1974 lines
64 KiB
Python
1974 lines
64 KiB
Python
from __future__ import annotations
|
|
|
|
import os
|
|
import re
|
|
from datetime import timezone
|
|
from enum import Enum
|
|
from enum import auto
|
|
from functools import wraps
|
|
from importlib.util import find_spec
|
|
from inspect import getattr_static
|
|
from inspect import getdoc
|
|
from secrets import token_hex
|
|
from typing import TYPE_CHECKING
|
|
from typing import Any
|
|
from typing import Callable
|
|
from typing import Container
|
|
from typing import Generic
|
|
from typing import Iterable
|
|
from typing import Iterator
|
|
from typing import Literal
|
|
from typing import Protocol
|
|
from typing import Sequence
|
|
from typing import TypeVar
|
|
from typing import Union
|
|
from typing import cast
|
|
from typing import overload
|
|
from warnings import warn
|
|
|
|
from narwhals._enum import NoAutoEnum
|
|
from narwhals.dependencies import get_cudf
|
|
from narwhals.dependencies import get_dask
|
|
from narwhals.dependencies import get_dask_dataframe
|
|
from narwhals.dependencies import get_duckdb
|
|
from narwhals.dependencies import get_ibis
|
|
from narwhals.dependencies import get_modin
|
|
from narwhals.dependencies import get_pandas
|
|
from narwhals.dependencies import get_polars
|
|
from narwhals.dependencies import get_pyarrow
|
|
from narwhals.dependencies import get_pyspark
|
|
from narwhals.dependencies import get_pyspark_connect
|
|
from narwhals.dependencies import get_pyspark_sql
|
|
from narwhals.dependencies import get_sqlframe
|
|
from narwhals.dependencies import is_cudf_series
|
|
from narwhals.dependencies import is_modin_series
|
|
from narwhals.dependencies import is_narwhals_series
|
|
from narwhals.dependencies import is_narwhals_series_int
|
|
from narwhals.dependencies import is_numpy_array_1d
|
|
from narwhals.dependencies import is_numpy_array_1d_int
|
|
from narwhals.dependencies import is_pandas_dataframe
|
|
from narwhals.dependencies import is_pandas_like_dataframe
|
|
from narwhals.dependencies import is_pandas_like_series
|
|
from narwhals.dependencies import is_pandas_series
|
|
from narwhals.dependencies import is_polars_series
|
|
from narwhals.dependencies import is_pyarrow_chunked_array
|
|
from narwhals.exceptions import ColumnNotFoundError
|
|
from narwhals.exceptions import DuplicateError
|
|
from narwhals.exceptions import InvalidOperationError
|
|
|
|
if TYPE_CHECKING:
|
|
from types import ModuleType
|
|
from typing import AbstractSet as Set
|
|
|
|
import pandas as pd
|
|
import pyarrow as pa
|
|
from typing_extensions import Concatenate
|
|
from typing_extensions import LiteralString
|
|
from typing_extensions import ParamSpec
|
|
from typing_extensions import Self
|
|
from typing_extensions import TypeAlias
|
|
from typing_extensions import TypeIs
|
|
|
|
from narwhals._compliant import CompliantExpr
|
|
from narwhals._compliant import CompliantExprT
|
|
from narwhals._compliant import CompliantFrameT
|
|
from narwhals._compliant import CompliantSeriesOrNativeExprT_co
|
|
from narwhals._compliant import CompliantSeriesT
|
|
from narwhals._compliant import NativeFrameT_co
|
|
from narwhals._compliant import NativeSeriesT_co
|
|
from narwhals._compliant.typing import EvalNames
|
|
from narwhals._namespace import EagerAllowedImplementation
|
|
from narwhals._namespace import Namespace
|
|
from narwhals._translate import ArrowStreamExportable
|
|
