REF: share more EA methods

pandas/core/arrays/_mixins.py

@@ -6,7 +6,7 @@
 from pandas.errors import AbstractMethodError
 from pandas.util._decorators import cache_readonly, doc
 
-from pandas.core.algorithms import searchsorted, take, unique
+from pandas.core.algorithms import take, unique
 from pandas.core.array_algos.transforms import shift
 from pandas.core.arrays.base import ExtensionArray
 
@@ -102,6 +102,9 @@ def T(self: _T) -> _T:
 
     # ------------------------------------------------------------------------
 
+    def _values_for_argsort(self):
+        return self._ndarray
+
     def copy(self: _T) -> _T:
         new_data = self._ndarray.copy()
         return self._from_backing_data(new_data)
@@ -135,7 +138,11 @@ def _concat_same_type(cls, to_concat, axis: int = 0):
 
     @doc(ExtensionArray.searchsorted)
     def searchsorted(self, value, side="left", sorter=None):
-        return searchsorted(self._ndarray, value, side=side, sorter=sorter)
+        value = self._validate_searchsorted_value(value)
+        return self._ndarray.searchsorted(value, side=side, sorter=sorter)
+
+    def _validate_searchsorted_value(self, value):
+        return value
 
     @doc(ExtensionArray.shift)
     def shift(self, periods=1, fill_value=None, axis=0):

pandas/core/arrays/categorical.py

@@ -12,7 +12,7 @@
 from pandas._libs import NaT, algos as libalgos, hashtable as htable, lib
 from pandas._typing import ArrayLike, Dtype, Ordered, Scalar
 from pandas.compat.numpy import function as nv
-from pandas.util._decorators import cache_readonly, deprecate_kwarg, doc
+from pandas.util._decorators import cache_readonly, deprecate_kwarg
 from pandas.util._validators import validate_bool_kwarg, validate_fillna_kwargs
 
 from pandas.core.dtypes.cast import (
@@ -45,12 +45,7 @@
 import pandas.core.algorithms as algorithms
 from pandas.core.algorithms import _get_data_algo, factorize, take_1d, unique1d
 from pandas.core.arrays._mixins import NDArrayBackedExtensionArray
-from pandas.core.base import (
-    ExtensionArray,
-    NoNewAttributesMixin,
-    PandasObject,
-    _shared_docs,
-)
+from pandas.core.base import ExtensionArray, NoNewAttributesMixin, PandasObject
 import pandas.core.common as com
 from pandas.core.construction import array, extract_array, sanitize_array
 from pandas.core.indexers import check_array_indexer, deprecate_ndim_indexing
@@ -1315,11 +1310,6 @@ def memory_usage(self, deep=False):
         """
         return self._codes.nbytes + self.dtype.categories.memory_usage(deep=deep)
 
-    @doc(_shared_docs["searchsorted"], klass="Categorical")
-    def searchsorted(self, value, side="left", sorter=None):
-        value = self._validate_searchsorted_value(value)
-        return self.codes.searchsorted(value, side=side, sorter=sorter)
-
     def isna(self):
         """
         Detect missing values
@@ -1428,9 +1418,6 @@ def check_for_ordered(self, op):
                 "Categorical to an ordered one\n"
             )
 
-    def _values_for_argsort(self):
-        return self._codes
-
     def argsort(self, ascending=True, kind="quicksort", **kwargs):
         """
         Return the indices that would sort the Categorical.
@@ -1879,7 +1866,7 @@ def __getitem__(self, key):
         if result.ndim > 1:
             deprecate_ndim_indexing(result)
             return result
-        return self._constructor(result, dtype=self.dtype, fastpath=True)
+        return self._from_backing_data(result)
 
     def __setitem__(self, key, value):
         """

pandas/core/arrays/datetimelike.py

@@ -545,15 +545,18 @@ def __getitem__(self, key):
             result = self._ndarray[key]
             if self.ndim == 1:
                 return self._box_func(result)
-            return self._simple_new(result, dtype=self.dtype)
+            return self._from_backing_data(result)
 
         key = self._validate_getitem_key(key)
         result = self._ndarray[key]
         if lib.is_scalar(result):
             return self._box_func(result)
 
+        result = self._from_backing_data(result)
+
         freq = self._get_getitem_freq(key)
-        return self._simple_new(result, dtype=self.dtype, freq=freq)
+        result._freq = freq
+        return result
 
     def _validate_getitem_key(self, key):
         if com.is_bool_indexer(key):
@@ -714,9 +717,6 @@ def _values_for_factorize(self):
     def _from_factorized(cls, values, original):
         return cls(values, dtype=original.dtype)
 
-    def _values_for_argsort(self):
-        return self._ndarray
-
     # ------------------------------------------------------------------
     # Validation Methods
     # TODO: try to de-duplicate these, ensure identical behavior
@@ -917,34 +917,6 @@ def _unbox(self, other, setitem: bool = False) -> Union[np.int64, np.ndarray]:
     #  These are not part of the EA API, but we implement them because
     #  pandas assumes they're there.
 
-    def searchsorted(self, value, side="left", sorter=None):
-        """
-        Find indices where elements should be inserted to maintain order.
-
-        Find the indices into a sorted array `self` such that, if the
-        corresponding elements in `value` were inserted before the indices,
-        the order of `self` would be preserved.
-
-        Parameters
-        ----------
-        value : array_like
-            Values to insert into `self`.
-        side : {'left', 'right'}, optional
-            If 'left', the index of the first suitable location found is given.
-            If 'right', return the last such index.  If there is no suitable
-            index, return either 0 or N (where N is the length of `self`).
-        sorter : 1-D array_like, optional
-            Optional array of integer indices that sort `self` into ascending
-            order. They are typically the result of ``np.argsort``.
-
-        Returns
-        -------
-        indices : array of ints
-            Array of insertion points with the same shape as `value`.
-        """
-        value = self._validate_searchsorted_value(value)
-        return self._data.searchsorted(value, side=side, sorter=sorter)
-
     def value_counts(self, dropna=False):
         """
         Return a Series containing counts of unique values.

pandas/core/arrays/numpy_.py

@@ -260,15 +260,19 @@ def __getitem__(self, item):
         return result
 
     def __setitem__(self, key, value) -> None:
-        value = extract_array(value, extract_numpy=True)
+        key = self._validate_setitem_key(key)
+        value = self._validate_setitem_value(value)
+        self._ndarray[key] = value
 
-        key = check_array_indexer(self, key)
-        scalar_value = lib.is_scalar(value)
+    def _validate_setitem_value(self, value):
+        value = extract_array(value, extract_numpy=True)
 
-        if not scalar_value:
+        if not lib.is_scalar(value):
             value = np.asarray(value, dtype=self._ndarray.dtype)
+        return value
 
-        self._ndarray[key] = value
+    def _validate_setitem_key(self, key):
+        return check_array_indexer(self, key)
 
     def isna(self) -> np.ndarray:
         return isna(self._ndarray)
@@ -308,9 +312,6 @@ def _validate_fill_value(self, fill_value):
             fill_value = self.dtype.na_value
         return fill_value
 
-    def _values_for_argsort(self) -> np.ndarray:
-        return self._ndarray
-
     def _values_for_factorize(self) -> Tuple[np.ndarray, int]:
         return self._ndarray, -1