CI/TST: fix tests for compat with numpy 2+ when using legacy promotion

pandas/compat/numpy/__init__.py

@@ -1,5 +1,6 @@
 """support numpy compatibility across versions"""
 
+import os
 import warnings
 
 import numpy as np
@@ -13,6 +14,10 @@
 np_version_gte1p24p3 = _nlv >= Version("1.24.3")
 np_version_gte1p25 = _nlv >= Version("1.25")
 np_version_gt2 = _nlv >= Version("2.0.0")
+np_version_gt2_not_legacy = (
+    _nlv >= Version("2.0.0")
+    and os.environ.get("NPY_PROMOTION_STATE", "weak") != "legacy"
+)
 is_numpy_dev = _nlv.dev is not None
 _min_numpy_ver = "1.23.5"
 

pandas/tests/dtypes/test_inference.py

@@ -34,7 +34,7 @@
     missing as libmissing,
     ops as libops,
 )
-from pandas.compat.numpy import np_version_gt2
+from pandas.compat.numpy import np_version_gt2_not_legacy
 
 from pandas.core.dtypes import inference
 from pandas.core.dtypes.cast import find_result_type
@@ -1989,7 +1989,7 @@ def test_ensure_int32():
         # find a smaller floating dtype
         (300.0, np.uint16),  # for integer floats, we convert them to ints
         (300.1, np.float64),
-        (np.int16(300), np.int16 if np_version_gt2 else np.uint16),
+        (np.int16(300), np.int16 if np_version_gt2_not_legacy else np.uint16),
     ],
 )
 def test_find_result_type_uint_int(right, result):

pandas/tests/indexing/test_coercion.py

@@ -13,7 +13,7 @@
     IS64,
     is_platform_windows,
 )
-from pandas.compat.numpy import np_version_gt2
+from pandas.compat.numpy import np_version_gt2_not_legacy
 
 import pandas as pd
 import pandas._testing as tm
@@ -231,7 +231,7 @@ def test_insert_float_index(
         obj = pd.Index([1.0, 2.0, 3.0, 4.0], dtype=dtype)
         coerced_dtype = coerced_dtype if coerced_dtype is not None else dtype
 
-        if np_version_gt2 and dtype == "float32" and coerced_val == 1.1:
+        if np_version_gt2_not_legacy and dtype == "float32" and coerced_val == 1.1:
             # Hack, in the 2nd test case, since 1.1 can be losslessly cast to float32
             # the expected dtype will be float32 if the original dtype was float32
             coerced_dtype = np.float32

pandas/tests/series/test_constructors.py

@@ -15,7 +15,10 @@
     lib,
 )
 from pandas.compat import HAS_PYARROW
-from pandas.compat.numpy import np_version_gt2
+from pandas.compat.numpy import (
+    np_version_gt2,
+    np_version_gt2_not_legacy,
+)
 from pandas.errors import IntCastingNaNError
 
 from pandas.core.dtypes.dtypes import CategoricalDtype
@@ -770,7 +773,7 @@ def test_constructor_cast(self):
 
     def test_constructor_signed_int_overflow_raises(self):
         # GH#41734 disallow silent overflow, enforced in 2.0
-        if np_version_gt2:
+        if np_version_gt2_not_legacy:
             msg = "The elements provided in the data cannot all be casted to the dtype"
             err = OverflowError
         else:
@@ -803,7 +806,7 @@ def test_constructor_numpy_uints(self, values):
 
     def test_constructor_unsigned_dtype_overflow(self, any_unsigned_int_numpy_dtype):
         # see gh-15832
-        if np_version_gt2:
+        if np_version_gt2_not_legacy:
             msg = (
                 f"The elements provided in the data cannot "
                 f"all be casted to the dtype {any_unsigned_int_numpy_dtype}"
@@ -1939,7 +1942,7 @@ def test_constructor_int64_dtype(self, any_int_dtype):
 
     def test_constructor_raise_on_lossy_conversion_of_strings(self):
         # GH#44923
-        if not np_version_gt2:
+        if not np_version_gt2_not_legacy:
             raises = pytest.raises(
                 ValueError, match="string values cannot be losslessly cast to int8"
             )