add code for analysis of data

1 files changed, 2010 insertions, 0 deletions

diff --git a/venv/lib/python3.8/site-packages/narwhals/_utils.py b/venv/lib/python3.8/site-packages/narwhals/_utils.py
new file mode 100644
index 0000000..3d2c89a
--- /dev/null
+++ b/venv/lib/python3.8/site-packages/narwhals/_utils.py
@@ -0,0 +1,2010 @@
+from __future__ import annotations
+
+import os
+import re
+from datetime import timezone
+from enum import Enum, auto
+from functools import wraps
+from importlib.util import find_spec
+from inspect import getattr_static, getdoc
+from secrets import token_hex
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    Container,
+    Generic,
+    Iterable,
+    Iterator,
+    Literal,
+    Protocol,
+    Sequence,
+    TypeVar,
+    Union,
+    cast,
+    overload,
+)
+from warnings import warn
+
+from narwhals._enum import NoAutoEnum
+from narwhals._typing_compat import deprecated
+from narwhals.dependencies import (
+    get_cudf,
+    get_dask,
+    get_dask_dataframe,
+    get_duckdb,
+    get_ibis,
+    get_modin,
+    get_pandas,
+    get_polars,
+    get_pyarrow,
+    get_pyspark,
+    get_pyspark_connect,
+    get_pyspark_sql,
+    get_sqlframe,
+    is_narwhals_series,
+    is_narwhals_series_int,
+    is_numpy_array_1d,
+    is_numpy_array_1d_int,
+    is_pandas_dataframe,
+    is_pandas_like_dataframe,
+    is_pandas_like_series,
+    is_pandas_series,
+    is_polars_series,
+    is_pyarrow_chunked_array,
+)
+from narwhals.exceptions import ColumnNotFoundError, DuplicateError, InvalidOperationError
+
+if TYPE_CHECKING:
+    from types import ModuleType
+    from typing import AbstractSet as Set
+
+    import pandas as pd
+    import polars as pl
+    import pyarrow as pa
+    from typing_extensions import (
+        Concatenate,
+        LiteralString,
+        ParamSpec,
+        Self,
+        TypeAlias,
+        TypeIs,
+    )
+
+    from narwhals._compliant import (
+        CompliantExpr,
+        CompliantExprT,
+        CompliantFrameT,
+        CompliantSeriesOrNativeExprT_co,
+        CompliantSeriesT,
+        NativeFrameT_co,
+        NativeSeriesT_co,
+    )
+    from narwhals._compliant.typing import EvalNames
+    from narwhals._namespace import EagerAllowedImplementation, Namespace
+    from narwhals._translate import ArrowStreamExportable, IntoArrowTable, ToNarwhalsT_co
+    from narwhals.dataframe import DataFrame, LazyFrame
+    from narwhals.dtypes import DType
+    from narwhals.series import Series
+    from narwhals.typing import (
+        CompliantDataFrame,
+        CompliantLazyFrame,
+        CompliantSeries,
+        DataFrameLike,
+        DTypes,
+        IntoSeriesT,
+        MultiIndexSelector,
+        SingleIndexSelector,
+        SizedMultiIndexSelector,
+        SizeUnit,
+        SupportsNativeNamespace,
+        TimeUnit,
+        _1DArray,
+        _SliceIndex,
+        _SliceName,
+        _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.IBIS:
+            import ibis  # ignore-banned-import
+
+            return ibis
+
+        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: {qualified_type_name(arg)!r}. 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: {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: 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]
+            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)
+
+
+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: 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(
+    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 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]:
+    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: Sequence[str], /, *, available: Sequence[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: 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 {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)
+
+    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
+
+
+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)