REF: standardize astype in EA subclasses

pandas/core/arrays/base.py

@@ -529,10 +529,8 @@ def astype(self, dtype, copy=True):
 
         Returns
         -------
-        array : ndarray
-            NumPy ndarray with 'dtype' for its dtype.
+        np.ndarray or ExtensionArray
         """
-        from pandas.core.arrays.string_ import StringDtype
 
         dtype = pandas_dtype(dtype)
         if is_dtype_equal(dtype, self.dtype):
@@ -541,10 +539,10 @@ def astype(self, dtype, copy=True):
             else:
                 return self.copy()
 
-        # FIXME: Really hard-code here?
-        if isinstance(dtype, StringDtype):
-            # allow conversion to StringArrays
-            return dtype.construct_array_type()._from_sequence(self, copy=False)
+        if isinstance(dtype, ExtensionDtype):
+            # allow conversion to e.g. StringArrays
+            cls = dtype.construct_array_type()
+            return cls._from_sequence(self, dtype=dtype, copy=copy)
 
         return np.array(self, dtype=dtype, copy=copy)
 

pandas/core/arrays/categorical.py

@@ -101,7 +101,6 @@
 )
 import pandas.core.common as com
 from pandas.core.construction import (
-    array as pd_array,
     extract_array,
     sanitize_array,
 )
@@ -494,19 +493,18 @@ def astype(self, dtype: Dtype, copy: bool = True) -> ArrayLike:
         """
         dtype = pandas_dtype(dtype)
         if self.dtype is dtype:
-            result = self.copy() if copy else self
+            return self.copy() if copy else self
 
         elif is_categorical_dtype(dtype):
             dtype = cast(Union[str, CategoricalDtype], dtype)
 
             # GH 10696/18593/18630
             dtype = self.dtype.update_dtype(dtype)
-            self = self.copy() if copy else self
-            result = self._set_dtype(dtype)
+            obj = self.copy() if copy else self
+            return obj._set_dtype(dtype)
 
-        # TODO: consolidate with ndarray case?
         elif isinstance(dtype, ExtensionDtype):
-            result = pd_array(self, dtype=dtype, copy=copy)
+            return super().astype(dtype, copy=copy)
 
         elif is_integer_dtype(dtype) and self.isna().any():
             raise ValueError("Cannot convert float NaN to integer")

pandas/core/arrays/datetimelike.py

@@ -69,7 +69,6 @@
     is_datetime64tz_dtype,
     is_datetime_or_timedelta_dtype,
     is_dtype_equal,
-    is_extension_array_dtype,
     is_float_dtype,
     is_integer_dtype,
     is_list_like,
@@ -82,6 +81,7 @@
 )
 from pandas.core.dtypes.dtypes import (
     DatetimeTZDtype,
+    ExtensionDtype,
     PeriodDtype,
 )
 from pandas.core.dtypes.missing import (
@@ -385,14 +385,13 @@ def astype(self, dtype, copy: bool = True):
         #   3. DatetimeArray.astype handles datetime -> period
         dtype = pandas_dtype(dtype)
 
+        if isinstance(dtype, ExtensionDtype):
+            return super().astype(dtype=dtype, copy=copy)
+
         if is_object_dtype(dtype):
             return self._box_values(self.asi8.ravel()).reshape(self.shape)
-        elif is_string_dtype(dtype) and not is_categorical_dtype(dtype):
-            if is_extension_array_dtype(dtype):
-                arr_cls = dtype.construct_array_type()
-                return arr_cls._from_sequence(self, dtype=dtype, copy=copy)
-            else:
-                return self._format_native_types()
+        elif is_string_dtype(dtype):
+            return self._format_native_types()
         elif is_integer_dtype(dtype):
             # we deliberately ignore int32 vs. int64 here.
             # See https://github.com/pandas-dev/pandas/issues/24381 for more.
@@ -422,9 +421,6 @@ def astype(self, dtype, copy: bool = True):
             # and conversions for any datetimelike to float
             msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"
             raise TypeError(msg)
-        elif is_categorical_dtype(dtype):
-            arr_cls = dtype.construct_array_type()
-            return arr_cls(self, dtype=dtype)
         else:
             return np.asarray(self, dtype=dtype)
 

