517 lines
17 KiB
Python
517 lines
17 KiB
Python
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Generic, Literal, Protocol
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from narwhals._compliant.any_namespace import (
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CatNamespace,
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DateTimeNamespace,
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ListNamespace,
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StringNamespace,
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StructNamespace,
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)
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from narwhals._compliant.typing import (
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CompliantSeriesT_co,
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EagerDataFrameAny,
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EagerSeriesT_co,
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NativeSeriesT,
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NativeSeriesT_co,
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)
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from narwhals._translate import FromIterable, FromNative, NumpyConvertible, ToNarwhals
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from narwhals._typing_compat import TypeVar, assert_never
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from narwhals._utils import (
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_StoresCompliant,
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_StoresNative,
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is_compliant_series,
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is_sized_multi_index_selector,
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unstable,
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)
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if TYPE_CHECKING:
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from collections.abc import Iterable, Iterator, Mapping, Sequence
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from types import ModuleType
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import pandas as pd
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import polars as pl
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import pyarrow as pa
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from typing_extensions import NotRequired, Self, TypedDict
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from narwhals._compliant.dataframe import CompliantDataFrame
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from narwhals._compliant.expr import CompliantExpr, EagerExpr
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from narwhals._compliant.namespace import CompliantNamespace, EagerNamespace
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from narwhals._utils import Implementation, Version, _LimitedContext
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from narwhals.dtypes import DType
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from narwhals.series import Series
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from narwhals.typing import (
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ClosedInterval,
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FillNullStrategy,
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Into1DArray,
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IntoDType,
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MultiIndexSelector,
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NonNestedLiteral,
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NumericLiteral,
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RankMethod,
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RollingInterpolationMethod,
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SizedMultiIndexSelector,
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TemporalLiteral,
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_1DArray,
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_SliceIndex,
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)
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class HistData(TypedDict, Generic[NativeSeriesT, "_CountsT_co"]):
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breakpoint: NotRequired[list[float] | _1DArray | list[Any]]
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count: NativeSeriesT | _1DArray | _CountsT_co | list[Any]
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_CountsT_co = TypeVar("_CountsT_co", bound="Iterable[Any]", covariant=True)
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__all__ = [
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"CompliantSeries",
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"EagerSeries",
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"EagerSeriesCatNamespace",
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"EagerSeriesDateTimeNamespace",
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"EagerSeriesHist",
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"EagerSeriesListNamespace",
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"EagerSeriesNamespace",
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"EagerSeriesStringNamespace",
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"EagerSeriesStructNamespace",
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]
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class CompliantSeries(
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NumpyConvertible["_1DArray", "Into1DArray"],
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FromIterable,
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FromNative[NativeSeriesT],
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ToNarwhals["Series[NativeSeriesT]"],
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Protocol[NativeSeriesT],
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):
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_implementation: Implementation
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_version: Version
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@property
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def dtype(self) -> DType: ...
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@property
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def name(self) -> str: ...
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@property
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def native(self) -> NativeSeriesT: ...
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def __narwhals_series__(self) -> Self:
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return self
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def __narwhals_namespace__(self) -> CompliantNamespace[Any, Any]: ...
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def __native_namespace__(self) -> ModuleType: ...
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def __array__(self, dtype: Any, *, copy: bool | None) -> _1DArray: ...
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def __contains__(self, other: Any) -> bool: ...
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def __getitem__(self, item: MultiIndexSelector[Self]) -> Any: ...
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def __iter__(self) -> Iterator[Any]: ...
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def __len__(self) -> int:
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return len(self.native)
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def _with_native(self, series: Any) -> Self: ...
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def _with_version(self, version: Version) -> Self: ...
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def _to_expr(self) -> CompliantExpr[Any, Self]: ...
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@classmethod
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def from_native(cls, data: NativeSeriesT, /, *, context: _LimitedContext) -> Self: ...
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@classmethod
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def from_numpy(cls, data: Into1DArray, /, *, context: _LimitedContext) -> Self: ...
