2023 lines
64 KiB
Python
2023 lines
64 KiB
Python
from __future__ import annotations
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import os
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import re
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from collections.abc import Collection, Container, Iterable, Iterator, Mapping, Sequence
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from datetime import timezone
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from enum import Enum, auto
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from functools import cache, lru_cache, wraps
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from importlib.util import find_spec
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from inspect import getattr_static, getdoc
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from secrets import token_hex
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Generic,
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Literal,
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Protocol,
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TypeVar,
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Union,
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cast,
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overload,
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)
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from narwhals._enum import NoAutoEnum
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from narwhals._exceptions import issue_deprecation_warning
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from narwhals._typing_compat import assert_never, deprecated
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from narwhals.dependencies import (
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get_cudf,
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get_dask_dataframe,
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get_duckdb,
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get_ibis,
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get_modin,
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get_pandas,
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get_polars,
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get_pyarrow,
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get_pyspark_connect,
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get_pyspark_sql,
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get_sqlframe,
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is_narwhals_series,
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is_narwhals_series_int,
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is_numpy_array_1d,
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is_numpy_array_1d_int,
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is_pandas_like_dataframe,
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is_pandas_like_series,
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is_polars_series,
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is_pyarrow_chunked_array,
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)
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from narwhals.exceptions import ColumnNotFoundError, DuplicateError, InvalidOperationError
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if TYPE_CHECKING:
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from collections.abc import Set # noqa: PYI025
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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 (
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Concatenate,
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LiteralString,
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ParamSpec,
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Self,
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TypeAlias,
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TypeIs,
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)
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from narwhals._compliant import (
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CompliantExpr,
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CompliantExprT,
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CompliantFrameT,
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CompliantSeriesOrNativeExprT_co,
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CompliantSeriesT,
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NativeFrameT_co,
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NativeSeriesT_co,
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)
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from narwhals._compliant.typing import EvalNames
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from narwhals._namespace import EagerAllowedImplementation, Namespace
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from narwhals._translate import ArrowStreamExportable, IntoArrowTable, ToNarwhalsT_co
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from narwhals.dataframe import DataFrame, LazyFrame
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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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CompliantDataFrame,
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CompliantLazyFrame,
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CompliantSeries,
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DataFrameLike,
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DTypes,
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IntoSeriesT,
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MultiIndexSelector,
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SingleIndexSelector,
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SizedMultiIndexSelector,
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SizeUnit,
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SupportsNativeNamespace,
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TimeUnit,
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_1DArray,
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_SliceIndex,
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_SliceName,
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_SliceNone,
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)
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FrameOrSeriesT = TypeVar(
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"FrameOrSeriesT", bound=Union[LazyFrame[Any], DataFrame[Any], Series[Any]]
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)
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_T1 = TypeVar("_T1")
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_T2 = TypeVar("_T2")
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_T3 = TypeVar("_T3")
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_T4 = TypeVar("_T4")
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_T5 = TypeVar("_T5")
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_T6 = TypeVar("_T6")
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_T7 = TypeVar("_T7")
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_Fn = TypeVar("_Fn", bound="Callable[..., Any]")
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P = ParamSpec("P")
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R = TypeVar("R")
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R1 = TypeVar("R1")
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R2 = TypeVar("R2")
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class _SupportsVersion(Protocol):
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__version__: str
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class _SupportsGet(Protocol): # noqa: PYI046
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def __get__(self, instance: Any, owner: Any | None = None, /) -> Any: ...
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class _StoresColumns(Protocol):
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@property
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def columns(self) -> Sequence[str]: ...
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_T = TypeVar("_T")
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NativeT_co = TypeVar("NativeT_co", covariant=True)
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CompliantT_co = TypeVar("CompliantT_co", covariant=True)
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_ContextT = TypeVar("_ContextT", bound="_FullContext")
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_Method: TypeAlias = "Callable[Concatenate[_ContextT, P], R]"
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_Constructor: TypeAlias = "Callable[Concatenate[_T, P], R2]"
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class _StoresNative(Protocol[NativeT_co]): # noqa: PYI046
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"""Provides access to a native object.
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Native objects have types like:
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>>> from pandas import Series
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>>> from pyarrow import Table
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"""
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@property
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def native(self) -> NativeT_co:
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"""Return the native object."""
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...
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class _StoresCompliant(Protocol[CompliantT_co]): # noqa: PYI046
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"""Provides access to a compliant object.
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Compliant objects have types like:
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>>> from narwhals._pandas_like.series import PandasLikeSeries
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>>> from narwhals._arrow.dataframe import ArrowDataFrame
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"""
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@property
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def compliant(self) -> CompliantT_co:
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"""Return the compliant object."""
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...
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class _StoresBackendVersion(Protocol):
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@property
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def _backend_version(self) -> tuple[int, ...]:
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"""Version tuple for a native package."""
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...
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class _StoresVersion(Protocol):
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_version: Version
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"""Narwhals API version (V1 or MAIN)."""
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class _StoresImplementation(Protocol):
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_implementation: Implementation
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"""Implementation of native object (pandas, Polars, PyArrow, ...)."""
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class _LimitedContext(_StoresImplementation, _StoresVersion, Protocol):
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"""Provides 2 attributes.
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- `_implementation`
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- `_version`
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"""
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class _FullContext(_StoresBackendVersion, _LimitedContext, Protocol):
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"""Provides 3 attributes.
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- `_implementation`
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- `_backend_version`
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- `_version`
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"""
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class ValidateBackendVersion(_StoresImplementation, Protocol):
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"""Ensure the target `Implementation` is on a supported version."""
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def _validate_backend_version(self) -> None:
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"""Raise if installed version below `nw._utils.MIN_VERSIONS`.
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**Only use this when moving between backends.**
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Otherwise, the validation will have taken place already.
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"""
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_ = self._implementation._backend_version()
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class Version(Enum):
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V1 = auto()
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V2 = auto()
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MAIN = auto()
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@property
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def namespace(self) -> type[Namespace[Any]]:
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if self is Version.V1:
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from narwhals.stable.v1._namespace import Namespace as NamespaceV1
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return NamespaceV1
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if self is Version.V2:
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from narwhals.stable.v2._namespace import Namespace as NamespaceV2
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return NamespaceV2
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from narwhals._namespace import Namespace
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return Namespace
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@property
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def dtypes(self) -> DTypes:
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if self is Version.V1:
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from narwhals.stable.v1 import dtypes as dtypes_v1
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return dtypes_v1
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if self is Version.V2:
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from narwhals.stable.v2 import dtypes as dtypes_v2
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return dtypes_v2
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from narwhals import dtypes
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return dtypes
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@property
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def dataframe(self) -> type[DataFrame[Any]]:
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if self is Version.V1:
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from narwhals.stable.v1 import DataFrame as DataFrameV1
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return DataFrameV1
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if self is Version.V2:
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from narwhals.stable.v2 import DataFrame as DataFrameV2
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return DataFrameV2
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from narwhals.dataframe import DataFrame
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return DataFrame
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@property
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def lazyframe(self) -> type[LazyFrame[Any]]:
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if self is Version.V1:
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from narwhals.stable.v1 import LazyFrame as LazyFrameV1
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return LazyFrameV1
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if self is Version.V2:
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from narwhals.stable.v2 import LazyFrame as LazyFrameV2
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return LazyFrameV2
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from narwhals.dataframe import LazyFrame
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return LazyFrame
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@property
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def series(self) -> type[Series[Any]]:
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if self is Version.V1:
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from narwhals.stable.v1 import Series as SeriesV1
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return SeriesV1
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if self is Version.V2:
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from narwhals.stable.v2 import Series as SeriesV2
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return SeriesV2
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from narwhals.series import Series
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return Series
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class Implementation(NoAutoEnum):
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"""Implementation of native object (pandas, Polars, PyArrow, ...)."""
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PANDAS = "pandas"
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"""pandas implementation."""
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MODIN = "modin"
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"""Modin implementation."""
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CUDF = "cudf"
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"""cuDF implementation."""
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PYARROW = "pyarrow"
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"""PyArrow implementation."""
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PYSPARK = "pyspark"
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"""PySpark implementation."""
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POLARS = "polars"
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"""Polars implementation."""
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DASK = "dask"
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"""Dask implementation."""
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DUCKDB = "duckdb"
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"""DuckDB implementation."""
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IBIS = "ibis"
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"""Ibis implementation."""
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SQLFRAME = "sqlframe"
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"""SQLFrame implementation."""
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PYSPARK_CONNECT = "pyspark[connect]"
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"""PySpark Connect implementation."""
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UNKNOWN = "unknown"
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"""Unknown implementation."""
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def __str__(self) -> str:
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return str(self.value)
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@classmethod
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def from_native_namespace(
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cls: type[Self], native_namespace: ModuleType
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) -> Implementation: # pragma: no cover
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"""Instantiate Implementation object from a native namespace module.
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Arguments:
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native_namespace: Native namespace.
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Returns:
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Implementation.
