diff --git a/doc/source/whatsnew/v0.24.0.txt b/doc/source/whatsnew/v0.24.0.txt
index 3e1711edb0f27..9203e98736924 100644
--- a/doc/source/whatsnew/v0.24.0.txt
+++ b/doc/source/whatsnew/v0.24.0.txt
@@ -238,6 +238,40 @@ For situations where you need an ``ndarray`` of ``Interval`` objects, use
    np.asarray(idx)
    idx.values.astype(object)
 
+
+.. _whatsnew_0240.api.types.is_scalar:
+
+Support for PEP 3141 numbers
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The `is_scalar` function now returns True when a `Number` or `Fraction` is passed.
+
+Previous Behavior:
+
+.. code-block:: ipython
+
+   In [1]: pandas.api.types.is_scalar(fractions.Fraction(1))
+   Out[1]:
+   False
+
+   In [2]: pandas.api.types.is_scalar(numbers.Number(1))
+   Out[2]:
+   False
+
+New Behavior:
+
+.. code-block:: ipython
+
+   In [1]: pandas.api.types.is_scalar(fractions.Fraction(1))
+   Out[1]:
+   True
+
+   In [2]: pandas.api.types.is_scalar(numbers.Number(1))
+   Out[2]:
+   True
+
+This mirrors ``numpy.isscalar``, which already supports PEP 3141 and is a requirement for `pandas`.
+
 .. _whatsnew_0240.api.timezone_offset_parsing:
 
 Parsing Datetime Strings with Timezone Offsets
@@ -829,4 +863,5 @@ Other
 - :meth:`DataFrame.nlargest` and :meth:`DataFrame.nsmallest` now returns the correct n values when keep != 'all' also when tied on the first columns (:issue:`22752`)
 - :meth:`~pandas.io.formats.style.Styler.bar` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` and setting clipping range with ``vmin`` and ``vmax`` (:issue:`21548` and :issue:`21526`). ``NaN`` values are also handled properly.
 - Logical operations ``&, |, ^`` between :class:`Series` and :class:`Index` will no longer raise ``ValueError`` (:issue:`22092`)
--
+- Support PEP 3141 numbers in `pandas.api.types.is_scalar` function
+- 
\ No newline at end of file
diff --git a/pandas/_libs/lib.pyx b/pandas/_libs/lib.pyx
index 0b9793a6ef97a..1b84d09ae6a85 100644
--- a/pandas/_libs/lib.pyx
+++ b/pandas/_libs/lib.pyx
@@ -119,7 +119,8 @@ def is_scalar(val: object) -> bint:
     - instances of decimal.Decimal
     - Interval
     - DateOffset
-
+    - Fraction
+    - Number
     """
 
     return (cnp.PyArray_IsAnyScalar(val)
@@ -134,8 +135,8 @@ def is_scalar(val: object) -> bint:
             or util.is_period_object(val)
             or is_decimal(val)
             or is_interval(val)
-            or util.is_offset_object(val))
-
+            or util.is_offset_object(val)
+            or np.isscalar(val))
 
 def item_from_zerodim(val: object) -> object:
     """
diff --git a/pandas/core/series.py b/pandas/core/series.py
index 83f80c305c5eb..9bc9777c59d8f 100644
--- a/pandas/core/series.py
+++ b/pandas/core/series.py
@@ -2266,30 +2266,41 @@ def _binop(self, other, func, level=None, fill_value=None):
 
     def combine(self, other, func, fill_value=None):
         """
+        Combine the Series with a `Series` or `Scalar` according to `func`.
+
         Perform elementwise binary operation on two Series using given function
         with optional fill value when an index is missing from one Series or
-        the other
+        the other.
 
         Parameters
         ----------
         other : Series or scalar value
+            The value(s) to be combined with the `Series`.
         func : function
-            Function that takes two scalars as inputs and return a scalar
+            Function that takes two scalars as inputs and return a scalar.
         fill_value : scalar value
+            The optional value to assume when an index 
+            is missing from one Series or the other,
             The default specifies to use the appropriate NaN value for
-            the underlying dtype of the Series
+            the underlying dtype of the Series.
 
         Returns
         -------
-        result : Series
+        result : the combined `Series` object
 
         Examples
         --------
         >>> s1 = pd.Series([1, 2])
-        >>> s2 = pd.Series([0, 3])
+        >>> s2 = pd.Series([0, 3, 4])
         >>> s1.combine(s2, lambda x1, x2: x1 if x1 < x2 else x2)
         0    0
         1    2
+        2    4
+        dtype: int64
+        >>> s1.combine(s2, lambda x1, x2: x1 if x1 > x2 else x2,fill_value=787)
+        0      1
+        1      3
+        2    787
         dtype: int64
 
         See Also
@@ -2333,12 +2344,16 @@ def combine(self, other, func, fill_value=None):
 
     def combine_first(self, other):
         """
-        Combine Series values, choosing the calling Series's values
-        first. Result index will be the union of the two indexes
+        Combine Series values, choosing the calling Series's values first.
+        
+        Notes
+        -----
+        Result index will be the union of the two indexes.
 
         Parameters
         ----------
         other : Series
+            The value(s) to be combined with the `Series`.
 
         Returns
         -------