scikit-learn
diff --git a/‎sklearn/utils/_indexing.py
Lines changed: 41 additions & 33 deletions b/‎sklearn/utils/_indexing.py
Lines changed: 41 additions & 33 deletions
diff --git a/‎sklearn/utils/tests/test_indexing.py
Lines changed: 1 addition & 1 deletion b/‎sklearn/utils/tests/test_indexing.py
Lines changed: 1 addition & 1 deletion
diff --git a/‎sklearn/utils/validation.py
Lines changed: 0 additions & 9 deletions b/‎sklearn/utils/validation.py
Lines changed: 0 additions & 9 deletions
@@ -2,11 +2,11 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 import numbers
-import sys
 import warnings
 from collections import UserList
 from itertools import compress, islice
 
+import narwhals as nw
 import numpy as np
 from scipy.sparse import issparse
 
@@ -17,7 +17,6 @@
     _check_sample_weight,
     _is_arraylike_not_scalar,
     _is_pandas_df,
-    _is_polars_df_or_series,
     _use_interchange_protocol,
     check_array,
     check_consistent_length,
@@ -37,6 +36,41 @@ def _array_indexing(array, key, key_dtype, axis):
     return array[key, ...] if axis == 0 else array[:, key]
 
 
+def _narwhals_indexing(X, key, key_dtype, axis):
+    """Index a narhals dataframe or series."""
+    X = nw.from_native(X, allow_series=True)
+    if (
+        not isinstance(key, (int, slice))
+        and not (isinstance(key, list) and key_dtype in ("bool", "str"))
+        and key is not None
+    ):
+        # Note that at least tuples should be converted to either list or ndarray as
+        # tupes in __getitem__ are special: x[(1, 2)] is equal to x[1, 2].
+        key = np.asarray(key)
+
+    if key_dtype in ("bool", "str") and not isinstance(key, (list, slice)):
+        key = key.tolist()
+
+    if axis == 1:
+        return X[:, key].to_native()
+
+    # From here on axis == 0:
+    if key_dtype == "bool":
+        X_indexed = X.filter(key)
+    else:
+        X_indexed = X[key]
+
+    if np.isscalar(key):
+        if len(X.shape) <= 1:
+            return X_indexed
+        # `X_indexed` is a DataFrame with a single row; we return a Series to be
+        # consistent with pandas
+        # Christian Lorentzen really dislikes this behaviour and favours to return a
+        # dataframe.
+        return np.array(X_indexed.row(0))
+    return X_indexed.to_native()
+
+
 def _pandas_indexing(X, key, key_dtype, axis):
     """Index a pandas dataframe or a series."""
     if _is_arraylike_not_scalar(key):
@@ -64,35 +98,6 @@ def _list_indexing(X, key, key_dtype):
     return [X[idx] for idx in key]
 
 
-def _polars_indexing(X, key, key_dtype, axis):
-    """Indexing X with polars interchange protocol."""
-    # Polars behavior is more consistent with lists
-    if isinstance(key, np.ndarray):
-        # Convert each element of the array to a Python scalar
-        key = key.tolist()
-    elif not (np.isscalar(key) or isinstance(key, slice)):
-        key = list(key)
-
-    if axis == 1:
-        # Here we are certain to have a polars DataFrame; which can be indexed with
-        # integer and string scalar, and list of integer, string and boolean
-        return X[:, key]
-
-    if key_dtype == "bool":
-        # Boolean mask can be indexed in the same way for Series and DataFrame (axis=0)
-        return X.filter(key)
-
-    # Integer scalar and list of integer can be indexed in the same way for Series and
-    # DataFrame (axis=0)
-    X_indexed = X[key]
-    if np.isscalar(key) and len(X.shape) == 2:
-        # `X_indexed` is a DataFrame with a single row; we return a Series to be
-        # consistent with pandas
-        pl = sys.modules["polars"]
-        return pl.Series(X_indexed.row(0))
-    return X_indexed
-
-
 def _determine_key_type(key, accept_slice=True):
     """Determine the data type of key.
 
@@ -264,9 +269,12 @@ def _safe_indexing(X, indices, *, axis=0):
     if hasattr(X, "iloc"):
         # TODO: we should probably use _is_pandas_df_or_series(X) instead but this
         # would require updating some tests such as test_train_test_split_mock_pandas.
+        # TODO: Should also work with _narwhals_indexing, but
+        # test_safe_indexing_pandas_no_settingwithcopy_warning
+        # does not pass.
         return _pandas_indexing(X, indices, indices_dtype, axis=axis)
-    elif _is_polars_df_or_series(X):
-        return _polars_indexing(X, indices, indices_dtype, axis=axis)
+    elif nw.dependencies.is_into_dataframe(X) or nw.dependencies.is_into_series(X):
+        return _narwhals_indexing(X, indices, indices_dtype, axis=axis)
     elif hasattr(X, "shape"):
         return _array_indexing(X, indices, indices_dtype, axis=axis)
     else:
 
@@ -452,7 +452,7 @@ def test_get_column_indices_interchange():
     pl = pytest.importorskip("polars")
 
     # Polars dataframes go down the interchange path.
-    df = pl.DataFrame([[1, 2, 3], [4, 5, 6]], schema=["a", "b", "c"])
+    df = pl.DataFrame([[1, 2, 3], [4, 5, 6]], schema=["a", "b", "c"], orient="row")
 
     key_results = [
         (slice(1, None), [1, 2]),
 
@@ -2349,15 +2349,6 @@ def _is_pandas_df(X):
     return isinstance(X, pd.DataFrame)
 
 
-def _is_polars_df_or_series(X):
-    """Return True if the X is a polars dataframe or series."""
-    try:
-        pl = sys.modules["polars"]
-    except KeyError:
-        return False
-    return isinstance(X, (pl.DataFrame, pl.Series))
-
-
 def _is_polars_df(X):
     """Return True if the X is a polars dataframe."""
     try: