Numpy: np_array[index_order_array] changes the order of elements in an array?

Question

I'm new to Python and Numpy

Why array = [1,2,3,4] and new_array = array[[3,2,0,1]] results in changing the order of elements as mentioned in the inner array?

import numpy as np

array = np.array([10,20,30,40,50])
array_link = np.array(['A','B','C','D','E'])

new_array = np.ndarray(5, dtype=np.int32)
new_array_link = np.ndarray(5, dtype=np.int32)

perm = np.random.permutation(array.shape[0])

new_array = array[perm]
new_array_link = array_link[perm]

print(new_array)
print(new_array_link)

# Output:
# [30 40 10 50 20]
# ['C' 'D' 'A' 'E' 'B']

Here is the Playground

Is this how it is supposed to work? Shouldn't it be initializing a new (perhaps 2D) array with the elements of inner array (as the first row)?

I have no idea what you are referring to. Everything seems normal to me. What are you expecting? — akuiper
– akuiper, Commented Mar 16, 2017 at 15:12
You are just creating new arrays that are permutations of the original ones. — user2261062
– user2261062, Commented Mar 16, 2017 at 15:15
As I have already mentioned in the question I'm wondering why new_array = array[[3,2,0,1]] wouldn't initialize a new (perhaps 2D) array with the elements of inner array (as the first row)? — Saravanabalagi Ramachandran
– Saravanabalagi Ramachandran, Commented Mar 16, 2017 at 15:21
I think you are confusing [] with(), the former is for indexing, and the latter is for constructing new arrays. — akuiper
– akuiper, Commented Mar 16, 2017 at 15:23
Just a note: it's good practice to avoid using the np.ndarray class to construct arrays. Use np.array, np.empty, etc. where possible. — Alex Riley
– Alex Riley, Commented Mar 16, 2017 at 15:24

hpaulj · Accepted Answer · 2017-03-16 17:14:56Z

The first of these 2 lines is useless. python does not require that you initialize or 'pre-define' a variable. The first creates an array; the second also creates one, and reassigns the variable. The original value of new_array is discarded.

new_array = np.ndarray(5, dtype=np.int32)
...
new_array = array[perm]

And as a general rule, np.ndarray is only used for advanced purposes. np.array, np.zeros etc are used to create new arrays.

array is a poor choice of variable name. array looks too much like np.array, and actually confused me when I first copied the above lines.

array = np.array([10,20,30,40,50])

In sum your code does:

In [28]: arr = np.array([10,20,30,40,50])
In [29]: perm = np.random.permutation(arr.shape[0])
In [30]: perm
Out[30]: array([2, 0, 1, 4, 3])
In [31]: arr1 = arr[perm]
In [32]: arr1
Out[32]: array([30, 10, 20, 50, 40])

arr1 is a new array with values selected from arr. arr itself is unchanged.

You could assign values to predefined array this way:

In [35]: arr2 = np.zeros(5, int)
In [36]: arr2
Out[36]: array([0, 0, 0, 0, 0])
In [37]: arr2[:] = arr[perm]
In [38]: arr2
Out[38]: array([30, 10, 20, 50, 40])

In arr[perm], the result is the same shape as perm, in this case a 5 element 1d array. If I turn perm into a (5,1) column array, the result is also a (5,1) array:

In [40]: arr[perm[:,None]]
Out[40]: 
array([[30],
       [10],
       [20],
       [50],
       [40]])
In [41]: _.shape
Out[41]: (5, 1)

Another example of array indexing - with a (2,2) array:

In [43]: arr[np.array([[0,1],[2,3]])]
Out[43]: 
array([[10, 20],
       [30, 40]])

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