How to Index One Numpy Array by Another?

Indexing One Array by Another in Numpy

Consider two matrices, A and B, where A contains arbitrary values and B holds indices of elements in A. The task is to extract elements from A based on the indices specified by B. This indexing allows for selective element retrieval.

Solution using Advanced Indexing:

Numpy's advanced indexing enables this operation using the expression:

A[np.arange(A.shape[0])[:,None], B]

This approach utilizes a combination of row indices and column indices retrieved from B to retrieve elements in A.

Solution using Linear Indexing:

An alternative approach involves linear indexing:

m, n = A.shape
out = np.take(A, B + n*np.arange(m)[:,None])

Here, m and n represent the dimensions of A, and the operations within the np.take() function ensure correct indexing of elements based on B.

Example:

Let's illustrate these solutions with an example:

import numpy as np

A = np.array([[2, 4, 5, 3],
              [1, 6, 8, 9],
              [8, 7, 0, 2]])

B = np.array([[0, 0, 1, 2],
              [0, 3, 2, 1],
              [3, 2, 1, 0]])

# Advanced indexing
result1 = A[np.arange(A.shape[0])[:,None], B]

# Linear indexing
m, n = A.shape
result2 = np.take(A, B + n*np.arange(m)[:,None])

print("Result using advanced indexing:")
print(result1)

print("Result using linear indexing:")
print(result2)

Output:

Result using advanced indexing:
[[2 2 4 5]
 [1 9 8 6]
 [2 0 7 8]]

Result using linear indexing:
[[2 2 4 5]
 [1 9 8 6]
 [2 0 7 8]]

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