Adjust according to comments

Author: topper-123

doc/source/whatsnew/v0.24.0.rst

@@ -1059,7 +1059,7 @@ Other API Changes
 - Slicing a single row of a DataFrame with multiple ExtensionArrays of the same type now preserves the dtype, rather than coercing to object (:issue:`22784`)
 - :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:`Series.searchsorted`, when supplied a scalar value to search for, now returns a scalar instead of an array (:issue:`xxxxx`).
+- :meth:`Series.searchsorted`, when supplied a scalar value to search for, now returns a scalar instead of an array (:issue:`23801`).
 - :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 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`).

pandas/core/base.py

@@ -1353,7 +1353,7 @@ def factorize(self, sort=False, na_sentinel=-1):
             same shape as `value`.
 
             .. versionchanged :: 0.24.0
-                Ìf `value`is a scalar, an int is now always returned.
+                If `value` is a scalar, an int is now always returned.
                 Previously, scalar inputs returned an 1-item array for
                 :class:`Series` and :class:`Categorical`.
 

pandas/tests/indexes/multi/test_monotonic.py

@@ -5,6 +5,7 @@
 
 import pandas as pd
 from pandas import Index, IntervalIndex, MultiIndex
+import pandas.util.testing as tm
 
 
 def test_is_monotonic_increasing():
@@ -182,22 +183,28 @@ def test_searchsorted_monotonic(indices):
     # test searchsorted only for increasing
     if indices.is_monotonic_increasing:
         ssm_left = indices._searchsorted_monotonic(value, side='left')
+        assert tm.is_scalar(ssm_left)
         assert expected_left == ssm_left
 
         ssm_right = indices._searchsorted_monotonic(value, side='right')
+        assert tm.is_scalar(ssm_right)
         assert expected_right == ssm_right
 
         ss_left = indices.searchsorted(value, side='left')
+        assert tm.is_scalar(ss_left)
         assert expected_left == ss_left
 
         ss_right = indices.searchsorted(value, side='right')
+        assert tm.is_scalar(ss_right)
         assert expected_right == ss_right
 
     elif indices.is_monotonic_decreasing:
         ssm_left = indices._searchsorted_monotonic(value, side='left')
+        assert tm.is_scalar(ssm_left)
         assert expected_left == ssm_left
 
         ssm_right = indices._searchsorted_monotonic(value, side='right')
+        assert tm.is_scalar(ssm_right)
         assert expected_right == ssm_right
 
     else:

pandas/util/testing.py

@@ -27,8 +27,8 @@
     is_bool, is_categorical_dtype, is_datetime64_dtype, is_datetime64tz_dtype,
     is_datetimelike_v_numeric, is_datetimelike_v_object,
     is_extension_array_dtype, is_interval_dtype, is_list_like, is_number,
-    is_period_dtype, is_scalar, is_sequence, is_timedelta64_dtype,
-    needs_i8_conversion)  # noqa
+    is_period_dtype, is_sequence, is_timedelta64_dtype, needs_i8_conversion)
+from pandas.core.dtypes.common import is_scalar  # noqa: F401
 from pandas.core.dtypes.missing import array_equivalent
 
 import pandas as pd