677 lines
25 KiB
Python
677 lines
25 KiB
Python
from __future__ import annotations
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import warnings
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from typing import TYPE_CHECKING
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from typing import Any
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from typing import Callable
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from typing import Literal
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from typing import Sequence
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from narwhals._compliant import LazyExpr
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from narwhals._compliant.expr import DepthTrackingExpr
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from narwhals._dask.expr_dt import DaskExprDateTimeNamespace
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from narwhals._dask.expr_str import DaskExprStringNamespace
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from narwhals._dask.utils import add_row_index
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from narwhals._dask.utils import maybe_evaluate_expr
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from narwhals._dask.utils import narwhals_to_native_dtype
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from narwhals._expression_parsing import ExprKind
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from narwhals._expression_parsing import evaluate_output_names_and_aliases
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from narwhals._pandas_like.utils import native_to_narwhals_dtype
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from narwhals.exceptions import ColumnNotFoundError
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from narwhals.exceptions import InvalidOperationError
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from narwhals.utils import Implementation
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from narwhals.utils import generate_temporary_column_name
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from narwhals.utils import not_implemented
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if TYPE_CHECKING:
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import dask.dataframe.dask_expr as dx
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from typing_extensions import Self
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from narwhals._compliant.typing import AliasNames
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from narwhals._compliant.typing import EvalNames
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from narwhals._compliant.typing import EvalSeries
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from narwhals._dask.dataframe import DaskLazyFrame
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from narwhals._dask.namespace import DaskNamespace
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from narwhals._expression_parsing import ExprKind
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from narwhals._expression_parsing import ExprMetadata
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from narwhals.dtypes import DType
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from narwhals.typing import FillNullStrategy
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from narwhals.typing import NonNestedLiteral
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from narwhals.typing import NumericLiteral
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from narwhals.typing import RollingInterpolationMethod
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from narwhals.typing import TemporalLiteral
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from narwhals.utils import Version
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from narwhals.utils import _FullContext
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class DaskExpr(
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LazyExpr["DaskLazyFrame", "dx.Series"],
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DepthTrackingExpr["DaskLazyFrame", "dx.Series"],
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):
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_implementation: Implementation = Implementation.DASK
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def __init__(
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self,
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call: EvalSeries[DaskLazyFrame, dx.Series],
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*,
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depth: int,
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function_name: str,
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evaluate_output_names: EvalNames[DaskLazyFrame],
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alias_output_names: AliasNames | None,
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backend_version: tuple[int, ...],
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version: Version,
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# Kwargs with metadata which we may need in group-by agg
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# (e.g. `ddof` for `std` and `var`).
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call_kwargs: dict[str, Any] | None = None,
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) -> None:
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self._call = call
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self._depth = depth
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self._function_name = function_name
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self._evaluate_output_names = evaluate_output_names
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self._alias_output_names = alias_output_names
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self._backend_version = backend_version
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self._version = version
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self._call_kwargs = call_kwargs or {}
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self._metadata: ExprMetadata | None = None
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def __call__(self, df: DaskLazyFrame) -> Sequence[dx.Series]:
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return self._call(df)
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def __narwhals_expr__(self) -> None: ...
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def __narwhals_namespace__(self) -> DaskNamespace: # pragma: no cover
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# Unused, just for compatibility with PandasLikeExpr
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from narwhals._dask.namespace import DaskNamespace
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return DaskNamespace(backend_version=self._backend_version, version=self._version)
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def broadcast(self, kind: Literal[ExprKind.AGGREGATION, ExprKind.LITERAL]) -> Self:
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def func(df: DaskLazyFrame) -> list[dx.Series]:
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return [result[0] for result in self(df)]
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return self.__class__(
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func,
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depth=self._depth,
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function_name=self._function_name,
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evaluate_output_names=self._evaluate_output_names,
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alias_output_names=self._alias_output_names,
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backend_version=self._backend_version,
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version=self._version,
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call_kwargs=self._call_kwargs,
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)
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@classmethod
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def from_column_names(
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cls: type[Self],
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evaluate_column_names: EvalNames[DaskLazyFrame],
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/,
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*,
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context: _FullContext,
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function_name: str = "",
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) -> Self:
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def func(df: DaskLazyFrame) -> list[dx.Series]:
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try:
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return [
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df._native_frame[column_name]
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for column_name in evaluate_column_names(df)
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]
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except KeyError as e:
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missing_columns = [
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x for x in evaluate_column_names(df) if x not in df.columns
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]
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raise ColumnNotFoundError.from_missing_and_available_column_names(
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missing_columns=missing_columns,
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available_columns=df.columns,
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) from e
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return cls(
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func,
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depth=0,
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function_name=function_name,
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evaluate_output_names=evaluate_column_names,
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alias_output_names=None,
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backend_version=context._backend_version,
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version=context._version,
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)
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@classmethod
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def from_column_indices(
