Mastering Array Interview Questions: The Sliding Window Technique in Python

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Weekly Python

4 min readMay 10, 2023

Introduction

Array problems are common in coding interviews, and one powerful technique for solving a variety of array-related challenges is the sliding window technique. This approach involves maintaining a variable-size window within the array and moving the window by adjusting the positions of two pointers. The sliding window technique is particularly useful for solving problems that involve finding subarrays that meet specific conditions. In this blog post, we will explore the sliding window technique in Python and work through several examples to help you get a firm grasp on this essential interview skill.

Understanding the Sliding Window Technique

The sliding window technique is a method that involves maintaining a window within the array and moving the window by adjusting the positions of two pointers. This technique is often used to solve problems that require finding subarrays that satisfy certain conditions, such as having a specific sum, product, or length. The sliding window technique can help optimize the time complexity of certain algorithms, leading to more efficient and elegant solutions.

Types of Sliding Window Techniques

Fixed-size sliding window

In this approach, the window size is fixed, and the window slides through the array by moving both the left and right pointers one position at a time.

Variable-size sliding window

In this approach, the window size can change, allowing for the window to expand or contract as needed. This technique is often used when the problem requires finding a subarray that meets a certain condition while minimizing or maximizing the window size.

Examples of Sliding Window Technique Problems

Example 1: Find the maximum subarray sum of size k

Problem statement: Given an array of integers and an integer k, find the maximum sum of a subarray of size k.

Initialize two pointers, left and right, both at the beginning of the array.
Initialize a variable to keep track of the current subarray sum.
Move the right pointer to the right, adding the element at the right pointer to the current sum.
When the distance between the left and right pointers is equal to k, subtract the element at the left pointer from the current sum and move the left pointer one position to the right.
Keep track of the maximum subarray sum encountered during the process.
Repeat steps 3–5 until the right pointer reaches the end of the array.

def max_subarray_sum(arr, k):
    if len(arr) < k:
        return None

    left = 0
    current_sum = sum(arr[:k])
    max_sum = current_sum

    for right in range(k, len(arr)):
        current_sum += arr[right] - arr[left]
        max_sum = max(max_sum, current_sum)
        left += 1

    return max_sum


# Example usage:
arr = [2, 1, 5, 1, 3, 2]
k = 3
result = max_subarray_sum(arr, k)
if result is not None:
    print(f"Maximum subarray sum of size {k}: {result}")
else:
    print("Invalid input: k is larger than the size of the array.")

Example 2: Find the smallest subarray with a sum greater than or equal to a target

Problem statement: Given an array of positive integers and a target sum, find the smallest subarray with a sum greater than or equal to the target.

Initialize two pointers, left and right, both at the beginning of the array.
Initialize a variable to keep track of the current subarray sum and another variable to keep track of the minimum subarray length.
Move the right pointer to the right, adding the element at the right pointer to the current sum.
When the current sum is greater than or equal to the target, update the minimum subarray length and subtract the element at the left pointer from the current sum, then move the left pointer one position to the right.
Repeat steps 3–4 until the right pointer reaches the end of the array.

def min_subarray_length(arr, target_sum):
    left = 0
    current_sum = 0
    min_length = float('inf')

    for right in range(len(arr)):
        current_sum += arr[right]

        while current_sum >= target_sum:
            min_length = min(min_length, right - left + 1)
            current_sum -= arr[left]
            left += 1

    return min_length if min_length != float('inf') else None


# Example usage:
arr = [2, 3, 1, 2, 4, 3]
target_sum = 7
result = min_subarray_length(arr, target_sum)
if result is not None:
    print(f"Smallest subarray with sum greater than or equal to {target_sum}: {result}")
else:
    print("No subarray with sum greater than or equal to the target sum.")

Tips for Using the Sliding Window Technique

Understand the problem

Before applying the sliding window technique, make sure you thoroughly understand the problem statement and requirements. Ensure that the technique is suitable for the given problem.

Choose the right approach

Depending on the problem, different types of sliding window techniques may be more appropriate. Be familiar with fixed-size and variable-size sliding window techniques and choose the most suitable method for the problem at hand.

Edge cases and input validation

Be mindful of edge cases and validate the input before applying the sliding window technique. Consider scenarios where the array is empty or has a single element, as well as cases where the target sum or window size is outside the array’s bounds.

Time and space complexity

Analyze the time and space complexity of your solution using the sliding window technique. In many cases, the technique can help optimize your solution by reducing the time complexity from quadratic to linear.

Conclusion

The sliding window technique is a powerful method for solving a wide range of array problems in coding interviews. By understanding the different types of sliding window techniques and practicing their implementation in Python, you can improve your problem-solving skills and increase your chances of success in coding interviews. Don’t forget to analyze the time and space complexity of your solutions and consider edge cases and input validation. With practice, you’ll become proficient in using the sliding window technique and be well-prepared to tackle any array problem that comes your way.

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