How to make array into array list in python -

from array

s = np.array([[35788, 41715, ... 34964],            [5047, 23529, ... 5165],            [12104, 33899, ... 11914],            [3646, 21031, ... 3814],            [8704, 7906, ... 8705]]) 

i have loop this

end =[] in range(len(s)):     j in range(i, len(s)):         out = mahalanobis(s[i], s[j], invcov)                end.append(out) print end 

and take output :

[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] 

but want output :

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

given 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 build desired 2-dimensional array using

import numpy 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) 

which 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 array filled zeros

2. np.triu_indices(s.shape[0], 0) returns indices upper-triangle of 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 upper-triangle values end.

3. take transpose of result , add result, making result symmetric.

this allows obtain result without calling both mahalanobis(s[i], s[j]) , mahalanobis(s[j], s[i]) unnecessary since mahalanbis distance symmetric.

---

note diagonal 0 since mahalanobis(x,x) equals 0 x. little added efficiency, exclude diagonal:

end =[] in range(len(s)):     j in range(i+1, len(s)):              # <-- note i+1         out = mahalanobis(s[i], s[j], invcov)                end.append(out) 

and build result same code before except can use

result[np.triu_indices(s.shape[0], 1)] = end      

instead of

result[np.triu_indices(s.shape[0], 0)] = end      

the second argument np.triu_indices controls diagonal offset. when offset 1, indices corresponding the main diagonal omitted.

in [96]: np.triu_indices(5, 1) out[96]: (array([0, 0, 0, 0, 1, 1, 1, 2, 2, 3]), array([1, 2, 3, 4, 2, 3, 4, 3, 4, 4]))