Convert list or numpy array of single element to float in python

np.asscalar(a) is deprecated since NumPy v1.16, use a.item() instead.

For example:

a = np.array([[0.6813]])
print(a.item())

gives:

0.6813

Use numpy.asscalar to convert a numpy array / matrix a scalar value:

>>> a=numpy.array([[[[42]]]])
>>> numpy.asscalar(a)
42

The output data type is the same type returned by the input’s item method.

It has built in error-checking if there is more than an single element:

>>> a=numpy.array([1, 2])
>>> numpy.asscalar(a)

gives:

ValueError: can only convert an array of size 1 to a Python scalar

---

Note: the object passed to asscalar must respond to item, so passing a list or tuple won't work.

You may want to use the ndarray.item method, as in a.item(). This is also equivalent to (the now deprecated) np.asscalar(a). This has the benefit of working in situations with views and superfluous axes, while the above solutions will currently break. For example,

>>> a = np.asarray(1).view()
>>> a.item()  # correct
1

>>> a[0]  # breaks
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
IndexError: too many indices for array


>>> a = np.asarray([[2]])
>>> a.item()  # correct
2

>>> a[0]  # bad result
array([2])

This also has the benefit of throwing an exception if the array is not actually a scalar, while the a[0] approach will silently proceed (which may lead to bugs sneaking through undetected).

>>> a = np.asarray([1, 2])
>>> a[0]  # silently proceeds
1
>>> a.item()  # detects incorrect size
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ValueError: can only convert an array of size 1 to a Python scalar

Just access the first item of the list/array, using the index access and the index 0:

>>> list_ = [4]
>>> list_[0]
4
>>> array_ = np.array([4])
>>> array_[0]
4

This will be an int since that was what you inserted in the first place. If you need it to be a float for some reason, you can call float() on it then:

>>> float(list_[0])
4.0