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up vote -2 down vote favorite

I have a function which can accept either a list or a numpy array.

In either case, the list/array has a single element (always). I just need to return a float.

So, e.g., I could receive:

list_ = [4]

or the numpy array:

array_ = array([4])

And I should return

 4.0

So, naturally (I would say), I employ float(...) on list_ and get:

TypeError: float() argument must be a string or a number

I do the same to array_ and this time it works by responding with "4.0". From this, I learn that Python's list cannot be converted to float this way.

Based on the success with the numpy array conversion to float this lead me to the approach:

float(np.asarray(list_))

And this works when list_ is both a Python list and when it is a numpy array.

Question

But it seems like this approach has an overhead first converting the list to a numpy array and then to float. Basically: Is there a better way of doing this?

share|improve this question
    
Can't you use slicing: float(list_[0]) = 4.0 – Kyrubas May 18 '15 at 19:17
    
either float(list_[0]) or float(''.join(list_)) – farhawa May 18 '15 at 19:21
up vote 2 down vote accepted

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
share|improve this answer
up vote 1 down vote

I would simply use,

np.asarray(input, dtype=np.float)[0]
  • If input is an ndarray of the right dtype, there is no overhead, since np.asarray does nothing in this case.
  • if input is a list, np.asarray makes sure the output is of the right type.
share|improve this answer

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