Increase numpy array elements using array as index

Question

I am trying to efficiently update some elements of a numpy array A, using another array b to indicate the indexes of the elements of A to be updated. However b can contain duplicates which are ignored whereas I would like to be taken into account. I would like to avoid for looping b. To illustrate it:

>>> A = np.arange(10).reshape(2,5)
>>> A[0, np.array([1,1,1,2])] += 1
>>> A
array([[0, 2, 3, 3, 4],
       [5, 6, 7, 8, 9]])

whereas I would like the output to be:

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

Any ideas?

In your index array, column index 1 occurs 3 times. If you want the elements to be incremented as many times as the index occurs, then element [0,1] should become 1 + 3 = 4. — Seppo Enarvi
– Seppo Enarvi, Commented Mar 11, 2016 at 13:05

Ruli · Accepted Answer · 2020-08-05 16:42:56Z

11

To correctly handle the duplicate indices, you'll need to use np.add.at instead of +=. Therefore to update the first row of A, the simplest way would probably be to do the following:

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

The documents for the ufunc.at method can be found here.

edited Aug 5, 2020 at 16:42

Ruli

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answered Dec 3, 2015 at 15:04

Alex Riley

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1 Comment

geompalik Over a year ago

Thank you. It works as expected. Thank you for the pointer, I didn't know this feature, it is very useful.

TheBlackCat · Accepted Answer · 2015-12-03 15:09:12Z

0

One approach is to use numpy.histogram to find out how many values there are at each index, then add the result to A:

A[0, :] += np.histogram(np.array([1,1,1,2]), bins=np.arange(A.shape[1]+1))[0]

answered Dec 3, 2015 at 15:09

TheBlackCat

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1 Comment

geompalik Over a year ago

Thanks for the answer, but I think the accepted one is more intuitive.

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