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"""
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mapping = {
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get_pandas(): Implementation.PANDAS,
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get_modin(): Implementation.MODIN,
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get_cudf(): Implementation.CUDF,
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get_pyarrow(): Implementation.PYARROW,
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get_pyspark_sql(): Implementation.PYSPARK,
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get_polars(): Implementation.POLARS,
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get_dask_dataframe(): Implementation.DASK,
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get_duckdb(): Implementation.DUCKDB,
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get_ibis(): Implementation.IBIS,
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get_sqlframe(): Implementation.SQLFRAME,
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get_pyspark_connect(): Implementation.PYSPARK_CONNECT,
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}
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return mapping.get(native_namespace, Implementation.UNKNOWN)
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@classmethod
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def from_string(
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cls: type[Self], backend_name: str
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) -> Implementation: # pragma: no cover
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"""Instantiate Implementation object from a native namespace module.
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Arguments:
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backend_name: Name of backend, expressed as string.
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Returns:
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Implementation.
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"""
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try:
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return cls(backend_name)
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except ValueError:
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return Implementation.UNKNOWN
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@classmethod
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def from_backend(
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cls: type[Self], backend: str | Implementation | ModuleType
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) -> Implementation:
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"""Instantiate from native namespace module, string, or Implementation.
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Arguments:
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backend: Backend to instantiate Implementation from.
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Returns:
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Implementation.
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"""
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return (
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cls.from_string(backend)
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if isinstance(backend, str)
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else backend
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if isinstance(backend, Implementation)
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else cls.from_native_namespace(backend)
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)
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def to_native_namespace(self) -> ModuleType:
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"""Return the native namespace module corresponding to Implementation.
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Returns:
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Native module.
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"""
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if self is Implementation.UNKNOWN:
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msg = "Cannot return native namespace from UNKNOWN Implementation"
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raise AssertionError(msg)
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self._backend_version()
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module_name = _IMPLEMENTATION_TO_MODULE_NAME.get(self, self.value)
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return _import_native_namespace(module_name)
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def is_pandas(self) -> bool:
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"""Return whether implementation is pandas.
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Returns:
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Boolean.
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Examples:
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>>> import pandas as pd
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>>> import narwhals as nw
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>>> df_native = pd.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_pandas()
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True
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"""
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return self is Implementation.PANDAS
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def is_pandas_like(self) -> bool:
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"""Return whether implementation is pandas, Modin, or cuDF.
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Returns:
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Boolean.
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Examples:
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>>> import pandas as pd
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>>> import narwhals as nw
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>>> df_native = pd.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_pandas_like()
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True
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"""
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return self in {Implementation.PANDAS, Implementation.MODIN, Implementation.CUDF}
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def is_spark_like(self) -> bool:
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"""Return whether implementation is pyspark or sqlframe.
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Returns:
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Boolean.
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Examples:
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>>> import pandas as pd
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>>> import narwhals as nw
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>>> df_native = pd.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_spark_like()
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False
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"""
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return self in {
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Implementation.PYSPARK,
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Implementation.SQLFRAME,
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Implementation.PYSPARK_CONNECT,
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}
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def is_polars(self) -> bool:
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"""Return whether implementation is Polars.
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Returns:
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Boolean.
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Examples:
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>>> import polars as pl
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>>> import narwhals as nw
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>>> df_native = pl.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_polars()
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True
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"""
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return self is Implementation.POLARS
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def is_cudf(self) -> bool:
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"""Return whether implementation is cuDF.
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Returns:
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Boolean.
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Examples:
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>>> import polars as pl
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>>> import narwhals as nw
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>>> df_native = pl.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_cudf()
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False
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"""
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return self is Implementation.CUDF # pragma: no cover
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def is_modin(self) -> bool:
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"""Return whether implementation is Modin.
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|
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|
Returns:
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Boolean.
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|
Examples:
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>>> import polars as pl
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>>> import narwhals as nw
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>>> df_native = pl.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_modin()
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False
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"""
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return self is Implementation.MODIN # pragma: no cover
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|
|
def is_pyspark(self) -> bool:
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"""Return whether implementation is PySpark.
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|
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|
Returns:
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Boolean.
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|
Examples:
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>>> import polars as pl
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>>> import narwhals as nw
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>>> df_native = pl.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_pyspark()
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False
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"""
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return self is Implementation.PYSPARK # pragma: no cover
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|
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def is_pyspark_connect(self) -> bool:
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"""Return whether implementation is PySpark.
|
|
|
|
Returns:
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Boolean.
|
|
|
|
Examples:
|
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>>> import polars as pl
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>>> import narwhals as nw
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>>> df_native = pl.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_pyspark_connect()
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False
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"""
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return self is Implementation.PYSPARK_CONNECT # pragma: no cover
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|
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def is_pyarrow(self) -> bool:
|
|
"""Return whether implementation is PyArrow.
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|
|
|
Returns:
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Boolean.
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|
|
Examples:
|
|
>>> import polars as pl
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>>> import narwhals as nw
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>>> df_native = pl.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_pyarrow()
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False
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"""
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return self is Implementation.PYARROW # pragma: no cover
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|
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def is_dask(self) -> bool:
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"""Return whether implementation is Dask.
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|
Returns:
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Boolean.
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|
Examples:
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>>> import polars as pl
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>>> import narwhals as nw
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>>> df_native = pl.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_dask()
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False
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"""
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return self is Implementation.DASK # pragma: no cover
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|
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def is_duckdb(self) -> bool:
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"""Return whether implementation is DuckDB.
|
|
|
|
Returns:
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Boolean.
|
|
|
|
Examples:
|
|
>>> import polars as pl
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>>> import narwhals as nw
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>>> df_native = pl.DataFrame({"a": [1, 2, 3]})
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>>> df = nw.from_native(df_native)
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>>> df.implementation.is_duckdb()
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False
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"""
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return self is Implementation.DUCKDB # pragma: no cover
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|
|
def is_ibis(self) -> bool:
|
|
"""Return whether implementation is Ibis.
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|
|
|
Returns:
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Boolean.
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|
|
|
Examples:
|
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>>> import polars as pl
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|
>>> import narwhals as nw
|
|
>>> df_native = pl.DataFrame({"a": [1, 2, 3]})
|
|
>>> df = nw.from_native(df_native)
|
|
>>> df.implementation.is_ibis()
|
|
False
|
|
"""
|
|
return self is Implementation.IBIS # pragma: no cover
|
|
|
|
def is_sqlframe(self) -> bool:
|
|
"""Return whether implementation is SQLFrame.
|
|
|
|
Returns:
|
|
Boolean.
|
|
|
|
Examples:
|
|
>>> import polars as pl
|
|
>>> import narwhals as nw
|
|
>>> df_native = pl.DataFrame({"a": [1, 2, 3]})
|
|
>>> df = nw.from_native(df_native)
|
|
>>> df.implementation.is_sqlframe()
|
|
False
|
|
"""
|
|
return self is Implementation.SQLFRAME # pragma: no cover
|
|
|
|
def _backend_version(self) -> tuple[int, ...]:
|
|
"""Returns backend version."""
|
|
return backend_version(self)
|
|
|
|
|
|
MIN_VERSIONS: Mapping[Implementation, tuple[int, ...]] = {
|
|
Implementation.PANDAS: (1, 1, 3),
|
|
Implementation.MODIN: (0, 8, 2),
|
|
Implementation.CUDF: (24, 10),
|
|
Implementation.PYARROW: (13,),
|
|
Implementation.PYSPARK: (3, 5),
|
|
Implementation.PYSPARK_CONNECT: (3, 5),
|
|
Implementation.POLARS: (0, 20, 4),
|
|
Implementation.DASK: (2024, 8),
|
|
Implementation.DUCKDB: (1,),
|
|
Implementation.IBIS: (6,),
|
|
Implementation.SQLFRAME: (3, 22, 0),
|
|
}
|
|
|
|
_IMPLEMENTATION_TO_MODULE_NAME: Mapping[Implementation, str] = {
|
|
Implementation.DASK: "dask.dataframe",
|
|
Implementation.MODIN: "modin.pandas",
|
|
Implementation.PYSPARK: "pyspark.sql",
|
|
Implementation.PYSPARK_CONNECT: "pyspark.sql.connect",
|
|
}
|
|
"""Stores non default mapping from Implementation to module name"""
|
|
|
|
|
|
@lru_cache(maxsize=16)
|
|
def _import_native_namespace(module_name: str) -> ModuleType:
|
|
from importlib import import_module
|
|
|
|
return import_module(module_name)
|
|
|
|
|
|
# NOTE: We can safely use an unbounded cache, the size is constrained by `len(Implementation._member_names_)`
|
|
# Faster than `lru_cache`
|
|
# https://docs.python.org/3/library/functools.html#functools.cache
|
|
@cache
|
|
def backend_version(implementation: Implementation, /) -> tuple[int, ...]:
|
|
if not isinstance(implementation, Implementation):
|
|
assert_never(implementation)
|
|
if implementation is Implementation.UNKNOWN: # pragma: no cover
|
|
msg = "Cannot return backend version from UNKNOWN Implementation"
|
|
raise AssertionError(msg)
|
|
into_version: ModuleType | str
|
|
impl = implementation
|
|
module_name = _IMPLEMENTATION_TO_MODULE_NAME.get(impl, impl.value)
|
|
native_namespace = _import_native_namespace(module_name)
|
|
if impl.is_sqlframe():
|
|
import sqlframe._version
|
|
|
|
into_version = sqlframe._version
|
|
elif impl.is_pyspark() or impl.is_pyspark_connect(): # pragma: no cover
|
|
import pyspark # ignore-banned-import
|
|
|
|
into_version = pyspark
|
|
elif impl.is_dask():
|
|
import dask # ignore-banned-import
|
|
|
|
into_version = dask
|
|
else:
|
|
into_version = native_namespace
|
|
version = parse_version(into_version)
|
|
if version < (min_version := MIN_VERSIONS[impl]):
|
|
msg = f"Minimum version of {impl} supported by Narwhals is {min_version}, found: {version}"
|
|
raise ValueError(msg)
|
|
return version
|
|
|
|
|
|
def flatten(args: Any) -> list[Any]:
|
|
return list(args[0] if (len(args) == 1 and _is_iterable(args[0])) else args)
|
|
|
|
|
|
def tupleify(arg: Any) -> Any:
|
|
if not isinstance(arg, (list, tuple)): # pragma: no cover
|
|
return (arg,)
|
|
return arg
|
|
|
|
|
|
def _is_iterable(arg: Any | Iterable[Any]) -> bool:
|
|
from narwhals.series import Series
|
|
|
|
if (
|
|
(pd := get_pandas()) is not None and isinstance(arg, (pd.Series, pd.DataFrame))
|
|
) or (
|
|
(pl := get_polars()) is not None
|
|
and isinstance(arg, (pl.Series, pl.Expr, pl.DataFrame, pl.LazyFrame))
|
|
):