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@classmethod
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def from_iterable(
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cls,
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data: Iterable[Any],
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/,
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*,
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context: _LimitedContext,
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name: str = "",
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dtype: IntoDType | None = None,
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) -> Self: ...
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def to_narwhals(self) -> Series[NativeSeriesT]:
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return self._version.series(self, level="full")
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# Operators
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def __add__(self, other: Any) -> Self: ...
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def __and__(self, other: Any) -> Self: ...
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def __eq__(self, other: object) -> Self: ... # type: ignore[override]
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def __floordiv__(self, other: Any) -> Self: ...
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def __ge__(self, other: Any) -> Self: ...
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def __gt__(self, other: Any) -> Self: ...
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def __invert__(self) -> Self: ...
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def __le__(self, other: Any) -> Self: ...
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def __lt__(self, other: Any) -> Self: ...
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def __mod__(self, other: Any) -> Self: ...
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def __mul__(self, other: Any) -> Self: ...
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def __ne__(self, other: object) -> Self: ... # type: ignore[override]
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def __or__(self, other: Any) -> Self: ...
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def __pow__(self, other: Any) -> Self: ...
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def __radd__(self, other: Any) -> Self: ...
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def __rand__(self, other: Any) -> Self: ...
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def __rfloordiv__(self, other: Any) -> Self: ...
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def __rmod__(self, other: Any) -> Self: ...
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def __rmul__(self, other: Any) -> Self: ...
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def __ror__(self, other: Any) -> Self: ...
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def __rpow__(self, other: Any) -> Self: ...
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def __rsub__(self, other: Any) -> Self: ...
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def __rtruediv__(self, other: Any) -> Self: ...
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def __sub__(self, other: Any) -> Self: ...
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def __truediv__(self, other: Any) -> Self: ...
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def abs(self) -> Self: ...
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def alias(self, name: str) -> Self: ...
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def all(self) -> bool: ...
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def any(self) -> bool: ...
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def arg_max(self) -> int: ...
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def arg_min(self) -> int: ...
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def arg_true(self) -> Self: ...
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def cast(self, dtype: IntoDType) -> Self: ...
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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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def count(self) -> int: ...
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def cum_count(self, *, reverse: bool) -> Self: ...
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def cum_max(self, *, reverse: bool) -> Self: ...
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def cum_min(self, *, reverse: bool) -> Self: ...
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def cum_prod(self, *, reverse: bool) -> Self: ...
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def cum_sum(self, *, reverse: bool) -> Self: ...
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def diff(self) -> Self: ...
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def drop_nulls(self) -> Self: ...
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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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def exp(self) -> Self: ...
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def sqrt(self) -> Self: ...
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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 filter(self, predicate: Any) -> Self: ...
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def gather_every(self, n: int, offset: int) -> Self: ...
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def head(self, n: int) -> Self: ...
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def is_between(
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self, lower_bound: Any, upper_bound: Any, closed: ClosedInterval
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) -> Self: ...
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def is_finite(self) -> Self: ...
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def is_first_distinct(self) -> Self: ...
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def is_in(self, other: Any) -> Self: ...
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def is_last_distinct(self) -> Self: ...
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def is_nan(self) -> Self: ...
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def is_null(self) -> Self: ...
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def is_sorted(self, *, descending: bool) -> bool: ...
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def is_unique(self) -> Self: ...
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def item(self, index: int | None) -> Any: ...
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def kurtosis(self) -> float | None: ...
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def len(self) -> int: ...
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def log(self, base: float) -> Self: ...
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def max(self) -> Any: ...
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def mean(self) -> float: ...
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def median(self) -> float: ...
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def min(self) -> Any: ...
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def mode(self) -> Self: ...
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def n_unique(self) -> int: ...
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def null_count(self) -> int: ...
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def quantile(
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self, quantile: float, interpolation: RollingInterpolationMethod
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) -> float: ...
