python: how to identify if a variable is an array or a scalar

Question

I have a function that takes the argument NBins. I want to make a call to this function with a scalar 50 or an array [0, 10, 20, 30].

Answer 1

You can check data type of variable.

N = [2,3,5]
P = 5
type(P)

It will give you out put as data type of P.

<type 'int'>

So that you can differentiate that it is an integer or an array.

Answer 2

Previous answers assume that the array is a python standard list. As someone who uses numpy often, I'd recommend a very pythonic test of:

if hasattr(N, "__len__")

Answer 3

Here is the best approach I have found: Check existence of __len__ and __getitem__.

You may ask why? The reasons includes:

1. The popular method isinstance(obj, abc.Sequence) fails on some objects including PyTorch's Tensor because they do not implement __contains__.
2. Unfortunately, there is nothing in Python's collections.abc that checks for only __len__ and __getitem__ which I feel are minimal methods for array-like objects.
3. It works on list, tuple, ndarray, Tensor etc.

So without further ado:

def is_array_like(obj, string_is_array=False, tuple_is_array=True):
    result = hasattr(obj, "__len__") and hasattr(obj, '__getitem__') 
    if result and not string_is_array and isinstance(obj, (str, abc.ByteString)):
        result = False
    if result and not tuple_is_array and isinstance(obj, tuple):
        result = False
    return result

Note that I've added default parameters because most of the time you might want to consider strings as values, not arrays. Similarly for tuples.

Answer 4

While, @jamylak's approach is the better one, here is an alternative approach

>>> N=[2,3,5]
>>> P = 5
>>> type(P) in (tuple, list)
False
>>> type(N) in (tuple, list)
True

Answer 5

Another alternative approach (use of class name property):

N = [2,3,5]
P = 5

type(N).__name__ == 'list'
True

type(P).__name__ == 'int'
True

type(N).__name__ in ('list', 'tuple')
True

No need to import anything.

Answer 6

Is there an equivalent to isscalar() in numpy? Yes.

>>> np.isscalar(3.1)
True
>>> np.isscalar([3.1])
False
>>> np.isscalar(False)
True