Weighted averaging a list

Question

Thanks for your responses. Yes, I was looking for the weighted average.

rate = [14.424, 14.421, 14.417, 14.413, 14.41]  amount = [3058.0, 8826.0, 56705.0, 30657.0, 12984.0] 

I want the weighted average of the top list based on each item of the bottom list.

So, if the first bottom-list item is small (such as 3,058 compared to the total 112,230), then the first top-list item should have less of an effect on the top-list average.

Here is some of what I have tried. It gives me an answer that looks right, but I am not sure if it follows what I am looking for.

for g in range(len(rate)):     rate[g] = rate[g] * (amount[g] / sum(amount)) rate = sum(rate) 

EDIT: After comparing other responses with my code, I decided to use the zip code to keep it as short as possible.

Answer 1

You could use numpy.average to calculate weighted average.

In [13]: import numpy as np  In [14]: rate = [14.424, 14.421, 14.417, 14.413, 14.41]  In [15]: amount = [3058.0, 8826.0, 56705.0, 30657.0, 12984.0]  In [17]: weighted_avg = np.average(rate, weights=amount)  In [19]: weighted_avg Out[19]: 14.415602815646439 

Answer 2

for g in range(len(rate)):    rate[g] = rate[g] * amount[g] / sum(amount) rate = sum(rate) 

is the same as:

sum(rate[g] * amount[g] / sum(amount) for g in range(len(rate))) 

which is the same as:

sum(rate[g] * amount[g] for g in range(len(rate))) / sum(amount) 

which is the same as:

sum(x * y for x, y in zip(rate, amount)) / sum(amount)

Result:

14.415602815646439