Python - Incremental Sublist Sum

An incremental sublist sum (also known as cumulative sum) calculates running totals of elements in a list. Each position contains the sum of all elements from the start up to that position.

For example, given the list [10, 20, 30, 40, 50]:

• Index 0: 10
• Index 1: 10 + 20 = 30
• Index 2: 10 + 20 + 30 = 60
• Index 3: 10 + 20 + 30 + 40 = 100
• Index 4: 10 + 20 + 30 + 40 + 50 = 150

The result is [10, 30, 60, 100, 150].

Using a Loop

The most straightforward approach uses a loop to accumulate the sum ?

def incremental_sublist_sum(numbers):
    result = []
    current_sum = 0
    for num in numbers:
        current_sum += num
        result.append(current_sum)
    return result

numbers = [100, 50, 120, 60, 30, 40]
cumulative_sum = incremental_sublist_sum(numbers)
print(cumulative_sum)

[100, 150, 270, 330, 360, 400]

Using List Comprehension

List comprehension with slicing provides a more concise solution ?

def incremental_sublist_sum(numbers):
    return [sum(numbers[:i+1]) for i in range(len(numbers))]

numbers = [78, 10, 88, 20]
cumulative_sum = incremental_sublist_sum(numbers)
print(cumulative_sum)

[78, 88, 176, 196]

Using itertools.accumulate()

The accumulate() function from itertools is designed specifically for cumulative operations ?

from itertools import accumulate

def incremental_sublist_sum(numbers):
    return list(accumulate(numbers))

numbers = [23, 43, 10]
cumulative_sum = incremental_sublist_sum(numbers)
print(cumulative_sum)

[23, 66, 76]

Using NumPy

NumPy's cumsum() function efficiently handles large arrays ?

import numpy as np

def incremental_sublist_sum(numbers):
    return np.cumsum(numbers).tolist()

numbers = [11, 21, 31, 41, 51]
cumulative_sum = incremental_sublist_sum(numbers)
print(cumulative_sum)

[11, 32, 63, 104, 155]

Performance Comparison

Conclusion

Use itertools.accumulate() for most cases as it's efficient and readable. For numerical computing with large datasets, NumPy's cumsum() offers superior performance.