1133 lines
38 KiB
Python
1133 lines
38 KiB
Python
from __future__ import annotations
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import sys
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from functools import partial
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from operator import methodcaller
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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 Generic
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from typing import Literal
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from typing import Mapping
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from typing import Protocol
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from typing import Sequence
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from narwhals._compliant.any_namespace import CatNamespace
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from narwhals._compliant.any_namespace import DateTimeNamespace
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from narwhals._compliant.any_namespace import ListNamespace
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from narwhals._compliant.any_namespace import NameNamespace
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from narwhals._compliant.any_namespace import StringNamespace
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from narwhals._compliant.any_namespace import StructNamespace
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from narwhals._compliant.namespace import CompliantNamespace
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from narwhals._compliant.typing import AliasName
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from narwhals._compliant.typing import AliasNames
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from narwhals._compliant.typing import CompliantExprT_co
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from narwhals._compliant.typing import CompliantFrameT
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from narwhals._compliant.typing import CompliantLazyFrameT
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from narwhals._compliant.typing import CompliantSeriesOrNativeExprT_co
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from narwhals._compliant.typing import EagerDataFrameT
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from narwhals._compliant.typing import EagerExprT
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from narwhals._compliant.typing import EagerSeriesT
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from narwhals._compliant.typing import LazyExprT
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from narwhals._compliant.typing import NativeExprT
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from narwhals._expression_parsing import evaluate_output_names_and_aliases
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from narwhals.dependencies import get_numpy
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from narwhals.dependencies import is_numpy_array
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from narwhals.dtypes import DType
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from narwhals.utils import _StoresCompliant
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from narwhals.utils import deprecated
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from narwhals.utils import not_implemented
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from narwhals.utils import unstable
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if not TYPE_CHECKING: # pragma: no cover
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if sys.version_info >= (3, 9):
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from typing import Protocol as Protocol38
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else:
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from typing import Generic as Protocol38
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else: # pragma: no cover
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# TODO @dangotbanned: Remove after dropping `3.8` (#2084)
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# - https://github.com/narwhals-dev/narwhals/pull/2064#discussion_r1965921386
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from typing import Protocol as Protocol38
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if TYPE_CHECKING:
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from typing import Mapping
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from typing_extensions import Self
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from typing_extensions import TypeIs
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from narwhals._compliant.namespace import CompliantNamespace
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from narwhals._compliant.namespace import EagerNamespace
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from narwhals._compliant.series import CompliantSeries
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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 TimeUnit
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from narwhals.utils import Implementation
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from narwhals.utils import Version
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from narwhals.utils import _FullContext
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__all__ = ["CompliantExpr", "EagerExpr", "LazyExpr", "NativeExpr"]
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class NativeExpr(Protocol):
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"""An `Expr`-like object from a package with [Lazy-only support](https://narwhals-dev.github.io/narwhals/extending/#levels-of-support).
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Protocol members are chosen *purely* for matching statically - as they
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are common to all currently supported packages.
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"""
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def between(self, *args: Any, **kwds: Any) -> Any: ...
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def isin(self, *args: Any, **kwds: Any) -> Any: ...
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class CompliantExpr(Protocol38[CompliantFrameT, CompliantSeriesOrNativeExprT_co]):
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_implementation: Implementation
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_backend_version: tuple[int, ...]
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_version: Version
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_evaluate_output_names: Callable[[CompliantFrameT], Sequence[str]]
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_alias_output_names: Callable[[Sequence[str]], Sequence[str]] | None
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_metadata: ExprMetadata | None
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def __call__(
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self, df: CompliantFrameT
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) -> Sequence[CompliantSeriesOrNativeExprT_co]: ...
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def __narwhals_expr__(self) -> None: ...
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def __narwhals_namespace__(
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self,
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) -> CompliantNamespace[CompliantFrameT, Self]: ...
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@classmethod
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def from_column_names(
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cls,
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evaluate_column_names: Callable[[CompliantFrameT], Sequence[str]],
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/,
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*,
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context: _FullContext,
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) -> Self: ...
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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 is_null(self) -> Self: ...
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def abs(self) -> Self: ...
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def all(self) -> Self: ...
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def any(self) -> Self: ...
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def alias(self, name: str) -> Self: ...
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def cast(self, dtype: DType | type[DType]) -> Self: ...
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def count(self) -> Self: ...
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def min(self) -> Self: ...