|
|
# Non-exhaustive check for common potential mistakes.
|
|
msg = (
|
|
f"Expected Narwhals class or scalar, got: {qualified_type_name(arg)!r}.\n\n"
|
|
"Hint: Perhaps you\n"
|
|
"- forgot a `nw.from_native` somewhere?\n"
|
|
"- used `pl.col` instead of `nw.col`?"
|
|
)
|
|
raise TypeError(msg)
|
|
|
|
return isinstance(arg, Iterable) and not isinstance(arg, (str, bytes, Series))
|
|
|
|
|
|
def parse_version(version: str | ModuleType | _SupportsVersion) -> tuple[int, ...]:
|
|
"""Simple version parser; split into a tuple of ints for comparison.
|
|
|
|
Arguments:
|
|
version: Version string, or object with one, to parse.
|
|
|
|
Returns:
|
|
Parsed version number.
|
|
"""
|
|
# lifted from Polars
|
|
# [marco]: Take care of DuckDB pre-releases which end with e.g. `-dev4108`
|
|
# and pandas pre-releases which end with e.g. .dev0+618.gb552dc95c9
|
|
version_str = version if isinstance(version, str) else version.__version__
|
|
version_str = re.sub(r"(\D?dev.*$)", "", version_str)
|
|
return tuple(int(re.sub(r"\D", "", v)) for v in version_str.split("."))
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: type, cls_or_tuple: type[_T]
|
|
) -> TypeIs[type[_T]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: object | type, cls_or_tuple: type[_T]
|
|
) -> TypeIs[_T | type[_T]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: type, cls_or_tuple: tuple[type[_T1], type[_T2]]
|
|
) -> TypeIs[type[_T1 | _T2]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: object | type, cls_or_tuple: tuple[type[_T1], type[_T2]]
|
|
) -> TypeIs[_T1 | _T2 | type[_T1 | _T2]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: type, cls_or_tuple: tuple[type[_T1], type[_T2], type[_T3]]
|
|
) -> TypeIs[type[_T1 | _T2 | _T3]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: object | type, cls_or_tuple: tuple[type[_T1], type[_T2], type[_T3]]
|
|
) -> TypeIs[_T1 | _T2 | _T3 | type[_T1 | _T2 | _T3]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: type, cls_or_tuple: tuple[type[_T1], type[_T2], type[_T3], type[_T4]]
|
|
) -> TypeIs[type[_T1 | _T2 | _T3 | _T4]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: object | type,
|
|
cls_or_tuple: tuple[type[_T1], type[_T2], type[_T3], type[_T4]],
|
|
) -> TypeIs[_T1 | _T2 | _T3 | _T4 | type[_T1 | _T2 | _T3 | _T4]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: type,
|
|
cls_or_tuple: tuple[type[_T1], type[_T2], type[_T3], type[_T4], type[_T5]],
|
|
) -> TypeIs[type[_T1 | _T2 | _T3 | _T4 | _T5]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: object | type,
|
|
cls_or_tuple: tuple[type[_T1], type[_T2], type[_T3], type[_T4], type[_T5]],
|
|
) -> TypeIs[_T1 | _T2 | _T3 | _T4 | _T5 | type[_T1 | _T2 | _T3 | _T4 | _T5]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: type,
|
|
cls_or_tuple: tuple[type[_T1], type[_T2], type[_T3], type[_T4], type[_T5], type[_T6]],
|
|
) -> TypeIs[type[_T1 | _T2 | _T3 | _T4 | _T5 | _T6]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: object | type,
|
|
cls_or_tuple: tuple[type[_T1], type[_T2], type[_T3], type[_T4], type[_T5], type[_T6]],
|
|
) -> TypeIs[
|
|
_T1 | _T2 | _T3 | _T4 | _T5 | _T6 | type[_T1 | _T2 | _T3 | _T4 | _T5 | _T6]
|
|
]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: type,
|
|
cls_or_tuple: tuple[
|
|
type[_T1], type[_T2], type[_T3], type[_T4], type[_T5], type[_T6], type[_T7]
|
|
],
|
|
) -> TypeIs[type[_T1 | _T2 | _T3 | _T4 | _T5 | _T6 | _T7]]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: object | type,
|
|
cls_or_tuple: tuple[
|
|
type[_T1], type[_T2], type[_T3], type[_T4], type[_T5], type[_T6], type[_T7]
|
|
],
|
|
) -> TypeIs[
|
|
_T1
|
|
| _T2
|
|
| _T3
|
|
| _T4
|
|
| _T5
|
|
| _T6
|
|
| _T7
|
|
| type[_T1 | _T2 | _T3 | _T4 | _T5 | _T6 | _T7]
|
|
]: ...
|
|
|
|
|
|
@overload
|
|
def isinstance_or_issubclass(
|
|
obj_or_cls: Any, cls_or_tuple: tuple[type, ...]
|
|
) -> TypeIs[Any]: ...
|
|
|
|
|
|
def isinstance_or_issubclass(obj_or_cls: Any, cls_or_tuple: Any) -> bool:
|
|
from narwhals.dtypes import DType
|
|
|
|
if isinstance(obj_or_cls, DType):
|
|
return isinstance(obj_or_cls, cls_or_tuple)
|
|
return isinstance(obj_or_cls, cls_or_tuple) or (
|
|
isinstance(obj_or_cls, type) and issubclass(obj_or_cls, cls_or_tuple)
|
|
)
|
|
|
|
|
|
def validate_laziness(items: Iterable[Any]) -> None:
|
|
from narwhals.dataframe import DataFrame, LazyFrame
|
|
|
|
if all(isinstance(item, DataFrame) for item in items) or (
|
|
all(isinstance(item, LazyFrame) for item in items)
|
|
):
|
|
return
|
|
msg = f"The items to concatenate should either all be eager, or all lazy, got: {[type(item) for item in items]}"
|
|
raise TypeError(msg)
|
|
|
|
|
|
def maybe_align_index(
|
|
lhs: FrameOrSeriesT, rhs: Series[Any] | DataFrame[Any] | LazyFrame[Any]
|
|
) -> FrameOrSeriesT:
|
|
"""Align `lhs` to the Index of `rhs`, if they're both pandas-like.
|
|
|
|
Arguments:
|
|
lhs: Dataframe or Series.
|
|
rhs: Dataframe or Series to align with.
|
|
|
|
Returns:
|
|
Same type as input.
|
|
|
|
Notes:
|
|
This is only really intended for backwards-compatibility purposes,
|
|
for example if your library already aligns indices for users.
|
|
If you're designing a new library, we highly encourage you to not
|
|
rely on the Index.
|
|
For non-pandas-like inputs, this only checks that `lhs` and `rhs`
|
|
are the same length.
