What is the most pythonic way to check if an object is a number?
Use Number from the numbers module to test isinstance(n, Number) (available since 2.6).
>>> from numbers import Number
... from decimal import Decimal
... from fractions import Fraction
... for n in [2, 2.0, Decimal('2.0'), complex(2, 0), Fraction(2, 1), '2']:
... print(f'{n!r:>14} {isinstance(n, Number)}')
2 True
2.0 True
Decimal('2.0') True
(2+0j) True
Fraction(2, 1) True
'2' False
This is, of course, contrary to duck typing. If you are more concerned about how an object acts rather than what it is, perform your operations as if you have a number and use exceptions to tell you otherwise.
You want to check if some object
acts like a number in certain circumstances
If you're using Python 2.5 or older, the only real way is to check some of those "certain circumstances" and see.
In 2.6 or better, you can use isinstance with numbers.Number -- an abstract base class (ABC) that exists exactly for this purpose (lots more ABCs exist in the collections module for various forms of collections/containers, again starting with 2.6; and, also only in those releases, you can easily add your own abstract base classes if you need to).
Bach to 2.5 and earlier,
"can be added to 0 and is not iterable" could be a good definition in some cases. But,
you really need to ask yourself, what it is that you're asking that what you want to consider "a number" must definitely be able to do, and what it must absolutely be unable to do -- and check.
This may also be needed in 2.6 or later, perhaps for the purpose of making your own registrations to add types you care about that haven't already be registered onto numbers.Numbers -- if you want to exclude some types that claim they're numbers but you just can't handle, that takes even more care, as ABCs have no unregister method [[for example you could make your own ABC WeirdNum and register there all such weird-for-you types, then first check for isinstance thereof to bail out before you proceed to checking for isinstance of the normal numbers.Number to continue successfully.
BTW, if and when you need to check if x can or cannot do something, you generally have to try something like:
try: 0 + x
except TypeError: canadd=False
else: canadd=True
The presence of __add__ per se tells you nothing useful, since e.g all sequences have it for the purpose of concatenation with other sequences. This check is equivalent to the definition "a number is something such that a sequence of such things is a valid single argument to the builtin function sum", for example. Totally weird types (e.g. ones that raise the "wrong" exception when summed to 0, such as, say, a ZeroDivisionError or ValueError &c) will propagate exception, but that's OK, let the user know ASAP that such crazy types are just not acceptable in good company;-); but, a "vector" that's summable to a scalar (Python's standard library doesn't have one, but of course they're popular as third party extensions) would also give the wrong result here, so (e.g.) this check should come after the "not allowed to be iterable" one (e.g., check that iter(x) raises TypeError, or for the presence of special method __iter__ -- if you're in 2.5 or earlier and thus need your own checks).
A brief glimpse at such complications may be sufficient to motivate you to rely instead on abstract base classes whenever feasible...;-).
This is a good example where exceptions really shine. Just do what you would do with the numeric types and catch the TypeError from everything else.
But obviously, this only checks if a operation works, not whether it makes sense! The only real solution for that is to never mix types and always know exactly what typeclass your values belong to.
Multiply the object by zero. Any number times zero is zero. Any other result means that the object is not a number (including exceptions)
def isNumber(x):
try:
return bool(0 == x*0)
except:
return False
Using isNumber thusly will give the following output:
class A: pass
def foo(): return 1
for x in [1,1.4, A(), range(10), foo, foo()]:
answer = isNumber(x)
print('{answer} == isNumber({x})'.format(**locals()))
Output:
True == isNumber(1)
True == isNumber(1.4)
False == isNumber(<__main__.A instance at 0x7ff52c15d878>)
False == isNumber([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
False == isNumber(<function foo at 0x7ff52c121488>)
True == isNumber(1)
There probably are some non-number objects in the world that define __mul__ to return zero when multiplied by zero but that is an extreme exception. This solution should cover all normal and sane code that you generate/encouter.
numpy.array example:
import numpy as np
def isNumber(x):
try:
return bool(x*0 == 0)
except:
return False
x = np.array([0,1])
answer = isNumber(x)
print('{answer} == isNumber({x})'.format(**locals()))
output:
False == isNumber([0 1])
To rephrase your question, you are trying to determine whether something is a collection or a single value. Trying to compare whether something is a vector or a number is comparing apples to oranges - I can have a vector of strings or numbers, and I can have a single string or single number. You are interested in how many you have (1 or more), not what type you actually have.
my solution for this problem is to check whether the input is a single value or a collection by checking the presence of __len__. For example:
def do_mult(foo, a_vector):
if hasattr(foo, '__len__'):
return sum([a*b for a,b in zip(foo, a_vector)])
else:
return [foo*b for b in a_vector]
Or, for the duck-typing approach, you can try iterating on foo first:
def do_mult(foo, a_vector):
try:
return sum([a*b for a,b in zip(foo, a_vector)])
except TypeError:
return [foo*b for b in a_vector]
Ultimately, it is easier to test whether something is vector-like than to test whether something is scalar-like. If you have values of different type (i.e. string, numeric, etc.) coming through, then the logic of your program may need some work - how did you end up trying to multiply a string by a numeric vector in the first place?