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Suppose I have a matrix A with some arbitrary values:
array([[ 2, 4, 5, 3], [ 1, 6, 8, 9], [ 8, 7, 0, 2]]) And a matrix B which contains indices of elements in A:
array([[0, 0, 1, 2], [0, 3, 2, 1], [3, 2, 1, 0]]) How do I select values from A pointed by B, i.e.:
A[B] = [[2, 2, 4, 5], [1, 9, 8, 6], [2, 0, 7, 8]] 
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Use numpy. concatenate() to merge the content of two or multiple arrays into a single array. This function takes several arguments along with the NumPy arrays to concatenate and returns a Numpy array ndarray. Note that this method also takes axis as another argument, when not specified it defaults to 0.
Array indexing is the same as accessing an array element. You can access an array element by referring to its index number. The indexes in NumPy arrays start with 0, meaning that the first element has index 0, and the second has index 1 etc.
Fancy indexing is conceptually simple: it means passing an array of indices to access multiple array elements at once. For example, consider the following array: import numpy as np rand = np. random. RandomState(42) x = rand.
EDIT: np.take_along_axis is a builtin function for this use case implemented since numpy 1.15. See @hpaulj 's answer below for how to use it.
You can use NumPy's advanced indexing -
A[np.arange(A.shape[0])[:,None],B] One can also use linear indexing -
m,n = A.shape out = np.take(A,B + n*np.arange(m)[:,None]) Sample run -
In [40]: A Out[40]: array([[2, 4, 5, 3], [1, 6, 8, 9], [8, 7, 0, 2]]) In [41]: B Out[41]: array([[0, 0, 1, 2], [0, 3, 2, 1], [3, 2, 1, 0]]) In [42]: A[np.arange(A.shape[0])[:,None],B] Out[42]: array([[2, 2, 4, 5], [1, 9, 8, 6], [2, 0, 7, 8]]) In [43]: m,n = A.shape In [44]: np.take(A,B + n*np.arange(m)[:,None]) Out[44]: array([[2, 2, 4, 5], [1, 9, 8, 6], [2, 0, 7, 8]]) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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