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cls: type[Self], *column_indices: int, context: _FullContext
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) -> Self:
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def func(df: DaskLazyFrame) -> list[dx.Series]:
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return [
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df._native_frame.iloc[:, column_index] for column_index in column_indices
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]
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return cls(
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func,
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depth=0,
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function_name="nth",
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evaluate_output_names=lambda df: [df.columns[i] for i in column_indices],
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alias_output_names=None,
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backend_version=context._backend_version,
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version=context._version,
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)
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def _with_callable(
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self,
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# First argument to `call` should be `dx.Series`
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call: Callable[..., dx.Series],
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/,
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expr_name: str = "",
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call_kwargs: dict[str, Any] | None = None,
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**expressifiable_args: Self | Any,
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) -> Self:
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def func(df: DaskLazyFrame) -> list[dx.Series]:
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native_results: list[dx.Series] = []
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native_series_list = self._call(df)
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other_native_series = {
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key: maybe_evaluate_expr(df, value)
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for key, value in expressifiable_args.items()
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}
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for native_series in native_series_list:
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result_native = call(native_series, **other_native_series)
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native_results.append(result_native)
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return native_results
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return self.__class__(
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func,
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depth=self._depth + 1,
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function_name=f"{self._function_name}->{expr_name}",
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evaluate_output_names=self._evaluate_output_names,
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alias_output_names=self._alias_output_names,
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backend_version=self._backend_version,
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version=self._version,
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call_kwargs=call_kwargs,
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)
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def _with_alias_output_names(self, func: AliasNames | None, /) -> Self:
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return type(self)(
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call=self._call,
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depth=self._depth,
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function_name=self._function_name,
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evaluate_output_names=self._evaluate_output_names,
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alias_output_names=func,
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backend_version=self._backend_version,
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version=self._version,
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call_kwargs=self._call_kwargs,
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)
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def __add__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__add__(other), "__add__", other=other
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)
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def __sub__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__sub__(other), "__sub__", other=other
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)
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def __rsub__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: other - _input, "__rsub__", other=other
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).alias("literal")
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def __mul__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__mul__(other), "__mul__", other=other
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)
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def __truediv__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__truediv__(other), "__truediv__", other=other
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)
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def __rtruediv__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: other / _input, "__rtruediv__", other=other
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).alias("literal")
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def __floordiv__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__floordiv__(other), "__floordiv__", other=other
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)
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def __rfloordiv__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: other // _input, "__rfloordiv__", other=other
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).alias("literal")
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def __pow__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__pow__(other), "__pow__", other=other
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)
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def __rpow__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: other**_input, "__rpow__", other=other
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).alias("literal")
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def __mod__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__mod__(other), "__mod__", other=other
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)
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def __rmod__(self, other: Any) -> Self:
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return self._with_callable(
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lambda _input, other: other % _input, "__rmod__", other=other
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).alias("literal")
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def __eq__(self, other: DaskExpr) -> Self: # type: ignore[override]
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return self._with_callable(
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lambda _input, other: _input.__eq__(other), "__eq__", other=other
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)
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def __ne__(self, other: DaskExpr) -> Self: # type: ignore[override]
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return self._with_callable(
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lambda _input, other: _input.__ne__(other), "__ne__", other=other
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)
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def __ge__(self, other: DaskExpr | Any) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__ge__(other), "__ge__", other=other
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)
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def __gt__(self, other: DaskExpr) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__gt__(other), "__gt__", other=other
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)
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def __le__(self, other: DaskExpr) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__le__(other), "__le__", other=other
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)
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def __lt__(self, other: DaskExpr) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__lt__(other), "__lt__", other=other
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)
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def __and__(self, other: DaskExpr | Any) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__and__(other), "__and__", other=other
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)
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def __or__(self, other: DaskExpr) -> Self:
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return self._with_callable(
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lambda _input, other: _input.__or__(other), "__or__", other=other
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)
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def __invert__(self) -> Self:
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return self._with_callable(lambda _input: _input.__invert__(), "__invert__")
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def mean(self) -> Self:
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return self._with_callable(lambda _input: _input.mean().to_series(), "mean")
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def median(self) -> Self:
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from narwhals.exceptions import InvalidOperationError
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def func(s: dx.Series) -> dx.Series:
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dtype = native_to_narwhals_dtype(s.dtype, self._version, Implementation.DASK)
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if not dtype.is_numeric():
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msg = "`median` operation not supported for non-numeric input type."
