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]. How can I identify within the function, what the length of NBins is? or said differently, if it is a scalar or a vector?

I tried this:

>>> N=[2,3,5]
>>> P = 5
>>> len(N)
3
>>> len(P)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: object of type 'int' has no len()
>>> 

As you see, I can’t apply len to P, since it’s not an array…. Is there something like isarray or isscalar in python?

thanks

>>> isinstance([0, 10, 20, 30], list)
True
>>> isinstance(50, list)
False

To support any type of sequence, check collections.Sequence instead of list.

note: isinstance also supports a tuple of classes, check type(x) in (..., ...) should be avoided and is unnecessary.

You may also wanna check not isinstance(x, (str, unicode))

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__")

Combining @jamylak and @jpaddison3’s answers together, if you need to be robust against numpy arrays as the input and handle them in the same way as lists, you should use

import numpy as np
isinstance(P, (list, tuple, np.ndarray))

This is robust against subclasses of list, tuple and numpy arrays.

And if you want to be robust against all other subclasses of sequence as well (not just list and tuple), use

import collections
import numpy as np
isinstance(P, (collections.Sequence, np.ndarray))

Why should you do things this way with isinstance and not compare type(P) with a target value? Here is an example, where we make and study the behaviour of NewList, a trivial subclass of list.

>>> class NewList(list):
...     isThisAList = '???'
... 
>>> x = NewList([0,1])
>>> y = list([0,1])
>>> print x
[0, 1]
>>> print y
[0, 1]
>>> x==y
True
>>> type(x)
<class '__main__.NewList'>
>>> type(x) is list
False
>>> type(y) is list
True
>>> type(x).__name__
'NewList'
>>> isinstance(x, list)
True

Despite x and y comparing as equal, handling them by type would result in different behaviour. However, since x is an instance of a subclass of list, using isinstance(x,list) gives the desired behaviour and treats x and y in the same manner.

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

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

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

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.

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.

>>> N=[2,3,5]
>>> P = 5
>>> type(P)==type(0)
True
>>> type([1,2])==type(N)
True
>>> type(P)==type([1,2])
False

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.

I am surprised that such a basic question doesn’t seem to have an immediate answer in python. It seems to me that nearly all proposed answers use some kind of type checking, that is usually not advised in python and they seem restricted to a specific case (they fail with different numerical types or generic iteratable objects that are not tuples or lists).

For me, what works better is importing numpy and using array.size, for example:

>>> a=1
>>> np.array(a)
Out[1]: array(1)

>>> np.array(a).size
Out[2]: 1

>>> np.array([1,2]).size
Out[3]: 2

>>> np.array('125')
Out[4]: 1

Note also:

>>> len(np.array([1,2]))

Out[5]: 2

but:

>>> len(np.array(a))
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-40-f5055b93f729> in <module>()
----> 1 len(np.array(a))

TypeError: len() of unsized object

Simply use size instead of len!

>>> from numpy import size
>>> N = [2, 3, 5]
>>> size(N)
3
>>> N = array([2, 3, 5])
>>> size(N)
3
>>> P = 5
>>> size(P)
1

preds_test[0] is of shape (128,128,1) Lets check its data type using isinstance() function isinstance takes 2 arguments. 1st argument is data 2nd argument is data type isinstance(preds_test[0], np.ndarray) gives Output as True. It means preds_test[0] is an array.

To answer the question in the title, a direct way to tell if a variable is a scalar is to try to convert it to a float. If you get TypeError, it’s not.

N = [1, 2, 3]
try:
    float(N)
except TypeError:
    print('it is not a scalar')
else:
    print('it is a scalar')