from __future__ import annotations import operator from functools import reduce from typing import TYPE_CHECKING, Iterable, Sequence, cast import dask.dataframe as dd import pandas as pd from narwhals._compliant import CompliantThen, CompliantWhen, LazyNamespace from narwhals._compliant.namespace import DepthTrackingNamespace from narwhals._dask.dataframe import DaskLazyFrame from narwhals._dask.expr import DaskExpr from narwhals._dask.selectors import DaskSelectorNamespace from narwhals._dask.utils import ( align_series_full_broadcast, narwhals_to_native_dtype, validate_comparand, ) from narwhals._expression_parsing import ( ExprKind, combine_alias_output_names, combine_evaluate_output_names, ) from narwhals._utils import Implementation if TYPE_CHECKING: import dask.dataframe.dask_expr as dx from narwhals._utils import Version from narwhals.typing import ConcatMethod, IntoDType, NonNestedLiteral class DaskNamespace( LazyNamespace[DaskLazyFrame, DaskExpr, dd.DataFrame], DepthTrackingNamespace[DaskLazyFrame, DaskExpr], ): _implementation: Implementation = Implementation.DASK @property def selectors(self) -> DaskSelectorNamespace: return DaskSelectorNamespace.from_namespace(self) @property def _expr(self) -> type[DaskExpr]: return DaskExpr @property def _lazyframe(self) -> type[DaskLazyFrame]: return DaskLazyFrame def __init__(self, *, backend_version: tuple[int, ...], version: Version) -> None: self._backend_version = backend_version self._version = version def lit(self, value: NonNestedLiteral, dtype: IntoDType | None) -> DaskExpr: def func(df: DaskLazyFrame) -> list[dx.Series]: if dtype is not None: native_dtype = narwhals_to_native_dtype(dtype, self._version) native_pd_series = pd.Series([value], dtype=native_dtype, name="literal") else: native_pd_series = pd.Series([value], name="literal") npartitions = df._native_frame.npartitions dask_series = dd.from_pandas(native_pd_series, npartitions=npartitions) return [dask_series[0].to_series()] return self._expr( func, depth=0, function_name="lit", evaluate_output_names=lambda _df: ["literal"], alias_output_names=None, backend_version=self._backend_version, version=self._version, ) def len(self) -> DaskExpr: def func(df: DaskLazyFrame) -> list[dx.Series]: # We don't allow dataframes with 0 columns, so `[0]` is safe. return [df._native_frame[df.columns[0]].size.to_series()] return self._expr( func, depth=0, function_name="len", evaluate_output_names=lambda _df: ["len"], alias_output_names=None, backend_version=self._backend_version, version=self._version, ) def all_horizontal(self, *exprs: DaskExpr) -> DaskExpr: def func(df: DaskLazyFrame) -> list[dx.Series]: series = align_series_full_broadcast( df, *(s for _expr in exprs for s in _expr(df)) ) return [reduce(operator.and_, series)] return self._expr( call=func, depth=max(x._depth for x in exprs) + 1, function_name="all_horizontal", 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: DaskExpr) -> DaskExpr: def func(df: DaskLazyFrame) -> list[dx.Series]: series = align_series_full_broadcast( df, *(s for _expr in exprs for s in _expr(df)) ) return [reduce(operator.or_, series)] return self._expr( call=func, depth=max(x._depth for x in exprs) + 1, function_name="any_horizontal", 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: DaskExpr) -> DaskExpr: def func(df: DaskLazyFrame) -> list[dx.Series]: series = align_series_full_broadcast( df, *(s for _expr in exprs for s in _expr(df)) ) return [dd.concat(series, axis=1).sum(axis=1)] return self._expr( call=func, depth=max(x._depth for x in exprs) + 1, function_name="sum_horizontal", 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 concat( self, items: Iterable[DaskLazyFrame], *, how: ConcatMethod ) -> DaskLazyFrame: if not items: msg = "No items to concatenate" # pragma: no cover raise AssertionError(msg) dfs = [i._native_frame for i in items] cols_0 = dfs[0].columns if how == "vertical": for i, df in enumerate(dfs[1:], start=1): cols_current = df.columns if not ( (len(cols_current) == len(cols_0)) and (cols_current == cols_0).all() ): msg = ( "unable to vstack, column names don't match:\n" f" - dataframe 0: {cols_0.to_list()}\n" f" - dataframe {i}: {cols_current.to_list()}\n" ) raise TypeError(msg) return DaskLazyFrame( dd.concat(dfs, axis=0, join="inner"), backend_version=self._backend_version, version=self._version, ) if