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def max(self) -> Self: ...
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def arg_min(self) -> Self: ...
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def arg_max(self) -> Self: ...
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def arg_true(self) -> Self: ...
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def mean(self) -> Self: ...
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def sum(self) -> Self: ...
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def median(self) -> Self: ...
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def skew(self) -> Self: ...
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def std(self, *, ddof: int) -> Self: ...
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def var(self, *, ddof: int) -> Self: ...
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def n_unique(self) -> Self: ...
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def null_count(self) -> Self: ...
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def drop_nulls(self) -> Self: ...
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def fill_null(
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self,
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value: Any | None,
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strategy: Literal["forward", "backward"] | None,
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limit: int | None,
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) -> Self: ...
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def diff(self) -> Self: ...
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def unique(self) -> Self: ...
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def len(self) -> Self: ...
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def round(self, decimals: int) -> Self: ...
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def mode(self) -> Self: ...
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def head(self, n: int) -> Self: ...
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def tail(self, n: int) -> Self: ...
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def shift(self, n: int) -> Self: ...
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def is_finite(self) -> Self: ...
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def is_nan(self) -> Self: ...
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def is_unique(self) -> Self: ...
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def is_first_distinct(self) -> Self: ...
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def is_last_distinct(self) -> Self: ...
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def cum_sum(self, *, reverse: bool) -> Self: ...
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def cum_count(self, *, reverse: bool) -> Self: ...
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def cum_min(self, *, reverse: bool) -> Self: ...
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def cum_max(self, *, reverse: bool) -> Self: ...
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def cum_prod(self, *, reverse: bool) -> Self: ...
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def is_in(self, other: Any) -> Self: ...
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def sort(self, *, descending: bool, nulls_last: bool) -> Self: ...
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def rank(
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self,
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method: Literal["average", "min", "max", "dense", "ordinal"],
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*,
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descending: bool,
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) -> Self: ...
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def replace_strict(
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self,
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old: Sequence[Any] | Mapping[Any, Any],
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new: Sequence[Any],
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*,
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return_dtype: DType | type[DType] | None,
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) -> Self: ...
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def over(self: Self, keys: Sequence[str], order_by: Sequence[str] | None) -> Self: ...
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def sample(
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self,
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n: int | None,
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*,
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fraction: float | None,
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with_replacement: bool,
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seed: int | None,
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) -> Self: ...
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def quantile(
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self,
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quantile: float,
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interpolation: Literal["nearest", "higher", "lower", "midpoint", "linear"],
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) -> Self: ...
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def map_batches(
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self,
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function: Callable[[CompliantSeries[Any]], CompliantExpr[Any, Any]],
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return_dtype: DType | type[DType] | None,
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) -> Self: ...
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@property
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def str(self) -> Any: ...
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@property
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def name(self) -> Any: ...
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@property
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def dt(self) -> Any: ...
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@property
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def cat(self) -> Any: ...
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@property
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def list(self) -> Any: ...
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@property
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def struct(self) -> Any: ...
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@unstable
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def ewm_mean(
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self,
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*,
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com: float | None,
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span: float | None,
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half_life: float | None,
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alpha: float | None,
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adjust: bool,
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min_samples: int,
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ignore_nulls: bool,
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) -> Self: ...
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@unstable
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def rolling_sum(
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self,
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window_size: int,
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*,
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min_samples: int,
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center: bool,
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) -> Self: ...
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@unstable
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def rolling_mean(
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self,
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window_size: int,
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*,
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min_samples: int,
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center: bool,
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) -> Self: ...
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@unstable
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def rolling_var(
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self,
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window_size: int,
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*,
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min_samples: int,
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center: bool,
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ddof: int,
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) -> Self: ...
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@unstable
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def rolling_std(
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self,
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window_size: int,
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*,
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min_samples: int,
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center: bool,
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ddof: int,
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) -> Self: ...
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@deprecated("Since `1.22.0`")
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def gather_every(self, n: int, offset: int) -> Self: ...
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def __and__(self, other: Any) -> Self: ...
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def __or__(self, other: Any) -> Self: ...
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def __add__(self, other: Any) -> Self: ...
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def __sub__(self, other: Any) -> Self: ...
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def __mul__(self, other: Any) -> Self: ...
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def __floordiv__(self, other: Any) -> Self: ...
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def __truediv__(self, other: Any) -> Self: ...
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def __mod__(self, other: Any) -> Self: ...
