Convert ArrowExtensionArray to proper NumPy dtype

doc/source/whatsnew/v2.2.0.rst

@@ -111,7 +111,7 @@ documentation.
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 ``to_numpy`` for NumPy nullable and Arrow types will now convert to a
-suitable NumPy dtype instead of ``object`` dtype for nullable extension dtypes.
+suitable NumPy dtype instead of ``object`` dtype for nullable and PyArrow backed extension dtypes.
 
 *Old behavior:*
 

pandas/core/arrays/_utils.py

@@ -0,0 +1,54 @@
+from __future__ import annotations
+
+from typing import (
+    TYPE_CHECKING,
+    Any,
+)
+
+import numpy as np
+
+from pandas._libs import lib
+from pandas.errors import LossySetitemError
+
+from pandas.core.dtypes.cast import np_can_hold_element
+from pandas.core.dtypes.common import is_numeric_dtype
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        npt,
+    )
+
+
+def to_numpy_dtype_inference(
+    arr: ArrayLike, dtype: npt.DTypeLike | None, na_value, hasna: bool
+) -> tuple[npt.DTypeLike, Any]:
+    if dtype is None and is_numeric_dtype(arr.dtype):
+        dtype_given = False
+        if hasna:
+            if arr.dtype.kind == "b":
+                dtype = np.dtype(np.object_)
+            else:
+                if arr.dtype.kind in "iu":
+                    dtype = np.dtype(np.float64)
+                else:
+                    dtype = arr.dtype.numpy_dtype  # type: ignore[union-attr]
+                if na_value is lib.no_default:
+                    na_value = np.nan
+        else:
+            dtype = arr.dtype.numpy_dtype  # type: ignore[union-attr]
+    elif dtype is not None:
+        dtype = np.dtype(dtype)
+        dtype_given = True
+    else:
+        dtype_given = True
+
+    if na_value is lib.no_default:
+        na_value = arr.dtype.na_value
+
+    if not dtype_given and hasna:
+        try:
+            np_can_hold_element(dtype, na_value)  # type: ignore[arg-type]
+        except LossySetitemError:
+            dtype = np.dtype(np.object_)
+    return dtype, na_value

pandas/core/arrays/arrow/array.py

@@ -39,6 +39,7 @@
     is_bool_dtype,
     is_integer,
     is_list_like,
+    is_numeric_dtype,
     is_scalar,
 )
 from pandas.core.dtypes.dtypes import DatetimeTZDtype
@@ -50,8 +51,10 @@
     ops,
     roperator,
 )
+from pandas.core.algorithms import map_array
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays._arrow_string_mixins import ArrowStringArrayMixin
+from pandas.core.arrays._utils import to_numpy_dtype_inference
 from pandas.core.arrays.base import (
     ExtensionArray,
     ExtensionArraySupportsAnyAll,
@@ -1317,12 +1320,7 @@ def to_numpy(
         copy: bool = False,
         na_value: object = lib.no_default,
     ) -> np.ndarray:
-        if dtype is not None:
-            dtype = np.dtype(dtype)
-
-        if na_value is lib.no_default:
-            na_value = self.dtype.na_value
-
+        dtype, na_value = to_numpy_dtype_inference(self, dtype, na_value, self._hasna)
         pa_type = self._pa_array.type
         if not self._hasna or isna(na_value) or pa.types.is_null(pa_type):
             data = self
@@ -1366,6 +1364,12 @@ def to_numpy(
             result[~mask] = data[~mask]._pa_array.to_numpy()
         return result
 