how == "diagonal": return DaskLazyFrame( dd.concat(dfs, axis=0, join="outer"), backend_version=self._backend_version, version=self._version, ) raise NotImplementedError def mean_horizontal(self, *exprs: DaskExpr) -> DaskExpr: def func(df: DaskLazyFrame) -> list[dx.Series]: expr_results = [s for _expr in exprs for s in _expr(df)] series = align_series_full_broadcast(df, *(s.fillna(0) for s in expr_results)) non_na = align_series_full_broadcast( df, *(1 - s.isna() for s in expr_results) ) num = reduce(lambda x, y: x + y, series) # pyright: ignore[reportOperatorIssue] den = reduce(lambda x, y: x + y, non_na) # pyright: ignore[reportOperatorIssue] return [cast("dx.Series", num / den)] # pyright: ignore[reportOperatorIssue] return self._expr( call=func, depth=max(x._depth for x in exprs) + 1, function_name="mean_horizontal", 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: DaskExpr) -> DaskExpr: def func(df: DaskLazyFrame) -> list[dx.Series]: series = align_series_full_broadcast( df, *(s for _expr in exprs for s in _expr(df)) ) return [dd.concat(series, axis=1).min(axis=1)] return self._expr( call=func, depth=max(x._depth for x in exprs) + 1, function_name="min_horizontal", 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: DaskExpr) -> DaskExpr: def func(df: DaskLazyFrame) -> list[dx.Series]: series = align_series_full_broadcast( df, *(s for _expr in exprs for s in _expr(df)) ) return [dd.concat(series, axis=1).max(axis=1)] return self._expr( call=func, depth=max(x._depth for x in exprs) + 1, function_name="max_horizontal", 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 when(self, predicate: DaskExpr) -> DaskWhen: return DaskWhen.from_expr(predicate, context=self) def concat_str( self, *exprs: DaskExpr, separator: str, ignore_nulls: bool ) -> DaskExpr: def func(df: DaskLazyFrame) -> list[dx.Series]: expr_results = [s for _expr in exprs for s in _expr(df)] series = ( s.astype(str) for s in align_series_full_broadcast(df, *expr_results) ) null_mask = [s.isna() for s in align_series_full_broadcast(df, *expr_results)] if not ignore_nulls: null_mask_result = reduce(operator.or_, null_mask) result = reduce(lambda x, y: x + separator + y, series).where( ~null_mask_result, None ) else: init_value, *values = [ s.where(~nm, "") for s, nm in zip(series, null_mask) ] separators = ( nm.map({True: "", False: separator}, meta=str) for nm in null_mask[:-1] ) result = reduce( operator.add, (s + v for s, v in zip(separators, values)), init_value ) return [result] return self._expr( call=func, depth=max(x._depth for x in exprs) + 1, function_name="concat_str", evaluate_output_names=getattr( exprs[0], "_evaluate_output_names", lambda _df: ["literal"] ), alias_output_names=getattr(exprs[0], "_alias_output_names", None), backend_version=self._backend_version, version=self._version, ) class DaskWhen(CompliantWhen[DaskLazyFrame, "dx.Series", DaskExpr]): @property def _then(self) -> type[DaskThen]: return DaskThen def __call__(self, df: DaskLazyFrame) -> Sequence[dx.Series]: then_value = ( self._then_value(df)[0] if isinstance(self._then_value, DaskExpr) else self._then_value ) otherwise_value = ( self._otherwise_value(df)[0] if isinstance(self._otherwise_value, DaskExpr) else self._otherwise_value ) condition = self._condition(df)[0] # re-evaluate DataFrame if the condition aggregates to force # then/otherwise to be evaluated against the aggregated frame assert self._condition._metadata is not None # noqa: S101 if self._condition._metadata.is_scalar_like: new_df = df._with_native(condition.to_frame()) condition = self._condition.broadcast(ExprKind.AGGREGATION)(df)[0] df = new_df if self._otherwise_value is None: (condition, then_series) = align_series_full_broadcast( df, condition, then_value ) validate_comparand(condition, then_series) return [then_series.where(condition)] # pyright: ignore[reportArgumentType] (condition, then_series, otherwise_series) = align_series_full_broadcast( df, condition, then_value, otherwise_value ) validate_comparand(condition, then_series) validate_comparand(condition, otherwise_series) return [then_series.where(condition, otherwise_series)] # pyright: ignore[reportArgumentType] class DaskThen(CompliantThen[DaskLazyFrame, "dx.Series", DaskExpr], DaskExpr): ...