|
|
|
|
Examples:
|
|
>>> import pandas as pd
|
|
>>> import polars as pl
|
|
>>> import narwhals as nw
|
|
>>> df_pd = pd.DataFrame({"a": [1, 2]}, index=[3, 4])
|
|
>>> s_pd = pd.Series([6, 7], index=[4, 3])
|
|
>>> df = nw.from_native(df_pd)
|
|
>>> s = nw.from_native(s_pd, series_only=True)
|
|
>>> nw.to_native(nw.maybe_align_index(df, s))
|
|
a
|
|
4 2
|
|
3 1
|
|
"""
|
|
from narwhals._pandas_like.dataframe import PandasLikeDataFrame
|
|
from narwhals._pandas_like.series import PandasLikeSeries
|
|
|
|
def _validate_index(index: Any) -> None:
|
|
if not index.is_unique:
|
|
msg = "given index doesn't have a unique index"
|
|
raise ValueError(msg)
|
|
|
|
lhs_any = cast("Any", lhs)
|
|
rhs_any = cast("Any", rhs)
|
|
if isinstance(
|
|
getattr(lhs_any, "_compliant_frame", None), PandasLikeDataFrame
|
|
) and isinstance(getattr(rhs_any, "_compliant_frame", None), PandasLikeDataFrame):
|
|
_validate_index(lhs_any._compliant_frame.native.index)
|
|
_validate_index(rhs_any._compliant_frame.native.index)
|
|
return lhs_any._with_compliant(
|
|
lhs_any._compliant_frame._with_native(
|
|
lhs_any._compliant_frame.native.loc[rhs_any._compliant_frame.native.index]
|
|
)
|
|
)
|
|
if isinstance(
|
|
getattr(lhs_any, "_compliant_frame", None), PandasLikeDataFrame
|
|
) and isinstance(getattr(rhs_any, "_compliant_series", None), PandasLikeSeries):
|
|
_validate_index(lhs_any._compliant_frame.native.index)
|
|
_validate_index(rhs_any._compliant_series.native.index)
|
|
return lhs_any._with_compliant(
|
|
lhs_any._compliant_frame._with_native(
|
|
lhs_any._compliant_frame.native.loc[
|
|
rhs_any._compliant_series.native.index
|
|
]
|
|
)
|
|
)
|
|
if isinstance(
|
|
getattr(lhs_any, "_compliant_series", None), PandasLikeSeries
|
|
) and isinstance(getattr(rhs_any, "_compliant_frame", None), PandasLikeDataFrame):
|
|
_validate_index(lhs_any._compliant_series.native.index)
|
|
_validate_index(rhs_any._compliant_frame.native.index)
|
|
return lhs_any._with_compliant(
|
|
lhs_any._compliant_series._with_native(
|
|
lhs_any._compliant_series.native.loc[
|
|
rhs_any._compliant_frame.native.index
|
|
]
|
|
)
|
|
)
|
|
if isinstance(
|
|
getattr(lhs_any, "_compliant_series", None), PandasLikeSeries
|
|
) and isinstance(getattr(rhs_any, "_compliant_series", None), PandasLikeSeries):
|
|
_validate_index(lhs_any._compliant_series.native.index)
|
|
_validate_index(rhs_any._compliant_series.native.index)
|
|
return lhs_any._with_compliant(
|
|
lhs_any._compliant_series._with_native(
|
|
lhs_any._compliant_series.native.loc[
|
|
rhs_any._compliant_series.native.index
|
|
]
|
|
)
|
|
)
|
|
if len(lhs_any) != len(rhs_any):
|
|
msg = f"Expected `lhs` and `rhs` to have the same length, got {len(lhs_any)} and {len(rhs_any)}"
|
|
raise ValueError(msg)
|
|
return lhs
|
|
|
|
|
|
def maybe_get_index(obj: DataFrame[Any] | LazyFrame[Any] | Series[Any]) -> Any | None:
|
|
"""Get the index of a DataFrame or a Series, if it's pandas-like.
|
|
|
|
Arguments:
|
|
obj: Dataframe or Series.
|
|
|
|
Returns:
|
|
Same type as input.
|
|
|
|
Notes:
|
|
This is only really intended for backwards-compatibility purposes,
|
|
for example if your library already aligns indices for users.
|
|
If you're designing a new library, we highly encourage you to not
|
|
rely on the Index.
|
|
For non-pandas-like inputs, this returns `None`.
|
|
|
|
Examples:
|
|
>>> import pandas as pd
|
|
>>> import polars as pl
|
|
>>> import narwhals as nw
|
|
>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]})
|
|
>>> df = nw.from_native(df_pd)
|
|
>>> nw.maybe_get_index(df)
|
|
RangeIndex(start=0, stop=2, step=1)
|
|
>>> series_pd = pd.Series([1, 2])
|
|
>>> series = nw.from_native(series_pd, series_only=True)
|
|
>>> nw.maybe_get_index(series)
|
|
RangeIndex(start=0, stop=2, step=1)
|
|
"""
|
|
obj_any = cast("Any", obj)
|
|
native_obj = obj_any.to_native()
|
|
if is_pandas_like_dataframe(native_obj) or is_pandas_like_series(native_obj):
|
|
return native_obj.index
|
|
return None
|
|
|
|
|
|
def maybe_set_index(
|
|
obj: FrameOrSeriesT,
|
|
column_names: str | list[str] | None = None,
|
|
*,
|
|
index: Series[IntoSeriesT] | list[Series[IntoSeriesT]] | None = None,
|
|
) -> FrameOrSeriesT:
|
|
"""Set the index of a DataFrame or a Series, if it's pandas-like.
|
|
|
|
Arguments:
|
|
obj: object for which maybe set the index (can be either a Narwhals `DataFrame`
|
|
or `Series`).
|
|
column_names: name or list of names of the columns to set as index.
|
|
For dataframes, only one of `column_names` and `index` can be specified but
|
|
not both. If `column_names` is passed and `df` is a Series, then a
|
|
`ValueError` is raised.
|
|
index: series or list of series to set as index.
|
|
|
|
Returns:
|
|
Same type as input.
|
|
|
|
Raises:
|
|
ValueError: If one of the following conditions happens
|
|
|
|
- none of `column_names` and `index` are provided
|
|
- both `column_names` and `index` are provided
|
|
- `column_names` is provided and `df` is a Series
|
|
|
|
Notes:
|
|
This is only really intended for backwards-compatibility purposes, for example if
|
|
your library already aligns indices for users.
|
|
If you're designing a new library, we highly encourage you to not
|
|
rely on the Index.
|
|
|
|
For non-pandas-like inputs, this is a no-op.
|
|
|
|
Examples:
|
|
>>> import pandas as pd
|
|
>>> import polars as pl
|
|
>>> import narwhals as nw
|
|
>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]})
|
|
>>> df = nw.from_native(df_pd)
|
|
>>> nw.to_native(nw.maybe_set_index(df, "b")) # doctest: +NORMALIZE_WHITESPACE
|
|
a
|
|
b
|
|
4 1
|
|
5 2
|
|
"""
|
|
from narwhals.translate import to_native
|
|
|
|
df_any = cast("Any", obj)
|
|
native_obj = df_any.to_native()
|
|
|
|
if column_names is not None and index is not None:
|
|
msg = "Only one of `column_names` or `index` should be provided"
|
|
raise ValueError(msg)
|
|
|
|
if not column_names and index is None:
|
|
msg = "Either `column_names` or `index` should be provided"
|
|
raise ValueError(msg)
|
|
|
|
if index is not None:
|
|
keys = (
|
|
[to_native(idx, pass_through=True) for idx in index]
|
|
if _is_iterable(index)
|
|
else to_native(index, pass_through=True)
|
|
)
|
|
else:
|
|
keys = column_names
|
|
|
|
if is_pandas_like_dataframe(native_obj):
|
|
return df_any._with_compliant(
|
|
df_any._compliant_frame._with_native(native_obj.set_index(keys))
|
|
)
|
|
elif is_pandas_like_series(native_obj):
|
|
from narwhals._pandas_like.utils import set_index
|
|
|
|
if column_names:
|
|
msg = "Cannot set index using column names on a Series"
|
|
raise ValueError(msg)
|
|
|
|
native_obj = set_index(
|
|
native_obj,
|
|
keys,
|
|
implementation=obj._compliant_series._implementation, # type: ignore[union-attr]
|
|
)
|
|
return df_any._with_compliant(df_any._compliant_series._with_native(native_obj))
|
|
else:
|
|
return df_any
|
|
|
|
|
|
def maybe_reset_index(obj: FrameOrSeriesT) -> FrameOrSeriesT:
|
|
"""Reset the index to the default integer index of a DataFrame or a Series, if it's pandas-like.
|
|
|
|
Arguments:
|
|
obj: Dataframe or Series.
|
|
|
|
Returns:
|
|
Same type as input.
|
|
|
|
Notes:
|
|
This is only really intended for backwards-compatibility purposes,
|
|
for example if your library already resets the index for users.
|
|
If you're designing a new library, we highly encourage you to not
|
|
rely on the Index.
|
|
For non-pandas-like inputs, this is a no-op.
|
|
|
|
Examples:
|
|
>>> import pandas as pd
|
|
>>> import polars as pl
|
|
>>> import narwhals as nw
|
|
>>> df_pd = pd.DataFrame({"a": [1, 2], "b": [4, 5]}, index=([6, 7]))
|
|
>>> df = nw.from_native(df_pd)
|
|
>>> nw.to_native(nw.maybe_reset_index(df))
|
|
a b
|
|
0 1 4
|
|
1 2 5
|
|
>>> series_pd = pd.Series([1, 2])
|
|
>>> series = nw.from_native(series_pd, series_only=True)
|
|
>>> nw.maybe_get_index(series)
|
|
RangeIndex(start=0, stop=2, step=1)
|
|
"""
|
|
obj_any = cast("Any", obj)
|
|
native_obj = obj_any.to_native()
|
|
if is_pandas_like_dataframe(native_obj):
|
|
native_namespace = obj_any.__native_namespace__()
|
|
if _has_default_index(native_obj, native_namespace):
|
|
return obj_any
|
|
return obj_any._with_compliant(
|
|
obj_any._compliant_frame._with_native(native_obj.reset_index(drop=True))
|
|
)
|
|
if is_pandas_like_series(native_obj):
|
|
native_namespace = obj_any.__native_namespace__()
|
|
if _has_default_index(native_obj, native_namespace):
|
|
return obj_any
|
|
return obj_any._with_compliant(
|
|
obj_any._compliant_series._with_native(native_obj.reset_index(drop=True))
|
|
)
|
|
return obj_any
|
|
|
|
|
|
def _is_range_index(obj: Any, native_namespace: Any) -> TypeIs[pd.RangeIndex]:
|
|
return isinstance(obj, native_namespace.RangeIndex)
|
|
|
|
|
|
def _has_default_index(
|
|
native_frame_or_series: pd.Series[Any] | pd.DataFrame, native_namespace: Any
|
|
) -> bool:
|
|
index = native_frame_or_series.index
|
|
return (
|
|
_is_range_index(index, native_namespace)
|
|
and index.start == 0
|
|
and index.stop == len(index)
|
|
and index.step == 1
|
|
)
|
|
|
|
|
|
def maybe_convert_dtypes(
|
|
obj: FrameOrSeriesT, *args: bool, **kwargs: bool | str
|
|
) -> FrameOrSeriesT:
|
|
"""Convert columns or series to the best possible dtypes using dtypes supporting ``pd.NA``, if df is pandas-like.
