Is there an efficient way of splitting a 2-D numpy array(an array of coordinates) to two 1-D numpy arrays?

Question

Here is my array and I would like to separate it to two arrays, one contains all first values of pairs and one with all second values of pairs. Is there an efficient way to do that?

array([[-0.43042553, -0.1224234 ],
       [-2.03524537,  0.56015045],
       [ 1.40186973, -0.74534088],
       [-0.04612834, -0.43374017],
       [-0.71154051, -0.54219998],
       [-0.10434967, -0.80116922],
       [-0.28625899, -1.15067218],
       [ 2.18190517,  0.52953827]])

Answer 1

Similar to other answers but more concise:

a1, a2 = arr.T

Answer 2

here is how you do it.

>>> arr = np.array([[-0.43042553, -0.1224234 ],
...        [-2.03524537,  0.56015045],
...        [ 1.40186973, -0.74534088],
...        [-0.04612834, -0.43374017],
...        [-0.71154051, -0.54219998],
...        [-0.10434967, -0.80116922],
...        [-0.28625899, -1.15067218],
...        [ 2.18190517,  0.52953827]])
>>>
>>> arr
array([[-0.43042553, -0.1224234 ],
       [-2.03524537,  0.56015045],
       [ 1.40186973, -0.74534088],
       [-0.04612834, -0.43374017],
       [-0.71154051, -0.54219998],
       [-0.10434967, -0.80116922],
       [-0.28625899, -1.15067218],
       [ 2.18190517,  0.52953827]])
>>> a1, a2 = arr[:,0], arr[:,1]
>>> a1
array([-0.43042553, -2.03524537,  1.40186973, -0.04612834, -0.71154051,
       -0.10434967, -0.28625899,  2.18190517])
>>> a2
array([-0.1224234 ,  0.56015045, -0.74534088, -0.43374017, -0.54219998,
       -0.80116922, -1.15067218,  0.52953827])

Answer 3

You can just slice

arr = np.array([[-0.43042553, -0.1224234 ],
                [-2.03524537,  0.56015045],
                [ 1.40186973, -0.74534088],
                [-0.04612834, -0.43374017],
                [-0.71154051, -0.54219998],
                [-0.10434967, -0.80116922],
                [-0.28625899, -1.15067218],
                [ 2.18190517,  0.52953827]])
arr1, arr2 = arr[:, 0], arr[:, 1]