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def rank(self, method: RankMethod, *, descending: bool) -> 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: IntoDType | None,
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) -> Self: ...
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def rolling_mean(
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self, window_size: int, *, min_samples: int, center: bool
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) -> Self: ...
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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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def rolling_sum(
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self, window_size: int, *, min_samples: int, center: bool
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) -> Self: ...
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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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def round(self, decimals: int) -> 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 scatter(self, indices: int | Sequence[int], values: Any) -> Self: ...
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def shift(self, n: int) -> Self: ...
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def skew(self) -> float | None: ...
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def sort(self, *, descending: bool, nulls_last: bool) -> Self: ...
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def std(self, *, ddof: int) -> float: ...
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def sum(self) -> float: ...
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def tail(self, n: int) -> Self: ...
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def to_arrow(self) -> pa.Array[Any]: ...
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def to_dummies(
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self, *, separator: str, drop_first: bool
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) -> CompliantDataFrame[Self, Any, Any, Any]: ...
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def to_frame(self) -> CompliantDataFrame[Self, Any, Any, Any]: ...
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def to_list(self) -> list[Any]: ...
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def to_pandas(self) -> pd.Series[Any]: ...
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def to_polars(self) -> pl.Series: ...
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def unique(self, *, maintain_order: bool) -> Self: ...
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def value_counts(
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self, *, sort: bool, parallel: bool, name: str | None, normalize: bool
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) -> CompliantDataFrame[Self, Any, Any, Any]: ...
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def var(self, *, ddof: int) -> float: ...
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def zip_with(self, mask: Any, other: Any) -> Self: ...
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@unstable
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def hist_from_bins(
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self, bins: list[float], *, include_breakpoint: bool
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) -> CompliantDataFrame[Self, Any, Any, Any]:
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"""`Series.hist(bins=..., bin_count=None)`."""
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...
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@unstable
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def hist_from_bin_count(
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self, bin_count: int, *, include_breakpoint: bool
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) -> CompliantDataFrame[Self, Any, Any, Any]:
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"""`Series.hist(bins=None, bin_count=...)`."""
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...
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@property
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def str(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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class EagerSeries(CompliantSeries[NativeSeriesT], Protocol[NativeSeriesT]):
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_native_series: Any
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_implementation: Implementation
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_version: Version
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_broadcast: bool
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@property
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def _backend_version(self) -> tuple[int, ...]:
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return self._implementation._backend_version()
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@classmethod
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def _align_full_broadcast(cls, *series: Self) -> Sequence[Self]:
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"""Ensure all of `series` have the same length (and index if `pandas`).
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Scalars get broadcasted to the full length of the longest Series.
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This is useful when you need to construct a full Series anyway, such as:
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DataFrame.select(...)
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It should not be used in binary operations, such as:
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nw.col("a") - nw.col("a").mean()
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because then it's more efficient to extract the right-hand-side's single element as a scalar.
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"""
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...
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def _from_scalar(self, value: Any) -> Self:
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return self.from_iterable([value], name=self.name, context=self)
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def _with_native(
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self, series: NativeSeriesT, *, preserve_broadcast: bool = False
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) -> Self:
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"""Return a new `CompliantSeries`, wrapping the native `series`.
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In cases when operations are known to not affect whether a result should
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be broadcast, we can pass `preserve_broadcast=True`.
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Set this with care - it should only be set for unary expressions which don't
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change length or order, such as `.alias` or `.fill_null`. If in doubt, don't
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set it, you probably don't need it.
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"""
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...
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def __narwhals_namespace__(
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self,
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) -> EagerNamespace[Any, Self, Any, Any, NativeSeriesT]: ...
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def _to_expr(self) -> EagerExpr[Any, Any]:
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return self.__narwhals_namespace__()._expr._from_series(self) # type: ignore[no-any-return]
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def _gather(self, rows: SizedMultiIndexSelector[NativeSeriesT]) -> Self: ...
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def _gather_slice(self, rows: _SliceIndex | range) -> Self: ...
