[FIXED] What's difference between np.zeros(img) and np.copy(img)*0?

Issue

What is the difference between these three:

np.zeros(img), np.zeros_like(img), and np.copy(img)*0?

When should I use each of them?

Solution

zeros is the basic operation, creating an array with a given shape and dtype:

In [313]: arr = np.zeros((3,4),int)
In [314]: arr
Out[314]: 
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]])

zeros_like is an alternative input to zeros, where you provide an array, and it copies its shape and dtype (but not values):

In [315]: bar = np.zeros_like(arr)
In [316]: bar
Out[316]: 
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]])

copy*0 also works, but generally I wouldn't recommend it. I did use N2 *=0 recently in while testing some code to reset an array.

In [317]: car = arr.copy()*0
In [318]: car
Out[318]: 
array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]])

Answered By - hpaulj