Is Binary Subarrays With Sum easy or hard?

Binary Subarrays With Sum is classified as a Medium problem on LeetCode with an acceptance rate around 68%. The challenge lies in recognizing the prefix sum pattern or the sliding window atMost trick that converts the problem into a linear-time solution.

Binary Subarrays With Sum Python/Java solution

Python and JavaScript implementations typically use the prefix sum plus hash map approach because dictionary lookups are straightforward and efficient. Java and C++ solutions often use the sliding window atMost technique for binary arrays since it runs in O(n) time and only uses constant extra space.

How to solve Binary Subarrays With Sum in O(n)?

Maintain a running prefix sum while iterating through the array and store frequencies of prefix sums in a hash map. For each index, check how many previous prefix sums equal currentSum - goal and add that count to the result. Another O(n) option for binary arrays is computing atMost(goal) minus atMost(goal - 1) using a sliding window.

What is the best approach for Binary Subarrays With Sum?

The prefix sum with hash map approach is the most widely used solution. It runs in O(n) time and O(n) space by storing counts of previous prefix sums and checking how many times curr - goal appears. For binary arrays specifically, a sliding window technique using the atMost trick can achieve O(n) time with O(1) space.

Is Binary Subarrays With Sum asked at Google/Amazon/Meta?

Binary Subarrays With Sum is a common interview-style problem involving prefix sums and sliding window techniques. Variants of subarray sum problems appear frequently in interviews at companies like Amazon, Google, and Meta because they test understanding of arrays, hashing, and linear-time optimization.

What data structure is used in Binary Subarrays With Sum?

The optimal prefix sum solution uses a hash map to store frequencies of prefix sums encountered so far. This allows constant-time lookup to determine how many previous prefixes produce the required subarray sum. The sliding window version relies on two pointers and a running sum instead of additional data structures.

What is the time complexity of Binary Subarrays With Sum?

The optimal solutions run in O(n) time where n is the length of the array. Prefix sum with a hash map processes each element once with constant-time lookups, while the sliding window approach moves each pointer at most n times. Both significantly outperform the O(n^2) brute force method.

Description Solution Video 20:27

930. Binary Subarrays With Sum

Medium69.7% acceptance7 companies

Given a binary array nums and an integer goal, return the number of non-empty subarrays with a sum goal.

A subarray is a contiguous part of the array.

Example 1:

Input: nums = [1,0,1,0,1], goal = 2
Output: 4
Explanation: The 4 subarrays are bolded and underlined below:
[1,0,1,0,1]
[1,0,1,0,1]
[1,0,1,0,1]
[1,0,1,0,1]

Example 2:

Input: nums = [0,0,0,0,0], goal = 0
Output: 15

Constraints:

1 <= nums.length <= 3 * 10⁴
nums[i] is either 0 or 1.
0 <= goal <= nums.length

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930. Binary Subarrays With Sum

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