more fixes

Author: tm9k1

doc/source/whatsnew/v0.24.0.txt

@@ -835,5 +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`)
-- Checking PEP 3141 numbers in `pandas.api.types.is_scalar` function returns ``True`` (:issue:`22903`)
-- 
+- Checking PEP 3141 numbers in :func:`~pandas.api.types.is_scalar` function returns ``True`` (:issue:`22903`)
+- 

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
@@ -15,9 +18,6 @@ from cpython.datetime cimport (PyDateTime_Check, PyDate_Check,
                                PyDateTime_IMPORT)
 PyDateTime_IMPORT
 
-from fractions import Fraction
-from numbers import Number
-
 import numpy as np
 cimport numpy as cnp
 from numpy cimport (ndarray, PyArray_NDIM, PyArray_GETITEM,
@@ -112,13 +112,22 @@ def is_scalar(val: object) -> bint:
 
     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.
+    val : input argument of any type 
 
+    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.
+
     Returns
     -------
     True if the given value is scalar, False otherwise.
@@ -128,23 +137,30 @@ def is_scalar(val: object) -> bint:
     >>> dt = pd.datetime.datetime(2018,10,3)
     >>> pd.is_scalar(dt)
     True
-
-    >>> from fractions import Fraction
-    >>> from numbers import Number
+    >>> pd.api.types.is_scalar([2,3])
+    False
+    
+    >>> pd.api.types.is_scalar({0:1,2:3})
+    False
 
-    >>> fract = Fraction(3,5)
-    >>> pd.is_scalar(fract)
+    >>> 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
 
-    >>> num = Number(2)
-    >>> pd.is_scalar(num)
+    >>> from numbers import Number    
+    >>> pd.api.types.is_scalar(Number())
     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, Number, Fraction))
             # We differ from numpy (as of 1.10), which claims that None is
             # not scalar in np.isscalar().
             or val is None
@@ -155,8 +171,6 @@ def is_scalar(val: object) -> bint:
             or is_decimal(val)
             or is_interval(val)
             or util.is_offset_object(val)
-            or isinstance(val, Number)
-            or isinstance(val, Fraction))
 
 def item_from_zerodim(val: object) -> object:
     """

pandas/tests/dtypes/test_inference.py

@@ -1121,6 +1121,8 @@ 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')
@@ -1185,11 +1187,6 @@ def test_is_scalar_pandas_containers(self):
         assert not is_scalar(Index([]))
         assert not is_scalar(Index([1]))
 
-    def tes_is_scalar_pep_3141(self):
-        assert is_scalar(Number())
-        assert is_scalar(Fraction())
-
-
 def test_datetimeindex_from_empty_datetime64_array():
     for unit in ['ms', 'us', 'ns']:
         idx = DatetimeIndex(np.array([], dtype='datetime64[%s]' % unit))