How to Compute Sliding Window Sum of Size k in Python

Compute the sum of every contiguous subarray of a fixed size k using an efficient O(n) sliding window technique.

Easy Python 3.9+ Aug 9, 2026 Lists & loops 13 views 0 copies

sliding-window list sum algorithm

Python code

20 lines

Python 3.9+

def sliding_window_sum(nums, k):
    """Return a list of sums for each contiguous subarray of size k."""
    if not nums or k <= 0 or k > len(nums):
        return []
    
    result = []
    window_sum = sum(nums[:k])
    result.append(window_sum)
    
    for i in range(k, len(nums)):
        window_sum += nums[i] - nums[i - k]
        result.append(window_sum)
    
    return result


if __name__ == "__main__":
    data = [1, 3, 5, 7, 9, 11]
    k = 3
    print(sliding_window_sum(data, k))

Output

stdout

[9, 15, 21, 27]

How it works

This function starts by summing the first k elements to initialize the window. Then, for each step forward, it adds the new element entering the window and subtracts the element leaving it, maintaining a running sum. This avoids recalculating the entire window sum from scratch each time, reducing time complexity from O(n*k) to O(n). The loop runs from index k to the end, updating the window sum in constant time per iteration. Edge cases like empty lists or invalid k are handled by returning an empty list.

Common mistakes

Not checking if k exceeds the list length, leading to an empty result or index error.
Using a naive O(n*k) approach by summing each window separately, which is slower.
Forgetting to subtract the element that leaves the window, causing incorrect sums.

Variations

Use itertools.accumulate to compute prefix sums, then derive each window sum as prefix[i+k] - prefix[i].
Use a list comprehension with slicing: [sum(nums[i:i+k]) for i in range(len(nums)-k+1)].
For streaming data, implement the window as a deque to add and remove elements.

Real-world use cases

Calculating moving averages for time-series metrics like server latency.
Computing rolling sums of transactions for anomaly detection in financial data.
Aggregating sensor readings over a fixed time window in IoT pipelines.

How to Compute Sliding Window Sum of Size k in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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