REF: share BooleanArray.astype+NumericArray.astype

pandas/core/arrays/boolean.py

@@ -1,10 +1,7 @@
 from __future__ import annotations
 
 import numbers
-from typing import (
-    TYPE_CHECKING,
-    overload,
-)
+from typing import TYPE_CHECKING
 
 import numpy as np
 
@@ -13,30 +10,19 @@
     missing as libmissing,
 )
 from pandas._typing import (
-    ArrayLike,
-    AstypeArg,
     Dtype,
     DtypeObj,
-    npt,
     type_t,
 )
 
 from pandas.core.dtypes.common import (
-    is_bool_dtype,
-    is_float_dtype,
-    is_integer_dtype,
     is_list_like,
     is_numeric_dtype,
-    pandas_dtype,
-)
-from pandas.core.dtypes.dtypes import (
-    ExtensionDtype,
-    register_extension_dtype,
 )
+from pandas.core.dtypes.dtypes import register_extension_dtype
 from pandas.core.dtypes.missing import isna
 
 from pandas.core import ops
-from pandas.core.arrays import ExtensionArray
 from pandas.core.arrays.masked import (
     BaseMaskedArray,
     BaseMaskedDtype,
@@ -360,67 +346,6 @@ def _coerce_to_array(
             assert dtype == "boolean"
         return coerce_to_array(value, copy=copy)
 
-    @overload
-    def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
-        ...
-
-    @overload
-    def astype(self, dtype: ExtensionDtype, copy: bool = ...) -> ExtensionArray:
-        ...
-
-    @overload
-    def astype(self, dtype: AstypeArg, copy: bool = ...) -> ArrayLike:
-        ...
-
-    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
-
-        """
-        Cast to a NumPy array or ExtensionArray with 'dtype'.
-
-        Parameters
-        ----------
-        dtype : str or dtype
-            Typecode or data-type to which the array is cast.
-        copy : bool, default True
-            Whether to copy the data, even if not necessary. If False,
-            a copy is made only if the old dtype does not match the
-            new dtype.
-
-        Returns
-        -------
-        ndarray or ExtensionArray
-            NumPy ndarray, BooleanArray or IntegerArray with 'dtype' for its dtype.
-
-        Raises
-        ------
-        TypeError
-            if incompatible type with an BooleanDtype, equivalent of same_kind
-            casting
-        """
-        dtype = pandas_dtype(dtype)
-
-        if isinstance(dtype, ExtensionDtype):
-            return super().astype(dtype, copy)
-
-        if is_bool_dtype(dtype):
-            # astype_nansafe converts np.nan to True
-            if self._hasna:
-                raise ValueError("cannot convert float NaN to bool")
-            else:
-                return self._data.astype(dtype, copy=copy)
-
-        # for integer, error if there are missing values
-        if is_integer_dtype(dtype) and self._hasna:
-            raise ValueError("cannot convert NA to integer")
-
-        # for float dtype, ensure we use np.nan before casting (numpy cannot
-        # deal with pd.NA)
-        na_value = self._na_value
-        if is_float_dtype(dtype):
-            na_value = np.nan
-        # coerce
-        return self.to_numpy(dtype=dtype, na_value=na_value, copy=False)
-
     def _logical_method(self, other, op):
 
         assert op.__name__ in {"or_", "ror_", "and_", "rand_", "xor", "rxor"}

pandas/core/arrays/masked.py

@@ -37,10 +37,12 @@
 )
 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,
     is_bool_dtype,
+    is_datetime64_dtype,
     is_dtype_equal,
     is_float,
     is_float_dtype,
@@ -450,7 +452,30 @@ def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
             eacls = dtype.construct_array_type()
             return eacls._from_sequence(self, dtype=dtype, copy=copy)
 
-        raise NotImplementedError("subclass must implement astype to np.dtype")
+        na_value: float | np.datetime64 | lib.NoDefault
+
+        # coerce
+        if is_float_dtype(dtype):
+            # In astype, we consider dtype=float to also mean na_value=np.nan
+            na_value = np.nan
+        elif is_datetime64_dtype(dtype):
+            na_value = np.datetime64("NaT")
+        else:
+            na_value = lib.no_default
+
+        # to_numpy will also raise, but we get somewhat nicer exception messages here
+        if is_integer_dtype(dtype) and self._hasna:
+            raise ValueError("cannot convert NA to integer")
+        if is_bool_dtype(dtype) and self._hasna:
+            # careful: astype_nansafe converts np.nan to True
+            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/core/arrays/numeric.py

