Remove _DtypeOpsMixin (#26331)

* Remove _DtypeOpsMixin * cleanup dead code

Author: TomAugspurger

pandas/core/dtypes/base.py

@@ -8,141 +8,7 @@
 from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
 
 
-class _DtypeOpsMixin:
- # Not all of pandas' extension dtypes are compatibile with
- # the new ExtensionArray interface. This means PandasExtensionDtype
- # can't subclass ExtensionDtype yet, as is_extension_array_dtype would
- # incorrectly say that these types are extension types.
- #
- # In the interim, we put methods that are shared between the two base
- # classes ExtensionDtype and PandasExtensionDtype here. Both those base
- # classes will inherit from this Mixin. Once everything is compatible, this
- # class's methods can be moved to ExtensionDtype and removed.
-
- # na_value is the default NA value to use for this type. This is used in
- # e.g. ExtensionArray.take. This should be the user-facing "boxed" version
- # of the NA value, not the physical NA vaalue for storage.
- # e.g. for JSONArray, this is an empty dictionary.
- na_value = np.nan
- _metadata = () # type: Tuple[str, ...]
-
- def __eq__(self, other):
- """Check whether 'other' is equal to self.
-
- By default, 'other' is considered equal if either
-
- * it's a string matching 'self.name'.
- * it's an instance of this type and all of the
- the attributes in ``self._metadata`` are equal between
- `self` and `other`.
-
- Parameters
- ----------
- other : Any
-
- Returns
- -------
- bool
- """
- if isinstance(other, str):
- try:
- other = self.construct_from_string(other)
- except TypeError:
- return False
- if isinstance(other, type(self)):
- return all(
- getattr(self, attr) == getattr(other, attr)
- for attr in self._metadata
- )
- return False
-
- def __hash__(self):
- return hash(tuple(getattr(self, attr) for attr in self._metadata))
-
- def __ne__(self, other):
- return not self.__eq__(other)
-
- @property
- def names(self) -> Optional[List[str]]:
- """Ordered list of field names, or None if there are no fields.
-
- This is for compatibility with NumPy arrays, and may be removed in the
- future.
- """
- return None
-
- @classmethod
- def is_dtype(cls, dtype):
- """Check if we match 'dtype'.
-
- Parameters
- ----------
- dtype : object
- The object to check.
-
- Returns
- -------
- is_dtype : bool
-
- Notes
- -----
- The default implementation is True if
-
- 1. ``cls.construct_from_string(dtype)`` is an instance
- of ``cls``.
- 2. ``dtype`` is an object and is an instance of ``cls``
- 3. ``dtype`` has a ``dtype`` attribute, and any of the above
- conditions is true for ``dtype.dtype``.
- """
- dtype = getattr(dtype, 'dtype', dtype)
-
- if isinstance(dtype, (ABCSeries, ABCIndexClass,
- ABCDataFrame, np.dtype)):
- # https://github.com/pandas-dev/pandas/issues/22960
- # avoid passing data to `construct_from_string`. This could
- # cause a FutureWarning from numpy about failing elementwise
- # comparison from, e.g., comparing DataFrame == 'category'.
- return False
- elif dtype is None:
- return False
- elif isinstance(dtype, cls):
- return True
- try:
- return cls.construct_from_string(dtype) is not None
- except TypeError:
- return False
-
- @property
- def _is_numeric(self) -> bool:
- """
- Whether columns with this dtype should be considered numeric.
-
- By default ExtensionDtypes are assumed to be non-numeric.
- They'll be excluded from operations that exclude non-numeric
- columns, like (groupby) reductions, plotting, etc.
- """
- return False
-
- @property
- def _is_boolean(self) -> bool:
- """
- Whether this dtype should be considered boolean.
-
- By default, ExtensionDtypes are assumed to be non-numeric.
- Setting this to True will affect the behavior of several places,
- e.g.
-
- * is_bool
- * boolean indexing
-
- Returns
- -------
- bool
- """
- return False
-
-
-class ExtensionDtype(_DtypeOpsMixin):
+class ExtensionDtype:
  """
  A custom data type, to be paired with an ExtensionArray.
 
