REF: share astype exception messages (pandas-dev#49655)

* REF: share astype exception messages * mypy fixup

Author: jbrockmendel

pandas/core/arrays/datetimes.py

@@ -641,10 +641,12 @@ def astype(self, dtype, copy: bool = True):
 
  elif self.tz is None and isinstance(dtype, DatetimeTZDtype):
  # pre-2.0 this did self.tz_localize(dtype.tz), which did not match
- # the Series behavior
+ # the Series behavior which did
+ # values.tz_localize("UTC").tz_convert(dtype.tz)
  raise TypeError(
  "Cannot use .astype to convert from timezone-naive dtype to "
- "timezone-aware dtype. Use obj.tz_localize instead."
+ "timezone-aware dtype. Use obj.tz_localize instead or "
+ "series.dt.tz_localize instead"
  )
 
  elif self.tz is not None and is_datetime64_dtype(dtype):

pandas/core/dtypes/astype.py

@@ -23,7 +23,6 @@
 
 from pandas.core.dtypes.common import (
  is_datetime64_dtype,
- is_datetime64tz_dtype,
  is_dtype_equal,
  is_integer_dtype,
  is_object_dtype,
@@ -211,16 +210,6 @@ def astype_array(values: ArrayLike, dtype: DtypeObj, copy: bool = False) -> Arra
  msg = rf"cannot astype a datetimelike from [{values.dtype}] to [{dtype}]"
  raise TypeError(msg)
 
- if is_datetime64tz_dtype(dtype) and is_datetime64_dtype(values.dtype):
- # Series.astype behavior pre-2.0 did
- # values.tz_localize("UTC").tz_convert(dtype.tz)
- # which did not match the DTA/DTI behavior.
- # We special-case here to give a Series-specific exception message.
- raise TypeError(
- "Cannot use .astype to convert from timezone-naive dtype to "
- "timezone-aware dtype. Use ser.dt.tz_localize instead."
- )
-
  if is_dtype_equal(values.dtype, dtype):
  if copy:
  return values.copy()

pandas/core/indexes/base.py

@@ -1020,7 +1020,7 @@ def astype(self, dtype, copy: bool = True):
  # NB: this must come before the ExtensionDtype check below
  # TODO: this differs from Series behavior; can/should we align them?
  raise TypeError(
- f"Cannot convert Float64Index to dtype {dtype}; integer "
+ f"Cannot convert dtype={self.dtype} to dtype {dtype}; integer "
  "values are required for conversion"
  )
 

pandas/io/parsers/base_parser.py

@@ -36,6 +36,7 @@
 from pandas._typing import (
  ArrayLike,
  DtypeArg,
+ DtypeObj,
  Scalar,
 )
 from pandas.errors import (
@@ -61,14 +62,18 @@
  is_string_dtype,
  pandas_dtype,
 )
-from pandas.core.dtypes.dtypes import CategoricalDtype
+from pandas.core.dtypes.dtypes import (
+ CategoricalDtype,
+ ExtensionDtype,
+)
 from pandas.core.dtypes.missing import isna
 
 from pandas import StringDtype
 from pandas.core import algorithms
 from pandas.core.arrays import (
  BooleanArray,
  Categorical,
+ ExtensionArray,
  FloatingArray,
  IntegerArray,
 )
@@ -599,14 +604,8 @@ def _convert_to_ndarrays(
  # type specified in dtype param or cast_type is an EA
  if cast_type and (not is_dtype_equal(cvals, cast_type) or is_ea):
  if not is_ea and na_count > 0:
- try:
- if is_bool_dtype(cast_type):
- raise ValueError(
- f"Bool column has NA values in column {c}"
- )
- except (AttributeError, TypeError):
- # invalid input to is_bool_dtype
- pass
+ if is_bool_dtype(cast_type):
+ raise ValueError(f"Bool column has NA values in column {c}")
  cast_type = pandas_dtype(cast_type)
  cvals = self._cast_types(cvals, cast_type, c)
 