|
|
|
|
Arguments:
|
|
obj: DataFrame or Series.
|
|
*args: Additional arguments which gets passed through.
|
|
**kwargs: Additional arguments which gets passed through.
|
|
|
|
Returns:
|
|
Same type as input.
|
|
|
|
Notes:
|
|
For non-pandas-like inputs, this is a no-op.
|
|
Also, `args` and `kwargs` just get passed down to the underlying library as-is.
|
|
|
|
Examples:
|
|
>>> import pandas as pd
|
|
>>> import polars as pl
|
|
>>> import narwhals as nw
|
|
>>> import numpy as np
|
|
>>> df_pd = pd.DataFrame(
|
|
... {
|
|
... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
|
|
... "b": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
|
|
... }
|
|
... )
|
|
>>> df = nw.from_native(df_pd)
|
|
>>> nw.to_native(
|
|
... nw.maybe_convert_dtypes(df)
|
|
... ).dtypes # doctest: +NORMALIZE_WHITESPACE
|
|
a Int32
|
|
b boolean
|
|
dtype: object
|
|
"""
|
|
obj_any = cast("Any", obj)
|
|
native_obj = obj_any.to_native()
|
|
if is_pandas_like_dataframe(native_obj):
|
|
return obj_any._with_compliant(
|
|
obj_any._compliant_frame._with_native(
|
|
native_obj.convert_dtypes(*args, **kwargs)
|
|
)
|
|
)
|
|
if is_pandas_like_series(native_obj):
|
|
return obj_any._with_compliant(
|
|
obj_any._compliant_series._with_native(
|
|
native_obj.convert_dtypes(*args, **kwargs)
|
|
)
|
|
)
|
|
return obj_any
|
|
|
|
|
|
def scale_bytes(sz: int, unit: SizeUnit) -> int | float:
|
|
"""Scale size in bytes to other size units (eg: "kb", "mb", "gb", "tb").
|
|
|
|
Arguments:
|
|
sz: original size in bytes
|
|
unit: size unit to convert into
|
|
|
|
Returns:
|
|
Integer or float.
|
|
"""
|
|
if unit in {"b", "bytes"}:
|
|
return sz
|
|
elif unit in {"kb", "kilobytes"}:
|
|
return sz / 1024
|
|
elif unit in {"mb", "megabytes"}:
|
|
return sz / 1024**2
|
|
elif unit in {"gb", "gigabytes"}:
|
|
return sz / 1024**3
|
|
elif unit in {"tb", "terabytes"}:
|
|
return sz / 1024**4
|
|
else:
|
|
msg = f"`unit` must be one of {{'b', 'kb', 'mb', 'gb', 'tb'}}, got {unit!r}"
|
|
raise ValueError(msg)
|
|
|
|
|
|
def is_ordered_categorical(series: Series[Any]) -> bool:
|
|
"""Return whether indices of categories are semantically meaningful.
|
|
|
|
This is a convenience function to accessing what would otherwise be
|
|
the `is_ordered` property from the DataFrame Interchange Protocol,
|
|
see https://data-apis.org/dataframe-protocol/latest/API.html.
|
|
|
|
- For Polars:
|
|
- Enums are always ordered.
|
|
- Categoricals are ordered if `dtype.ordering == "physical"`.
|
|
- For pandas-like APIs:
|
|
- Categoricals are ordered if `dtype.cat.ordered == True`.
|
|
- For PyArrow table:
|
|
- Categoricals are ordered if `dtype.type.ordered == True`.
|
|
|
|
Arguments:
|
|
series: Input Series.
|
|
|
|
Returns:
|
|
Whether the Series is an ordered categorical.
|
|
|
|
Examples:
|
|
>>> import narwhals as nw
|
|
>>> import pandas as pd
|
|
>>> import polars as pl
|
|
>>> data = ["x", "y"]
|
|
>>> s_pd = pd.Series(data, dtype=pd.CategoricalDtype(ordered=True))
|
|
>>> s_pl = pl.Series(data, dtype=pl.Categorical(ordering="physical"))
|
|
|
|
Let's define a library-agnostic function:
|
|
|
|
>>> @nw.narwhalify
|
|
... def func(s):
|
|
... return nw.is_ordered_categorical(s)
|
|
|
|
Then, we can pass any supported library to `func`:
|
|
|
|
>>> func(s_pd)
|
|
True
|
|
>>> func(s_pl)
|
|
True
|
|
"""
|
|
from narwhals._interchange.series import InterchangeSeries
|
|
|
|
dtypes = series._compliant_series._version.dtypes
|
|
compliant = series._compliant_series
|
|
# If it doesn't match any branches, let's just play it safe and return False.
|
|
result: bool = False
|
|
if isinstance(compliant, InterchangeSeries) and isinstance(
|
|
series.dtype, dtypes.Categorical
|
|
):
|
|
result = compliant.native.describe_categorical["is_ordered"]
|
|
elif series.dtype == dtypes.Enum:
|
|
result = True
|
|
elif series.dtype != dtypes.Categorical:
|
|
result = False
|
|
else:
|
|
native = series.to_native()
|
|
if is_polars_series(native):
|
|
result = cast("pl.Categorical", native.dtype).ordering == "physical"
|
|
elif is_pandas_like_series(native):
|
|
result = bool(native.cat.ordered)
|
|
elif is_pyarrow_chunked_array(native):
|
|
from narwhals._arrow.utils import is_dictionary
|
|
|
|
result = is_dictionary(native.type) and native.type.ordered
|
|
return result
|
|
|
|
|
|
def generate_unique_token(
|
|
n_bytes: int, columns: Container[str]
|
|
) -> str: # pragma: no cover
|
|
msg = (
|
|
"Use `generate_temporary_column_name` instead. `generate_unique_token` is "
|
|
"deprecated and it will be removed in future versions"
|
|
)
|
|
issue_deprecation_warning(msg, _version="1.13.0")
|
|
return generate_temporary_column_name(n_bytes=n_bytes, columns=columns)
|
|
|
|
|
|
def generate_temporary_column_name(n_bytes: int, columns: Container[str]) -> str:
|
|
"""Generates a unique column name that is not present in the given list of columns.
|
|
|
|
It relies on [python secrets token_hex](https://docs.python.org/3/library/secrets.html#secrets.token_hex)
|
|
function to return a string nbytes random bytes.
|
|
|
|
Arguments:
|
|
n_bytes: The number of bytes to generate for the token.
|
|
columns: The list of columns to check for uniqueness.
|
|
|
|
Returns:
|
|
A unique token that is not present in the given list of columns.
|
|
|
|
Raises:
|
|
AssertionError: If a unique token cannot be generated after 100 attempts.