from narwhals._translate import IntoArrowTable
|
|
from narwhals._translate import ToNarwhalsT_co
|
|
from narwhals.dataframe import DataFrame
|
|
from narwhals.dataframe import LazyFrame
|
|
from narwhals.dtypes import DType
|
|
from narwhals.series import Series
|
|
from narwhals.typing import CompliantDataFrame
|
|
from narwhals.typing import CompliantLazyFrame
|
|
from narwhals.typing import CompliantSeries
|
|
from narwhals.typing import DataFrameLike
|
|
from narwhals.typing import DTypes
|
|
from narwhals.typing import IntoSeriesT
|
|
from narwhals.typing import MultiIndexSelector
|
|
from narwhals.typing import SingleIndexSelector
|
|
from narwhals.typing import SizedMultiIndexSelector
|
|
from narwhals.typing import SizeUnit
|
|
from narwhals.typing import SupportsNativeNamespace
|
|
from narwhals.typing import TimeUnit
|
|
from narwhals.typing import _1DArray
|
|
from narwhals.typing import _SliceIndex
|
|
from narwhals.typing import _SliceName
|
|
from narwhals.typing import _SliceNone
|
|
|
|
FrameOrSeriesT = TypeVar(
|
|
"FrameOrSeriesT", bound=Union[LazyFrame[Any], DataFrame[Any], Series[Any]]
|
|
)
|
|
|
|
_T1 = TypeVar("_T1")
|
|
_T2 = TypeVar("_T2")
|
|
_T3 = TypeVar("_T3")
|
|
_Fn = TypeVar("_Fn", bound="Callable[..., Any]")
|
|
P = ParamSpec("P")
|
|
R = TypeVar("R")
|
|
R1 = TypeVar("R1")
|
|
R2 = TypeVar("R2")
|
|
|
|
class _SupportsVersion(Protocol):
|
|
__version__: str
|
|
|
|
class _SupportsGet(Protocol): # noqa: PYI046
|
|
def __get__(self, instance: Any, owner: Any | None = None, /) -> Any: ...
|
|
|
|
class _StoresImplementation(Protocol):
|
|
_implementation: Implementation
|
|
"""Implementation of native object (pandas, Polars, PyArrow, ...)."""
|
|
|
|
class _StoresBackendVersion(Protocol):
|
|
_backend_version: tuple[int, ...]
|
|
"""Version tuple for a native package."""
|
|
|
|
class _StoresVersion(Protocol):
|
|
_version: Version
|
|
"""Narwhals API version (V1 or MAIN)."""
|
|
|
|
class _LimitedContext(_StoresBackendVersion, _StoresVersion, Protocol):
|
|
"""Provides 2 attributes.
|
|
|
|
- `_backend_version`
|
|
- `_version`
|
|
"""
|
|
|
|
class _FullContext(_StoresImplementation, _LimitedContext, Protocol):
|
|
"""Provides 3 attributes.
|
|
|
|
- `_implementation`
|
|
- `_backend_version`
|
|
- `_version`
|
|
"""
|
|
|
|
class _StoresColumns(Protocol):
|
|
@property
|
|
def columns(self) -> Sequence[str]: ...
|
|
|
|
|
|
_T = TypeVar("_T")
|
|
NativeT_co = TypeVar("NativeT_co", covariant=True)
|
|
CompliantT_co = TypeVar("CompliantT_co", covariant=True)
|
|
_ContextT = TypeVar("_ContextT", bound="_FullContext")
|
|
_Method: TypeAlias = "Callable[Concatenate[_ContextT, P], R]"
|
|
_Constructor: TypeAlias = "Callable[Concatenate[_T, P], R2]"
|
|
|
|
|
|
class _StoresNative(Protocol[NativeT_co]): # noqa: PYI046
|
|
"""Provides access to a native object.
|
|
|
|
Native objects have types like:
|
|
|
|
>>> from pandas import Series
|
|
>>> from pyarrow import Table
|
|
"""
|
|
|
|
@property
|
|
def native(self) -> NativeT_co:
|
|
"""Return the native object."""
|
|
...
|
|
|
|
|
|
class _StoresCompliant(Protocol[CompliantT_co]): # noqa: PYI046
|
|
"""Provides access to a compliant object.
|
|
|
|
Compliant objects have types like:
|
|
|
|
>>> from narwhals._pandas_like.series import PandasLikeSeries
|
|
>>> from narwhals._arrow.dataframe import ArrowDataFrame
|
|
"""
|
|
|
|
@property
|
|
def compliant(self) -> CompliantT_co:
|
|
"""Return the compliant object."""
|
|
...