@@ -686,7 +685,7 @@ def _set(x) -> int:
 
  def _infer_types(
  self, values, na_values, no_dtype_specified, try_num_bool: bool = True
- ):
+ ) -> tuple[ArrayLike, int]:
  """
  Infer types of values, possibly casting
 
@@ -700,7 +699,7 @@ def _infer_types(
 
  Returns
  -------
- converted : ndarray
+ converted : ndarray or ExtensionArray
  na_count : int
  """
  na_count = 0
@@ -777,48 +776,50 @@ def _infer_types(
 
  return result, na_count
 
- def _cast_types(self, values, cast_type, column):
+ def _cast_types(self, values: ArrayLike, cast_type: DtypeObj, column) -> ArrayLike:
  """
  Cast values to specified type
 
  Parameters
  ----------
- values : ndarray
- cast_type : string or np.dtype
+ values : ndarray or ExtensionArray
+ cast_type : np.dtype or ExtensionDtype
  dtype to cast values to
  column : string
  column name - used only for error reporting
 
  Returns
  -------
- converted : ndarray
+ converted : ndarray or ExtensionArray
  """
  if is_categorical_dtype(cast_type):
  known_cats = (
  isinstance(cast_type, CategoricalDtype)
  and cast_type.categories is not None
  )
 
- if not is_object_dtype(values) and not known_cats:
+ if not is_object_dtype(values.dtype) and not known_cats:
  # TODO: this is for consistency with
  # c-parser which parses all categories
  # as strings
 
- values = astype_nansafe(values, np.dtype(str))
+ values = lib.ensure_string_array(
+ values, skipna=False, convert_na_value=False
+ )
 
  cats = Index(values).unique().dropna()
  values = Categorical._from_inferred_categories(
  cats, cats.get_indexer(values), cast_type, true_values=self.true_values
  )
 
  # use the EA's implementation of casting
- elif is_extension_array_dtype(cast_type):
- # ensure cast_type is an actual dtype and not a string
- cast_type = pandas_dtype(cast_type)
+ elif isinstance(cast_type, ExtensionDtype):
  array_type = cast_type.construct_array_type()
  try:
  if is_bool_dtype(cast_type):
- return array_type._from_sequence_of_strings(
+ # error: Unexpected keyword argument "true_values" for
+ # "_from_sequence_of_strings" of "ExtensionArray"
+ return array_type._from_sequence_of_strings( # type: ignore[call-arg] # noqa:E501
  values,
  dtype=cast_type,
  true_values=self.true_values,
@@ -832,6 +833,8 @@ def _cast_types(self, values, cast_type, column):
  "_from_sequence_of_strings in order to be used in parser methods"
  ) from err
 
+ elif isinstance(values, ExtensionArray):
+ values = values.astype(cast_type, copy=False)
  else:
  try:
  values = astype_nansafe(values, cast_type, copy=True, skipna=True)

pandas/tests/arrays/test_datetimes.py

@@ -373,7 +373,7 @@ def test_astype_copies(self, dtype, other):
 
  if err:
  if dtype == "datetime64[ns]":
- msg = "Use ser.dt.tz_localize instead"
+ msg = "Use obj.tz_localize instead or series.dt.tz_localize instead"
  else:
  msg = "from timezone-aware dtype to timezone-naive dtype"
  with pytest.raises(TypeError, match=msg):

pandas/tests/indexes/numeric/test_astype.py

@@ -70,7 +70,7 @@ def test_cannot_cast_to_datetimelike(self, dtype):
  idx = Index([0, 1.1, 2], dtype=np.float64)
 
  msg = (
- f"Cannot convert Float64Index to dtype {pandas_dtype(dtype)}; "
+ f"Cannot convert dtype=float64 to dtype {pandas_dtype(dtype)}; "
  f"integer values are required for conversion"
  )
  with pytest.raises(TypeError, match=re.escape(msg)):