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.

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.

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

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
```

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