Sorting a 2D numpy array on to the proximity of each element to a certain point

Question

I have (n,2) numpy array which contains the coordination of n points now I want to sort them based on approximation of each element to the specific point (x,y) and pick the closest one. How can I achieve this?

Right now I have:

def find_nearest(array,value):
    xlist = (np.abs(array[:, 0]-value[:, 0]))
    ylist = (np.abs(array[:, 1]-value[:, 1]))
    newList = np.vstack((xlist,ylist))
    // SORT NEW LIST and return the 0 elemnt

In my solution I need to sort newList based on Proximity to (0,0) and I don't know how? Any solution for this or any other solution?

My array of points looks like:

array([[ 0.1648,  0.227 ],
       [ 0.2116,  0.2472],
       [ 0.78  ,  0.546 ],
       [ 0.9752,  1.    ],
       [ 0.384 ,  0.4862],
       [ 0.4428,  0.2204],
       [ 0.4448,  0.4146],
       [ 0.1046,  0.2658],
       [ 0.5668,  0.7792],
       [ 0.1664,  0.0746],
       [ 0.5636,  0.6372],
       [ 0.7822,  0.5536],
       [ 0.7718,  0.8276],
       [ 0.9916,  1.    ],
       [ 0.    ,  0.    ],
       [ 0.8206,  0.817 ],
       [ 0.4858,  0.4652],
       [ 0.    ,  0.    ],
       [ 0.1574,  0.3114],
       [ 0.    ,  0.0022],
       [ 0.874 ,  0.714 ],
       [ 0.148 ,  0.6624],
       [ 0.0656,  0.5912],
       [ 0.1148,  0.607 ],
       [ 0.069 ,  0.6296]])

Answer 1

Sorting to find the nearest point is not a good idea. If you want the closest point then just find the closest point instead, sorting for that is overkilling.

def closest_point(arr, x, y):
    dist = (arr[:, 0] - x)**2 + (arr[:, 1] - y)**2
    return arr[dist.argmin()]

Moreover if you need to repeat the search many times with a fixed or quasi fixed set of points there are specific data structures that can speed up this kind of query a lot (the search time becomes sub-linear).