efficient way of removing None's from numpy array

Question

Is there an efficient way to remove Nones from numpy arrays and resize the array to its new size?

For example, how would you remove the None from this frame without iterating through it in python. I can easily iterate through it but was working on an api call that would be potentially called many times.

a = np.array([1,45,23,23,1234,3432,-1232,-34,233,None]) 

Answer 1

In [17]: a[a != np.array(None)] Out[17]: array([1, 45, 23, 23, 1234, 3432, -1232, -34, 233], dtype=object) 

The above works because a != np.array(None) is a boolean array which maps out non-None values:

In [20]: a != np.array(None) Out[20]: array([ True,  True,  True,  True,  True,  True,  True,  True,  True, False], dtype=bool) 

Selecting elements of an array in this manner is called boolean array indexing.

Answer 2

I use the following which I find simpler than the accepted answer:

a = a[a != None] 

Caveat: PEP8 warns against using the equality operator with singletons such as None. I didn't know about this when I posted this answer. That said, for numpy arrays I find this too Pythonic and pretty to not use. See discussion in comments.