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def __getitem__(self, item: MultiIndexSelector[Self]) -> Self:
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if isinstance(item, (slice, range)):
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return self._gather_slice(item)
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elif is_compliant_series(item):
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return self._gather(item.native)
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||
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|
elif is_sized_multi_index_selector(item):
|
||
|
|
return self._gather(item)
|
||
|
|
else:
|
||
|
|
assert_never(item)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def str(self) -> EagerSeriesStringNamespace[Self, NativeSeriesT]: ...
|
||
|
|
@property
|
||
|
|
def dt(self) -> EagerSeriesDateTimeNamespace[Self, NativeSeriesT]: ...
|
||
|
|
@property
|
||
|
|
def cat(self) -> EagerSeriesCatNamespace[Self, NativeSeriesT]: ...
|
||
|
|
@property
|
||
|
|
def list(self) -> EagerSeriesListNamespace[Self, NativeSeriesT]: ...
|
||
|
|
@property
|
||
|
|
def struct(self) -> EagerSeriesStructNamespace[Self, NativeSeriesT]: ...
|
||
|
|
|
||
|
|
|
||
|
|
class _SeriesNamespace( # type: ignore[misc]
|
||
|
|
_StoresCompliant[CompliantSeriesT_co],
|
||
|
|
_StoresNative[NativeSeriesT_co],
|
||
|
|
Protocol[CompliantSeriesT_co, NativeSeriesT_co],
|
||
|
|
):
|
||
|
|
_compliant_series: CompliantSeriesT_co
|
||
|
|
|
||
|
|
@property
|
||
|
|
def compliant(self) -> CompliantSeriesT_co:
|
||
|
|
return self._compliant_series
|
||
|
|
|
||
|
|
@property
|
||
|
|
def implementation(self) -> Implementation:
|
||
|
|
return self.compliant._implementation
|
||
|
|
|
||
|
|
@property
|
||
|
|
def backend_version(self) -> tuple[int, ...]:
|
||
|
|
return self.implementation._backend_version()
|
||
|
|
|
||
|
|
@property
|
||
|
|
def version(self) -> Version:
|
||
|
|
return self.compliant._version
|
||
|
|
|
||
|
|
@property
|
||
|
|
def native(self) -> NativeSeriesT_co:
|
||
|
|
return self._compliant_series.native # type: ignore[no-any-return]
|
||
|
|
|
||
|
|
def with_native(self, series: Any, /) -> CompliantSeriesT_co:
|
||
|
|
return self.compliant._with_native(series)
|
||
|
|
|
||
|
|
|
||
|
|
class EagerSeriesNamespace(
|
||
|
|
_SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
Generic[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
):
|
||
|
|
_compliant_series: EagerSeriesT_co
|
||
|
|
|
||
|
|
def __init__(self, series: EagerSeriesT_co, /) -> None:
|
||
|
|
self._compliant_series = series
|
||
|
|
|
||
|
|
|
||
|
|
class EagerSeriesCatNamespace( # type: ignore[misc]
|
||
|
|
_SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
CatNamespace[EagerSeriesT_co],
|
||
|
|
Protocol[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
): ...
|
||
|
|
|
||
|
|
|
||
|
|
class EagerSeriesDateTimeNamespace( # type: ignore[misc]
|
||
|
|
_SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
DateTimeNamespace[EagerSeriesT_co],
|
||
|
|
Protocol[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
): ...
|
||
|
|
|
||
|
|
|
||
|
|
class EagerSeriesListNamespace( # type: ignore[misc]
|
||
|
|
_SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
ListNamespace[EagerSeriesT_co],
|
||
|
|
Protocol[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
): ...
|
||
|
|
|
||
|
|
|
||
|
|
class EagerSeriesStringNamespace( # type: ignore[misc]
|
||
|
|
_SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
StringNamespace[EagerSeriesT_co],
|
||
|
|
Protocol[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
): ...
