fix(python): don't convert "ns"-precision temporal types via pyarrow

py-polars/polars/internals/dataframe/frame.py

@@ -6771,18 +6771,13 @@ def iter_rows(
 
         Returns
         -------
-        An iterator of tuples (default) or dictionaries of row values.
+        An iterator of tuples (default) or dictionaries of python row values.
 
         Warnings
         --------
         Row iteration is not optimal as the underlying data is stored in columnar form;
         where possible, prefer export via one of the dedicated export/output methods.
 
-        Notes
-        -----
-        If you are planning to materialise all frame data at once you should prefer
-        calling ``rows()``, which will be faster.
-
         Examples
         --------
         >>> df = pl.DataFrame(
@@ -6809,7 +6804,12 @@ def iter_rows(
         if buffer_size:
             for offset in range(0, self.height, buffer_size):
                 zerocopy_slice = self.slice(offset, buffer_size)
-                if named and _PYARROW_AVAILABLE:
+                if (
+                    named
+                    and _PYARROW_AVAILABLE
+                    # note: 'ns' precision instantiates values as pandas types - avoid
+                    and not any((getattr(tp, "tu", None) == "ns") for tp in self.dtypes)
+                ):
                     yield from zerocopy_slice.to_arrow().to_batches()[0].to_pylist()
                 else:
                     rows_chunk = zerocopy_slice.rows(named=False)

py-polars/tests/unit/test_datelike.py

@@ -356,6 +356,25 @@ def test_timezone() -> None:
     assert s.cast(int).series_equal(tz_s.cast(int))
 
 
+def test_to_dicts() -> None:
+    now = datetime.now()
+    data = {
+        "a": now,
+        "b": now.date(),
+        "c": now.time(),
+        "d": timedelta(days=1, seconds=43200),
+    }
+    df = pl.DataFrame(
+        data, schema_overrides={"a": pl.Datetime("ns"), "d": pl.Duration("ns")}
+    )
+    assert len(df) == 1
+
+    d = df.to_dicts()[0]
+    for col in data:
+        assert d[col] == data[col]
+        assert isinstance(d[col], type(data[col]))
+
+
 def test_to_list() -> None:
     s = pl.Series("date", [123543, 283478, 1243]).cast(pl.Date)