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raise InvalidOperationError(msg)
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return s.median_approximate().to_series()
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return self._with_callable(func, "median")
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def min(self) -> Self:
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return self._with_callable(lambda _input: _input.min().to_series(), "min")
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def max(self) -> Self:
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return self._with_callable(lambda _input: _input.max().to_series(), "max")
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def std(self, ddof: int) -> Self:
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return self._with_callable(
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lambda _input: _input.std(ddof=ddof).to_series(),
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"std",
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call_kwargs={"ddof": ddof},
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)
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def var(self, ddof: int) -> Self:
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return self._with_callable(
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lambda _input: _input.var(ddof=ddof).to_series(),
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"var",
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call_kwargs={"ddof": ddof},
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)
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def skew(self) -> Self:
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return self._with_callable(lambda _input: _input.skew().to_series(), "skew")
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def shift(self, n: int) -> Self:
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return self._with_callable(lambda _input: _input.shift(n), "shift")
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def cum_sum(self, *, reverse: bool) -> Self:
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if reverse: # pragma: no cover
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# https://github.com/dask/dask/issues/11802
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msg = "`cum_sum(reverse=True)` is not supported with Dask backend"
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raise NotImplementedError(msg)
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return self._with_callable(lambda _input: _input.cumsum(), "cum_sum")
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def cum_count(self, *, reverse: bool) -> Self:
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if reverse: # pragma: no cover
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msg = "`cum_count(reverse=True)` is not supported with Dask backend"
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raise NotImplementedError(msg)
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return self._with_callable(
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lambda _input: (~_input.isna()).astype(int).cumsum(), "cum_count"
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)
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def cum_min(self, *, reverse: bool) -> Self:
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if reverse: # pragma: no cover
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msg = "`cum_min(reverse=True)` is not supported with Dask backend"
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raise NotImplementedError(msg)
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return self._with_callable(lambda _input: _input.cummin(), "cum_min")
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def cum_max(self, *, reverse: bool) -> Self:
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if reverse: # pragma: no cover
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msg = "`cum_max(reverse=True)` is not supported with Dask backend"
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raise NotImplementedError(msg)
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return self._with_callable(lambda _input: _input.cummax(), "cum_max")
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def cum_prod(self, *, reverse: bool) -> Self:
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if reverse: # pragma: no cover
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msg = "`cum_prod(reverse=True)` is not supported with Dask backend"
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raise NotImplementedError(msg)
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return self._with_callable(lambda _input: _input.cumprod(), "cum_prod")
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def rolling_sum(self, window_size: int, *, min_samples: int, center: bool) -> Self:
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return self._with_callable(
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lambda _input: _input.rolling(
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window=window_size, min_periods=min_samples, center=center
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).sum(),
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"rolling_sum",
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)
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def rolling_mean(self, window_size: int, *, min_samples: int, center: bool) -> Self:
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return self._with_callable(
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lambda _input: _input.rolling(
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window=window_size, min_periods=min_samples, center=center
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).mean(),
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"rolling_mean",
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)
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def rolling_var(
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self, window_size: int, *, min_samples: int, center: bool, ddof: int
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) -> Self:
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if ddof == 1:
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return self._with_callable(
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lambda _input: _input.rolling(
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window=window_size, min_periods=min_samples, center=center
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).var(),
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"rolling_var",
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)
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else:
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msg = "Dask backend only supports `ddof=1` for `rolling_var`"
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raise NotImplementedError(msg)
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def rolling_std(
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self, window_size: int, *, min_samples: int, center: bool, ddof: int
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) -> Self:
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if ddof == 1:
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return self._with_callable(
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lambda _input: _input.rolling(
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window=window_size, min_periods=min_samples, center=center
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).std(),
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"rolling_std",
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)
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else:
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msg = "Dask backend only supports `ddof=1` for `rolling_std`"
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raise NotImplementedError(msg)
