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.