adjust for that is_monotonic uses fused types

Author: topper-123

doc/source/whatsnew/v0.24.0.txt

@@ -591,9 +591,10 @@ Removal of prior version deprecations/changes
 Performance Improvements
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-- Very large improvement in performance of slicing when the index is a :class:`CategoricalIndex`,
-  both when indexing by label (using .loc) and position(.iloc).
-  Likewise, slicing a ``CategoricalIndex`` itself (i.e. ``ci[100:200]``) shows similar speed improvements (:issue:`21659`)
+- Slicing Series and Dataframe with an monotonically increasing :class:`CategoricalIndex`
+  is now very fast and has speed comparable to slicing with an ``Int64Index``.
+  The speed increase is both when indexing by label (using .loc) and position(.iloc) (:issue:`20395`)
+- Slicing a ``CategoricalIndex`` itself (i.e. ``ci[1000:2000]``) shows similar speed improvements as above (:issue:`21659`)
 - 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`)

pandas/_libs/algos.pyx

@@ -361,9 +361,13 @@ ctypedef fused algos_t:
     float64_t
     float32_t
     object
-    int32_t
     int64_t
+    int32_t
+    int16_t
+    int8_t
     uint64_t
+    uint32_t
+    uint16_t
     uint8_t
 
 

pandas/_libs/index_class_helper.pxi.in

@@ -33,7 +33,7 @@ cdef class {{name}}Engine(IndexEngine):
     _dtype = '{{dtype}}'
 
     def _call_monotonic(self, values):
-        return algos.is_monotonic_{{dtype}}(values, timelike=False)
+        return algos.is_monotonic(values, timelike=False)
 
     def get_backfill_indexer(self, other, limit=None):
         return algos.backfill_{{dtype}}(self._get_index_values(),