pandas/core/arrays/interval.py

@@ -48,7 +48,10 @@
     needs_i8_conversion,
     pandas_dtype,
 )
-from pandas.core.dtypes.dtypes import IntervalDtype
+from pandas.core.dtypes.dtypes import (
+    ExtensionDtype,
+    IntervalDtype,
+)
 from pandas.core.dtypes.generic import (
     ABCDataFrame,
     ABCDatetimeIndex,
@@ -70,7 +73,6 @@
     ExtensionArray,
     _extension_array_shared_docs,
 )
-from pandas.core.arrays.categorical import Categorical
 import pandas.core.common as com
 from pandas.core.construction import (
     array as pd_array,
@@ -827,7 +829,6 @@ def astype(self, dtype, copy: bool = True):
             ExtensionArray or NumPy ndarray with 'dtype' for its dtype.
         """
         from pandas import Index
-        from pandas.core.arrays.string_ import StringDtype
 
         if dtype is not None:
             dtype = pandas_dtype(dtype)
@@ -848,13 +849,11 @@ def astype(self, dtype, copy: bool = True):
                 )
                 raise TypeError(msg) from err
             return self._shallow_copy(new_left, new_right)
-        elif is_categorical_dtype(dtype):
-            return Categorical(np.asarray(self), dtype=dtype)
-        elif isinstance(dtype, StringDtype):
-            return dtype.construct_array_type()._from_sequence(self, copy=False)
 
-        # TODO: This try/except will be repeated.
         try:
+            if isinstance(dtype, ExtensionDtype):
+                cls = dtype.construct_array_type()
+                return cls._from_sequence(self, dtype=dtype, copy=copy)
             return np.asarray(self).astype(dtype, copy=copy)
         except (TypeError, ValueError) as err:
             msg = f"Cannot cast {type(self).__name__} to dtype {dtype}"

pandas/core/arrays/masked.py

@@ -320,8 +320,7 @@ def astype(self, dtype: Dtype, copy: bool = True) -> ArrayLike:
             return cls(data, mask, copy=False)
 
         if isinstance(dtype, ExtensionDtype):
-            eacls = dtype.construct_array_type()
-            return eacls._from_sequence(self, dtype=dtype, copy=copy)
+            return super().astype(dtype=dtype, copy=copy)
 
         raise NotImplementedError("subclass must implement astype to np.dtype")
 

pandas/core/arrays/string_.py

@@ -435,8 +435,8 @@ def astype(self, dtype, copy=True):
             values = arr.astype(dtype.numpy_dtype)
             return FloatingArray(values, mask, copy=False)
         elif isinstance(dtype, ExtensionDtype):
-            cls = dtype.construct_array_type()
-            return cls._from_sequence(self, dtype=dtype, copy=copy)
+            return super().astype(dtype=dtype, copy=copy)
+
         elif np.issubdtype(dtype, np.floating):
             arr = self._ndarray.copy()
             mask = self.isna()

pandas/core/dtypes/cast.py

@@ -1103,7 +1103,7 @@ def astype_nansafe(
     if issubclass(dtype.type, str):
         return lib.ensure_string_array(arr, skipna=skipna, convert_na_value=False)
 
-    elif is_datetime64_dtype(arr):
+    elif is_datetime64_dtype(arr.dtype):
         if dtype == np.int64:
             warnings.warn(
                 f"casting {arr.dtype} values to int64 with .astype(...) "
@@ -1123,7 +1123,7 @@ def astype_nansafe(
 
         raise TypeError(f"cannot astype a datetimelike from [{arr.dtype}] to [{dtype}]")
 
-    elif is_timedelta64_dtype(arr):
+    elif is_timedelta64_dtype(arr.dtype):
         if dtype == np.int64:
             warnings.warn(
                 f"casting {arr.dtype} values to int64 with .astype(...) "