@@ -5,35 +5,25 @@
 from typing import (
     TYPE_CHECKING,
     TypeVar,
-    overload,
 )
 
 import numpy as np
 
 from pandas._libs import (
     Timedelta,
-    lib,
     missing as libmissing,
 )
-from pandas._typing import (
-    ArrayLike,
-    AstypeArg,
-    Dtype,
-    npt,
-)
+from pandas._typing import Dtype
 from pandas.compat.numpy import function as nv
 
-from pandas.core.dtypes.astype import astype_nansafe
 from pandas.core.dtypes.common import (
-    is_datetime64_dtype,
     is_float,
     is_float_dtype,
     is_integer,
     is_integer_dtype,
     is_list_like,
     pandas_dtype,
 )
-from pandas.core.dtypes.dtypes import ExtensionDtype
 
 from pandas.core.arrays.masked import (
     BaseMaskedArray,
@@ -43,7 +33,6 @@
 if TYPE_CHECKING:
     import pyarrow
 
-    from pandas.core.arrays import ExtensionArray
 
 T = TypeVar("T", bound="NumericArray")
 
@@ -112,66 +101,6 @@ def _from_sequence_of_strings(
         scalars = to_numeric(strings, errors="raise")
         return cls._from_sequence(scalars, dtype=dtype, copy=copy)
 
-    @overload
-    def astype(self, dtype: npt.DTypeLike, copy: bool = ...) -> np.ndarray:
-        ...
-
-    @overload
-    def astype(self, dtype: ExtensionDtype, copy: bool = ...) -> ExtensionArray:
-        ...
-
-    @overload
-    def astype(self, dtype: AstypeArg, copy: bool = ...) -> ArrayLike:
-        ...
-
-    def astype(self, dtype: AstypeArg, copy: bool = True) -> ArrayLike:
-        """
-        Cast to a NumPy array or ExtensionArray with 'dtype'.
-
-        Parameters
-        ----------
-        dtype : str or dtype
-            Typecode or data-type to which the array is cast.
-        copy : bool, default True
-            Whether to copy the data, even if not necessary. If False,
-            a copy is made only if the old dtype does not match the
-            new dtype.
-
-        Returns
-        -------
-        ndarray or ExtensionArray
-            NumPy ndarray, or BooleanArray, IntegerArray or FloatingArray with
-            'dtype' for its dtype.
-
-        Raises
-        ------
-        TypeError
-            if incompatible type with our dtype, equivalent of same_kind
-            casting
-        """
-        dtype = pandas_dtype(dtype)
-
-        if isinstance(dtype, ExtensionDtype):
-            return super().astype(dtype, copy=copy)
-
-        na_value: float | np.datetime64 | lib.NoDefault
-
-        # coerce
-        if is_float_dtype(dtype):
-            # In astype, we consider dtype=float to also mean na_value=np.nan
-            na_value = np.nan
-        elif is_datetime64_dtype(dtype):
-            na_value = np.datetime64("NaT")
-        else:
-            na_value = lib.no_default
-
-        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
-
     def _arith_method(self, other, op):
         op_name = op.__name__
         omask = None

pandas/tests/arrays/floating/test_astype.py

@@ -9,10 +9,10 @@ def test_astype():
     # with missing values
     arr = pd.array([0.1, 0.2, None], dtype="Float64")
 
-    with pytest.raises(ValueError, match="cannot convert to 'int64'-dtype NumPy"):
+    with pytest.raises(ValueError, match="cannot convert NA to integer"):
         arr.astype("int64")
 
-    with pytest.raises(ValueError, match="cannot convert to 'bool'-dtype NumPy"):
+    with pytest.raises(ValueError, match="cannot convert float NaN to bool"):
         arr.astype("bool")
 
     result = arr.astype("float64")

pandas/tests/arrays/integer/test_dtypes.py

@@ -54,7 +54,7 @@ def test_preserve_dtypes(op):
 def test_astype_nansafe():
     # see gh-22343
     arr = pd.array([np.nan, 1, 2], dtype="Int8")
-    msg = "cannot convert to 'uint32'-dtype NumPy array with missing values."
+    msg = "cannot convert NA to integer"
 
     with pytest.raises(ValueError, match=msg):
         arr.astype("uint32")
@@ -136,7 +136,7 @@ def test_astype(all_data):
 
     # coerce to same numpy_dtype - mixed
     s = pd.Series(mixed)
-    msg = r"cannot convert to .*-dtype NumPy array with missing values.*"
+    msg = "cannot convert NA to integer"
     with pytest.raises(ValueError, match=msg):
         s.astype(all_data.dtype.numpy_dtype)