Given a list nested with numpy lists, I want to go through each index in each corresponding list and keep track of the count depending on the element, which would be stored into a single list.

The minimum runnable code example below better showcases the problem.

import numpy as np lst = [ np.array([1,0,1]), np.array([1,1,1]), np.array([2,2,1]) ] print(lst) #Problem, take count of each corresponding index within list #If the element == 2, subtract one. If it's 1, add 1. 0 has no value #Make the output just one list with the calculated count for each index #Expected output: #lst = [1, 0, 3] # 1 in index 0 because there's two 1s in each lists first index, but a 2 subtracts one from the count # 0 in index 1 since there's one 1 which brings the count to 1, but a 2 subtracts one from the count # 3 in index 2 since there's three 1's, adding 3 to the count.

I have looked into `from collections import defaultdict`

and `from itertools import groupby`

, but I couldn’t understand how to use that to do anything else but sort a nested list.

Apologies if this issue is within the two sources I provided above.

If all the inner arrays have the same length you could transform it into a 2-dimensional array to leverage numpy vectorization:

# transform into two dimensional array arr = np.array(lst) # set -1 where values are equals to 2 t = np.where(arr == 2, -1, arr) # sum across first axis (each corresponding positions on the inners lists) res = t.sum(axis=0).tolist() print(res)

**Output**

[1, 0, 3]

## Recent Comments