REF: Separate values-casting from Block.astype (#38455)

* REF: Separate values-casting from Block.astype * fix xfail

Author: jbrockmendel

pandas/core/internals/blocks.py

@@ -10,7 +10,7 @@
 from pandas._libs.internals import BlockPlacement
 from pandas._libs.tslibs import conversion
 from pandas._libs.tslibs.timezones import tz_compare
-from pandas._typing import ArrayLike, Scalar, Shape
+from pandas._typing import ArrayLike, DtypeObj, Scalar, Shape
 from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.cast import (
@@ -68,7 +68,7 @@
 )
 from pandas.core.base import PandasObject
 import pandas.core.common as com
-from pandas.core.construction import extract_array
+from pandas.core.construction import array as pd_array, extract_array
 from pandas.core.indexers import (
  check_setitem_lengths,
  is_empty_indexer,
@@ -593,7 +593,7 @@ def astype(self, dtype, copy: bool = False, errors: str = "raise"):
  dtype : str, dtype convertible
  copy : bool, default False
  copy if indicated
- errors : str, {'raise', 'ignore'}, default 'ignore'
+ errors : str, {'raise', 'ignore'}, default 'raise'
  - ``raise`` : allow exceptions to be raised
  - ``ignore`` : suppress exceptions. On error return original object
 
@@ -617,69 +617,23 @@ def astype(self, dtype, copy: bool = False, errors: str = "raise"):
  )
  raise TypeError(msg)
 
- if dtype is not None:
- dtype = pandas_dtype(dtype)
-
- # may need to convert to categorical
- if is_categorical_dtype(dtype):
-
- if is_categorical_dtype(self.values.dtype):
- # GH 10696/18593: update an existing categorical efficiently
- return self.make_block(self.values.astype(dtype, copy=copy))
-
- return self.make_block(Categorical(self.values, dtype=dtype))
-
  dtype = pandas_dtype(dtype)
 
- # astype processing
- if is_dtype_equal(self.dtype, dtype):
- if copy:
- return self.copy()
- return self
-
- # force the copy here
- if self.is_extension:
- try:
- values = self.values.astype(dtype)
- except (ValueError, TypeError):
- if errors == "ignore":
- values = self.values
- else:
- raise
- else:
- if issubclass(dtype.type, str):
-
- # use native type formatting for datetime/tz/timedelta
- if self.is_datelike:
- values = self.to_native_types().values
-
- # astype formatting
- else:
- # Because we have neither is_extension nor is_datelike,
- # self.values already has the correct shape
- values = self.values
-
+ try:
+ new_values = self._astype(dtype, copy=copy)
+ except (ValueError, TypeError):
+ # e.g. astype_nansafe can fail on object-dtype of strings
+ # trying to convert to float
+ if errors == "ignore":
+ new_values = self.values
  else:
- values = self.get_values(dtype=dtype)
-
- # _astype_nansafe works fine with 1-d only
- vals1d = values.ravel()
- try:
- values = astype_nansafe(vals1d, dtype, copy=True)
- except (ValueError, TypeError):
- # e.g. astype_nansafe can fail on object-dtype of strings
- # trying to convert to float
- if errors == "raise":
- raise
- newb = self.copy() if copy else self
- return newb
-
- # TODO(EA2D): special case not needed with 2D EAs
- if isinstance(values, np.ndarray):
- values = values.reshape(self.shape)
+ raise
 
- newb = self.make_block(values)
+ if isinstance(new_values, np.ndarray):
+ # TODO(EA2D): special case not needed with 2D EAs
+ new_values = new_values.reshape(self.shape)
 
+ newb = self.make_block(new_values)
  if newb.is_numeric and self.is_numeric:
  if newb.shape != self.shape:
  raise TypeError(
@@ -689,6 +643,50 @@ def astype(self, dtype, copy: bool = False, errors: str = "raise"):
  )
  return newb
 
+ def _astype(self, dtype: DtypeObj, copy: bool) -> ArrayLike:
+ values = self.values
+
+ if is_categorical_dtype(dtype):
+
+ if is_categorical_dtype(values.dtype):
+ # GH#10696/GH#18593: update an existing categorical efficiently
+ return values.astype(dtype, copy=copy)
+
+ return Categorical(values, dtype=dtype)
+
+ if is_dtype_equal(values.dtype, dtype):
+ if copy:
+ return values.copy()
+ return values
+
+ if isinstance(values, ExtensionArray):
+ values = values.astype(dtype, copy=copy)
+
+ else:
+ if issubclass(dtype.type, str):
+ if values.dtype.kind in ["m", "M"]:
+ # use native type formatting for datetime/tz/timedelta
+ arr = pd_array(values)
+ # Note: in the case where dtype is an np.dtype, i.e. not
+ # StringDtype, this matches arr.astype(dtype), xref GH#36153
+ values = arr._format_native_types(na_rep="NaT")
+
+ elif is_object_dtype(dtype):
+ if values.dtype.kind in ["m", "M"]:
+ # Wrap in Timedelta/Timestamp
+ arr = pd_array(values)
+ values = arr.astype(object)
+ else:
+ values = values.astype(object)
+ # We still need to go through astype_nansafe for
+ # e.g. dtype = Sparse[object, 0]
+
+ # astype_nansafe works with 1-d only
+ vals1d = values.ravel()
+ values = astype_nansafe(vals1d, dtype, copy=True)
+
+ return values
+
  def convert(
  self,
  copy: bool = True,

pandas/tests/arrays/string_/test_string.py

@@ -5,6 +5,8 @@
 
 import pandas.util._test_decorators as td
 
+from pandas.core.dtypes.common import is_dtype_equal
+
 import pandas as pd
 import pandas._testing as tm
 from pandas.core.arrays.string_arrow import ArrowStringArray, ArrowStringDtype
@@ -127,11 +129,14 @@ def test_astype_roundtrip(dtype, request):
  mark = pytest.mark.xfail(reason=reason)
  request.node.add_marker(mark)
 
- s = pd.Series(pd.date_range("2000", periods=12))
- s[0] = None
+ ser = pd.Series(pd.date_range("2000", periods=12))
+ ser[0] = None
+
+ casted = ser.astype(dtype)
+ assert is_dtype_equal(casted.dtype, dtype)
 
- result = s.astype(dtype).astype("datetime64[ns]")
- tm.assert_series_equal(result, s)
+ result = casted.astype("datetime64[ns]")
+ tm.assert_series_equal(result, ser)
 
 
 def test_add(dtype, request):