API: make FloatingArray.astype consistent with IntegerArray/BooleanArray (#50028)

Author: jbrockmendel

pandas/core/arrays/masked.py

@@ -38,7 +38,6 @@
 from pandas.util._decorators import doc
 from pandas.util._validators import validate_fillna_kwargs
 
-from pandas.core.dtypes.astype import astype_nansafe
 from pandas.core.dtypes.base import ExtensionDtype
 from pandas.core.dtypes.common import (
     is_bool,
@@ -492,10 +491,6 @@ def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
             raise ValueError("cannot convert float NaN to bool")
 
         data = self.to_numpy(dtype=dtype, na_value=na_value, copy=copy)
-        if self.dtype.kind == "f":
-            # TODO: make this consistent between IntegerArray/FloatingArray,
-            #  see test_astype_str
-            return astype_nansafe(data, dtype, copy=False)
         return data
 
     __array_priority__ = 1000  # higher than ndarray so ops dispatch to us

pandas/tests/arrays/floating/test_astype.py

@@ -65,7 +65,7 @@ def test_astype_to_integer_array():
 
 def test_astype_str():
     a = pd.array([0.1, 0.2, None], dtype="Float64")
-    expected = np.array(["0.1", "0.2", "<NA>"], dtype=object)
+    expected = np.array(["0.1", "0.2", "<NA>"], dtype="U32")
 
     tm.assert_numpy_array_equal(a.astype(str), expected)
     tm.assert_numpy_array_equal(a.astype("str"), expected)