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GFG : Count distinct elements in every window

Posted on March 6, 2024March 6, 2024 By thecodepathshala No Comments on GFG : Count distinct elements in every window

    Problem :

    Given an array of integers and a number K. Find the count of distinct elements in every window of size K in the array.

    Example 1:
    
    Input:
    N = 7, K = 4
    A[] = {1,2,1,3,4,2,3}
    Output: 3 4 4 3
    Explanation: Window 1 of size k = 4 is
    1 2 1 3. Number of distinct elements in
    this window are 3. 
    Window 2 of size k = 4 is 2 1 3 4. Number
    of distinct elements in this window are 4.
    Window 3 of size k = 4 is 1 3 4 2. Number
    of distinct elements in this window are 4.
    Window 4 of size k = 4 is 3 4 2 3. Number
    of distinct elements in this window are 3.
    Example 2:
    
    Input:
    N = 3, K = 2
    A[] = {4,1,1}
    Output: 2 1
    Your Task:
    Your task is to complete the function countDistinct() which takes the array A[], the size of the array(N) and the window size(K) as inputs and returns an array containing the count of distinct elements in every contiguous window of size K in the array A[].
    
    Expected Time Complexity: O(N).
    Expected Auxiliary Space: O(N).

    Solution:

    class Solution{
      public:
        vector <int> countDistinct (int A[], int n, int k)
        {
            //code here.
            vector<int> ret;
            map<int, int> mp;
            for(int i=0; i<n; i++) {
                if(i>=k){
                    if(--mp[A[i-k]] == 0){
                        mp.erase(A[i-k]);
                    }
                }
                mp[A[i]]++;
                if(i>=k-1){
                    ret.push_back(mp.size());
                }
            }
            
            return ret;
        }
    };
    Algo, Competitive Programming, DS & Algo, GFG, Leetcode Problems Tags:coding interview, dsa, gfg, sliding window

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    🚀 LeetCode #1 – Two Sum Explained in Under 2 Minutes!
Learn the most asked coding interview problem using the Hash Map approach and understand why it runs in O(n) time instead of O(n²).
In this Short, you'll learn:
✅ Problem statement
✅ Optimized Hash Map approach
✅ Step-by-step dry run
✅ Time & Space Complexity
✅ Interview tips
If you're preparing for FAANG, Microsoft, Amazon, Google, or any software engineering interview, this series is for you.
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    Want to clearly understand Time Complexity and Space Complexity of Queue for DSA and coding interviews? 🤔
In this video, we explain Queue Data Structure Complexity using simple examples, Big-O notation, and interview-focused concepts.

🚀 What You’ll Learn in This Video:

What is a Queue in Data Structures?

Queue Principle: FIFO (First In First Out)

Types of Queue (Simple, Circular, Priority, Deque)

Time Complexity of Queue Operations

Enqueue → O(1)

Dequeue → O(1)

Front / Peek → O(1)

Rear → O(1)

Search → O(n)

Space Complexity of Queue

Queue Implementation (Array vs Linked List)

Real-World Applications of Queue

Most Asked Queue Interview Questions

🎯 Why Watch This Video?
✔ Easy explanation of Big O Notation
✔ Important for Coding Interviews & Exams
✔ Useful for C, C++, Java, Python learners
✔ Beginner-friendly & exam-oriented

📌 Who Should Watch?

DSA Beginners

Computer Science Students

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Competitive Programmers

📈 Master DSA Concepts Step-by-Step!

👍 Like | 💬 Comment | 🔔 Subscribe for more Data Structures & Algorithms content


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    🔥 Time & Space Complexity of Queue | Queue Data Structure | Big-O Explained
    Want to master Time Complexity and Space Complexity of Stack for DSA and coding interviews? 🤔
In this video, we explain Stack Data Structure Complexity using simple examples, Big-O notation, and interview-oriented explanations.

🚀 What You’ll Learn in This Video:

What is a Stack in Data Structures?

Stack Principle: LIFO (Last In First Out)

Time Complexity of Stack Operations

Push → O(1)

Pop → O(1)

Peek / Top → O(1)

Search → O(n)

Space Complexity of Stack

Stack Implementation (Array vs Linked List)

Real-World Applications of Stack

Common Stack Interview Questions

🎯 Why Watch This Video?
✔ Clear explanation of Big O Notation
✔ Essential for DSA Interviews & Exams
✔ Helpful for C, C++, Java, Python learners
✔ Beginner-friendly & concept-focused

📌 Who Should Watch?

DSA Beginners

Computer Science Students

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Competitive Programmers

📈 Build Strong DSA Foundations – One Concept at a Time!

👍 Like | 💬 Comment | 🔔 Subscribe for more Data Structures & Algorithms videos


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    🔥 Time & Space Complexity of Stack | Stack Data Structure | Big-O Explained
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