|
|
|
|
|
|
class Version(Enum):
|
|
V1 = auto()
|
|
MAIN = auto()
|
|
|
|
@property
|
|
def namespace(self) -> type[Namespace[Any]]:
|
|
if self is Version.MAIN:
|
|
from narwhals._namespace import Namespace
|
|
|
|
return Namespace
|
|
from narwhals.stable.v1._namespace import Namespace
|
|
|
|
return Namespace
|
|
|
|
@property
|
|
def dtypes(self) -> DTypes:
|
|
if self is Version.MAIN:
|
|
from narwhals import dtypes
|
|
|
|
return dtypes
|
|
from narwhals.stable.v1 import dtypes as v1_dtypes
|
|
|
|
return v1_dtypes
|
|
|
|
@property
|
|
def dataframe(self) -> type[DataFrame[Any]]:
|
|
if self is Version.MAIN:
|
|
from narwhals.dataframe import DataFrame
|
|
|
|
return DataFrame
|
|
from narwhals.stable.v1 import DataFrame as DataFrameV1
|
|
|
|
return DataFrameV1
|
|
|
|
@property
|
|
def lazyframe(self) -> type[LazyFrame[Any]]:
|
|
if self is Version.MAIN:
|
|
from narwhals.dataframe import LazyFrame
|
|
|
|
return LazyFrame
|
|
from narwhals.stable.v1 import LazyFrame as LazyFrameV1
|
|
|
|
return LazyFrameV1
|
|
|
|
@property
|
|
def series(self) -> type[Series[Any]]:
|
|
if self is Version.MAIN:
|
|
from narwhals.series import Series
|
|
|
|
return Series
|
|
from narwhals.stable.v1 import Series as SeriesV1
|
|
|
|
return SeriesV1
|
|
|
|
|
|
class Implementation(NoAutoEnum):
|
|
"""Implementation of native object (pandas, Polars, PyArrow, ...)."""
|
|
|
|
PANDAS = "pandas"
|
|
"""pandas implementation."""
|
|
MODIN = "modin"
|
|
"""Modin implementation."""
|
|
CUDF = "cudf"
|
|
"""cuDF implementation."""
|
|
PYARROW = "pyarrow"
|
|
"""PyArrow implementation."""
|
|
PYSPARK = "pyspark"
|
|
"""PySpark implementation."""
|
|
POLARS = "polars"
|
|
"""Polars implementation."""
|
|
DASK = "dask"
|
|
"""Dask implementation."""
|
|
DUCKDB = "duckdb"
|
|
"""DuckDB implementation."""
|
|
IBIS = "ibis"
|
|
"""Ibis implementation."""
|
|
SQLFRAME = "sqlframe"
|
|
"""SQLFrame implementation."""
|
|
PYSPARK_CONNECT = "pyspark[connect]"
|
|
"""PySpark Connect implementation."""
|
|
UNKNOWN = "unknown"
|
|
"""Unknown implementation."""
|
|
|
|
def __str__(self) -> str:
|
|
return str(self.value)
|
|
|
|
@classmethod
|
|
def from_native_namespace(
|
|
cls: type[Self], native_namespace: ModuleType
|
|
) -> Implementation: # pragma: no cover
|
|
"""Instantiate Implementation object from a native namespace module.
|
|
|
|
Arguments:
|
|
native_namespace: Native namespace.
|
|
|
|
Returns:
|
|
Implementation.
|
|
"""
|
|
mapping = {
|
|
get_pandas(): Implementation.PANDAS,
|
|
get_modin(): Implementation.MODIN,
|
|
get_cudf(): Implementation.CUDF,
|
|
get_pyarrow(): Implementation.PYARROW,
|
|
get_pyspark_sql(): Implementation.PYSPARK,
|
|
get_polars(): Implementation.POLARS,
|
|
get_dask_dataframe(): Implementation.DASK,
|
|
get_duckdb(): Implementation.DUCKDB,
|
|
get_ibis(): Implementation.IBIS,
|
|
get_sqlframe(): Implementation.SQLFRAME,
|
|
get_pyspark_connect(): Implementation.PYSPARK_CONNECT,
|
|
}
|
|
return mapping.get(native_namespace, Implementation.UNKNOWN)
|
|
|
|
@classmethod
|
|
def from_string(
|
|
cls: type[Self], backend_name: str
|
|
) -> Implementation: # pragma: no cover
|
|
"""Instantiate Implementation object from a native namespace module.