|
|
|
|
Examples:
|
|
>>> import narwhals as nw
|
|
>>> columns = ["abc", "xyz"]
|
|
>>> nw.generate_temporary_column_name(n_bytes=8, columns=columns) not in columns
|
|
True
|
|
"""
|
|
counter = 0
|
|
while True:
|
|
# Prepend `'nw'` to ensure it always starts with a character
|
|
# https://github.com/narwhals-dev/narwhals/issues/2510
|
|
token = f"nw{token_hex(n_bytes - 1)}"
|
|
if token not in columns:
|
|
return token
|
|
|
|
counter += 1
|
|
if counter > 100:
|
|
msg = (
|
|
"Internal Error: Narwhals was not able to generate a column name with "
|
|
f"{n_bytes=} and not in {columns}"
|
|
)
|
|
raise AssertionError(msg)
|
|
|
|
|
|
def parse_columns_to_drop(
|
|
frame: _StoresColumns, subset: Iterable[str], /, *, strict: bool
|
|
) -> list[str]:
|
|
if not strict:
|
|
return list(set(frame.columns).intersection(subset))
|
|
to_drop = list(subset)
|
|
if error := check_columns_exist(to_drop, available=frame.columns):
|
|
raise error
|
|
return to_drop
|
|
|
|
|
|
def is_sequence_but_not_str(sequence: Sequence[_T] | Any) -> TypeIs[Sequence[_T]]:
|
|
return isinstance(sequence, Sequence) and not isinstance(sequence, str)
|
|
|
|
|
|
def is_slice_none(obj: Any) -> TypeIs[_SliceNone]:
|
|
return isinstance(obj, slice) and obj == slice(None)
|
|
|
|
|
|
def is_sized_multi_index_selector(
|
|
obj: Any,
|
|
) -> TypeIs[SizedMultiIndexSelector[Series[Any] | CompliantSeries[Any]]]:
|
|
return (
|
|
(
|
|
is_sequence_but_not_str(obj)
|
|
and ((len(obj) > 0 and isinstance(obj[0], int)) or (len(obj) == 0))
|
|
)
|
|
or is_numpy_array_1d_int(obj)
|
|
or is_narwhals_series_int(obj)
|
|
or is_compliant_series_int(obj)
|
|
)
|
|
|
|
|
|
def is_sequence_like(
|
|
obj: Sequence[_T] | Any,
|
|
) -> TypeIs[Sequence[_T] | Series[Any] | _1DArray]:
|
|
return (
|
|
is_sequence_but_not_str(obj)
|
|
or is_numpy_array_1d(obj)
|
|
or is_narwhals_series(obj)
|
|
or is_compliant_series(obj)
|
|
)
|
|
|
|
|
|
def is_slice_index(obj: Any) -> TypeIs[_SliceIndex]:
|
|
return isinstance(obj, slice) and (
|
|
isinstance(obj.start, int)
|
|
or isinstance(obj.stop, int)
|
|
or (isinstance(obj.step, int) and obj.start is None and obj.stop is None)
|
|
)
|
|
|
|
|
|
def is_range(obj: Any) -> TypeIs[range]:
|
|
return isinstance(obj, range)
|
|
|
|
|
|
def is_single_index_selector(obj: Any) -> TypeIs[SingleIndexSelector]:
|
|
return bool(isinstance(obj, int) and not isinstance(obj, bool))
|
|
|
|
|
|
def is_index_selector(
|
|
obj: Any,
|
|
) -> TypeIs[SingleIndexSelector | MultiIndexSelector[Series[Any] | CompliantSeries[Any]]]:
|
|
return (
|
|
is_single_index_selector(obj)
|
|
or is_sized_multi_index_selector(obj)
|
|
or is_slice_index(obj)
|
|
)
|
|
|
|
|
|
def is_list_of(obj: Any, tp: type[_T]) -> TypeIs[list[_T]]:
|
|
# Check if an object is a list of `tp`, only sniffing the first element.
|
|
return bool(isinstance(obj, list) and obj and isinstance(obj[0], tp))
|
|
|
|
|
|
def is_sequence_of(obj: Any, tp: type[_T]) -> TypeIs[Sequence[_T]]:
|
|
# Check if an object is a sequence of `tp`, only sniffing the first element.
|
|
return bool(
|
|
is_sequence_but_not_str(obj)
|
|
and (first := next(iter(obj), None))
|
|
and isinstance(first, tp)
|
|
)
|
|
|
|
|
|
def validate_strict_and_pass_though(
|
|
strict: bool | None, # noqa: FBT001
|
|
pass_through: bool | None, # noqa: FBT001
|
|
*,
|
|
pass_through_default: bool,
|
|
) -> bool:
|
|
if strict is None and pass_through is None:
|
|
pass_through = pass_through_default
|
|
elif strict is not None and pass_through is None:
|
|
pass_through = not strict
|
|
elif strict is None and pass_through is not None:
|
|
pass
|
|
else:
|
|
msg = "Cannot pass both `strict` and `pass_through`"
|
|
raise ValueError(msg)
|
|
return pass_through
|
|
|
|
|
|
def deprecate_native_namespace(
|
|
*, warn_version: str = "", required: bool = False
|
|
) -> Callable[[Callable[P, R]], Callable[P, R]]:
|
|
"""Decorator to transition from `native_namespace` to `backend` argument.
|
|
|
|
Arguments:
|
|
warn_version: Emit a deprecation warning from this version.
|
|
required: Raise when both `native_namespace`, `backend` are `None`.
|
|
|
|
Returns:
|
|
Wrapped function, with `native_namespace` **removed**.
|
|
"""
|
|
|
|
def decorate(fn: Callable[P, R], /) -> Callable[P, R]:
|
|
@wraps(fn)
|
|
def wrapper(*args: P.args, **kwds: P.kwargs) -> R:
|
|
backend = kwds.pop("backend", None)
|
|
native_namespace = kwds.pop("native_namespace", None)
|
|
if native_namespace is not None and backend is None:
|
|
if warn_version:
|
|
msg = (
|
|
"`native_namespace` is deprecated, please use `backend` instead.\n\n"
|
|
"Note: `native_namespace` will remain available in `narwhals.stable.v1`.\n"
|
|
"See https://narwhals-dev.github.io/narwhals/backcompat/ for more information.\n"
|
|
)
|
|
issue_deprecation_warning(msg, _version=warn_version)
|
|
backend = native_namespace
|
|
elif native_namespace is not None and backend is not None:
|
|
msg = "Can't pass both `native_namespace` and `backend`"
|
|
raise ValueError(msg)
|
|
elif native_namespace is None and backend is None and required:
|
|
msg = f"`backend` must be specified in `{fn.__name__}`."
|
|
raise ValueError(msg)
|
|
kwds["backend"] = backend
|
|
return fn(*args, **kwds)
|
|
|
|
return wrapper
|
|
|
|
return decorate
|
|
|
|
|
|
def _validate_rolling_arguments(
|
|
window_size: int, min_samples: int | None
|
|
) -> tuple[int, int]:
|
|
ensure_type(window_size, int, param_name="window_size")
|
|
ensure_type(min_samples, int, type(None), param_name="min_samples")
|
|
|
|
if window_size < 1:
|
|
msg = "window_size must be greater or equal than 1"
|
|
raise ValueError(msg)
|
|
|
|
if min_samples is not None:
|
|
if min_samples < 1:
|
|
msg = "min_samples must be greater or equal than 1"
|
|
raise ValueError(msg)
|
|
|
|
if min_samples > window_size:
|
|
msg = "`min_samples` must be less or equal than `window_size`"
|
|
raise InvalidOperationError(msg)
|
|
else:
|
|
min_samples = window_size
|
|
|
|
return window_size, min_samples
|
|
|
|
|
|
def generate_repr(header: str, native_repr: str) -> str:
|
|
try:
|
|
terminal_width = os.get_terminal_size().columns
|
|
except OSError:
|
|
terminal_width = int(os.getenv("COLUMNS", 80)) # noqa: PLW1508
|
|
native_lines = native_repr.expandtabs().splitlines()
|
|
max_native_width = max(len(line) for line in native_lines)
|
|
|
|
if max_native_width + 2 <= terminal_width:
|
|
length = max(max_native_width, len(header))
|
|
output = f"┌{'─' * length}┐\n"
|
|
header_extra = length - len(header)
|
|
output += f"|{' ' * (header_extra // 2)}{header}{' ' * (header_extra // 2 + header_extra % 2)}|\n"
|
|
output += f"|{'-' * (length)}|\n"
|
|
start_extra = (length - max_native_width) // 2
|
|
end_extra = (length - max_native_width) // 2 + (length - max_native_width) % 2
|
|
for line in native_lines:
|
|
output += f"|{' ' * (start_extra)}{line}{' ' * (end_extra + max_native_width - len(line))}|\n"
|
|
output += f"└{'─' * length}┘"
|
|
return output
|
|
|
|
diff = 39 - len(header)
|
|
return (
|
|
f"┌{'─' * (39)}┐\n"
|
|
f"|{' ' * (diff // 2)}{header}{' ' * (diff // 2 + diff % 2)}|\n"
|
|
"| Use `.to_native` to see native output |\n└"
|
|
f"{'─' * 39}┘"
|
|
)
|
|
|
|
|
|
def check_columns_exist(
|
|
subset: Collection[str], /, *, available: Collection[str]
|
|
) -> ColumnNotFoundError | None:
|
|
if missing := set(subset).difference(available):
|
|
return ColumnNotFoundError.from_missing_and_available_column_names(
|
|
missing, available
|
|
)
|
|
return None
|
|
|
|
|
|
def check_column_names_are_unique(columns: Collection[str]) -> None:
|
|
if len(columns) != len(set(columns)):
|
|
from collections import Counter
|
|
|
|
counter = Counter(columns)
|
|
duplicates = {k: v for k, v in counter.items() if v > 1}
|
|
msg = "".join(f"\n- '{k}' {v} times" for k, v in duplicates.items())
|
|
msg = f"Expected unique column names, got:{msg}"
|
|
raise DuplicateError(msg)
|
|
|
|
|
|
def _parse_time_unit_and_time_zone(
|
|
time_unit: TimeUnit | Iterable[TimeUnit] | None,
|
|
time_zone: str | timezone | Iterable[str | timezone | None] | None,
|
|
) -> tuple[Set[TimeUnit], Set[str | None]]:
|
|
time_units: Set[TimeUnit] = (
|
|
{"ms", "us", "ns", "s"}
|
|
if time_unit is None
|
|
else {time_unit}
|
|
if isinstance(time_unit, str)
|
|
else set(time_unit)
|
|
