How to rearrange array based upon index array

Question

I'm looking for a one line solution that would help me do the following.

Suppose I have

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

I'd like to rearrange it based upon an input ordering. If there were a numpy function called arrange, it would do the following:

newarray = np.arrange(array, [1, 0, 3, 4, 2]) print newarray      [20, 10, 40, 50, 30] 

Formally, if the array to be reordered is m x n, and the "index" array is 1 x n, the ordering would be determined by the array called "index".

Does numpy have a function like this?

Answer 1

You can simply use your "index" list directly, as, well, an index array:

>>> arr = np.array([10, 20, 30, 40, 50]) >>> idx = [1, 0, 3, 4, 2] >>> arr[idx] array([20, 10, 40, 50, 30]) 

It tends to be much faster if idx is already an ndarray and not a list, even though it'll work either way:

>>> %timeit arr[idx] 100000 loops, best of 3: 2.11 µs per loop >>> ai = np.array(idx) >>> %timeit arr[ai] 1000000 loops, best of 3: 296 ns per loop 

Answer 2

for those whose index is 2d array, you can use map function. Here is an example:

a = np.random.randn(3, 3) print(a) print(np.argsort(a))  print(np.array(list(map(lambda x, y: y[x], np.argsort(a), a)))) 

the output is

[[-1.42167035  0.62520498  2.02054623]  [-0.17966393 -0.01561566  0.24480554]  [ 1.10568543  0.00298402 -0.71397599]] [[0 1 2]  [0 1 2]  [2 1 0]] [[-1.42167035  0.62520498  2.02054623]  [-0.17966393 -0.01561566  0.24480554]  [-0.71397599  0.00298402  1.10568543]]