|
||
|
|
|
||
|
|
|
||
|
|
class EagerSeriesStructNamespace( # type: ignore[misc]
|
||
|
|
_SeriesNamespace[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
StructNamespace[EagerSeriesT_co],
|
||
|
|
Protocol[EagerSeriesT_co, NativeSeriesT_co],
|
||
|
|
): ...
|
||
|
|
|
||
|
|
|
||
|
|
class EagerSeriesHist(Protocol[NativeSeriesT, _CountsT_co]):
|
||
|
|
_series: EagerSeries[NativeSeriesT]
|
||
|
|
_breakpoint: bool
|
||
|
|
_data: HistData[NativeSeriesT, _CountsT_co]
|
||
|
|
|
||
|
|
@property
|
||
|
|
def native(self) -> NativeSeriesT:
|
||
|
|
return self._series.native
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def from_series(
|
||
|
|
cls, series: EagerSeries[NativeSeriesT], *, include_breakpoint: bool
|
||
|
|
) -> Self:
|
||
|
|
obj = cls.__new__(cls)
|
||
|
|
obj._series = series
|
||
|
|
obj._breakpoint = include_breakpoint
|
||
|
|
return obj
|
||
|
|
|
||
|
|
def to_frame(self) -> EagerDataFrameAny: ...
|
||
|
|
def _linear_space( # NOTE: Roughly `pl.linear_space`
|
||
|
|
self,
|
||
|
|
start: float,
|
||
|
|
end: float,
|
||
|
|
num_samples: int,
|
||
|
|
*,
|
||
|
|
closed: Literal["both", "none"] = "both",
|
||
|
|
) -> _1DArray: ...
|
||
|
|
|
||
|
|
# NOTE: *Could* be handled at narwhals-level
|
||
|
|
def is_empty_series(self) -> bool: ...
|
||
|
|
|
||
|
|
# NOTE: **Should** be handled at narwhals-level
|
||
|
|
def data_empty(self) -> HistData[NativeSeriesT, _CountsT_co]:
|
||
|
|
return {"breakpoint": [], "count": []} if self._breakpoint else {"count": []}
|
||
|
|
|
||
|
|
# NOTE: *Could* be handled at narwhals-level, **iff** we add `nw.repeat`, `nw.linear_space`
|
||
|
|
# See https://github.com/narwhals-dev/narwhals/pull/2839#discussion_r2215630696
|
||
|
|
def series_empty(
|
||
|
|
self, arg: int | list[float], /
|
||
|
|
) -> HistData[NativeSeriesT, _CountsT_co]: ...
|
||
|
|
|
||
|
|
def with_bins(self, bins: list[float], /) -> Self:
|
||
|
|
if len(bins) <= 1:
|
||
|
|
self._data = self.data_empty()
|
||
|
|
elif self.is_empty_series():
|
||
|
|
self._data = self.series_empty(bins)
|
||
|
|
else:
|
||
|
|
self._data = self._calculate_hist(bins)
|
||
|
|
return self
|
||
|
|
|
||
|
|
def with_bin_count(self, bin_count: int, /) -> Self:
|
||
|
|
if bin_count == 0:
|
||
|
|
self._data = self.data_empty()
|
||
|
|
elif self.is_empty_series():
|
||
|
|
self._data = self.series_empty(bin_count)
|
||
|
|
else:
|
||
|
|
self._data = self._calculate_hist(self._calculate_bins(bin_count))
|
||
|
|
return self
|
||
|
|
|
||
|
|
def _calculate_breakpoint(self, arg: int | list[float], /) -> list[float] | _1DArray:
|
||
|
|
bins = self._linear_space(0, 1, arg + 1) if isinstance(arg, int) else arg
|
||
|
|
return bins[1:]
|
||
|
|
|
||
|
|
def _calculate_bins(self, bin_count: int) -> _1DArray: ...
|
||
|
|
def _calculate_hist(
|
||
|
|
self, bins: list[float] | _1DArray
|
||
|
|
) -> HistData[NativeSeriesT, _CountsT_co]: ...
|