fix(python): DataFrame init from collections.namedtuple values

py-polars/polars/utils/_construction.py

@@ -388,7 +388,7 @@ def sequence_to_pyseries(
         if (
             dataclasses.is_dataclass(value)
             or is_pydantic_model(value)
-            or is_namedtuple(value.__class__, annotated=True)
+            or is_namedtuple(value.__class__)
         ):
             return pl.DataFrame(values).to_struct(name)._s
         elif isinstance(value, range):
@@ -1080,12 +1080,13 @@ def _sequence_of_tuple_to_pydf(
     if is_namedtuple(first_element.__class__):
         if schema is None:
             schema = first_element._fields  # type: ignore[attr-defined]
-            if len(first_element.__annotations__) == len(schema):
+            annotations = getattr(first_element, "__annotations__", None)
+            if annotations and len(annotations) == len(schema):
                 schema = [
                     (name, py_type_to_dtype(tp, raise_unmatched=False))
                     for name, tp in first_element.__annotations__.items()
                 ]
-        elif orient is None:
+        if orient is None:
             orient = "row"
 
     # ...then defer to generic sequence processing

py-polars/tests/unit/test_constructors.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import sys
+from collections import namedtuple
 from datetime import date, datetime, timedelta, timezone
 from decimal import Decimal
 from random import shuffle
@@ -462,6 +463,24 @@ class ABC:
     assert dataclasses.asdict(abc) == df.rows(named=True)[0]
 
 
+def test_collections_namedtuple() -> None:
+    TestData = namedtuple("TestData", ["id", "info"])
+    nt_data = [TestData(1, "a"), TestData(2, "b"), TestData(3, "c")]
+
+    df1 = pl.DataFrame(nt_data)
+    assert df1.to_dict(False) == {"id": [1, 2, 3], "info": ["a", "b", "c"]}
+
+    df2 = pl.DataFrame({"data": nt_data, "misc": ["x", "y", "z"]})
+    assert df2.to_dict(False) == {
+        "data": [
+            {"id": 1, "info": "a"},
+            {"id": 2, "info": "b"},
+            {"id": 3, "info": "c"},
+        ],
+        "misc": ["x", "y", "z"],
+    }
+
+
 def test_init_ndarray(monkeypatch: Any) -> None:
     # Empty array
     df = pl.DataFrame(np.array([]))