The "Contains Duplicate" problem is a common coding challenge found on LeetCode and in technical interviews. It requires determining whether an array contains any duplicate elements. In this blog post, we'll explore different approaches to solving this problem in Python, including a straightforward method, an alternative solution by sorting the array and finding duplicates, and a more efficient approach using a hash set. We'll discuss their time and space complexities, offering insights into problem-solving strategies along the way.

Problem Overview

Given an array of integers, determine if the array contains any duplicates. If any value appears at least twice in the array, return True. If every element is distinct, return False.

Example

Input: [1, 2, 3, 1] Output: True

Input: [1, 2, 3, 4] Output: False

Brute Force Approach: Exploring Every Element

Overview: The brute force approach involves checking each element in the array against every other element to find duplicates.

Algorithm:

1. Use two nested loops to iterate through the array.

2. The outer loop iterates through each element in the array.

3. The inner loop iterates through the remaining elements to check for duplicates.

4. If a duplicate is found, return True.

5. If no duplicates are found after both loops are complete, return False.

def containsDuplicateBruteForce(nums):
    n = len(nums)
    for i in range(n):
        for j in range(i + 1, n):
            if nums[i] == nums[j]:
                return True
    return False

Intuition: This approach is similar to searching for a needle in a haystack. By systematically comparing each element with every other element, we can identify duplicates. However, it has a time complexity of O(n^2), making it inefficient for large arrays.

Time Complexity: O(n^2) - Two nested loops iterate through the entire array.

Space Complexity: O(1) - Constant space is used for variables.

Alternative Approach: Sorting and Comparing Adjacent Elements

Overview: An alternative approach involves sorting the array and finding duplicates by comparing adjacent elements. This method leverages the fact that duplicates if they exist, will be adjacent to each other after sorting.

Algorithm:

1. Sort the array in non-decreasing order.

2. Iterate through the sorted array and check if any adjacent elements are the same.

3. If you find any such elements, return True.

4. If no duplicates are found after the loop completes, return False.

def containsDuplicateSort(nums):
    nums.sort()
    for i in range(1, len(nums)):
        if nums[i] == nums[i - 1]:
            return True
    return False

Intuition: By sorting the array, we bring duplicates together, making it easy to identify them by comparing adjacent elements. This approach offers a time complexity of O(n * log(n)), which is efficient for moderate-sized arrays.

Time Complexity: O(n log(n)) - Sorting the array takes O(n log(n)) time.

Space Complexity: O(1) - We use a constant amount of space for variables.

Better Solution: Leveraging a Hash Set

Overview: The better approach involves using a hash set to efficiently track unique elements in the array. If a duplicate is found, we can return True.

Algorithm:

1. Initialize an empty hash set.

2. Iterate through the array.

3. For each element, check if it is already in the hash set.

4. If it is, return True (a duplicate is found).

5. If it is not, add it to the hash set.

6. After the loop completes, return False (no duplicates were found).

def containsDuplicateHashSet(nums):
    num_set = set()
    for num in nums:
        if num in num_set:
            return True
        num_set.add(num)
    return False

Intuition: This approach optimizes the time complexity to O(n) by using a hash set to efficiently store and check for duplicates. It's like keeping a record of visited places to avoid revisiting them during a journey.

Time Complexity: O(n) - We iterate through the array once, and hash set operations are typically O(1) on average.

Space Complexity: O(n) - In the worst case, we may need to store all elements of the array in the hash set.

Conclusion

In this blog post, we explored three different solutions to the "Contains Duplicate" problem: the brute force approach, an alternative solution by sorting the array and finding duplicates, and a more efficient approach using a hash set. Each solution has its own time and space complexity characteristics. Understanding these trade-offs is crucial in solving coding problems effectively.

Whether you're preparing for a technical interview or aiming to improve your problem-solving skills, these approaches provide valuable insights into tackling the "Contains Duplicate" problem efficiently. By practicing these solutions and experimenting with other problem-solving techniques, you'll become a more proficient programmer.

We hope you found this blog post helpful. If you have any questions or comments, please feel free to leave them below. Don't forget to like and share this post if you found it useful, and subscribe for more coding tutorials and problem-solving strategies.

Happy coding!

Additional Information

Set in Python

In Python, a set is a built-in data type that represents an unordered collection of distinct elements. Sets are a fundamental part of the Python language and provide several useful operations for working with unique data.

Key Characteristics of Sets:

1. Uniqueness: Sets don't allow duplicate elements.

2. Unordered: Elements in a set have no specific order.

3. Mutable: You can add and remove elements from a set.

4. Set Operations: Sets support operations like union, intersection, and difference.

Creating Sets:

You can create a set in Python using {} or the set() constructor.

Basic Set Operations:

• Adding Elements: Use add() to add elements.

• Removing Elements: Use remove() or discard().

• Set Operations: Perform union, intersection, and difference with methods or operators.

Use Cases for Sets:

Sets are handy in various situations, such as:

• Removing duplicates from a list.

• Checking for membership (if an element is in the set or not).

• Performing set operations on data.

Note: Sets are distinct from lists and tuples, which allow duplicates and have a defined order. Additionally, sets cannot contain mutable elements like lists, but they can contain immutable types like numbers, strings, and tuples.

Sets are a versatile and efficient tool for working with collections of unique items in Python, and they can simplify many programming tasks involving distinct elements.