Better doc-string and whatsnew

Author: tm9k1

doc/source/whatsnew/v0.24.0.txt

@@ -239,39 +239,6 @@ For situations where you need an ``ndarray`` of ``Interval`` objects, use
    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
@@ -868,5 +835,5 @@ 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
+- Checking PEP 3141 numbers in `pandas.api.types.is_scalar` function returns ``True`` (:issue:`22903`)
 - 

pandas/_libs/lib.pyx

@@ -107,20 +107,37 @@ def memory_usage_of_objects(arr: object[:]) -> int64_t:
 def is_scalar(val: object) -> bint:
     """
     Return True if given value is scalar.
+    
+    Parameters
+    ----------
+    val : numpy array scalar (e.g. np.int64), Python builtin numerics,
+          Python builtin byte arrays and strings, None,
+          instances of datetime.datetime, instances of datetime.timedelta,
+          Period, instances of decimal.Decimal, Interval, DateOffset,
+          Fraction, Number
+          The value to be checked.
+    
+    Returns
+    -------
+    True if the given value is scalar, False otherwise.
+    
+    Examples
+    --------
+    >>> dt = pd.datetime.datetime(2018,10,3)
+    >>> pd.is_scalar(dt)
+    True
+
+    >>> from fractions import Fraction
+    >>> from numbers import Number
+    
+    >>> fract = Fraction(3,5)
+    >>> pd.is_scalar(fract)
+    True
+
+    >>> num = Number(2)
+    >>> pd.is_scalar(num)
+    True
 
-    This includes:
-    - numpy array scalar (e.g. np.int64)
-    - Python builtin numerics
-    - Python builtin byte arrays and strings
-    - None
-    - instances of datetime.datetime
-    - instances of datetime.timedelta
-    - Period
-    - instances of decimal.Decimal
-    - Interval
-    - DateOffset
-    - Fraction
-    - Number
     """
 
     return (cnp.PyArray_IsAnyScalar(val)
@@ -136,7 +153,8 @@ def is_scalar(val: object) -> bint:
             or is_decimal(val)
             or is_interval(val)
             or util.is_offset_object(val)
-            or np.isscalar(val))
+            or isinstance(val,Number)
+            or ininstance(val,Fraction))
 
 def item_from_zerodim(val: object) -> object:
     """