track back to use _codes_for_values

Author: topper-123

doc/source/whatsnew/v0.24.0.rst

@@ -961,7 +961,7 @@ Other API Changes
 - :class:`DateOffset` attribute `_cacheable` and method `_should_cache` have been removed (:issue:`23118`)
 - Comparing :class:`Timedelta` to be less or greater than unknown types now raises a ``TypeError`` instead of returning ``False`` (:issue:`20829`)
 - :meth:`Categorical.searchsorted`, when supplied a scalar value to search for, now returns a scalar instead of an array (:issue:`23466`).
-- :meth:`Categorical.searchsorted` now raises a ``keyError`` rather that a ``ValueError``, if a search for key is not found in its categories (:issue:`23466`).
+- :meth:`Categorical.searchsorted` now raises a ``keyError`` rather that a ``ValueError``, if a searched for key is not found in its categories (:issue:`23466`).
 - :meth:`Index.hasnans` and :meth:`Series.hasnans` now always return a python boolean. Previously, a python or a numpy boolean could be returned, depending on circumstances (:issue:`23294`).
 
 .. _whatsnew_0240.deprecations:
@@ -1080,7 +1080,6 @@ Performance Improvements
 - Improved performance of :func:`Series.describe` in case of numeric dtpyes (:issue:`21274`)
 - Improved performance of :func:`pandas.core.groupby.GroupBy.rank` when dealing with tied rankings (:issue:`21237`)
 - Improved performance of :func:`DataFrame.set_index` with columns consisting of :class:`Period` objects (:issue:`21582`, :issue:`21606`)
-- Improved performance of :meth:`Categorical.searchsorted` (:issue:`23466`)
 - Improved performance of membership checks in :class:`Categorical` and :class:`CategoricalIndex`
   (i.e. ``x in cat``-style checks are much faster). :meth:`CategoricalIndex.contains`
   is likewise much faster (:issue:`21369`, :issue:`21508`)

pandas/core/arrays/categorical.py

@@ -1335,16 +1335,6 @@ def memory_usage(self, deep=False):
         return self._codes.nbytes + self.dtype.categories.memory_usage(
             deep=deep)
 
-    def _ensure_codes_dtype(self, code):
-        """
-        Ensure ``code`` has the same dtype as self.codes.
-        """
-        dtype = self.codes.dtype
-        if is_scalar(code):
-            return dtype.type(code)
-        else:
-            return np.array(code, dtype=dtype)
-
     @Substitution(klass='Categorical')
     @Appender(_shared_docs['searchsorted'])
     def searchsorted(self, value, side='left', sorter=None):
@@ -1353,13 +1343,12 @@ def searchsorted(self, value, side='left', sorter=None):
                              ".as_ordered() to change the Categorical to an "
                              "ordered one")
 
-        if is_scalar(value):
-            codes = self.categories.get_loc(value)
-        else:
-            codes = self.categories.get_indexer(value)
-            if -1 in codes:
-                raise KeyError("All values not in self.categories")
-        codes = self._ensure_codes_dtype(codes)
+        from pandas.core.series import Series
+        codes = _get_codes_for_values(Series(value).values, self.categories)
+        if -1 in codes:
+            raise KeyError("Value(s) to be inserted must be in categories.")
+
+        codes = codes[0] if len(codes) == 1 else codes
 
         return self.codes.searchsorted(codes, side=side, sorter=sorter)
 

pandas/core/indexes/category.py

@@ -465,7 +465,7 @@ def get_loc(self, key, method=None):
         array([False,  True, False,  True], dtype=bool)
         """
         code = self.categories.get_loc(key)
-        code = self.values._ensure_codes_dtype(code)
+        code = self.codes.dtype.type(code)
         try:
             return self._engine.get_loc(code)
         except KeyError:

pandas/tests/arrays/categorical/test_analytics.py

@@ -88,15 +88,13 @@ def test_searchsorted(self):
         assert res_cat == 2
 
         res_ser = s1.searchsorted('apple')
-        exp = np.array([2], dtype=np.intp)
-        tm.assert_numpy_array_equal(res_ser, exp)
+        assert res_ser == 2
 
         # Searching for single item array, side='left' (default)
         res_cat = c1.searchsorted(['bread'])
         res_ser = s1.searchsorted(['bread'])
-        exp = np.array([3], dtype=np.intp)
-        tm.assert_numpy_array_equal(res_cat, exp)
-        tm.assert_numpy_array_equal(res_ser, exp)
+        assert res_cat == 3
+        assert res_ser == 3
 
         # Searching for several items array, side='right'
         res_cat = c1.searchsorted(['apple', 'bread'], side='right')