Three Clever Ways to Sum Columns in a 2D Python List

1. Introduction

A fan asked how to sum each column of a regular two‑dimensional Python list. The naïve approach uses separate accumulator variables for each column, which becomes unwieldy when the sub‑lists contain many elements.

lst = [[1, 2, 3, 4], [1, 5, 1, 2], [2, 3, 4, 5], [5, 3, 1, 3]]
s1 = 0
s2 = 0
s3 = 0
s4 = 0
for i in lst:
    s1 += i[0]
    s2 += i[1]
    s3 += i[2]
    s4 += i[3]
print(list([s1, s2, s3, s4]))

This works but is not scalable.

2. Solutions

Solution by Yuliang

Using list unpacking with zip to transpose the matrix and sum each column.

lst = [[1, 2, 3, 4], [1, 5, 1, 2], [2, 3, 4, 5], [5, 3, 1, 3]]
[print(sum(i) for i in zip(*lst))]

Solution by Daler

Leveraging NumPy to compute sums along both axes.

import numpy as np

lst = [[1, 2, 3, 4], [1, 5, 1, 2], [2, 3, 4, 5], [5, 3, 1, 3]]
list1 = np.sum(lst, axis=0)  # column‑wise sum
list2 = np.sum(lst, axis=1)  # row‑wise sum
print(list1)
print(list2)

Solution by Moon God

Applying functools.reduce with map and a lambda to aggregate columns.

from functools import reduce

lst = [[1, 2, 3, 4],
       [1, 5, 1, 2, 6],
       [2, 3, 4, 5],
       [5, 3, 1, 3]]
print(list(reduce(lambda x, y: map(lambda i, j: i + j, x, y), lst)))

3. Conclusion

The three methods—list unpacking with zip, NumPy aggregation, and functional reduction—provide concise and scalable ways to sum corresponding elements of sub‑lists in a 2D list. Choose the approach that best fits your project’s dependencies and readability preferences.