REF: move sharable methods to ExtensionIndex

pandas/core/indexes/category.py

@@ -414,7 +414,7 @@ def astype(self, dtype, copy=True):
             if dtype == self.dtype:
                 return self.copy() if copy else self
 
-        return super().astype(dtype=dtype, copy=copy)
+        return Index.astype(self, dtype=dtype, copy=copy)
 
     @cache_readonly
     def _isnan(self):

pandas/core/indexes/datetimelike.py

@@ -105,17 +105,6 @@ class DatetimeIndexOpsMixin(ExtensionIndex, ExtensionOpsMixin):
     def is_all_dates(self) -> bool:
         return True
 
-    def unique(self, level=None):
-        if level is not None:
-            self._validate_index_level(level)
-
-        result = self._data.unique()
-
-        # Note: if `self` is already unique, then self.unique() should share
-        #  a `freq` with self.  If not already unique, then self.freq must be
-        #  None, so again sharing freq is correct.
-        return self._shallow_copy(result._data)
-
     @classmethod
     def _create_comparison_method(cls, op):
         """
@@ -549,18 +538,6 @@ def _concat_same_dtype(self, to_concat, name):
 
         return self._simple_new(new_data, **attribs)
 
-    @Appender(_index_shared_docs["astype"])
-    def astype(self, dtype, copy=True):
-        if is_dtype_equal(self.dtype, dtype) and copy is False:
-            # Ensure that self.astype(self.dtype) is self
-            return self
-
-        new_values = self._data.astype(dtype, copy=copy)
-
-        # pass copy=False because any copying will be done in the
-        #  _data.astype call above
-        return Index(new_values, dtype=new_values.dtype, name=self.name, copy=False)
-
     def shift(self, periods=1, freq=None):
         """
         Shift index by desired number of time frequency increments.

pandas/core/indexes/datetimes.py

@@ -891,20 +891,6 @@ def slice_indexer(self, start=None, end=None, step=None, kind=None):
             else:
                 raise
 
-    # --------------------------------------------------------------------
-    # Wrapping DatetimeArray
-
-    def __getitem__(self, key):
-        result = self._data.__getitem__(key)
-        if is_scalar(result):
-            return result
-        elif result.ndim > 1:
-            # To support MPL which performs slicing with 2 dim
-            # even though it only has 1 dim by definition
-            assert isinstance(result, np.ndarray), result
-            return result
-        return type(self)(result, name=self.name)
-
     # --------------------------------------------------------------------
 
     @Substitution(klass="DatetimeIndex")

pandas/core/indexes/extension.py

@@ -3,16 +3,18 @@
 """
 from typing import List
 
+import numpy as np
+
 from pandas.compat.numpy import function as nv
-from pandas.util._decorators import cache_readonly
+from pandas.util._decorators import Appender, cache_readonly
 
-from pandas.core.dtypes.common import ensure_platform_int
+from pandas.core.dtypes.common import ensure_platform_int, is_dtype_equal
 from pandas.core.dtypes.generic import ABCSeries
 
 from pandas.core.arrays import ExtensionArray
 from pandas.core.ops import get_op_result_name
 
-from .base import Index
+from .base import Index, _index_shared_docs
 
 
 def inherit_from_data(name: str, delegate, cache: bool = False):
@@ -164,6 +166,57 @@ class ExtensionIndex(Index):
 
     _data: ExtensionArray
 
+    def __getitem__(self, key):
+        result = self._data[key]
+        if isinstance(result, type(self._data)):
+            return type(self)(result, name=self.name)
+
+        # Includes cases where we get a 2D ndarray back for MPL compat
+        return result
+
+    def __iter__(self):
+        return self._data.__iter__()
+
+    @property
+    def _ndarray_values(self) -> np.ndarray:
+        return self._data._ndarray_values
+
+    @Appender(_index_shared_docs["astype"])
+    def astype(self, dtype, copy=True):
+        if is_dtype_equal(self.dtype, dtype) and copy is False:
+            # Ensure that self.astype(self.dtype) is self
+            return self
+
+        new_values = self._data.astype(dtype, copy=copy)
+
+        # pass copy=False because any copying will be done in the
+        #  _data.astype call above
+        return Index(new_values, dtype=new_values.dtype, name=self.name, copy=False)
+
+    def dropna(self, how="any"):
+        if how not in ("any", "all"):
+            raise ValueError(f"invalid how option: {how}")
+
+        if self.hasnans:
+            return self._shallow_copy(self._data[~self._isnan])
+        return self._shallow_copy()
+
+    def _get_unique_index(self, dropna=False):
+        if self.is_unique and not dropna:
+            return self
+
+        result = self._data.unique()
+        if dropna and self.hasnans:
+            result = result[~result.isna()]
+        return self._shallow_copy(result)
+
+    def unique(self, level=None):
+        if level is not None:
+            self._validate_index_level(level)
+
+        result = self._data.unique()
+        return self._shallow_copy(result)
+
     def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs):
         nv.validate_take(tuple(), kwargs)
         indices = ensure_platform_int(indices)

pandas/core/indexes/interval.py

@@ -439,7 +439,7 @@ def astype(self, dtype, copy=True):
             new_values = self.values.astype(dtype, copy=copy)
         if is_interval_dtype(new_values):
             return self._shallow_copy(new_values.left, new_values.right)
-        return super().astype(dtype, copy=copy)
+        return Index.astype(self, dtype, copy=copy)
 
     @property
     def inferred_type(self) -> str:
@@ -1003,14 +1003,6 @@ def take(self, indices, axis=0, allow_fill=True, fill_value=None, **kwargs):
         )
         return self._shallow_copy(result)
 
-    def __getitem__(self, value):
-        result = self._data[value]
-        if isinstance(result, IntervalArray):
-            return self._shallow_copy(result)
-        else:
-            # scalar
-            return result
-
     # --------------------------------------------------------------------
     # Rendering Methods
     # __repr__ associated methods are based on MultiIndex

pandas/core/indexes/period.py

@@ -588,15 +588,6 @@ def get_indexer_non_unique(self, target):
         indexer, missing = self._int64index.get_indexer_non_unique(target)
         return ensure_platform_int(indexer), missing
 
-    def _get_unique_index(self, dropna=False):
-        if self.is_unique and not dropna:
-            return self
-
-        result = self._data.unique()
-        if dropna and self.hasnans:
-            result = result[~result.isna()]
-        return self._shallow_copy(result)
-
     def get_loc(self, key, method=None, tolerance=None):
         """
         Get integer location for requested label

pandas/core/indexes/timedeltas.py

@@ -222,15 +222,6 @@ def _formatter_func(self):
 
         return _get_format_timedelta64(self, box=True)
 
-    # -------------------------------------------------------------------
-    # Wrapping TimedeltaArray
-
-    def __getitem__(self, key):
-        result = self._data.__getitem__(key)
-        if is_scalar(result):
-            return result
-        return type(self)(result, name=self.name)
-
     # -------------------------------------------------------------------
 
     @Appender(_index_shared_docs["astype"])