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def __pow__(self, other: Any) -> Self: ...
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def __gt__(self, other: Any) -> Self: ...
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def __ge__(self, other: Any) -> Self: ...
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def __lt__(self, other: Any) -> Self: ...
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def __le__(self, other: Any) -> Self: ...
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def __invert__(self) -> Self: ...
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def broadcast(
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self, kind: Literal[ExprKind.AGGREGATION, ExprKind.LITERAL]
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) -> Self: ...
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def _is_multi_output_unnamed(self) -> bool:
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"""Return `True` for multi-output aggregations without names.
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For example, column `'a'` only appears in the output as a grouping key:
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df.group_by('a').agg(nw.all().sum())
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It does not get included in:
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nw.all().sum().
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"""
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assert self._metadata is not None # noqa: S101
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return self._metadata.expansion_kind.is_multi_unnamed()
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class DepthTrackingExpr(
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CompliantExpr[CompliantFrameT, CompliantSeriesOrNativeExprT_co],
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Protocol38[CompliantFrameT, CompliantSeriesOrNativeExprT_co],
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):
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_depth: int
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_function_name: str
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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: Callable[[CompliantFrameT], Sequence[str]],
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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 _is_elementary(self) -> bool:
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"""Check if expr is elementary.
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Examples:
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- nw.col('a').mean() # depth 1
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- nw.mean('a') # depth 1
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- nw.len() # depth 0
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as opposed to, say
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- nw.col('a').filter(nw.col('b')>nw.col('c')).max()
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Elementary expressions are the only ones supported properly in
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pandas, PyArrow, and Dask.
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"""
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return self._depth < 2
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def __repr__(self) -> str: # pragma: no cover
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return f"{type(self).__name__}(depth={self._depth}, function_name={self._function_name})"
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class EagerExpr(
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DepthTrackingExpr[EagerDataFrameT, EagerSeriesT],
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Protocol38[EagerDataFrameT, EagerSeriesT],
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):
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_call: Callable[[EagerDataFrameT], Sequence[EagerSeriesT]]
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_call_kwargs: dict[str, Any]
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def __init__(
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self: Self,
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call: Callable[[EagerDataFrameT], Sequence[EagerSeriesT]],
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*,
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depth: int,
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function_name: str,
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evaluate_output_names: Callable[[EagerDataFrameT], Sequence[str]],
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alias_output_names: Callable[[Sequence[str]], Sequence[str]] | None,
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implementation: Implementation,
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backend_version: tuple[int, ...],
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version: Version,
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call_kwargs: dict[str, Any] | None = None,
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) -> None: ...
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def __call__(self, df: EagerDataFrameT) -> Sequence[EagerSeriesT]:
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return self._call(df)
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def __narwhals_namespace__(
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self,
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) -> EagerNamespace[EagerDataFrameT, EagerSeriesT, Self]: ...
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def __narwhals_expr__(self) -> None: ...
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@classmethod
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def _from_callable(
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cls,
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func: Callable[[EagerDataFrameT], Sequence[EagerSeriesT]],
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*,
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depth: int,
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function_name: str,
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evaluate_output_names: Callable[[EagerDataFrameT], Sequence[str]],
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alias_output_names: Callable[[Sequence[str]], Sequence[str]] | None,
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context: _FullContext,
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call_kwargs: dict[str, Any] | None = None,
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) -> Self:
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return cls(
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func,
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depth=depth,
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function_name=function_name,
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evaluate_output_names=evaluate_output_names,
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alias_output_names=alias_output_names,
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implementation=context._implementation,
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backend_version=context._backend_version,
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version=context._version,
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call_kwargs=call_kwargs,
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)
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@classmethod
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def _from_series(cls, series: EagerSeriesT) -> Self:
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return cls(
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lambda _df: [series],
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depth=0,
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function_name="series",
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evaluate_output_names=lambda _df: [series.name],
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alias_output_names=None,
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implementation=series._implementation,
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backend_version=series._backend_version,
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version=series._version,
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)
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def _reuse_series(
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self: Self,
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method_name: str,
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*,
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returns_scalar: bool = False,
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call_kwargs: dict[str, Any] | None = None,
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**expressifiable_args: Any,
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) -> Self:
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"""Reuse Series implementation for expression.
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If Series.foo is already defined, and we'd like Expr.foo to be the same, we can
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leverage this method to do that for us.
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Arguments:
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method_name: name of method.
