Test for setitem/construction

Author: jbrockmendel

pandas/core/arrays/arrow/array.py

@@ -452,7 +452,7 @@ def _box_pa_scalar(cls, value, pa_type: pa.DataType | None = None) -> pa.Scalar:
         """
         if isinstance(value, pa.Scalar):
             pa_scalar = value
-        elif isna(value) and not lib.is_float(value):
+        elif isna(value) and not (lib.is_float(value) and not is_nan_na()):
             pa_scalar = pa.scalar(None, type=pa_type)
         else:
             # Workaround https://github.com/apache/arrow/issues/37291

pandas/io/json/_json.py

@@ -18,6 +18,8 @@
 
 import numpy as np
 
+from pandas._config import option_context
+
 from pandas._libs import lib
 from pandas._libs.json import (
     ujson_dumps,
@@ -994,16 +996,13 @@ def _read_ujson(self) -> DataFrame | Series:
         else:
             obj = self._get_object_parser(self.data)
         if self.dtype_backend is not lib.no_default:
-            if self.dtype_backend == "pyarrow":
+            with option_context("mode.nan_is_na", True):
                 # The construction above takes "null" to NaN, which we want to
                 #  convert to NA. But .convert_dtypes to pyarrow doesn't allow
                 #  that, so we do a 2-step conversion through numpy-nullable.
-                obj = obj.convert_dtypes(
-                    infer_objects=False, dtype_backend="numpy_nullable"
+                return obj.convert_dtypes(
+                    infer_objects=False, dtype_backend=self.dtype_backend
                 )
-            return obj.convert_dtypes(
-                infer_objects=False, dtype_backend=self.dtype_backend
-            )
         else:
             return obj
 
@@ -1078,16 +1077,13 @@ def __next__(self) -> DataFrame | Series:
             raise ex
 
         if self.dtype_backend is not lib.no_default:
-            if self.dtype_backend == "pyarrow":
+            with option_context("mode.nan_is_na", True):
                 # The construction above takes "null" to NaN, which we want to
                 #  convert to NA. But .convert_dtypes to pyarrow doesn't allow
                 #  that, so we do a 2-step conversion through numpy-nullable.
-                obj = obj.convert_dtypes(
-                    infer_objects=False, dtype_backend="numpy_nullable"
+                return obj.convert_dtypes(
+                    infer_objects=False, dtype_backend=self.dtype_backend
                 )
-            return obj.convert_dtypes(
-                infer_objects=False, dtype_backend=self.dtype_backend
-            )
         else:
             return obj
 

pandas/tests/extension/test_arrow.py

@@ -3612,3 +3612,35 @@ def test_ops_with_nan_is_na(using_nan_is_na):
         assert result.isna()[1]
     else:
         assert not result.isna()[1]
+
+
+def test_setitem_float_nan_is_na(using_nan_is_na):
+    # GH#61732
+    import pyarrow as pa
+
+    ser = pd.Series([-1, 0, 1], dtype="int64[pyarrow]")
+
+    if using_nan_is_na:
+        ser[1] = np.nan
+        assert ser.isna()[1]
+    else:
+        msg = "Could not convert nan with type float: tried to convert to int64"
+        with pytest.raises(pa.lib.ArrowInvalid, match=msg):
+            ser[1] = np.nan
+
+    ser = pd.Series([-1, np.nan, 1], dtype="float64[pyarrow]")
+    if using_nan_is_na:
+        assert ser.isna()[1]
+        assert ser[1] is pd.NA
+
+        ser[1] = np.nan
+        assert ser[1] is pd.NA
+
+    else:
+        assert not ser.isna()[1]
+        assert isinstance(ser[1], float)
+        assert np.isnan(ser[1])
+
+        ser[2] = np.nan
+        assert isinstance(ser[2], float)
+        assert np.isnan(ser[2])