@@ -202,10 +68,52 @@ class property**.
  ``pandas.errors.AbstractMethodError`` and no ``register`` method is
  provided for registering virtual subclasses.
  """
+ # na_value is the default NA value to use for this type. This is used in
+ # e.g. ExtensionArray.take. This should be the user-facing "boxed" version
+ # of the NA value, not the physical NA value for storage.
+ # e.g. for JSONArray, this is an empty dictionary.
+ na_value = np.nan
+ _metadata = () # type: Tuple[str, ...]
 
  def __str__(self):
  return self.name
 
+ def __eq__(self, other):
+ """Check whether 'other' is equal to self.
+
+ By default, 'other' is considered equal if either
+
+ * it's a string matching 'self.name'.
+ * it's an instance of this type and all of the
+ the attributes in ``self._metadata`` are equal between
+ `self` and `other`.
+
+ Parameters
+ ----------
+ other : Any
+
+ Returns
+ -------
+ bool
+ """
+ if isinstance(other, str):
+ try:
+ other = self.construct_from_string(other)
+ except TypeError:
+ return False
+ if isinstance(other, type(self)):
+ return all(
+ getattr(self, attr) == getattr(other, attr)
+ for attr in self._metadata
+ )
+ return False
+
+ def __hash__(self):
+ return hash(tuple(getattr(self, attr) for attr in self._metadata))
+
+ def __ne__(self, other):
+ return not self.__eq__(other)
+
  @property
  def type(self) -> Type:
  """
@@ -243,6 +151,15 @@ def name(self) -> str:
  """
  raise AbstractMethodError(self)
 
+ @property
+ def names(self) -> Optional[List[str]]:
+ """Ordered list of field names, or None if there are no fields.
+
+ This is for compatibility with NumPy arrays, and may be removed in the
+ future.
+ """
+ return None
+
  @classmethod
  def construct_array_type(cls):
  """
@@ -286,3 +203,73 @@ def construct_from_string(cls, string):
  ... "'{}'".format(cls, string))
  """
  raise AbstractMethodError(cls)
+
+ @classmethod
+ def is_dtype(cls, dtype):
+ """Check if we match 'dtype'.
+
+ Parameters
+ ----------
+ dtype : object
+ The object to check.
+
+ Returns
+ -------
+ is_dtype : bool
+
+ Notes
+ -----
+ The default implementation is True if
+
+ 1. ``cls.construct_from_string(dtype)`` is an instance
+ of ``cls``.
+ 2. ``dtype`` is an object and is an instance of ``cls``
+ 3. ``dtype`` has a ``dtype`` attribute, and any of the above
+ conditions is true for ``dtype.dtype``.
+ """
+ dtype = getattr(dtype, 'dtype', dtype)
+
+ if isinstance(dtype, (ABCSeries, ABCIndexClass,
+ ABCDataFrame, np.dtype)):
+ # https://github.com/pandas-dev/pandas/issues/22960
+ # avoid passing data to `construct_from_string`. This could
+ # cause a FutureWarning from numpy about failing elementwise
+ # comparison from, e.g., comparing DataFrame == 'category'.
+ return False
+ elif dtype is None:
+ return False
+ elif isinstance(dtype, cls):
+ return True
+ try:
+ return cls.construct_from_string(dtype) is not None
+ except TypeError:
+ return False
+
+ @property
+ def _is_numeric(self) -> bool:
+ """
+ Whether columns with this dtype should be considered numeric.
+
+ By default ExtensionDtypes are assumed to be non-numeric.
+ They'll be excluded from operations that exclude non-numeric
+ columns, like (groupby) reductions, plotting, etc.
+ """
+ return False
+
+ @property
+ def _is_boolean(self) -> bool:
+ """
+ Whether this dtype should be considered boolean.
+
+ By default, ExtensionDtypes are assumed to be non-numeric.
+ Setting this to True will affect the behavior of several places,
+ e.g.
+
+ * is_bool
+ * boolean indexing
+
+ Returns
+ -------
+ bool
+ """
+ return False

pandas/core/dtypes/cast.py

@@ -18,8 +18,7 @@
  is_integer, is_integer_dtype, is_object_dtype, is_scalar, is_string_dtype,
  is_timedelta64_dtype, is_timedelta64_ns_dtype, is_unsigned_integer_dtype,
  pandas_dtype)
-from .dtypes import (
- DatetimeTZDtype, ExtensionDtype, PandasExtensionDtype, PeriodDtype)
+from .dtypes import DatetimeTZDtype, ExtensionDtype, PeriodDtype
 from .generic import (
  ABCDatetimeArray, ABCDatetimeIndex, ABCPeriodArray, ABCPeriodIndex,
  ABCSeries)
@@ -1108,8 +1107,7 @@ def find_common_type(types):
  if all(is_dtype_equal(first, t) for t in types[1:]):
  return first
 