|
|
|
|
Arguments:
|
|
backend_name: Name of backend, expressed as string.
|
|
|
|
Returns:
|
|
Implementation.
|
|
"""
|
|
try:
|
|
return cls(backend_name)
|
|
except ValueError:
|
|
return Implementation.UNKNOWN
|
|
|
|
@classmethod
|
|
def from_backend(
|
|
cls: type[Self], backend: str | Implementation | ModuleType
|
|
) -> Implementation:
|
|
"""Instantiate from native namespace module, string, or Implementation.
|
|
|
|
Arguments:
|
|
backend: Backend to instantiate Implementation from.
|
|
|
|
Returns:
|
|
Implementation.
|
|
"""
|
|
return (
|
|
cls.from_string(backend)
|
|
if isinstance(backend, str)
|
|
else backend
|
|
if isinstance(backend, Implementation)
|
|
else cls.from_native_namespace(backend)
|
|
)
|
|
|
|
def to_native_namespace(self) -> ModuleType: # noqa: C901, PLR0911
|
|
"""Return the native namespace module corresponding to Implementation.
|
|
|
|
Returns:
|
|
Native module.
|
|
"""
|
|
if self is Implementation.PANDAS:
|
|
import pandas as pd # ignore-banned-import
|
|
|
|
return pd
|
|
if self is Implementation.MODIN:
|
|
import modin.pandas
|
|
|
|
return modin.pandas
|
|
if self is Implementation.CUDF: # pragma: no cover
|
|
import cudf # ignore-banned-import
|
|
|
|
return cudf
|
|
if self is Implementation.PYARROW:
|
|
import pyarrow as pa # ignore-banned-import
|
|
|
|
return pa
|
|
if self is Implementation.PYSPARK: # pragma: no cover
|
|
import pyspark.sql
|
|
|
|
return pyspark.sql
|
|
if self is Implementation.POLARS:
|
|
import polars as pl # ignore-banned-import
|
|
|
|
return pl
|
|
if self is Implementation.DASK:
|
|
import dask.dataframe # ignore-banned-import
|
|
|
|
return dask.dataframe
|
|
|
|
if self is Implementation.DUCKDB:
|
|
import duckdb # ignore-banned-import
|
|
|
|
return duckdb
|
|
|
|
if self is Implementation.SQLFRAME:
|
|
import sqlframe # ignore-banned-import
|
|
|
|
return sqlframe
|
|
|
|
if self is Implementation.PYSPARK_CONNECT: # pragma: no cover
|
|
import pyspark.sql.connect # ignore-banned-import
|
|
|
|
return pyspark.sql.connect
|
|
|
|
msg = "Not supported Implementation" # pragma: no cover
|
|
raise AssertionError(msg)
|
|
|
|
def is_pandas(self) -> bool:
|
|
"""Return whether implementation is pandas.
|
|
|
|
Returns:
|
|
Boolean.
|
|
|
|
Examples:
|
|
>>> import pandas as pd
|
|
>>> import narwhals as nw
|
|
>>> df_native = pd.DataFrame({"a": [1, 2, 3]})
|
|
>>> df = nw.from_native(df_native)
|
|
>>> df.implementation.is_pandas()
|
|
True
|
|
"""
|
|
return self is Implementation.PANDAS
|
|
|
|
def is_pandas_like(self) -> bool:
|
|
"""Return whether implementation is pandas, Modin, or cuDF.
|
|
|
|
Returns:
|
|
Boolean.
|
|
|
|
Examples:
|
|
>>> import pandas as pd
|
|
>>> import narwhals as nw
|
|
>>> df_native = pd.DataFrame({"a": [1, 2, 3]})
|
|
>>> df = nw.from_native(df_native)
|
|
>>> df.implementation.is_pandas_like()
|
|
True
|
|
"""
|
|
return self in {
|
|
Implementation.PANDAS,
|
|
Implementation.MODIN,
|
|
Implementation.CUDF,
|
|
}
|
|
|
|
def is_spark_like(self) -> bool:
|
|
"""Return whether implementation is pyspark or sqlframe.
