How to make array into array list in python

You need to loop differently in at least two ways:

end =[]
for s1 in s:
    end.append([mahalanobis(s1, s2, invcov) for s2 in s])

The most important thing is that the inner loop needs to be on the whole s again, else you will never get a square but 1 + 2 + ... + len(s) items (15 in this case as len(s) is 5).

Next, the inner loop must be enclosed in a list, since you want a list of lists.

Less important but nice: I've changed the inner loop to a list comprehension; and I've changed both loops to be directly on s since there's really no reason to go over the indirection of looping over indices then using those indices to get the s items you care about.

So I made four changes in all, but the first two are what you really need to get the result you desire, the other two are just nice improvements:-).

your loop is fine, if you just add a counter and a second array, then you can group results in groups of 5 elements in one array (if this is the desired result)

end =[]
tmp =[]
for i in range(len(s)):
    for j in range(i, len(s)):
        out = mahalanobis(s[i], s[j], invcov)
        if k % 5 == 0 and k != 0:
            end.append(tmp)
            tmp =[]       
        k += 1
        tmp.append(out)
if len(tmp) > 0:
    end.append(tmp)
print end

No matter what your input then, your output will be an array with n arrays each with 5 members [[1,2,3,4,5],[...],[...], ... ]

Another way is to use your code, and underneath it:

end2 = []
for repeat in range(len(end)/5):
    end2.append(end[0:4])
    end = end[5:]

Given the list,

end = [0.0, 12.99, 5.85, 10.22, 3.95, 0.0, 5.12, 3.45, 4.10, 0.0, 5.05, 8.10, 0.0, 15.45, 0.0]

you could build the desired 2-dimensional array using

import numpy as np
result = np.zeros((s.shape[0],)*2)               # 1
result[np.triu_indices(s.shape[0], 0)] = end     # 2
result += result.T                               # 3
print(result)

yields

[[  0.    12.99   5.85  10.22   3.95]
 [ 12.99   0.     5.12   3.45   4.1 ]
 [  5.85   5.12   0.     5.05   8.1 ]
 [ 10.22   3.45   5.05   0.    15.45]
 [  3.95   4.1    8.1   15.45   0.  ]]

1. make an array filled with zeros

2. np.triu_indices(s.shape[0], 0) returns the indices for the upper-triangle of an array of shape (s.shape[0], s.shape[0]).

   In [95]: np.triu_indices(5, 0)
   Out[95]: 
   (array([0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3, 4]),
    array([0, 1, 2, 3, 4, 1, 2, 3, 4, 2, 3, 4, 3, 4, 4]))
   

   result[...] = end fills the upper-triangle with the values from end.

3. Take the transpose of result and add that to result, thus making result symmetric.

This allows you to obtain the result without calling both mahalanobis(s[i], s[j]) and mahalanobis(s[j], s[i]) which is unnecessary since mahalanbis distance is symmetric.