numpy - scalar multiplication of column vector times row vector

Question

What is the best and most efficient way to solve the following in python numpy:

given a weight vector:

weights = numpy.array([1, 5, 2])

and a value vector:

values = numpy.array([1, 3, 10, 4, 2])

as result I need a matrix, which contains on each row the values vector scalar multiplied with the value of weights[row]:

result = [
    [1,  3, 10,  4,  2],
    [5, 15, 50, 20, 10],
    [2,  6, 20,  8,  4]
]

One solution which I found is the following:

result = numpy.array([ weights[n]*values for n in range(len(weights)) ])

Is there a better way?

Answer 1

This operation is called the outer product. It can be performed using numpy.outer():

In [6]: numpy.outer(weights, values)
Out[6]: 
array([[ 1,  3, 10,  4,  2],
       [ 5, 15, 50, 20, 10],
       [ 2,  6, 20,  8,  4]])