|
|
|
|
Returns:
|
|
Boolean.
|
|
|
|
Examples:
|
|
>>> import pandas as pd
|
|
>>> import narwhals as nw
|
|
>>> df_native = pd.DataFrame({"a": [1, 2, 3]})
|
|
>>> df = nw.from_native(df_native)
|
|
>>> df.implementation.is_spark_like()
|
|
False
|
|
"""
|
|
return self in {
|
|
Implementation.PYSPARK,
|
|
Implementation.SQLFRAME,
|
|
Implementation.PYSPARK_CONNECT,
|
|
}
|
|
|
|
def is_polars(self) -> bool:
|
|
"""Return whether implementation is Polars.
|
|
|
|
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_polars()
|
|
True
|
|
"""
|
|
return self is Implementation.POLARS
|
|
|
|
def is_cudf(self) -> bool:
|
|
"""Return whether implementation is cuDF.
|
|
|
|
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_cudf()
|
|
False
|
|
"""
|
|
return self is Implementation.CUDF # pragma: no cover
|
|
|
|
def is_modin(self) -> bool:
|
|
"""Return whether implementation is Modin.
|
|
|
|
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_modin()
|
|
False
|
|
"""
|
|
return self is Implementation.MODIN # pragma: no cover
|
|
|
|
def is_pyspark(self) -> bool:
|
|
"""Return whether implementation is PySpark.
|
|
|
|
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_pyspark()
|
|
False
|
|
"""
|
|
return self is Implementation.PYSPARK # pragma: no cover
|
|
|
|
def is_pyspark_connect(self) -> bool:
|
|
"""Return whether implementation is PySpark.
|
|
|
|
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_pyspark_connect()
|
|
False
|
|
"""
|
|
return self is Implementation.PYSPARK_CONNECT # pragma: no cover
|
|
|
|
def is_pyarrow(self) -> bool:
|
|
"""Return whether implementation is PyArrow.
|
|
|
|
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_pyarrow()
|
|
False
|
|
"""
|
|
return self is Implementation.PYARROW # pragma: no cover
|
|
|
|
def is_dask(self) -> bool:
|
|
"""Return whether implementation is Dask.
|
|
|
|
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_dask()
|
|
False
|
|
"""
|
|
return self is Implementation.DASK # pragma: no cover
|
|
|
|
def is_duckdb(self) -> bool:
|
|
"""Return whether implementation is DuckDB.
|
|
|
|
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_duckdb()
|
|
False
|
|
"""
|
|
return self is Implementation.DUCKDB # pragma: no cover
|
|
|
|
def is_ibis(self) -> bool:
|
|
"""Return whether implementation is Ibis.
|
|
|
|
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_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, ...]:
|
|
native = self.to_native_namespace()
|
|
into_version: Any
|
|
if self not in {
|
|
Implementation.PYSPARK,
|
|
Implementation.PYSPARK_CONNECT,
|
|
Implementation.DASK,
|
|
Implementation.SQLFRAME,
|
|
}:
|
|
into_version = native
|
|
elif self in {Implementation.PYSPARK, Implementation.PYSPARK_CONNECT}:
|
|
into_version = get_pyspark() # pragma: no cover
|
|
elif self is Implementation.DASK:
|
|
into_version = get_dask()
|
|
else:
|
|
import sqlframe._version
|
|
|
|
into_version = sqlframe._version
|
|
return parse_version(into_version)
|
|
|
|
|
|
MIN_VERSIONS: dict[Implementation, tuple[int, ...]] = {
|
|
Implementation.PANDAS: (0, 25, 3),
|
|
Implementation.MODIN: (0, 25, 3),
|
|
Implementation.CUDF: (24, 10),
|
|
Implementation.PYARROW: (11,),
|
|
Implementation.PYSPARK: (3, 5),
|
|
Implementation.PYSPARK_CONNECT: (3, 5),
|
|
Implementation.POLARS: (0, 20, 3),
|
|
Implementation.DASK: (2024, 8),
|
|
Implementation.DUCKDB: (1,),
|
|
Implementation.IBIS: (6,),
|
|
Implementation.SQLFRAME: (3, 22, 0),
|
|
}
|
|
|
|
|
|
def validate_backend_version(
|
|
implementation: Implementation, backend_version: tuple[int, ...]