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def sum(self) -> Self:
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return self._with_callable(lambda _input: _input.sum().to_series(), "sum")
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def count(self) -> Self:
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return self._with_callable(lambda _input: _input.count().to_series(), "count")
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def round(self, decimals: int) -> Self:
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return self._with_callable(lambda _input: _input.round(decimals), "round")
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def unique(self) -> Self:
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return self._with_callable(lambda _input: _input.unique(), "unique")
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def drop_nulls(self) -> Self:
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return self._with_callable(lambda _input: _input.dropna(), "drop_nulls")
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def abs(self) -> Self:
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return self._with_callable(lambda _input: _input.abs(), "abs")
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def all(self) -> Self:
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return self._with_callable(
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lambda _input: _input.all(
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axis=None, skipna=True, split_every=False, out=None
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).to_series(),
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"all",
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)
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def any(self) -> Self:
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return self._with_callable(
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lambda _input: _input.any(axis=0, skipna=True, split_every=False).to_series(),
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"any",
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)
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def fill_null(
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self,
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value: Self | NonNestedLiteral,
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strategy: FillNullStrategy | None,
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limit: int | None,
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) -> Self:
|
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def func(_input: dx.Series) -> dx.Series:
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if value is not None:
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res_ser = _input.fillna(value)
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else:
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res_ser = (
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_input.ffill(limit=limit)
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if strategy == "forward"
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else _input.bfill(limit=limit)
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)
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return res_ser
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return self._with_callable(func, "fillna")
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def clip(
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self,
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lower_bound: Self | NumericLiteral | TemporalLiteral | None,
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upper_bound: Self | NumericLiteral | TemporalLiteral | None,
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) -> Self:
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return self._with_callable(
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lambda _input, lower_bound, upper_bound: _input.clip(
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lower=lower_bound, upper=upper_bound
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),
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"clip",
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lower_bound=lower_bound,
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upper_bound=upper_bound,
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)
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def diff(self) -> Self:
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return self._with_callable(lambda _input: _input.diff(), "diff")
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def n_unique(self) -> Self:
|
|
return self._with_callable(
|
|
lambda _input: _input.nunique(dropna=False).to_series(), "n_unique"
|
|
)
|
|
|
|
def is_null(self) -> Self:
|
|
return self._with_callable(lambda _input: _input.isna(), "is_null")
|
|
|
|
def is_nan(self) -> Self:
|
|
def func(_input: dx.Series) -> dx.Series:
|
|
dtype = native_to_narwhals_dtype(
|
|
_input.dtype, self._version, self._implementation
|
|
)
|
|
if dtype.is_numeric():
|
|
return _input != _input # pyright: ignore[reportReturnType] # noqa: PLR0124
|
|
msg = f"`.is_nan` only supported for numeric dtypes and not {dtype}, did you mean `.is_null`?"
|
|
raise InvalidOperationError(msg)
|
|
|
|
return self._with_callable(func, "is_null")
|
|
|
|
def len(self) -> Self:
|
|
return self._with_callable(lambda _input: _input.size.to_series(), "len")
|
|
|
|
def quantile(
|
|
self, quantile: float, interpolation: RollingInterpolationMethod
|
|
) -> Self:
|
|
if interpolation == "linear":
|
|
|
|
def func(_input: dx.Series, quantile: float) -> dx.Series:
|
|
if _input.npartitions > 1:
|
|
msg = "`Expr.quantile` is not supported for Dask backend with multiple partitions."
|
|
raise NotImplementedError(msg)
|
|
return _input.quantile(
|
|
q=quantile, method="dask"
|
|
).to_series() # pragma: no cover
|
|
|
|
return self._with_callable(func, "quantile", quantile=quantile)
|
|
else:
|
|
msg = "`higher`, `lower`, `midpoint`, `nearest` - interpolation methods are not supported by Dask. Please use `linear` instead."
|
|
raise NotImplementedError(msg)
|
|
|
|
def is_first_distinct(self) -> Self:
|
|
def func(_input: dx.Series) -> dx.Series:
|
|
_name = _input.name
|
|
col_token = generate_temporary_column_name(n_bytes=8, columns=[_name])
|
|
frame = add_row_index(
|
|
_input.to_frame(), col_token, self._backend_version, self._implementation
|
|
)
|
|
first_distinct_index = frame.groupby(_name).agg({col_token: "min"})[col_token]
|
|
return frame[col_token].isin(first_distinct_index)
|
|
|
|
return self._with_callable(func, "is_first_distinct")
|
|
|
|
def is_last_distinct(self) -> Self:
|
|
def func(_input: dx.Series) -> dx.Series:
|
|
_name = _input.name
|
|
col_token = generate_temporary_column_name(n_bytes=8, columns=[_name])
|
|
frame = add_row_index(
|
|
_input.to_frame(), col_token, self._backend_version, self._implementation
|
|
)
|
|
last_distinct_index = frame.groupby(_name).agg({col_token: "max"})[col_token]
|
|
return frame[col_token].isin(last_distinct_index)
|
|
|
|
return self._with_callable(func, "is_last_distinct")
|
|
|
|
def is_unique(self) -> Self:
|
|
def func(_input: dx.Series) -> dx.Series:
|
|
_name = _input.name
|
|
return (
|
|
_input.to_frame()
|
|
.groupby(_name, dropna=False)
|
|
.transform("size", meta=(_name, int))
|
|
== 1
|
|
)
|
|
|
|
return self._with_callable(func, "is_unique")
|
|
|
|
def is_in(self, other: Any) -> Self:
|
|
return self._with_callable(lambda _input: _input.isin(other), "is_in")
|
|
|
|
def null_count(self) -> Self:
|
|
return self._with_callable(
|
|
lambda _input: _input.isna().sum().to_series(), "null_count"
|
|
)
|
|
|
|
def over(
|
|
self,
|
|
partition_by: Sequence[str],
|
|
order_by: Sequence[str] | None,
|
|
) -> Self:
|
|
# pandas is a required dependency of dask so it's safe to import this
|
|
from narwhals._pandas_like.group_by import PandasLikeGroupBy
|
|
|
|
if not partition_by:
