Indexing 3D arrays with Numpy

Question

I have an array in three dimensions (x, y, z) and an indexing vector. This vector has a size equal to the dimension x of the array. Its objective is to index a specific y bringing their respective z, i.e., the expected result has dimension (x, z).

I wrote a code that works as expected, but does anyone know if a Numpy function can replace the for loop and solve the problem more optimally?

arr = np.random.rand(100,5,2)
result = np.random.rand(100,2)
id = [np.random.randint(0, 5) for _ in range(100)]
for i in range(100): 
    result[i] = arr[i,id[i]]

maousi · Accepted Answer · 2021-03-25 22:12:05Z

1

You can achieve this with this piece of code:

import numpy as np
arr = np.random.randn(100, 5, 2)
ids = np.random.randint(0, 5, size=100)
res = arr[range(100), ids]
res.shape # (100, 2)

answered Mar 25, 2021 at 22:12

maousi

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1 Comment

Kevin Over a year ago

Not sure if OP meant to only index the y dimension. If so the first dimension will not be indexed: res = arr[:, ids].

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