Add pandas 3.0 compatibility tests

changelog_entry.yaml

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+- bump: patch
+  changes:
+    added:
+      - Added pandas 3.0 compatibility tests to verify region parameter lookups and string variables work correctly

policyengine_uk/tests/core/test_pandas3_compatibility.py

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+"""
+Tests for pandas 3.0.0 compatibility in policyengine-uk.
+
+These tests verify that policyengine-uk works correctly with pandas 3.0.0,
+which introduces PyArrow-backed strings as default (StringDtype).
+
+These tests will FAIL if policyengine-core < 3.9.1 is used, which doesn't
+have the pandas 3 compatibility fixes.
+"""
+
+import numpy as np
+
+from policyengine_uk import Simulation
+
+
+class TestRegionParameterLookupWithPandas3:
+    """
+    Test that region-based parameter lookup works with pandas 3 StringArray.
+
+    In pandas 3, string columns use StringDtype by default. When looking up
+    region-specific parameters using vectorial indexing, the region codes
+    may be StringArray instead of numpy array.
+
+    policyengine-core >= 3.9.1 converts StringArray to numpy before lookup.
+    """
+
+    def test_region_parameter_lookup(self):
+        """
+        Test that region-based parameter lookup works for multiple regions.
+
+        This exercises the VectorialParameterNodeAtInstant.__getitem__ fix
+        that converts pandas StringArray to numpy array.
+        """
+        # Create a simulation with households in different regions
+        sim = Simulation(
+            situation={
+                "people": {
+                    "person1": {"age": {"2024": 30}},
+                    "person2": {"age": {"2024": 40}},
+                },
+                "households": {
+                    "household1": {
+                        "members": ["person1"],
+                        "region": {"2024": "LONDON"},
+                    },
+                    "household2": {
+                        "members": ["person2"],
+                        "region": {"2024": "SCOTLAND"},
+                    },
+                },
+            }
+        )
+
+        # This calculation involves region-based parameter lookups
+        # If pandas 3 StringArray handling is broken, this would raise:
+        # TypeError: unhashable type: 'StringArray'
+        result = sim.calculate("household_net_income", "2024")
+
+        # Basic sanity check - should return an array
+        assert isinstance(result, np.ndarray)
+        assert len(result) == 2  # Two households
+
+
+class TestFilledArrayWithStringDtype:
+    """
+    Test that population.filled_array works with pandas StringDtype.
+
+    In pandas 3, numpy.full() cannot handle StringDtype. policyengine-core
+    >= 3.9.1 converts StringDtype to object dtype before calling numpy.full().
+    """
+
+    def test_string_variable_default_value(self):
+        """
+        Test that string-typed variables work correctly.
+
+        Variables with value_type=str use filled_array with a string dtype.
+        In pandas 3, this would fail with:
+        TypeError: Cannot interpret '<StringDtype>' as a data type
+        """
+        # Create a simple simulation
+        sim = Simulation(
+            situation={
+                "people": {
+                    "person1": {"age": {"2024": 30}},
+                },
+                "households": {
+                    "household1": {
+                        "members": ["person1"],
+                    },
+                },
+            }
+        )
+
+        # region is a string/enum variable - calculating it exercises filled_array
+        result = sim.calculate("region", "2024")
+
+        # Should return valid results without error
+        assert len(result) == 1
+
+
+class TestEnumVariableWithPandas3:
+    """
+    Test that Enum variables work correctly with pandas 3.
+
+    Enum variables involve string-based parameter lookups which can
+    trigger the StringArray issue in pandas 3.
+    """
+
+    def test_tenure_type_enum(self):
+        """
+        Test that tenure_type enum works correctly.
+        """
+        sim = Simulation(
+            situation={
+                "people": {
+                    "person1": {"age": {"2024": 30}},
+                },
+                "households": {
+                    "household1": {
+                        "members": ["person1"],
+                    },
+                },
+            }
+        )
+
+        # tenure_type is an enum variable
+        result = sim.calculate("tenure_type", "2024")
+
+        # Should return valid results
+        assert len(result) == 1