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LeetCode 274: H-Index | Python Solution | Sorting & Counting Approach

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📝 In this video, I solve LeetCode's H-Index problem using sorting and counting techniques. The h-index measures a researcher's productivity and citation impact. #leetcode #sorting #arrays #pythonsolution
⏱️ **Time Complexity**: O(n log n) - dominated by the sorting operation
🗂️ **Space Complexity**: O(1) - constant extra space
## Timestamps
00:00 - Understanding problem statement
00:46 - Using Brute force
01:49 - Brute Force - Big O Notation
02:16 - Using Sorting approach
04:13 - Sorting approach - Big O Notation
04:45 - Optimized solution - Using Counters
07:12 - Optimal solution - Big O Notation
07:53 - Python code walk-through
08:54 - Solution analysis - runtime + memory
🔑 **Key Concepts**:
- Sorting algorithms
- Binary search application
- Understanding citation metrics
- Array manipulation techniques
- Counting sort optimization
💡 **Main Takeaways**:
- How to approach problems requiring ranked metrics
- Efficiently calculating bibliometric indices
- Optimizing sorting-based solutions
- Handling edge cases in citation counting
📚 **Related LeetCode Problems**:
- LeetCode 275: H-Index II (Binary Search)
- LeetCode 347: Top K Frequent Elements
- LeetCode 451: Sort Characters By Frequency
- LeetCode 692: Top K Frequent Words
👨💻 **Target Audience**: Software engineering candidates preparing for technical interviews, particularly those focused on algorithmic problem-solving with Python.
📋 **Prerequisites**: Basic understanding of arrays, sorting algorithms, and Python syntax.
## Links
🔔 If you found this tutorial helpful, please subscribe to the channel for weekly coding problems explained step-by-step! Drop your questions or alternative approaches in the comments section below. #codinginterviews #pythonprogramming #algorithmsandatastructures
## Additional Tips
- Emphasize the definition of h-index, as it's not intuitive for many viewers
- Discuss the O(n) counting sort solution as a follow-up optimization
- Explain why sorting in descending order simplifies the solution logic
⏱️ **Time Complexity**: O(n log n) - dominated by the sorting operation
🗂️ **Space Complexity**: O(1) - constant extra space
## Timestamps
00:00 - Understanding problem statement
00:46 - Using Brute force
01:49 - Brute Force - Big O Notation
02:16 - Using Sorting approach
04:13 - Sorting approach - Big O Notation
04:45 - Optimized solution - Using Counters
07:12 - Optimal solution - Big O Notation
07:53 - Python code walk-through
08:54 - Solution analysis - runtime + memory
🔑 **Key Concepts**:
- Sorting algorithms
- Binary search application
- Understanding citation metrics
- Array manipulation techniques
- Counting sort optimization
💡 **Main Takeaways**:
- How to approach problems requiring ranked metrics
- Efficiently calculating bibliometric indices
- Optimizing sorting-based solutions
- Handling edge cases in citation counting
📚 **Related LeetCode Problems**:
- LeetCode 275: H-Index II (Binary Search)
- LeetCode 347: Top K Frequent Elements
- LeetCode 451: Sort Characters By Frequency
- LeetCode 692: Top K Frequent Words
👨💻 **Target Audience**: Software engineering candidates preparing for technical interviews, particularly those focused on algorithmic problem-solving with Python.
📋 **Prerequisites**: Basic understanding of arrays, sorting algorithms, and Python syntax.
## Links
🔔 If you found this tutorial helpful, please subscribe to the channel for weekly coding problems explained step-by-step! Drop your questions or alternative approaches in the comments section below. #codinginterviews #pythonprogramming #algorithmsandatastructures
## Additional Tips
- Emphasize the definition of h-index, as it's not intuitive for many viewers
- Discuss the O(n) counting sort solution as a follow-up optimization
- Explain why sorting in descending order simplifies the solution logic
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