- if any(isinstance(t, (PandasExtensionDtype, ExtensionDtype))
- for t in types):
+ if any(isinstance(t, ExtensionDtype) for t in types):
  return np.object
 
  # take lowest unit

pandas/core/dtypes/common.py

@@ -9,7 +9,7 @@
 
 from pandas.core.dtypes.dtypes import (
  CategoricalDtype, DatetimeTZDtype, ExtensionDtype, IntervalDtype,
- PandasExtensionDtype, PeriodDtype, registry)
+ PeriodDtype, registry)
 from pandas.core.dtypes.generic import (
  ABCCategorical, ABCDateOffset, ABCDatetimeIndex, ABCIndexClass,
  ABCPeriodArray, ABCPeriodIndex, ABCSeries)
@@ -1888,7 +1888,7 @@ def _is_dtype_type(arr_or_dtype, condition):
  if isinstance(arr_or_dtype, np.dtype):
  return condition(arr_or_dtype.type)
  elif isinstance(arr_or_dtype, type):
- if issubclass(arr_or_dtype, (PandasExtensionDtype, ExtensionDtype)):
+ if issubclass(arr_or_dtype, ExtensionDtype):
  arr_or_dtype = arr_or_dtype.type
  return condition(np.dtype(arr_or_dtype).type)
  elif arr_or_dtype is None:
@@ -1936,7 +1936,7 @@ def infer_dtype_from_object(dtype):
  if isinstance(dtype, type) and issubclass(dtype, np.generic):
  # Type object from a dtype
  return dtype
- elif isinstance(dtype, (np.dtype, PandasExtensionDtype, ExtensionDtype)):
+ elif isinstance(dtype, (np.dtype, ExtensionDtype)):
  # dtype object
  try:
  _validate_date_like_dtype(dtype)
@@ -2021,7 +2021,7 @@ def pandas_dtype(dtype):
  # short-circuit
  if isinstance(dtype, np.ndarray):
  return dtype.dtype
- elif isinstance(dtype, (np.dtype, PandasExtensionDtype, ExtensionDtype)):
+ elif isinstance(dtype, (np.dtype, ExtensionDtype)):
  return dtype
 
  # registered extension types

pandas/core/dtypes/dtypes.py

@@ -12,7 +12,7 @@
 from pandas.core.dtypes.generic import (
  ABCCategoricalIndex, ABCDateOffset, ABCIndexClass)
 
-from .base import ExtensionDtype, _DtypeOpsMixin
+from .base import ExtensionDtype
 from .inference import is_list_like
 
 str_type = str
@@ -68,7 +68,7 @@ def register(self, dtype):
  ----------
  dtype : ExtensionDtype
  """
- if not issubclass(dtype, (PandasExtensionDtype, ExtensionDtype)):
+ if not issubclass(dtype, ExtensionDtype):
  raise ValueError("can only register pandas extension dtypes")
 
  self.dtypes.append(dtype)
@@ -104,7 +104,7 @@ def find(self, dtype):
 registry = Registry()
 
 
-class PandasExtensionDtype(_DtypeOpsMixin):
+class PandasExtensionDtype(ExtensionDtype):
  """
  A np.dtype duck-typed class, suitable for holding a custom dtype.
 
@@ -577,7 +577,7 @@ def _is_boolean(self):
 
 
 @register_extension_dtype
-class DatetimeTZDtype(PandasExtensionDtype, ExtensionDtype):
+class DatetimeTZDtype(PandasExtensionDtype):
  """
  An ExtensionDtype for timezone-aware datetime data.
 
@@ -737,7 +737,7 @@ def __setstate__(self, state):
 
 
 @register_extension_dtype
-class PeriodDtype(ExtensionDtype, PandasExtensionDtype):
+class PeriodDtype(PandasExtensionDtype):
  """
  An ExtensionDtype for Period data.
 
@@ -894,7 +894,7 @@ def construct_array_type(cls):
 
 
 @register_extension_dtype
-class IntervalDtype(PandasExtensionDtype, ExtensionDtype):
+class IntervalDtype(PandasExtensionDtype):
  """
  An ExtensionDtype for Interval data.