|
|
) -> None:
|
|
if backend_version < (min_version := MIN_VERSIONS[implementation]):
|
|
msg = f"Minimum version of {implementation} supported by Narwhals is {min_version}, found: {backend_version}"
|
|
raise ValueError(msg)
|
|
|
|
|
|
def remove_prefix(text: str, prefix: str) -> str: # pragma: no cover
|
|
if text.startswith(prefix):
|
|
return text[len(prefix) :]
|
|
return text
|
|
|
|
|
|
def remove_suffix(text: str, suffix: str) -> str: # pragma: no cover
|
|
if text.endswith(suffix):
|
|
return text[: -len(suffix)]
|
|
return text # pragma: no cover
|
|
|
|
|
|
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 is_pandas_dataframe(arg) or is_pandas_series(arg):
|
|
msg = f"Expected Narwhals class or scalar, got: {type(arg)}. Perhaps you forgot a `nw.from_native` somewhere?"
|
|
raise TypeError(msg)
|
|
if (pl := get_polars()) is not None and isinstance(
|
|
arg, (pl.Series, pl.Expr, pl.DataFrame, pl.LazyFrame)
|
|
):
|
|
msg = (
|
|
f"Expected Narwhals class or scalar, got: {type(arg)}.\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: 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
|
|
from narwhals.dataframe import 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]
|
|
backend_version=obj._compliant_series._backend_version, # 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)
|
|
|
|
|
|
# NOTE: Remove ignore(s) after release w/ (https://github.com/pandas-dev/pandas-stubs/pull/1115)
|
|
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: # noqa: PLR0911
|
|
"""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 isinstance(compliant, InterchangeSeries) and isinstance(
|
|
series.dtype, dtypes.Categorical
|
|
):
|
|
return compliant.native.describe_categorical["is_ordered"]
|
|
if series.dtype == dtypes.Enum:
|
|
return True
|
|
if series.dtype != dtypes.Categorical:
|
|
return False
|
|
native_series = series.to_native()
|
|
if is_polars_series(native_series):
|
|
return native_series.dtype.ordering == "physical" # type: ignore[attr-defined]
|
|
if is_pandas_series(native_series):
|
|
return bool(native_series.cat.ordered)
|
|
if is_modin_series(native_series): # pragma: no cover
|
|
return native_series.cat.ordered
|
|
if is_cudf_series(native_series): # pragma: no cover
|
|
return native_series.cat.ordered
|
|
if is_pyarrow_chunked_array(native_series):
|
|
from narwhals._arrow.utils import is_dictionary
|
|
|
|
return is_dictionary(native_series.type) and native_series.type.ordered
|
|
# If it doesn't match any of the above, let's just play it safe and return False.
|
|
return False # pragma: no cover
|
|
|
|
|
|
def generate_unique_token(
|
|
n_bytes: int, columns: Sequence[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: Sequence[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:
|
|
token = token_hex(n_bytes)
|
|
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(
|
|
compliant_frame: Any,
|
|
columns: Iterable[str],
|
|
strict: bool, # noqa: FBT001
|
|
) -> list[str]:
|
|
cols = compliant_frame.columns
|
|
to_drop = list(columns)
|
|
if strict:
|
|
missing_columns = [x for x in to_drop if x not in cols]
|
|
if missing_columns:
|
|
raise ColumnNotFoundError.from_missing_and_available_column_names(
|
|
missing_columns=missing_columns, available_columns=cols
|
|
)
|
|
else:
|
|
to_drop = list(set(cols).intersection(set(to_drop)))
|
|
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 find_stacklevel() -> int:
|
|
"""Find the first place in the stack that is not inside narwhals.
|
|
|
|
Returns:
|
|
Stacklevel.
|
|
|
|
Taken from:
|
|
https://github.com/pandas-dev/pandas/blob/ab89c53f48df67709a533b6a95ce3d911871a0a8/pandas/util/_exceptions.py#L30-L51
|
|
"""
|
|
import inspect
|
|
from pathlib import Path
|
|
|
|
import narwhals as nw
|
|
|
|
pkg_dir = str(Path(nw.__file__).parent)
|
|
|
|
# https://stackoverflow.com/questions/17407119/python-inspect-stack-is-slow
|
|
frame = inspect.currentframe()
|
|
n = 0
|
|
try:
|
|
while frame:
|
|
fname = inspect.getfile(frame)
|
|
if fname.startswith(pkg_dir) or (
|
|
(qualname := getattr(frame.f_code, "co_qualname", None))
|
|
# ignore @singledispatch wrappers
|
|
and qualname.startswith("singledispatch.")
