Adjust according to comments

Author: topper-123

pandas/core/base.py

@@ -1355,7 +1355,7 @@ 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