fix: support correct numpy construction for dbjson dtype in pandas 1.5

db_dtypes/json.py

@@ -231,3 +231,16 @@ def _reduce(
         if name in ["min", "max"]:
             raise TypeError("JSONArray does not support min/max reducntion.")
         super()._reduce(name, skipna=skipna, keepdims=keepdims, **kwargs)
+
+    def __array__(self, dtype=None, copy: bool | None = None) -> np.ndarray:
+        """Correctly construct numpy arrays when passed to `np.asarray()`."""
+        pa_type = self.pa_data.type
+        data = self
+        if dtype is None:
+            empty = pa.array([], type=pa_type).to_numpy(zero_copy_only=False)
+            dtype = empty.dtype
+        result = np.empty(len(data), dtype=dtype)
+        mask = data.isna()
+        result[mask] = self._dtype.na_value
+        result[~mask] = data[~mask].pa_data.to_numpy()
+        return result

testing/constraints-3.9.txt

@@ -1,3 +1,3 @@
-# Make sure we test with pandas 1.5.0. The Python version isn't that relevant.
+# Make sure we test with pandas 1.5.3. The Python version isn't that relevant.
 pandas==1.5.3
 numpy==1.24.0

tests/unit/test_json.py

@@ -12,7 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
 
+import numpy as np
 import pandas as pd
 import pytest
 
@@ -81,3 +83,34 @@ def test_deterministic_json_serialization():
     y = {"b": 1, "a": 0}
     data = db_dtypes.JSONArray._from_sequence([y])
     assert data[0] == x
+
+
+def test_to_numpy():
+    """
+    Verifies that JSONArray can be cast to a NumPy array.
+    This test ensures compatibility with Python 3.9 and replicates the behavior
+    of the `test_to_numpy` test from `test_json_compliance.py::TestJSONArrayCasting`,
+    which is run with Python 3.12 environments only.
+    """
+    data = db_dtypes.JSONArray._from_sequence(JSON_DATA.values())
+    expected = np.asarray(data)
+
+    result = data.to_numpy()
+    pd._testing.assert_equal(result, expected)
+
+    result = pd.Series(data).to_numpy()
+    pd._testing.assert_equal(result, expected)
+
+
+def test_as_numpy_array():
+    data = db_dtypes.JSONArray._from_sequence(JSON_DATA.values())
+    result = np.asarray(data)
+    expected = np.asarray(
+        [
+            json.dumps(value, sort_keys=True, separators=(",", ":"))
+            if value is not None
+            else pd.NA
+            for value in JSON_DATA.values()
+        ]
+    )
+    pd._testing.assert_equal(result, expected)