ci: Unpin NumPy in type checking workflow (pola-rs#20792)

.github/workflows/lint-python.yml

@@ -58,9 +58,7 @@ jobs:
 
       - name: Install Python dependencies
         working-directory: py-polars
-        # TODO: Fix typing issues for newer NumPy versions
-        # https://github.com/pola-rs/polars/issues/20561
-        run: uv pip install -r requirements-dev.txt -r requirements-lint.txt 'numpy<2.1'
+        run: uv pip install -r requirements-dev.txt -r requirements-lint.txt
 
       # Allow untyped calls for older Python versions
       - name: Run mypy

py-polars/polars/series/series.py

@@ -1421,7 +1421,7 @@ def __array_ufunc__(
 
             # Get minimum dtype needed to be able to cast all input arguments to the
             # same dtype.
-            dtype_char_minimum = np.result_type(*args).char
+            dtype_char_minimum: str = np.result_type(*args).char
 
             # Get all possible output dtypes for ufunc.
             # Input dtypes and output dtypes seem to always match for ufunc.types,
@@ -4756,7 +4756,8 @@ def scatter(
         └─────────┘
         """
         if not isinstance(indices, Iterable):
-            indices = [indices]  # type: ignore[list-item]
+            index: Any = indices  # Workaround for older NumPy versions
+            indices = [index]
         indices = Series(values=indices)
         if indices.is_empty():
             return self

py-polars/tests/unit/interop/numpy/test_to_numpy_df.py

@@ -160,7 +160,7 @@ def test_df_to_numpy_zero_copy_path_temporal() -> None:
     s = pl.Series(values)
     df = pl.DataFrame({"a": s[:4], "b": s[4:8], "c": s[8:]})
 
-    result = df.to_numpy(allow_copy=False)
+    result: npt.NDArray[np.generic] = df.to_numpy(allow_copy=False)
     assert result.flags.f_contiguous is True
     assert result.flags.writeable is False
     assert result.tolist() == [list(row) for row in df.iter_rows()]

py-polars/tests/unit/interop/numpy/test_to_numpy_series.py

@@ -52,7 +52,7 @@ def test_series_to_numpy_numeric_zero_copy(
     dtype: PolarsDataType, expected_dtype: npt.DTypeLike
 ) -> None:
     s = pl.Series([1, 2, 3]).cast(dtype)
-    result = s.to_numpy(allow_copy=False)
+    result: npt.NDArray[np.generic] = s.to_numpy(allow_copy=False)
 
     assert_zero_copy(s, result)
     assert result.tolist() == s.to_list()
@@ -78,7 +78,7 @@ def test_series_to_numpy_numeric_with_nulls(
     dtype: PolarsDataType, expected_dtype: npt.DTypeLike
 ) -> None:
     s = pl.Series([1, 2, None], dtype=dtype, strict=False)
-    result = s.to_numpy()
+    result: npt.NDArray[np.generic] = s.to_numpy()
 
     assert result.tolist()[:-1] == s.to_list()[:-1]
     assert np.isnan(result[-1])
@@ -104,7 +104,7 @@ def test_series_to_numpy_temporal_zero_copy(
 ) -> None:
     values = [0, 2_000, 1_000_000]
     s = pl.Series(values, dtype=dtype, strict=False)
-    result = s.to_numpy(allow_copy=False)
+    result: npt.NDArray[np.generic] = s.to_numpy(allow_copy=False)
 
     assert_zero_copy(s, result)
     # NumPy tolist returns integers for ns precision
@@ -118,7 +118,7 @@ def test_series_to_numpy_temporal_zero_copy(
 def test_series_to_numpy_datetime_with_tz_zero_copy() -> None:
     values = [datetime(1970, 1, 1), datetime(2024, 2, 28)]
     s = pl.Series(values).dt.convert_time_zone("Europe/Amsterdam").rechunk()
-    result = s.to_numpy(allow_copy=False)
+    result: npt.NDArray[np.generic] = s.to_numpy(allow_copy=False)
 
     assert_zero_copy(s, result)
     assert result.tolist() == values
@@ -129,7 +129,7 @@ def test_series_to_numpy_date() -> None:
     values = [date(1970, 1, 1), date(2024, 2, 28)]
     s = pl.Series(values)
 
-    result = s.to_numpy()
+    result: npt.NDArray[np.generic] = s.to_numpy()
 
     assert s.to_list() == result.tolist()
     assert result.dtype == np.dtype("datetime64[D]")
@@ -168,7 +168,7 @@ def test_series_to_numpy_temporal_with_nulls(
 ) -> None:
     values = [0, 2_000, 1_000_000, None]
     s = pl.Series(values, dtype=dtype, strict=False)
-    result = s.to_numpy()
+    result: npt.NDArray[np.generic] = s.to_numpy()
 