)
|
|
time_zones: Set[str | None] = (
|
|
{None}
|
|
if time_zone is None
|
|
else {str(time_zone)}
|
|
if isinstance(time_zone, (str, timezone))
|
|
else {str(tz) if tz is not None else None for tz in time_zone}
|
|
)
|
|
return time_units, time_zones
|
|
|
|
|
|
def dtype_matches_time_unit_and_time_zone(
|
|
dtype: DType, dtypes: DTypes, time_units: Set[TimeUnit], time_zones: Set[str | None]
|
|
) -> bool:
|
|
return (
|
|
isinstance(dtype, dtypes.Datetime)
|
|
and (dtype.time_unit in time_units)
|
|
and (
|
|
dtype.time_zone in time_zones
|
|
or ("*" in time_zones and dtype.time_zone is not None)
|
|
)
|
|
)
|
|
|
|
|
|
def get_column_names(frame: _StoresColumns, /) -> Sequence[str]:
|
|
return frame.columns
|
|
|
|
|
|
def exclude_column_names(frame: _StoresColumns, names: Container[str]) -> Sequence[str]:
|
|
return [col_name for col_name in frame.columns if col_name not in names]
|
|
|
|
|
|
def passthrough_column_names(names: Sequence[str], /) -> EvalNames[Any]:
|
|
def fn(_frame: Any, /) -> Sequence[str]:
|
|
return names
|
|
|
|
return fn
|
|
|
|
|
|
def _hasattr_static(obj: Any, attr: str) -> bool:
|
|
sentinel = object()
|
|
return getattr_static(obj, attr, sentinel) is not sentinel
|
|
|
|
|
|
def is_compliant_dataframe(
|
|
obj: CompliantDataFrame[
|
|
CompliantSeriesT, CompliantExprT, NativeFrameT_co, ToNarwhalsT_co
|
|
]
|
|
| Any,
|
|
) -> TypeIs[
|
|
CompliantDataFrame[CompliantSeriesT, CompliantExprT, NativeFrameT_co, ToNarwhalsT_co]
|
|
]:
|
|
return _hasattr_static(obj, "__narwhals_dataframe__")
|
|
|
|
|
|
def is_compliant_lazyframe(
|
|
obj: CompliantLazyFrame[CompliantExprT, NativeFrameT_co, ToNarwhalsT_co] | Any,
|
|
) -> TypeIs[CompliantLazyFrame[CompliantExprT, NativeFrameT_co, ToNarwhalsT_co]]:
|
|
return _hasattr_static(obj, "__narwhals_lazyframe__")
|
|
|
|
|
|
def is_compliant_series(
|
|
obj: CompliantSeries[NativeSeriesT_co] | Any,
|
|
) -> TypeIs[CompliantSeries[NativeSeriesT_co]]:
|
|
return _hasattr_static(obj, "__narwhals_series__")
|
|
|
|
|
|
def is_compliant_series_int(
|
|
obj: CompliantSeries[NativeSeriesT_co] | Any,
|
|
) -> TypeIs[CompliantSeries[NativeSeriesT_co]]:
|
|
return is_compliant_series(obj) and obj.dtype.is_integer()
|
|
|
|
|
|
def is_compliant_expr(
|
|
obj: CompliantExpr[CompliantFrameT, CompliantSeriesOrNativeExprT_co] | Any,
|
|
) -> TypeIs[CompliantExpr[CompliantFrameT, CompliantSeriesOrNativeExprT_co]]:
|
|
return hasattr(obj, "__narwhals_expr__")
|
|
|
|
|
|
def is_eager_allowed(obj: Implementation) -> TypeIs[EagerAllowedImplementation]:
|
|
return obj in {
|
|
Implementation.PANDAS,
|
|
Implementation.MODIN,
|
|
Implementation.CUDF,
|
|
Implementation.POLARS,
|
|
Implementation.PYARROW,
|
|
}
|
|
|
|
|
|
def has_native_namespace(obj: Any) -> TypeIs[SupportsNativeNamespace]:
|
|
return _hasattr_static(obj, "__native_namespace__")
|
|
|
|
|
|
def _supports_dataframe_interchange(obj: Any) -> TypeIs[DataFrameLike]:
|
|
return hasattr(obj, "__dataframe__")
|
|
|
|
|
|
def supports_arrow_c_stream(obj: Any) -> TypeIs[ArrowStreamExportable]:
|
|
return _hasattr_static(obj, "__arrow_c_stream__")
|
|
|
|
|
|
def _remap_full_join_keys(
|
|
left_on: Collection[str], right_on: Collection[str], suffix: str
|
|
) -> dict[str, str]:
|
|
"""Remap join keys to avoid collisions.
|
|
|
|
If left keys collide with the right keys, append the suffix.
|
|
If there's no collision, let the right keys be.
|
|
|
|
Arguments:
|
|
left_on: Left keys.
|
|
right_on: Right keys.
|
|
suffix: Suffix to append to right keys.
|
|
|
|
Returns:
|
|
A map of old to new right keys.
|
|
"""
|
|
right_keys_suffixed = (
|
|
f"{key}{suffix}" if key in left_on else key for key in right_on
|
|
)
|
|
return dict(zip(right_on, right_keys_suffixed))
|
|
|
|
|
|
def _into_arrow_table(data: IntoArrowTable, context: _LimitedContext, /) -> pa.Table:
|
|
"""Guards `ArrowDataFrame.from_arrow` w/ safer imports.
|
|
|
|
Arguments:
|
|
data: Object which implements `__arrow_c_stream__`.
|
|
context: Initialized compliant object.
|
|
|
|
Returns:
|
|
A PyArrow Table.
|
|
"""
|
|
if find_spec("pyarrow"):
|
|
ns = context._version.namespace.from_backend("pyarrow").compliant
|
|
return ns._dataframe.from_arrow(data, context=ns).native
|
|
else: # pragma: no cover
|
|
msg = f"'pyarrow>=14.0.0' is required for `from_arrow` for object of type {qualified_type_name(data)!r}."
|
|
raise ModuleNotFoundError(msg)
|
|
|
|
|
|
# TODO @dangotbanned: Extend with runtime behavior for `v1.*`
|
|
# See `narwhals.exceptions.NarwhalsUnstableWarning`
|
|
def unstable(fn: _Fn, /) -> _Fn:
|
|
"""Visual-only marker for unstable functionality.
|
|
|
|
Arguments:
|
|
fn: Function to decorate.
|
|
|
|
Returns:
|
|
Decorated function (unchanged).
|
|
|
|
Examples:
|
|
>>> from narwhals._utils import unstable
|
|
>>> @unstable
|
|
... def a_work_in_progress_feature(*args):
|
|
... return args
|
|
>>>
|
|
>>> a_work_in_progress_feature.__name__
|
|
'a_work_in_progress_feature'
|
|
>>> a_work_in_progress_feature(1, 2, 3)
|
|
(1, 2, 3)
|
|
"""
|
|
return fn
|
|
|
|
|
|
def _is_naive_format(format: str) -> bool:
|
|
"""Determines if a datetime format string is 'naive', i.e., does not include timezone information.
|
|
|
|
A format is considered naive if it does not contain any of the following
|
|
|
|
- '%s': Unix timestamp
|
|
- '%z': UTC offset
|
|
- 'Z' : UTC timezone designator
|
|
|
|
Arguments:
|
|
format: The datetime format string to check.
|
|
|
|
Returns:
|
|
bool: True if the format is naive (does not include timezone info), False otherwise.
|
|
"""
|
|
return not any(x in format for x in ("%s", "%z", "Z"))
|
|
|
|
|
|
class not_implemented: # noqa: N801
|
|
"""Mark some functionality as unsupported.
|
|
|
|
Arguments:
|
|
alias: optional name used instead of the data model hook [`__set_name__`].
|
|
|
|
Returns:
|
|
An exception-raising [descriptor].
|
|
|
|
Notes:
|
|
- Attribute/method name *doesn't* need to be declared twice
|
|
- Allows different behavior when looked up on the class vs instance
|
|
- Allows us to use `isinstance(...)` instead of monkeypatching an attribute to the function
|
|
|
|
Examples:
|
|
>>> from narwhals._utils import not_implemented
|
|
>>> class Thing:
|
|
... def totally_ready(self) -> str:
|
|
... return "I'm ready!"
|
|
...
|
|
... not_ready_yet = not_implemented()
|
|
>>>
|
|
>>> thing = Thing()
|
|
>>> thing.totally_ready()
|
|
"I'm ready!"
|
|
>>> thing.not_ready_yet()
|
|
Traceback (most recent call last):
|
|
...
|
|
NotImplementedError: 'not_ready_yet' is not implemented for: 'Thing'.
|
|
...
|
|
>>> isinstance(Thing.not_ready_yet, not_implemented)
|
|
True
|
|
|
|
[`__set_name__`]: https://docs.python.org/3/reference/datamodel.html#object.__set_name__
|
|
[descriptor]: https://docs.python.org/3/howto/descriptor.html
|
|
"""
|
|
|
|
def __init__(self, alias: str | None = None, /) -> None:
|
|
# NOTE: Don't like this
|
|
# Trying to workaround `mypy` requiring `@property` everywhere
|
|
self._alias: str | None = alias
|
|
|
|
def __repr__(self) -> str:
|
|
return f"<{type(self).__name__}>: {self._name_owner}.{self._name}"
|
|
|
|
def __set_name__(self, owner: type[_T], name: str) -> None:
|
|
# https://docs.python.org/3/howto/descriptor.html#customized-names
|
|
self._name_owner: str = owner.__name__
|
|
self._name: str = self._alias or name
|
|
|
|
def __get__(
|
|
self, instance: _T | Literal["raise"] | None, owner: type[_T] | None = None, /
|
|
) -> Any:
|
|
if instance is None:
|
|
# NOTE: Branch for `cls._name`
|
|
# We can check that to see if an instance of `type(self)` for
|
|
# https://narwhals-dev.github.io/narwhals/api-completeness/expr/
|
|
return self
|
|
# NOTE: Prefer not exposing the actual class we're defining in
|
|
# `_implementation` may not be available everywhere
|
|
who = getattr(instance, "_implementation", self._name_owner)
|
|
_raise_not_implemented_error(self._name, who)
|
|
return None # pragma: no cover
|
|
|
|
def __call__(self, *args: Any, **kwds: Any) -> Any:
|
|
# NOTE: Purely to duck-type as assignable to **any** instance method
|
|
# Wouldn't be reachable through *regular* attribute access
|
|
return self.__get__("raise")
|
|
|
|
@classmethod
|
|
def deprecated(cls, message: LiteralString, /) -> Self:
|
|
"""Alt constructor, wraps with `@deprecated`.