|
|
assert order_by is not None # help type checkers # noqa: S101
|
|
|
|
# This is something like `nw.col('a').cum_sum().order_by(key)`
|
|
# which we can always easily support, as it doesn't require grouping.
|
|
def func(df: DaskLazyFrame) -> Sequence[dx.Series]:
|
|
return self(df.sort(*order_by, descending=False, nulls_last=False))
|
|
elif not self._is_elementary(): # pragma: no cover
|
|
msg = (
|
|
"Only elementary expressions are supported for `.over` in dask.\n\n"
|
|
"Please see: "
|
|
"https://narwhals-dev.github.io/narwhals/pandas_like_concepts/improve_group_by_operation/"
|
|
)
|
|
raise NotImplementedError(msg)
|
|
elif order_by:
|
|
# Wrong results https://github.com/dask/dask/issues/11806.
|
|
msg = "`over` with `order_by` is not yet supported in Dask."
|
|
raise NotImplementedError(msg)
|
|
else:
|
|
function_name = PandasLikeGroupBy._leaf_name(self)
|
|
try:
|
|
dask_function_name = PandasLikeGroupBy._REMAP_AGGS[function_name]
|
|
except KeyError:
|
|
# window functions are unsupported: https://github.com/dask/dask/issues/11806
|
|
msg = (
|
|
f"Unsupported function: {function_name} in `over` context.\n\n"
|
|
f"Supported functions are {', '.join(PandasLikeGroupBy._REMAP_AGGS)}\n"
|
|
)
|
|
raise NotImplementedError(msg) from None
|
|
|
|
def func(df: DaskLazyFrame) -> Sequence[dx.Series]:
|
|
output_names, aliases = evaluate_output_names_and_aliases(self, df, [])
|
|
|
|
with warnings.catch_warnings():
|
|
# https://github.com/dask/dask/issues/11804
|
|
warnings.filterwarnings(
|
|
"ignore",
|
|
message=".*`meta` is not specified",
|
|
category=UserWarning,
|
|
)
|
|
grouped = df.native.groupby(partition_by)
|
|
if dask_function_name == "size":
|
|
if len(output_names) != 1: # pragma: no cover
|
|
msg = "Safety check failed, please report a bug."
|
|
raise AssertionError(msg)
|
|
res_native = grouped.transform(
|
|
dask_function_name, **self._call_kwargs
|
|
).to_frame(output_names[0])
|
|
else:
|
|
res_native = grouped[list(output_names)].transform(
|
|
dask_function_name, **self._call_kwargs
|
|
)
|
|
result_frame = df._with_native(
|
|
res_native.rename(columns=dict(zip(output_names, aliases)))
|
|
).native
|
|
return [result_frame[name] for name 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 cast(self, dtype: DType | type[DType]) -> Self:
|
|
def func(_input: dx.Series) -> dx.Series:
|
|
native_dtype = narwhals_to_native_dtype(dtype, self._version)
|
|
return _input.astype(native_dtype)
|
|
|
|
return self._with_callable(func, "cast")
|
|
|
|
def is_finite(self) -> Self:
|
|
import dask.array as da
|
|
|
|
return self._with_callable(da.isfinite, "is_finite")
|
|
|
|
@property
|
|
def str(self) -> DaskExprStringNamespace:
|
|
return DaskExprStringNamespace(self)
|
|
|
|
@property
|
|
def dt(self) -> DaskExprDateTimeNamespace:
|
|
return DaskExprDateTimeNamespace(self)
|
|
|
|
list = not_implemented() # pyright: ignore[reportAssignmentType]
|
|
struct = not_implemented() # pyright: ignore[reportAssignmentType]
|
|
rank = not_implemented() # pyright: ignore[reportAssignmentType]
|