# Numpy Broadcasting Rules

They say that all arithmetic operations in Numpy behave like their element-wise cousins in Matlab. This is wrong, and seriously tripped me up last week.

In particular, this is what happens when you multiply an array with a matrix[1] in Numpy:

[[ 1], [[1, 2, 3], [[ 1, 2, 3], [ 10], * [4, 5, 6], = [ 40, 50, 60], [100]] [7, 8, 9]] [700, 800, 900]] [ 1, 10, 100] [[1, 2, 3], [[ 1, 20, 300], OR * [4, 5, 6], = [ 4, 50, 600], [[ 1, 10, 100]] [7, 8, 9]] [ 7, 80, 900]]

They behave as if each row was evaluated separately, and singular dimensions are repeated where necessary. It helps to think about them as row-wise, instead of element-wise. This is particularly important in the second example, where the *whole* 1d-array is multiplied with *every row* of the 2d-array.

Note that this is *not* equivalent to multiplying every *element* as in `[a[n]*b[n] for n in range(len(a))]`

. I guess that's why this is called *broadcasting*, and not *element-wise*.

[1] "matrix" here refers to a 2-d `numpy.array`

. There is also a `numpy.matrix`

, where multiplication is matrix multiplication, but this is not what I'm talking about.