For example, I have two numpy arrays,
A = np.array( [[0,1], [2,3], [4,5]]) B = np.array( [[1], [0], [1]], dtype='int')
and I want to extract one element from each row of A, and that element is indexed by B, so I want the following results:
C = np.array( [[1], [2], [5]])
I tried A[:, B.ravel()], but it’ll broadcast B, not what I want. Also looked into np.take, seems not the right solution to my problem.
However, I could use np.choose by transposing A,
np.choose(B.ravel(), A.T)
but any other better solution?
Answers:
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Method 1
You can use NumPy's purely integer array indexing –
A[np.arange(A.shape[0]),B.ravel()]
Sample run –
In [57]: A
Out[57]:
array([[0, 1],
[2, 3],
[4, 5]])
In [58]: B
Out[58]:
array([[1],
[0],
[1]])
In [59]: A[np.arange(A.shape[0]),B.ravel()]
Out[59]: array([1, 2, 5])
Please note that if B is a 1D array or a list of such column indices, you could simply skip the flattening operation with .ravel().
Sample run –
In [186]: A
Out[186]:
array([[0, 1],
[2, 3],
[4, 5]])
In [187]: B
Out[187]: [1, 0, 1]
In [188]: A[np.arange(A.shape[0]),B]
Out[188]: array([1, 2, 5])
Method 2
C = np.array([A[i][j] for i,j in enumerate(B)])
All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0