What is the best approach to solve Running Sum of 1D Array?

The optimal approach for Running Sum of 1D Array is the "Optimal — In-Place Prefix Sum" technique. It runs in O(n) time and uses O(1) space. Accumulate the sum in-place. Each element becomes the sum of itself and all previous elements. Build the prefix sum array in a single left-to-right pass.

What is the time complexity of Running Sum of 1D Array?

The optimal solution for Running Sum of 1D Array has a time complexity of O(n) and a space complexity of O(1), achieved using the Prefix Sum pattern.

Is Running Sum of 1D Array a Easy LeetCode problem?

Yes, LeetCode #1480 (Running Sum of 1D Array) is rated Easy. It uses the Prefix Sum pattern and is commonly asked in interviews at Amazon, Google.

How to solve Running Sum of 1D Array in Python, Java, C++, and JavaScript?

LumiChats provides complete solutions for Running Sum of 1D Array in Python, Java, C++, and JavaScript, along with step-by-step visual animations and time/space complexity analysis. All solutions are free to view.

#1480EasyPrefix Sum

Running Sum of 1D Array

Build a prefix sum array — foundation of all range query problems.

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Problem Statement

Given an array nums, define the running sum of an array as runningSum[i] = sum(nums[0]...nums[i]). Return the running sum of nums.

Examples

Example 1

Input:nums = [1,2,3,4]

Output:[1,3,6,10]

Explanation: Running sum: [1, 1+2, 1+2+3, 1+2+3+4]

Example 2

Input:nums = [1,1,1,1,1]

Output:[1,2,3,4,5]

Constraints

1 ≤ nums.length ≤ 1000
-10⁶ ≤ nums[i] ≤ 10⁶

Solutions

Extra Array

TimeO(n²)SpaceO(n)

Create a result array. For each index, sum all elements from 0 to i. Straightforward but O(n²).

Visual Animation

def runningSum(nums: list[int]) -> list[int]:
    return [sum(nums[:i+1]) for i in range(len(nums))]

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