Is there a difference between adding a scalar to a vector inside a for loop and outside it, using numpy?

Question

I was trying to take advantage of the Broadcasting property of Python while replacing the for loop of this snippet:

import numpy as np
B = np.random.randn(10,1)
k = 25
for i in range(len(B)):
  B[i][0]= B[i][0] + k

with this:

for i in range((lenB)):
  B=B+k

I observed that I was getting different results. When I tried outside the loop, B = B+k, gave the same results as what I was expecting with B[i][0] = B[i][0] + k

Why is this so? Does Broadcasting follow different rules inside loops?

I think you have a typo. 'for in in range(len(B))'? Also, are you using numpy? — eSurfsnake, Aug 26 '18 at 05:43
Please make a [mcve] by initialising `B` and `k` helpfully. Fix the seeming mistakes around `in` and `lenB` if they are not also in the code you are asking about. Add all relevant imports. — Yunnosch, Aug 26 '18 at 05:44
broadcasting is associated with `numpy`, Python itself doesn't really have a notion of "scalar" vs "vector" — juanpa.arrivillaga, Aug 26 '18 at 05:46
Surely you are now adding `k` multiple times. Remove the loop. — John Zwinck, Aug 26 '18 at 05:52

Eran Moshe · Accepted Answer · 2018-08-28T07:49:31.717

In your 2nd option you ment to do the following:

B=B+k

As you see, you don't need the for loop and it is MUCH faster than looping over the "vector" (numpy array).

It is some form of "Vectorization" calculation instead of iterative calculation which is better in terms of complexity and readability. Both will yield the same result.

You can see a lot of examples on Vectorization vs Iteration, including running time, here.

And you can see a great video of Andrew Ng going over numpy broadcasting property.

Is there a difference between adding a scalar to a vector inside a for loop and outside it, using numpy?

1 Answers1