Support for PEP 3141 numbers

doc/source/whatsnew/v0.24.0.rst

@@ -465,6 +465,7 @@ For situations where you need an ``ndarray`` of ``Interval`` objects, use
    np.asarray(idx)
    idx.values.astype(object)
 
+
 .. _whatsnew_0240.api.timezone_offset_parsing:
 
 Parsing Datetime Strings with Timezone Offsets
@@ -1471,6 +1472,7 @@ 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`)
+- Checking PEP 3141 numbers in :func:`~pandas.api.types.is_scalar` function returns ``True`` (:issue:`22903`)
 - Bug in :meth:`DataFrame.combine_first` in which column types were unexpectedly converted to float (:issue:`20699`)
 
 .. _whatsnew_0.24.0.contributors:
@@ -1479,3 +1481,4 @@ Contributors
 ~~~~~~~~~~~~
 
 .. contributors:: v0.23.4..HEAD
+

pandas/_libs/lib.pyx

@@ -1,5 +1,8 @@
 # -*- coding: utf-8 -*-
 from decimal import Decimal
+from fractions import Fraction
+from numbers import Number
+
 import sys
 
 import cython
@@ -15,7 +18,6 @@ from cpython.datetime cimport (PyDateTime_Check, PyDate_Check,
                                PyDateTime_IMPORT)
 PyDateTime_IMPORT
 
-
 import numpy as np
 cimport numpy as cnp
 from numpy cimport (ndarray, PyArray_GETITEM,
@@ -105,23 +107,54 @@ def is_scalar(val: object) -> bool:
     """
     Return True if given value is scalar.
 
-    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
+    Parameters
+    ----------
+    val : object
+        This includes:
+
+        - numpy array scalar (e.g. np.int64)
+        - Python builtin numerics
+        - Python builtin byte arrays and strings
+        - None
+        - datetime.datetime
+        - datetime.timedelta
+        - Period
+        - decimal.Decimal
+        - Interval
+        - DateOffset
+        - Fraction
+        - Number
+
+    Returns
+    -------
+    bool
+        Return True if given object is scalar, False otherwise
+
+    Examples
+    --------
+    >>> dt = pd.datetime.datetime(2018, 10, 3)
+    >>> pd.is_scalar(dt)
+    True
+
+    >>> pd.api.types.is_scalar([2, 3])
+    False
+
+    >>> pd.api.types.is_scalar({0: 1, 2: 3})
+    False
+
+    >>> pd.api.types.is_scalar((0, 2))
+    False
+
+    pandas supports PEP 3141 numbers:
 
+    >>> from fractions import Fraction
+    >>> pd.api.types.is_scalar(Fraction(3, 5))
+    True
     """
 
     return (cnp.PyArray_IsAnyScalar(val)
             # As of numpy-1.9, PyArray_IsAnyScalar misses bytearrays on Py3.
-            or isinstance(val, bytes)
+            or isinstance(val, (bytes, Fraction, Number))
             # We differ from numpy (as of 1.10), which claims that None is
             # not scalar in np.isscalar().
             or val is None

pandas/tests/dtypes/test_inference.py

@@ -10,10 +10,11 @@
 import re
 from datetime import datetime, date, timedelta, time
 from decimal import Decimal
+from numbers import Number
+from fractions import Fraction
 import numpy as np
 import pytz
 import pytest
-
 import pandas as pd
 from pandas._libs import lib, iNaT, missing as libmissing
 from pandas import (Series, Index, DataFrame, Timedelta,
@@ -1183,6 +1184,8 @@ def test_is_scalar_builtin_scalars(self):
         assert is_scalar(None)
         assert is_scalar(True)
         assert is_scalar(False)
+        assert is_scalar(Number())
+        assert is_scalar(Fraction())
         assert is_scalar(0.)
         assert is_scalar(np.nan)
         assert is_scalar('foobar')