|
|
):
|
|
frame = frame.f_back
|
|
n += 1
|
|
else: # pragma: no cover
|
|
break
|
|
else: # pragma: no cover
|
|
pass
|
|
finally:
|
|
# https://docs.python.org/3/library/inspect.html
|
|
# > Though the cycle detector will catch these, destruction of the frames
|
|
# > (and local variables) can be made deterministic by removing the cycle
|
|
# > in a finally clause.
|
|
del frame
|
|
return n
|
|
|
|
|
|
def issue_deprecation_warning(message: str, _version: str) -> None:
|
|
"""Issue a deprecation warning.
|
|
|
|
Arguments:
|
|
message: The message associated with the warning.
|
|
_version: Narwhals version when the warning was introduced. Just used for internal
|
|
bookkeeping.
|
|
"""
|
|
warn(message=message, category=DeprecationWarning, stacklevel=find_stacklevel())
|
|
|
|
|
|
def validate_strict_and_pass_though(
|
|
strict: bool | None, # noqa: FBT001
|
|
pass_through: bool | None, # noqa: FBT001
|
|
*,
|
|
pass_through_default: bool,
|
|
emit_deprecation_warning: 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:
|
|
if emit_deprecation_warning:
|
|
msg = (
|
|
"`strict` in `from_native` is deprecated, please use `pass_through` instead.\n\n"
|
|
"Note: `strict` 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="1.13.0")
|
|
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]:
|
|
if window_size < 1:
|
|
msg = "window_size must be greater or equal than 1"
|
|
raise ValueError(msg)
|
|
|
|
if not isinstance(window_size, int):
|
|
_type = window_size.__class__.__name__
|
|
msg = (
|
|
f"argument 'window_size': '{_type}' object cannot be "
|
|
"interpreted as an integer"
|
|
)
|
|
raise TypeError(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 not isinstance(min_samples, int):
|
|
_type = min_samples.__class__.__name__
|
|
msg = (
|
|
f"argument 'min_samples': '{_type}' object cannot be "
|
|
"interpreted as an integer"
|
|
)
|
|
raise TypeError(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_column_exists(columns: Sequence[str], subset: Sequence[str] | None) -> None:
|
|
if subset is not None and (missing := set(subset).difference(columns)):
|
|
msg = f"Column(s) {sorted(missing)} not found in {columns}"
|
|
raise ColumnNotFoundError(msg)
|
|
|
|
|
|
def check_column_names_are_unique(columns: Sequence[str]) -> None:
|
|
len_unique_columns = len(set(columns))
|
|
if len(columns) != len_unique_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(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: Sequence[str], right_on: Sequence[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: _FullContext, /) -> 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"):
|
|
import pyarrow as pa # ignore-banned-import
|
|
|
|
from narwhals._arrow.namespace import ArrowNamespace
|
|
|
|
version = context._version
|
|
ns = ArrowNamespace(backend_version=parse_version(pa), version=version)
|
|
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 {type(data).__name__!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
|
|
|
|
|
|
if TYPE_CHECKING:
|
|
import sys
|
|
|
|
if sys.version_info >= (3, 13):
|
|
# NOTE: avoids `mypy`
|
|
# error: Module "narwhals.utils" does not explicitly export attribute "deprecated" [attr-defined]
|
|
from warnings import deprecated as deprecated # noqa: PLC0414
|
|
else:
|
|
from typing_extensions import deprecated as deprecated # noqa: PLC0414
|
|
else:
|
|
|
|
def deprecated(message: str, /) -> Callable[[_Fn], _Fn]: # noqa: ARG001
|
|
def wrapper(func: _Fn, /) -> _Fn:
|
|
return func
|
|
|
|
return wrapper
|
|
|
|
|
|
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)
|
|
|
|
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 _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"
|
|
)
|
|
return 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
|
|
|
|
|
|
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)
|