Python joint enumerate with multiple variables

Question

So I would like to iterate over two lists simultaneously and I also need the index as done with the following code. Both are straightforward to me, but is it possible two use enumerate in the second case as well? Of course, assuming both var arrays are of the same length.

import numpy as np

def _eqn(y, variables, sign=-1.0):
    f = 0
    for i,x in enumerate(variables):
        f += x/(1+0.06)**(i+1)
    return float(sign*(y + f))


_eqn(1,np.array([1,1]))

def _eqn2(y, vars1, vars2, sign=-1.0):
    f = 0
    n = len(vars1)
    for x,y,i in zip(vars1, vars2, range(n)):
        f += (x+y)/(1+0.06)**(i+1)
    return float(sign*(y + f))


_eqn2(1,np.array([1,1]),np.array([1,1]))

Did you try it?

Ignacio Vergara Kausel
– Ignacio Vergara Kausel

2017-11-17 13:07:47 +00:00
Commented Nov 17, 2017 at 13:07 — Ignacio Vergara Kausel
– Ignacio Vergara Kausel, Commented Nov 17, 2017 at 13:07

Ignacio Vergara Kausel · Accepted Answer · 2017-11-17 13:30:24Z

5

Yes, it is possible... although with a slight change

def _eqn2(y, vars1, vars2, sign=-1.0):
    f = 0
    for i, (v1,v2) in enumerate(zip(vars1, vars2)):
        f += (v1+v2)/(1+0.06)**(i+1)
    return float(sign*(y + f))

You enumerate tuples of the zip of your vars.

Although since you're working with numpy arrays, I'm sure that there is a better way to achieve this same goal without using a python for loop.

Also took the liberty of changing the names of variables since you were usign y in two different contexts and could be confusing.

edited Nov 17, 2017 at 13:30

answered Nov 17, 2017 at 13:09

Ignacio Vergara Kausel

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4 Comments

Peterhack Over a year ago

Ok, can you give a hint solving this without a for loop?

Ignacio Vergara Kausel Over a year ago

You make an array of numerators and another one of denominators (a**b in numpy works as element-wise exponentiation) and then you divide numerator by denominator which is also a vectorized operation and in the end you do a sum of that array. That will eliminate the whole for loop. Eventually if your lists/arrays are big enough it can be performance-worthy.

Peterhack Over a year ago

You are right, that this can easily be achieved with vectorized operations. And indeed it gets faster with bigger arrays. You think this is the best way? f = ((vars1+vars2)/(1+0.06)**(np.arange(len(vars1))+1)).sum()

Ignacio Vergara Kausel Over a year ago

That looks like what I would have done. You can explicitly ass 1 with 0.06 to avoid one addition.

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