Closest pair in n dimension

Is there any efficient library function in python to find a pair of points which are closest/farthest? The input is a list of points which lie in a k-dimensional cube of edge size 1. For finding the closest, the following code is taking too much time. TC: O( n**2 * k ) where n is the size of input. In my case, input n is around 4000 and value of k is around 300.

def euclid_dist( p1, p2 ):
    val = 0
    for i in range(len(p1)):
        val += (p1[i]-p2[i])**2
    return val

def find_close_cluster( points ):
    ans1 = 0
    ans2 = 1
    min_dist = 1000000000000
    for i in range( len(clusters) ):
        for j in range( i+1,len(clusters)):
            current_dist = euclid_dist(clusters[i],clusters[j])
            if( current_dist < min_dist ):
                ans1 = i
                ans2 = j
                min_dist = current_dist
    return ( ans1, ans2 )

You should use numpy ndarrays and the scipy.spatial.distance.cdist function. numpy gives you a efficient container to manipulate data in a vectorized form, so the code runs much faster than doing for loops on iterators or lists. The scipy.spatial.distance.cdist uses numpy arrays to compute the distances between all elements. Take a look on the documentation for more details. The code below should work:

import numpy as np
from scipy.spatial.distance import cdist

your_data = np.asarray([[first_sample], [second_sample], [...])
d = cdist(your_data, your_data)
number_samples = your_data.shape[0]
# Select d(a_i,a_j), i != j without repetitions
il1 = np.tril_indices(number_samples, k=-1) 
dist = d[il1]
arg_min = dist.argmin()
min = dist.min()
arg_max = dist.argmax()
max = dist.max()