I have two numpy array a and b
a=np.array([[1,2,3],[4,5,6],[7,8,9]])
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
b = np.array([1,2,3])
array([1, 2, 3])
I would like to substract to each row of a the correspondent element of b (ie. to the first row of a, the first element of b, etc) so that c is
array([[0, 1, 2],
[2, 3, 4],
[4, 5, 6]])
Is there a python command to do this?
Is there a python command to do this?
Yes, the - operator.
In addition you need to make b into a column vector so that broadcasting can do the rest for you:
a - b[:, np.newaxis]
# array([[0, 1, 2],
# [2, 3, 4],
# [4, 5, 6]])
yup! You just need to make b a column vector first
a - b[:, np.newaxis]
Reshape b into a column vector, then subtract:
a - b.reshape(3, 1)
b isn't altered in place, but the result of the reshape method call will be the column vector:
array([[1],
[2],
[3]])
Allowing the "shape" of the subtraction you wanted. A little more general reshape operation would be:
b.reshape(b.size, 1)
Taking however many elements b has, and molding them into an N x 1 vector.
Update: A quick benchmark shows kazemakase's answer, using b[:, np.newaxis] as the reshaping strategy, to be ~7% faster. For small vectors, those few extra fractions of a µs won't matter. But for large vectors or inner loops, prefer his approach. It's a less-general reshape, but more performant for this use.
b.reshape(-1, 1) lets reshape set the size of one dimension automatically without explicitly querying b.shape.
-1 wildcard, but since you bring it up, it tunes performance with no loss of generality. Thumbs up.