+    def map(self, mapper, na_action=None):
+        if is_numeric_dtype(self.dtype):
+            return map_array(self.to_numpy(), mapper, na_action=None)
+        else:
+            return super().map(mapper, na_action)
+
     @doc(ExtensionArray.duplicated)
     def duplicated(
         self, keep: Literal["first", "last", False] = "first"

pandas/core/arrays/masked.py

@@ -35,15 +35,11 @@
     IS64,
     is_platform_windows,
 )
-from pandas.errors import (
-    AbstractMethodError,
-    LossySetitemError,
-)
+from pandas.errors import AbstractMethodError
 from pandas.util._decorators import doc
 from pandas.util._validators import validate_fillna_kwargs
 
 from pandas.core.dtypes.base import ExtensionDtype
-from pandas.core.dtypes.cast import np_can_hold_element
 from pandas.core.dtypes.common import (
     is_bool,
     is_integer_dtype,
@@ -80,6 +76,7 @@
 )
 from pandas.core.array_algos.quantile import quantile_with_mask
 from pandas.core.arraylike import OpsMixin
+from pandas.core.arrays._utils import to_numpy_dtype_inference
 from pandas.core.arrays.base import ExtensionArray
 from pandas.core.construction import (
     array as pd_array,
@@ -477,32 +474,7 @@ def to_numpy(
         array([ True, False, False])
         """
         hasna = self._hasna
-
-        if dtype is None:
-            dtype_given = False
-            if hasna:
-                if self.dtype.kind == "b":
-                    dtype = object
-                else:
-                    if self.dtype.kind in "iu":
-                        dtype = np.dtype(np.float64)
-                    else:
-                        dtype = self.dtype.numpy_dtype
-                    if na_value is lib.no_default:
-                        na_value = np.nan
-            else:
-                dtype = self.dtype.numpy_dtype
-        else:
-            dtype = np.dtype(dtype)
-            dtype_given = True
-        if na_value is lib.no_default:
-            na_value = libmissing.NA
-
-        if not dtype_given and hasna:
-            try:
-                np_can_hold_element(dtype, na_value)  # type: ignore[arg-type]
-            except LossySetitemError:
-                dtype = object
+        dtype, na_value = to_numpy_dtype_inference(self, dtype, na_value, hasna)
 
         if hasna:
             if (

pandas/tests/extension/test_arrow.py

@@ -278,7 +278,13 @@ def test_map(self, data_missing, na_action):
             expected = data_missing.to_numpy(dtype=object)
             tm.assert_numpy_array_equal(result, expected)
         else:
-            super().test_map(data_missing, na_action)
+            result = data_missing.map(lambda x: x, na_action=na_action)
+            if data_missing.dtype == "float32[pyarrow]":
+                # map roundtrips through objects, which converts to float64
+                expected = data_missing.to_numpy(dtype="float64", na_value=np.nan)
+            else:
+                expected = data_missing.to_numpy()
+            tm.assert_numpy_array_equal(result, expected)
 
     def test_astype_str(self, data, request):
         pa_dtype = data.dtype.pyarrow_dtype
@@ -1585,7 +1591,7 @@ def test_to_numpy_with_defaults(data):
     else:
         expected = np.array(data._pa_array)
 
-    if data._hasna:
+    if data._hasna and not is_numeric_dtype(data.dtype):
         expected = expected.astype(object)
         expected[pd.isna(data)] = pd.NA
 
@@ -1597,8 +1603,8 @@ def test_to_numpy_int_with_na():
     data = [1, None]
     arr = pd.array(data, dtype="int64[pyarrow]")
     result = arr.to_numpy()
-    expected = np.array([1, pd.NA], dtype=object)
-    assert isinstance(result[0], int)
+    expected = np.array([1, np.nan])
+    assert isinstance(result[0], float)
     tm.assert_numpy_array_equal(result, expected)
 
 

pandas/tests/series/test_npfuncs.py

@@ -5,6 +5,8 @@
 import numpy as np
 import pytest
 
+import pandas.util._test_decorators as td
+
 from pandas import Series
 import pandas._testing as tm
 
@@ -33,3 +35,12 @@ def test_numpy_argwhere(index):
     expected = np.array([[3], [4]], dtype=np.int64)
 
     tm.assert_numpy_array_equal(result, expected)
+
+
+@td.skip_if_no("pyarrow")
+def test_log_arrow_backed_missing_value():
+    # GH#56285
+    ser = Series([1, 2, None], dtype="float64[pyarrow]")
+    result = np.log(ser)
+    expected = np.log(Series([1, 2, None], dtype="float64"))
+    tm.assert_series_equal(result, expected)