|
|
|
|
Arguments:
|
|
message: **Static-only** deprecation message, emitted in an IDE.
|
|
|
|
Returns:
|
|
An exception-raising [descriptor].
|
|
|
|
[descriptor]: https://docs.python.org/3/howto/descriptor.html
|
|
"""
|
|
obj = cls()
|
|
return deprecated(message)(obj)
|
|
|
|
|
|
def _raise_not_implemented_error(what: str, who: str, /) -> NotImplementedError:
|
|
msg = (
|
|
f"{what!r} is not implemented for: {who!r}.\n\n"
|
|
"If you would like to see this functionality in `narwhals`, "
|
|
"please open an issue at: https://github.com/narwhals-dev/narwhals/issues"
|
|
)
|
|
raise NotImplementedError(msg)
|
|
|
|
|
|
class requires: # noqa: N801
|
|
"""Method decorator for raising under certain constraints.
|
|
|
|
Attributes:
|
|
_min_version: Minimum backend version.
|
|
_hint: Optional suggested alternative.
|
|
|
|
Examples:
|
|
>>> from narwhals._utils import requires, Implementation
|
|
>>> class SomeBackend:
|
|
... _implementation = Implementation.PYARROW
|
|
... _backend_version = 20, 0, 0
|
|
...
|
|
... @requires.backend_version((9000, 0, 0))
|
|
... def really_complex_feature(self) -> str:
|
|
... return "hello"
|
|
>>> backend = SomeBackend()
|
|
>>> backend.really_complex_feature()
|
|
Traceback (most recent call last):
|
|
...
|
|
NotImplementedError: `really_complex_feature` is only available in 'pyarrow>=9000.0.0', found version '20.0.0'.
|
|
"""
|
|
|
|
_min_version: tuple[int, ...]
|
|
_hint: str
|
|
|
|
@classmethod
|
|
def backend_version(cls, minimum: tuple[int, ...], /, hint: str = "") -> Self:
|
|
"""Method decorator for raising below a minimum `_backend_version`.
|
|
|
|
Arguments:
|
|
minimum: Minimum backend version.
|
|
hint: Optional suggested alternative.
|
|
|
|
Returns:
|
|
An exception-raising decorator.
|
|
"""
|
|
obj = cls.__new__(cls)
|
|
obj._min_version = minimum
|
|
obj._hint = hint
|
|
return obj
|
|
|
|
@staticmethod
|
|
def _unparse_version(backend_version: tuple[int, ...], /) -> str:
|
|
return ".".join(f"{d}" for d in backend_version)
|
|
|
|
def _ensure_version(self, instance: _FullContext, /) -> None:
|
|
if instance._backend_version >= self._min_version:
|
|
return
|
|
method = self._wrapped_name
|
|
backend = instance._implementation
|
|
minimum = self._unparse_version(self._min_version)
|
|
found = self._unparse_version(instance._backend_version)
|
|
msg = f"`{method}` is only available in '{backend}>={minimum}', found version {found!r}."
|
|
if self._hint:
|
|
msg = f"{msg}\n{self._hint}"
|
|
raise NotImplementedError(msg)
|
|
|
|
def __call__(self, fn: _Method[_ContextT, P, R], /) -> _Method[_ContextT, P, R]:
|
|
self._wrapped_name = fn.__name__
|
|
|
|
@wraps(fn)
|
|
def wrapper(instance: _ContextT, *args: P.args, **kwds: P.kwargs) -> R:
|
|
self._ensure_version(instance)
|
|
return fn(instance, *args, **kwds)
|
|
|
|
# NOTE: Only getting a complaint from `mypy`
|
|
return wrapper # type: ignore[return-value]
|
|
|
|
|
|
def convert_str_slice_to_int_slice(
|
|
str_slice: _SliceName, columns: Sequence[str]
|
|
) -> tuple[int | None, int | None, Any]:
|
|
start = columns.index(str_slice.start) if str_slice.start is not None else None
|
|
stop = columns.index(str_slice.stop) + 1 if str_slice.stop is not None else None
|
|
step = str_slice.step
|
|
return (start, stop, step)
|
|
|
|
|
|
def inherit_doc(
|
|
tp_parent: Callable[P, R1], /
|
|
) -> Callable[[_Constructor[_T, P, R2]], _Constructor[_T, P, R2]]:
|
|
"""Steal the class-level docstring from parent and attach to child `__init__`.
|
|
|
|
Returns:
|
|
Decorated constructor.
|
|
|
|
Notes:
|
|
- Passes static typing (mostly)
|
|
- Passes at runtime
|
|
"""
|
|
|
|
def decorate(init_child: _Constructor[_T, P, R2], /) -> _Constructor[_T, P, R2]:
|
|
if init_child.__name__ == "__init__" and issubclass(type(tp_parent), type):
|
|
init_child.__doc__ = getdoc(tp_parent)
|
|
return init_child
|
|
else: # pragma: no cover
|
|
msg = (
|
|
f"`@{inherit_doc.__name__}` is only allowed to decorate an `__init__` with a class-level doc.\n"
|
|
f"Method: {init_child.__qualname__!r}\n"
|
|
f"Parent: {tp_parent!r}"
|
|
)
|
|
raise TypeError(msg)
|
|
|
|
return decorate
|
|
|
|
|
|
def qualified_type_name(obj: object | type[Any], /) -> str:
|
|
tp = obj if isinstance(obj, type) else type(obj)
|
|
module = tp.__module__ if tp.__module__ != "builtins" else ""
|
|
return f"{module}.{tp.__name__}".lstrip(".")
|
|
|
|
|
|
def ensure_type(obj: Any, /, *valid_types: type[Any], param_name: str = "") -> None:
|
|
"""Validate that an object is an instance of one or more specified types.
|
|
|
|
Parameters:
|
|
obj: The object to validate.
|
|
*valid_types: One or more valid types that `obj` is expected to match.
|
|
param_name: The name of the parameter being validated.
|
|
Used to improve error message clarity.
|
|
|
|
Raises:
|
|
TypeError: If `obj` is not an instance of any of the provided `valid_types`.
|
|
|
|
Examples:
|
|
>>> from narwhals._utils import ensure_type
|
|
>>> ensure_type(42, int, float)
|
|
>>> ensure_type("hello", str)
|
|
|
|
>>> ensure_type("hello", int, param_name="test")
|
|
Traceback (most recent call last):
|
|
...
|
|
TypeError: Expected 'int', got: 'str'
|
|
test='hello'
|
|
^^^^^^^
|
|
>>> import polars as pl
|
|
>>> import pandas as pd
|
|
>>> df = pl.DataFrame([[1], [2], [3], [4], [5]], schema=[*"abcde"])
|
|
>>> ensure_type(df, pd.DataFrame, param_name="df")
|
|
Traceback (most recent call last):
|
|
...
|
|
TypeError: Expected 'pandas.core.frame.DataFrame', got: 'polars.dataframe.frame.DataFrame'
|
|
df=polars.dataframe.frame.DataFrame(...)
|
|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
|
"""
|
|
if not isinstance(obj, valid_types): # pragma: no cover
|
|
tp_names = " | ".join(qualified_type_name(tp) for tp in valid_types)
|
|
msg = f"Expected {tp_names!r}, got: {qualified_type_name(obj)!r}"
|
|
if param_name:
|
|
left_pad = " " * 4
|
|
val = repr(obj)
|
|
if len(val) > 40: # truncate long reprs
|
|
val = f"{qualified_type_name(obj)}(...)"
|
|
assign = f"{left_pad}{param_name}="
|
|
underline = (" " * len(assign)) + ("^" * len(val))
|
|
msg = f"{msg}\n{assign}{val}\n{underline}"
|
|
raise TypeError(msg)
|
|
|
|
|
|
class _DeferredIterable(Generic[_T]):
|
|
"""Store a callable producing an iterable to defer collection until we need it."""
|
|
|
|
def __init__(self, into_iter: Callable[[], Iterable[_T]], /) -> None:
|
|
self._into_iter: Callable[[], Iterable[_T]] = into_iter
|
|
|
|
def __iter__(self) -> Iterator[_T]:
|
|
yield from self._into_iter()
|
|
|
|
def to_tuple(self) -> tuple[_T, ...]:
|
|
# Collect and return as a `tuple`.
|
|
it = self._into_iter()
|
|
return it if isinstance(it, tuple) else tuple(it)
|