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returns_scalar: whether the Series version returns a scalar. In this case,
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the expression version should return a 1-row Series.
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call_kwargs: non-expressifiable args which we may need to reuse in `agg` or `over`,
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such as `ddof` for `std` and `var`.
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expressifiable_args: keyword arguments to pass to function, which may
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be expressifiable (e.g. `nw.col('a').is_between(3, nw.col('b')))`).
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"""
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func = partial(
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self._reuse_series_inner,
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method_name=method_name,
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returns_scalar=returns_scalar,
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call_kwargs=call_kwargs or {},
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expressifiable_args=expressifiable_args,
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)
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return self._from_callable(
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func,
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depth=self._depth + 1,
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function_name=f"{self._function_name}->{method_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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call_kwargs=call_kwargs,
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context=self,
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)
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# For PyArrow.Series, we return Python Scalars (like Polars does) instead of PyArrow Scalars.
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# However, when working with expressions, we keep everything PyArrow-native.
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def _reuse_series_extra_kwargs(
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self, *, returns_scalar: bool = False
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) -> dict[str, Any]:
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return {}
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|
@classmethod
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|
def _is_expr(cls, obj: Self | Any) -> TypeIs[Self]:
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return hasattr(obj, "__narwhals_expr__")
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|
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def _reuse_series_inner(
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self,
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df: EagerDataFrameT,
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*,
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method_name: str,
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returns_scalar: bool,
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call_kwargs: dict[str, Any],
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expressifiable_args: dict[str, Any],
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) -> Sequence[EagerSeriesT]:
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kwargs = {
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**call_kwargs,
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**{
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name: df._evaluate_expr(value) if self._is_expr(value) else value
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for name, value in expressifiable_args.items()
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},
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}
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method = methodcaller(
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method_name,
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**self._reuse_series_extra_kwargs(returns_scalar=returns_scalar),
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**kwargs,
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)
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out: Sequence[EagerSeriesT] = [
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series._from_scalar(method(series)) if returns_scalar else method(series)
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for series in self(df)
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]
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|
_, aliases = evaluate_output_names_and_aliases(self, df, [])
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|
if [s.name for s in out] != list(aliases): # pragma: no cover
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|
msg = (
|
|
f"Safety assertion failed, please report a bug to https://github.com/narwhals-dev/narwhals/issues\n"
|
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f"Expression aliases: {aliases}\n"
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f"Series names: {[s.name for s in out]}"
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)
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raise AssertionError(msg)
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return out
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|
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def _reuse_series_namespace(
|
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self: Self,
|
|
series_namespace: Literal["cat", "dt", "list", "name", "str", "struct"],
|
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method_name: str,
|
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**kwargs: Any,
|
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) -> Self:
|
|
"""Reuse Series implementation for expression.
|
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|
Just like `_reuse_series`, but for e.g. `Expr.dt.foo` instead
|
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of `Expr.foo`.
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|
Arguments:
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series_namespace: The Series namespace.
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|
method_name: name of method, within `series_namespace`.
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kwargs: keyword arguments to pass to function.
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"""
|
|
return self._from_callable(
|
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lambda df: [
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getattr(getattr(series, series_namespace), method_name)(**kwargs)
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|
for series in self(df)
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],
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depth=self._depth + 1,
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function_name=f"{self._function_name}->{series_namespace}.{method_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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call_kwargs={**self._call_kwargs, **kwargs},
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context=self,
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)
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|
|
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,
|
|
call_kwargs=self._call_kwargs,
|
|
)
|
|
|
|
def cast(self, dtype: DType | type[DType]) -> 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, call_kwargs={"ddof": ddof})
|
|
|
|
def var(self, *, ddof: int) -> Self:
|
|
return self._reuse_series("var", returns_scalar=True, call_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: Any, upper_bound: Any) -> 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: Any | None,
|
|
strategy: Literal["forward", "backward"] | 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: DType | type[DType] | 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: 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,
|
|
call_kwargs=self._call_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: Literal["nearest", "higher", "lower", "midpoint", "linear"],
|
|
) -> 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: Self,
|
|
function: Callable[[Any], Any],
|
|
return_dtype: DType | type[DType] | 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( # type: ignore[misc]
|
|
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]
|
|
|
|
@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: ...
|
|
|
|
@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")
|
|
|
|
|
|
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,
|
|
call_kwargs=expr._call_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
|
|
)
|