feat(python): Avoid importing interchange module by default

py-polars/polars/__init__.py

@@ -16,6 +16,7 @@
 from polars.config import Config
 from polars.convert import (
     from_arrow,
+    from_dataframe,
     from_dict,
     from_dicts,
     from_numpy,
@@ -160,7 +161,6 @@
     when,
     zeros,
 )
-from polars.interchange.from_dataframe import from_dataframe
 from polars.io import (
     read_avro,
     read_csv,

py-polars/polars/convert.py

@@ -17,6 +17,7 @@
 if TYPE_CHECKING:
     from polars import DataFrame, Series
     from polars.dependencies import numpy as np
+    from polars.interchange.protocol import SupportsInterchange
     from polars.type_aliases import Orientation, SchemaDefinition, SchemaDict
 
 
@@ -730,3 +731,56 @@ def from_pandas(
         raise TypeError(
             f"expected pandas DataFrame or Series, got {type(data).__name__!r}"
         )
+
+
+def from_dataframe(df: SupportsInterchange, *, allow_copy: bool = True) -> DataFrame:
+    """
+    Build a Polars DataFrame from any dataframe supporting the interchange protocol.
+
+    Parameters
+    ----------
+    df
+        Object supporting the dataframe interchange protocol, i.e. must have implemented
+        the `__dataframe__` method.
+    allow_copy
+        Allow memory to be copied to perform the conversion. If set to False, causes
+        conversions that are not zero-copy to fail.
+
+    Notes
+    -----
+    Details on the Python dataframe interchange protocol:
+    https://data-apis.org/dataframe-protocol/latest/index.html
+
+    Using a dedicated function like :func:`from_pandas` or :func:`from_arrow` is a more
+    efficient method of conversion.
+
+    Polars currently relies on pyarrow's implementation of the dataframe interchange
+    protocol for `from_dataframe`. Therefore, pyarrow>=11.0.0 is required for this
+    function to work.
+
+    Because Polars can not currently guarantee zero-copy conversion from Arrow for
+    categorical columns, `allow_copy=False` will not work if the dataframe contains
+    categorical data.
+
+    Examples
+    --------
+    Convert a pandas dataframe to Polars through the interchange protocol.
+
+    >>> import pandas as pd
+    >>> df_pd = pd.DataFrame({"a": [1, 2], "b": [3.0, 4.0], "c": ["x", "y"]})
+    >>> dfi = df_pd.__dataframe__()
+    >>> pl.from_dataframe(dfi)
+    shape: (2, 3)
+    ┌─────┬─────┬─────┐
+    │ a   ┆ b   ┆ c   │
+    │ --- ┆ --- ┆ --- │
+    │ i64 ┆ f64 ┆ str │
+    ╞═════╪═════╪═════╡
+    │ 1   ┆ 3.0 ┆ x   │
+    │ 2   ┆ 4.0 ┆ y   │
+    └─────┴─────┴─────┘
+
+    """
+    from polars.interchange.from_dataframe import from_dataframe
+
+    return from_dataframe(df, allow_copy=allow_copy)

py-polars/polars/interchange/from_dataframe.py

@@ -26,41 +26,6 @@ def from_dataframe(df: SupportsInterchange, *, allow_copy: bool = True) -> DataF
     allow_copy
         Allow memory to be copied to perform the conversion. If set to False, causes
         conversions that are not zero-copy to fail.
-
-    Notes
-    -----
-    Details on the Python dataframe interchange protocol:
-    https://data-apis.org/dataframe-protocol/latest/index.html
-
-    Using a dedicated function like :func:`from_pandas` or :func:`from_arrow` is a more
-    efficient method of conversion.
-
-    Polars currently relies on pyarrow's implementation of the dataframe interchange
-    protocol for `from_dataframe`. Therefore, pyarrow>=11.0.0 is required for this
-    function to work.
-
-    Because Polars can not currently guarantee zero-copy conversion from Arrow for
-    categorical columns, `allow_copy=False` will not work if the dataframe contains
-    categorical data.
-
-    Examples
-    --------
-    Convert a pandas dataframe to Polars through the interchange protocol.
-
-    >>> import pandas as pd
-    >>> df_pd = pd.DataFrame({"a": [1, 2], "b": [3.0, 4.0], "c": ["x", "y"]})
-    >>> dfi = df_pd.__dataframe__()
-    >>> pl.from_dataframe(dfi)
-    shape: (2, 3)
-    ┌─────┬─────┬─────┐
-    │ a   ┆ b   ┆ c   │
-    │ --- ┆ --- ┆ --- │
-    │ i64 ┆ f64 ┆ str │
-    ╞═════╪═════╪═════╡
-    │ 1   ┆ 3.0 ┆ x   │
-    │ 2   ┆ 4.0 ┆ y   │
-    └─────┴─────┴─────┘
-
     """
     if isinstance(df, pl.DataFrame):
         return df

py-polars/tests/unit/interchange/test_from_dataframe.py

@@ -102,7 +102,7 @@ def test_from_dataframe_pyarrow_min_version(monkeypatch: Any) -> None:
     dfi = pl.DataFrame({"a": [1, 2]}).to_arrow().__dataframe__()
 
     monkeypatch.setattr(
-        pl.convert.pa,  # type: ignore[attr-defined]
+        pl.interchange.from_dataframe.pa,  # type: ignore[attr-defined]
         "__version__",
         "10.0.0",
     )