211 lines
7.2 KiB
Python
211 lines
7.2 KiB
Python
from __future__ import annotations
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from functools import partial
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from typing import TYPE_CHECKING, Any, Protocol, overload
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from narwhals._compliant.typing import (
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CompliantExprT,
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CompliantFrameT,
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CompliantLazyFrameT,
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DepthTrackingExprT,
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EagerDataFrameT,
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EagerExprT,
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EagerSeriesT,
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LazyExprT,
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NativeFrameT,
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NativeFrameT_co,
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NativeSeriesT,
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)
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from narwhals._utils import (
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exclude_column_names,
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get_column_names,
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passthrough_column_names,
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)
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from narwhals.dependencies import is_numpy_array_2d
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if TYPE_CHECKING:
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from collections.abc import Container, Iterable, Mapping, Sequence
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from typing_extensions import TypeAlias
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from narwhals._compliant.selectors import CompliantSelectorNamespace
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from narwhals._compliant.when_then import CompliantWhen, EagerWhen
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from narwhals._utils import Implementation, Version
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from narwhals.dtypes import DType
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from narwhals.schema import Schema
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from narwhals.typing import (
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ConcatMethod,
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Into1DArray,
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IntoDType,
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NonNestedLiteral,
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_2DArray,
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)
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Incomplete: TypeAlias = Any
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__all__ = [
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"CompliantNamespace",
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"DepthTrackingNamespace",
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"EagerNamespace",
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"LazyNamespace",
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]
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class CompliantNamespace(Protocol[CompliantFrameT, CompliantExprT]):
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_implementation: Implementation
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_version: Version
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def all(self) -> CompliantExprT:
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return self._expr.from_column_names(get_column_names, context=self)
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def col(self, *column_names: str) -> CompliantExprT:
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return self._expr.from_column_names(
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passthrough_column_names(column_names), context=self
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)
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def exclude(self, excluded_names: Container[str]) -> CompliantExprT:
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return self._expr.from_column_names(
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partial(exclude_column_names, names=excluded_names), context=self
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)
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def nth(self, *column_indices: int) -> CompliantExprT:
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return self._expr.from_column_indices(*column_indices, context=self)
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def len(self) -> CompliantExprT: ...
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def lit(self, value: NonNestedLiteral, dtype: IntoDType | None) -> CompliantExprT: ...
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def all_horizontal(
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self, *exprs: CompliantExprT, ignore_nulls: bool
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) -> CompliantExprT: ...
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def any_horizontal(
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self, *exprs: CompliantExprT, ignore_nulls: bool
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) -> CompliantExprT: ...
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def sum_horizontal(self, *exprs: CompliantExprT) -> CompliantExprT: ...
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def mean_horizontal(self, *exprs: CompliantExprT) -> CompliantExprT: ...
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def min_horizontal(self, *exprs: CompliantExprT) -> CompliantExprT: ...
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def max_horizontal(self, *exprs: CompliantExprT) -> CompliantExprT: ...
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def concat(
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self, items: Iterable[CompliantFrameT], *, how: ConcatMethod
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) -> CompliantFrameT: ...
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def when(
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self, predicate: CompliantExprT
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) -> CompliantWhen[CompliantFrameT, Incomplete, CompliantExprT]: ...
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def concat_str(
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self, *exprs: CompliantExprT, separator: str, ignore_nulls: bool
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) -> CompliantExprT: ...
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@property
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def selectors(self) -> CompliantSelectorNamespace[Any, Any]: ...
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@property
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def _expr(self) -> type[CompliantExprT]: ...
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def coalesce(self, *exprs: CompliantExprT) -> CompliantExprT: ...
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class DepthTrackingNamespace(
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CompliantNamespace[CompliantFrameT, DepthTrackingExprT],
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Protocol[CompliantFrameT, DepthTrackingExprT],
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):
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def all(self) -> DepthTrackingExprT:
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return self._expr.from_column_names(
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get_column_names, function_name="all", context=self
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)
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def col(self, *column_names: str) -> DepthTrackingExprT:
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return self._expr.from_column_names(
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passthrough_column_names(column_names), function_name="col", context=self
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)
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def exclude(self, excluded_names: Container[str]) -> DepthTrackingExprT:
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return self._expr.from_column_names(
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partial(exclude_column_names, names=excluded_names),
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function_name="exclude",
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context=self,
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)
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class LazyNamespace(
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CompliantNamespace[CompliantLazyFrameT, LazyExprT],
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Protocol[CompliantLazyFrameT, LazyExprT, NativeFrameT_co],
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):
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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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@property
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def _lazyframe(self) -> type[CompliantLazyFrameT]: ...
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def from_native(self, data: NativeFrameT_co | Any, /) -> CompliantLazyFrameT:
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if self._lazyframe._is_native(data):
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return self._lazyframe.from_native(data, context=self)
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else: # pragma: no cover
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msg = f"Unsupported type: {type(data).__name__!r}"
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raise TypeError(msg)
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class EagerNamespace(
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DepthTrackingNamespace[EagerDataFrameT, EagerExprT],
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Protocol[EagerDataFrameT, EagerSeriesT, EagerExprT, NativeFrameT, NativeSeriesT],
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):
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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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@property
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def _dataframe(self) -> type[EagerDataFrameT]: ...
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@property
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def _series(self) -> type[EagerSeriesT]: ...
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def when(
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self, predicate: EagerExprT
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) -> EagerWhen[EagerDataFrameT, EagerSeriesT, EagerExprT, NativeSeriesT]: ...
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@overload
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def from_native(self, data: NativeFrameT, /) -> EagerDataFrameT: ...
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@overload
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def from_native(self, data: NativeSeriesT, /) -> EagerSeriesT: ...
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def from_native(
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self, data: NativeFrameT | NativeSeriesT | Any, /
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) -> EagerDataFrameT | EagerSeriesT:
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if self._dataframe._is_native(data):
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return self._dataframe.from_native(data, context=self)
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elif self._series._is_native(data):
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return self._series.from_native(data, context=self)
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msg = f"Unsupported type: {type(data).__name__!r}"
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raise TypeError(msg)
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@overload
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def from_numpy(self, data: Into1DArray, /, schema: None = ...) -> EagerSeriesT: ...
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@overload
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def from_numpy(
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self,
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data: _2DArray,
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/,
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schema: Mapping[str, DType] | Schema | Sequence[str] | None,
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) -> EagerDataFrameT: ...
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def from_numpy(
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self,
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data: Into1DArray | _2DArray,
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/,
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schema: Mapping[str, DType] | Schema | Sequence[str] | None = None,
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) -> EagerDataFrameT | EagerSeriesT:
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if is_numpy_array_2d(data):
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return self._dataframe.from_numpy(data, schema=schema, context=self)
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return self._series.from_numpy(data, context=self)
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def _concat_diagonal(self, dfs: Sequence[NativeFrameT], /) -> NativeFrameT: ...
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def _concat_horizontal(
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self, dfs: Sequence[NativeFrameT | Any], /
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) -> NativeFrameT: ...
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def _concat_vertical(self, dfs: Sequence[NativeFrameT], /) -> NativeFrameT: ...
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def concat(
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self, items: Iterable[EagerDataFrameT], *, how: ConcatMethod
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) -> EagerDataFrameT:
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dfs = [item.native for item in items]
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if how == "horizontal":
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native = self._concat_horizontal(dfs)
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elif how == "vertical":
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native = self._concat_vertical(dfs)
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elif how == "diagonal":
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native = self._concat_diagonal(dfs)
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else: # pragma: no cover
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raise NotImplementedError
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return self._dataframe.from_native(native, context=self)
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