     # NumPy tolist returns integers for ns precision
     if getattr(s.dtype, "time_unit", None) == "ns":
@@ -182,7 +182,7 @@ def test_series_to_numpy_temporal_with_nulls(
 def test_series_to_numpy_datetime_with_tz_with_nulls() -> None:
     values = [datetime(1970, 1, 1), datetime(2024, 2, 28), None]
     s = pl.Series(values).dt.convert_time_zone("Europe/Amsterdam")
-    result = s.to_numpy()
+    result: npt.NDArray[np.generic] = s.to_numpy()
 
     assert result.tolist() == values
     assert result.dtype == np.dtype("datetime64[us]")
@@ -209,7 +209,7 @@ def test_to_numpy_object_dtypes(
         values.append(None)
 
     s = pl.Series(values, dtype=dtype)
-    result = s.to_numpy()
+    result: npt.NDArray[np.generic] = s.to_numpy()
 
     assert result.tolist() == values
     assert result.dtype == np.object_
@@ -218,7 +218,7 @@ def test_to_numpy_object_dtypes(
 
 def test_series_to_numpy_bool() -> None:
     s = pl.Series([True, False])
-    result = s.to_numpy()
+    result: npt.NDArray[np.generic] = s.to_numpy()
 
     assert s.to_list() == result.tolist()
     assert result.dtype == np.bool_
@@ -228,7 +228,7 @@ def test_series_to_numpy_bool() -> None:
 
 def test_series_to_numpy_bool_with_nulls() -> None:
     s = pl.Series([True, False, None])
-    result = s.to_numpy()
+    result: npt.NDArray[np.generic] = s.to_numpy()
 
     assert s.to_list() == result.tolist()
     assert result.dtype == np.object_
@@ -249,7 +249,7 @@ def test_series_to_numpy_array_of_int() -> None:
 def test_series_to_numpy_array_of_str() -> None:
     values = [["1", "2", "3"], ["4", "5", "10000"]]
     s = pl.Series(values, dtype=pl.Array(pl.String, 3))
-    result = s.to_numpy()
+    result: npt.NDArray[np.generic] = s.to_numpy()
     assert result.tolist() == values
     assert result.dtype == np.object_
 
@@ -343,7 +343,7 @@ def test_to_numpy_chunked() -> None:
     s2 = pl.Series([3, 4])
     s = pl.concat([s1, s2], rechunk=False)
 
-    result = s.to_numpy()
+    result: npt.NDArray[np.generic] = s.to_numpy()
 
     assert result.tolist() == s.to_list()
     assert result.dtype == np.int64
@@ -362,7 +362,7 @@ def test_to_numpy_chunked_temporal_nested() -> None:
     s2 = pl.Series([[datetime(2022, 1, 1)], [datetime(2023, 1, 1)]], dtype=dtype)
     s = pl.concat([s1, s2], rechunk=False)
 
-    result = s.to_numpy()
+    result: npt.NDArray[np.generic] = s.to_numpy()
 
     assert result.tolist() == s.to_list()
     assert result.dtype == np.dtype("datetime64[us]")
@@ -375,7 +375,7 @@ def test_zero_copy_only_deprecated() -> None:
     values = [1, 2]
     s = pl.Series([1, 2])
     with pytest.deprecated_call():
-        result = s.to_numpy(zero_copy_only=True)
+        result: npt.NDArray[np.generic] = s.to_numpy(zero_copy_only=True)
     assert result.tolist() == values
 
 

py-polars/tests/unit/series/test_series.py

@@ -498,35 +498,40 @@ def test_cast() -> None:
         pl.Series(["1", "2", "3", "4", "foobar"]).cast(int)
 
 
-def test_to_pandas() -> None:
-    for test_data in (
+@pytest.mark.parametrize(
+    "test_data",
+    [
         [1, None, 2],
         ["abc", None, "xyz"],
         [None, datetime.now()],
         [[1, 2], [3, 4], None],
-    ):
-        a = pl.Series("s", test_data)
-        b = a.to_pandas()
-
-        assert a.name == b.name
-        assert b.isnull().sum() == 1
-
-        if a.dtype == pl.List:
-            vals_b = [(None if x is None else x.tolist()) for x in b]
-        else:
-            vals_b = b.replace({np.nan: None}).values.tolist()
-
-        assert vals_b == test_data
-
-        try:
-            c = a.to_pandas(use_pyarrow_extension_array=True)
-            assert a.name == c.name
-            assert c.isnull().sum() == 1
-            vals_c = [None if x is pd.NA else x for x in c.tolist()]
-            assert vals_c == test_data
-        except ModuleNotFoundError:
-            # Skip test if pandas>=1.5.0 or Pyarrow>=8.0.0 is not installed.
-            pass
+    ],
+)
+def test_to_pandas(test_data: list[Any]) -> None:
+    a = pl.Series("s", test_data)
+    b = a.to_pandas()
+
+    assert a.name == b.name
+    assert b.isnull().sum() == 1
+
+    vals_b: list[Any]
+    if a.dtype == pl.List:
+        vals_b = [(None if x is None else x.tolist()) for x in b]
+    else:
+        v = b.replace({np.nan: None}).values.tolist()
+        vals_b = cast(list[Any], v)
+
+    assert vals_b == test_data
+
+    try:
+        c = a.to_pandas(use_pyarrow_extension_array=True)
+        assert a.name == c.name
+        assert c.isnull().sum() == 1
+        vals_c = [None if x is pd.NA else x for x in c.tolist()]
+        assert vals_c == test_data
+    except ModuleNotFoundError:
+        # Skip test if pandas>=1.5.0 or Pyarrow>=8.0.0 is not installed.
+        pass
 
 
 def test_series_to_list() -> None: