How to Index Multiple Arrays in NumPy?

Indexing Multiple Arrays in NumPy

In NumPy, indexing beyond single-dimensional arrays requires advanced techniques. One scenario is indexing one array based on the values of another array, known as multidimensional indexing.

Consider matrices A with arbitrary values:

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

And matrix B containing indices of elements in A:

array([[0, 0, 1, 2],
       [0, 3, 2, 1],
       [3, 2, 1, 0]])

To select values from A using the indices in B, you can employ NumPy's advanced indexing:

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

This indexing approach combines the row indices (0, 1, 2) with the indices specified in B.

Alternatively, you can use linear indexing:

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

Here, m and n represent the number of rows and columns in A, respectively. np.take() extracts elements from A based on the linear indices generated by the sum of B and n multiplied by the range of row indices.

Using either technique, the output will be:

[[2, 2, 4, 5],
 [1, 9, 8, 6],
 [2, 0, 7, 8]]

This indexing method provides flexibility in accessing and manipulating elements based on multiple criteria, enhancing the versatility of NumPy arrays for complex data processing scenarios.

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