and then # again calling `sort_indices`. `ArrowSeries.scatter` would also sort. sorting_indices = pc.sort_indices(df.get_column(token).native) return [s._with_native(s.native.take(sorting_indices)) for s in result] else: def func(df: ArrowDataFrame) -> Sequence[ArrowSeries]: output_names, aliases = evaluate_output_names_and_aliases(self, df, []) if overlap := set(output_names).intersection(partition_by): # E.g. `df.select(nw.all().sum().over('a'))`. This is well-defined, # we just don't support it yet. msg = ( f"Column names {overlap} appear in both expression output names and in `over` keys.\n" "This is not yet supported." ) raise NotImplementedError(msg) tmp = df.group_by(partition_by, drop_null_keys=False).agg(self) tmp = df.simple_select(*partition_by).join( tmp, how="left", left_on=partition_by, right_on=partition_by, suffix="_right", ) return [tmp.get_column(alias) for alias in aliases] return self.__class__( func, depth=self._depth + 1, function_name=self._function_name + "->over", evaluate_output_names=self._evaluate_output_names, alias_output_names=self._alias_output_names, backend_version=self._backend_version, version=self._version, ) def cum_count(self, *, reverse: bool) -> Self: return self._reuse_series("cum_count", reverse=reverse) def cum_min(self, *, reverse: bool) -> Self: return self._reuse_series("cum_min", reverse=reverse) def cum_max(self, *, reverse: bool) -> Self: return self._reuse_series("cum_max", reverse=reverse) def cum_prod(self, *, reverse: bool) -> Self: return self._reuse_series("cum_prod", reverse=reverse) def rank(self, method: RankMethod, *, descending: bool) -> Self: return self._reuse_series("rank", method=method, descending=descending) def log(self, base: float) -> Self: return self._reuse_series("log", base=base) def exp(self) -> Self: return self._reuse_series("exp") ewm_mean = not_implemented()