[python] Support NumPy 2 (#2934) (#3248)

* [python] NumPy 2 experiment [WIP]

* fixups from latest CI run

Co-authored-by: John Kerl <kerl.john.r@gmail.com>

apis/python/setup.py

@@ -332,7 +332,7 @@ def run(self):
         "pyparsing~=3.1.4",
         "attrs>=22.2",
         "numba>=0.58.0",
-        "numpy<2.0",
+        "numpy",
         "pandas",
         "pyarrow",
         "scanpy>=1.9.2",

apis/python/tests/test_basic_anndata_io.py

@@ -745,7 +745,7 @@ def test_null_obs(conftest_pbmc_small, tmp_path: Path):
     seed = 42
     #   Create column of all null values
     conftest_pbmc_small.obs["empty_categorical_all"] = pd.Categorical(
-        [np.NaN] * conftest_pbmc_small.n_obs,
+        [np.nan] * conftest_pbmc_small.n_obs,
         dtype=pd.CategoricalDtype(categories=[], ordered=False),
     )
     conftest_pbmc_small.obs["empty_extension_all"] = pd.Series(
@@ -755,7 +755,7 @@ def test_null_obs(conftest_pbmc_small, tmp_path: Path):
     rng = np.random.RandomState(seed)
 
     conftest_pbmc_small.obs["empty_categorical_partial"] = rng.choice(
-        (np.NaN, 1.0), conftest_pbmc_small.n_obs, True
+        (np.nan, 1.0), conftest_pbmc_small.n_obs, True
     )
     conftest_pbmc_small.obs["empty_extension_partial"] = pd.Series(
         [1] * conftest_pbmc_small.n_obs + [np.nan], dtype=pd.Int64Dtype()

apis/python/tests/test_dataframe.py

@@ -1212,7 +1212,7 @@ def test_extend_enumerations(tmp_path):
                 np.array([0, 1.1, 2.1, 0, 1.1, 2.1], dtype=np.float64), dtype="category"
             ),
             "float64_w_non_finite": pd.Series(
-                np.array([0, 1.1, 2.1, 0, np.Inf, np.NINF], dtype=np.float64),
+                np.array([0, 1.1, 2.1, 0, np.inf, -np.inf], dtype=np.float64),
                 dtype="category",
             ),
             "str_ordered": pd.Series(

apis/python/tests/test_regression.py

@@ -10,9 +10,7 @@ def test_nd_dense_non_contiguous_write(tmp_path):
     """Test regression dected in GitHub Issue #2537"""
     # Create data.
     data = (
-        np.arange(np.product(24), dtype=np.uint8)
-        .reshape((4, 3, 2))
-        .transpose((2, 0, 1))
+        np.arange(np.prod(24), dtype=np.uint8).reshape((4, 3, 2)).transpose((2, 0, 1))
     )
     coords = tuple(slice(0, dim_len) for dim_len in data.shape)
     tensor = pa.Tensor.from_numpy(data)