done

lib/python3.11/site-packages/narwhals/stable/v2/_namespace.py

@@ -0,0 +1,10 @@
+from __future__ import annotations
+
+from narwhals._compliant.typing import CompliantNamespaceT_co
+from narwhals._namespace import Namespace as NwNamespace
+from narwhals._utils import Version
+
+__all__ = ["Namespace"]
+
+
+class Namespace(NwNamespace[CompliantNamespaceT_co], version=Version.V2): ...

lib/python3.11/site-packages/narwhals/stable/v2/dependencies.py

@@ -0,0 +1,3 @@
+from __future__ import annotations
+
+from narwhals.dependencies import *  # noqa: F403

lib/python3.11/site-packages/narwhals/stable/v2/dtypes.py

@@ -0,0 +1,3 @@
+from __future__ import annotations
+
+from narwhals.dtypes import *  # noqa: F403

lib/python3.11/site-packages/narwhals/stable/v2/selectors.py

@@ -0,0 +1,3 @@
+from __future__ import annotations
+
+from narwhals.selectors import *  # noqa: F403

lib/python3.11/site-packages/narwhals/stable/v2/typing.py

@@ -0,0 +1,186 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Protocol, TypeVar, Union
+
+if TYPE_CHECKING:
+    import sys
+    from collections.abc import Iterable, Sized
+
+    from narwhals.stable.v2 import DataFrame, LazyFrame
+
+    if sys.version_info >= (3, 10):
+        from typing import TypeAlias
+    else:
+        from typing_extensions import TypeAlias
+
+    from narwhals.stable.v2 import Expr, Series
+
+    # All dataframes supported by Narwhals have a
+    # `columns` property. Their similarities don't extend
+    # _that_ much further unfortunately...
+    class NativeFrame(Protocol):
+        @property
+        def columns(self) -> Any: ...
+
+        def join(self, *args: Any, **kwargs: Any) -> Any: ...
+
+    class NativeDataFrame(Sized, NativeFrame, Protocol): ...
+
+    class NativeLazyFrame(NativeFrame, Protocol):
+        def explain(self, *args: Any, **kwargs: Any) -> Any: ...
+
+    class NativeSeries(Sized, Iterable[Any], Protocol):
+        def filter(self, *args: Any, **kwargs: Any) -> Any: ...
+
+
+IntoExpr: TypeAlias = Union["Expr", str, "Series[Any]"]
+"""Anything which can be converted to an expression.
+
+Use this to mean "either a Narwhals expression, or something
+which can be converted into one". For example, `exprs` in `DataFrame.select` is
+typed to accept `IntoExpr`, as it can either accept a `nw.Expr`
+(e.g. `df.select(nw.col('a'))`) or a string which will be interpreted as a
+`nw.Expr`, e.g. `df.select('a')`.
+"""
+
+IntoDataFrame: TypeAlias = "NativeDataFrame"
+"""Anything which can be converted to a Narwhals DataFrame.
+
+Use this if your function accepts a narwhalifiable object but doesn't care about its backend.
+
+Examples:
+    >>> import narwhals as nw
+    >>> from narwhals.typing import IntoDataFrame
+    >>> def agnostic_shape(df_native: IntoDataFrame) -> tuple[int, int]:
+    ...     df = nw.from_native(df_native, eager_only=True)
+    ...     return df.shape
+"""
+
+IntoLazyFrame: TypeAlias = "NativeLazyFrame"
+
+IntoFrame: TypeAlias = Union["IntoDataFrame", "IntoLazyFrame"]
+"""Anything which can be converted to a Narwhals DataFrame or LazyFrame.
+
+Use this if your function can accept an object which can be converted to either
+`nw.DataFrame` or `nw.LazyFrame` and it doesn't care about its backend.
+
+Examples:
+    >>> import narwhals as nw
+    >>> from narwhals.typing import IntoFrame
+    >>> def agnostic_columns(df_native: IntoFrame) -> list[str]:
+    ...     df = nw.from_native(df_native)
+    ...     return df.collect_schema().names()
+"""
+
+Frame: TypeAlias = Union["DataFrame[Any]", "LazyFrame[Any]"]
+"""Narwhals DataFrame or Narwhals LazyFrame.
+
+Use this if your function can work with either and your function doesn't care
+about its backend.
+
+Examples:
+    >>> import narwhals as nw
+    >>> from narwhals.typing import Frame
+    >>> @nw.narwhalify
+    ... def agnostic_columns(df: Frame) -> list[str]:
+    ...     return df.columns
+"""
+
+IntoSeries: TypeAlias = "NativeSeries"
+"""Anything which can be converted to a Narwhals Series.
+
+Use this if your function can accept an object which can be converted to `nw.Series`
+and it doesn't care about its backend.
+
+Examples:
+    >>> from typing import Any
+    >>> import narwhals as nw
+    >>> from narwhals.typing import IntoSeries
+    >>> def agnostic_to_list(s_native: IntoSeries) -> list[Any]:
+    ...     s = nw.from_native(s_native)
+    ...     return s.to_list()
+"""
+
+IntoFrameT = TypeVar("IntoFrameT", bound="IntoFrame")
+"""TypeVar bound to object convertible to Narwhals DataFrame or Narwhals LazyFrame.
+
+Use this if your function accepts an object which is convertible to `nw.DataFrame`
+or `nw.LazyFrame` and returns an object of the same type.
+
+Examples:
+    >>> import narwhals as nw
+    >>> from narwhals.typing import IntoFrameT
+    >>> def agnostic_func(df_native: IntoFrameT) -> IntoFrameT:
+    ...     df = nw.from_native(df_native)
+    ...     return df.with_columns(c=nw.col("a") + 1).to_native()
+"""
+
+IntoDataFrameT = TypeVar("IntoDataFrameT", bound="IntoDataFrame")
+"""TypeVar bound to object convertible to Narwhals DataFrame.
+
+Use this if your function accepts an object which can be converted to `nw.DataFrame`
+and returns an object of the same class.
+
+Examples:
+    >>> import narwhals as nw
+    >>> from narwhals.typing import IntoDataFrameT
+    >>> def agnostic_func(df_native: IntoDataFrameT) -> IntoDataFrameT:
+    ...     df = nw.from_native(df_native, eager_only=True)
+    ...     return df.with_columns(c=df["a"] + 1).to_native()
+"""
+
+FrameT = TypeVar("FrameT", "DataFrame[Any]", "LazyFrame[Any]")
+"""TypeVar bound to Narwhals DataFrame or Narwhals LazyFrame.
+
+Use this if your function accepts either `nw.DataFrame` or `nw.LazyFrame` and returns
+an object of the same kind.
+
+Examples:
+    >>> import narwhals as nw
+    >>> from narwhals.typing import FrameT
+    >>> @nw.narwhalify
+    ... def agnostic_func(df: FrameT) -> FrameT:
+    ...     return df.with_columns(c=nw.col("a") + 1)
+"""
+
+DataFrameT = TypeVar("DataFrameT", bound="DataFrame[Any]")
+"""TypeVar bound to Narwhals DataFrame.
+
+Use this if your function can accept a Narwhals DataFrame and returns a Narwhals
+DataFrame backed by the same backend.
+
+Examples:
+    >>> import narwhals as nw
+    >>> from narwhals.typing import DataFrameT
+    >>> @nw.narwhalify
+    >>> def func(df: DataFrameT) -> DataFrameT:
+    ...     return df.with_columns(c=df["a"] + 1)
+"""
+
+IntoSeriesT = TypeVar("IntoSeriesT", bound="IntoSeries")
+"""TypeVar bound to object convertible to Narwhals Series.
+
+Use this if your function accepts an object  which can be converted to `nw.Series`
+and returns an object of the same class.
+
+Examples:
+    >>> import narwhals as nw
+    >>> from narwhals.typing import IntoSeriesT
+    >>> def agnostic_abs(s_native: IntoSeriesT) -> IntoSeriesT:
+    ...     s = nw.from_native(s_native, series_only=True)
+    ...     return s.abs().to_native()
+"""
+
+
+__all__ = [
+    "DataFrameT",
+    "Frame",
+    "FrameT",
+    "IntoDataFrame",
+    "IntoDataFrameT",
+    "IntoExpr",
+    "IntoFrame",
+    "IntoFrameT",
+    "IntoSeries",
+    "IntoSeriesT",
+]