Solved LeetCode Top K Frequent Elements with Optimal Bucket Sort

🚀 Day 15 of DSA Grind — Sliding Window Maximum (Hard) 🔥 Today’s problem pushed my thinking to the next level — not just solving, but optimizing. 💡 Problem: Given an array, find the maximum in every sliding window of size k. ⚡ Naive Approach: Check every window → O(n * k) ❌ (Too slow) 🔥 Optimized Approach (Deque / Monotonic Queue): Maintain elements in decreasing order Remove useless elements Front always gives maximum ✅ Time Complexity: O(n) ✅ Space Complexity: O(k) 🧠 Key Learning: This problem teaches how to avoid recomputation and use data structures smartly — a must-know pattern for interviews. 📌 Example: Input: [1,2,1,0,4,2,6], k = 3 Output: [2,2,4,4,6] 💬 Big Takeaway: Don’t just solve — optimize like an engineer. Consistency > Motivation 💯 #DSA #LeetCode #SlidingWindow #Python #CodingJourney #TechInterview #100DaysOfCode #SoftwareEngineering #ProblemSolving

𝐂𝐥𝐞𝐚𝐧𝐞𝐫 𝐂𝐨𝐝𝐞 > 𝐌𝐨𝐫𝐞 𝐂𝐨𝐝𝐞 (𝐀 𝐒𝐦𝐚𝐥𝐥 𝐏𝐚𝐧𝐝𝐚𝐬 𝐑𝐞𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧) Ran into something interesting while working with pandas today — chaining operations actually makes a huge difference. Instead of writing step-by-step code for filtering, grouping, and aggregating, combining them in a single flow made the analysis much cleaner and faster to read. Something like filtering a dataset → grouping by category → calculating averages — all in one pipeline. Feels closer to how real analysis should look instead of breaking everything into isolated steps. Still improving, but this felt like a shift from “practice code” to more structured data work. #Python #Pandas #DataAnalytics #LearningByDoing

🚀 Day 1 — Building Problem Solving Skills I’m continuing my journey to improve problem-solving skills by staying consistent, disciplined, and accountable — one problem at a time. 🧩 Problem: • Two Sum (LeetCode #1) 📚 Topic: Arrays (Pair Traversal) 💡 Key Insight: Checking all possible pairs works, but it highlights the need for more efficient approaches as input size grows. ⚡ Approach: • Pick an element using index i • Traverse remaining elements using index j • Check if nums[i] + nums[j] == target • Return indices when condition is satisfied 🎯 Takeaway: Even simple problems help strengthen logic and introduce optimization thinking. ⏱ Complexity: Time → O(n²) Space → O(1) 💻 Sample Input: nums = [2, 7, 11, 15] target = 9 ✅ Output: [0, 1] Consistency > Perfection 💪 #DSA #LeetCode #ProblemSolving #Python #CodingJourney #LearningInPublic #Arrays #TechGrowth

Day 22/100 – DSA Journey Problem: Find Mode in Binary Search Tree (BST) Today’s problem focused on understanding how Binary Search Trees (BST) behave and how we can efficiently extract useful insights from them. Understanding the BST A Binary Search Tree follows a structured property: Left subtree → values ≤ root Right subtree → values ≥ root Because of this, when we perform an Inorder Traversal (Left → Root → Right), the values are visited in sorted order. Why Inorder Traversal? Since duplicates appear consecutively in a sorted sequence, inorder traversal allows us to: Track frequency without using extra space Compare current value with previous value Efficiently determine the most frequent element (mode) Approach Used Traverse the BST using inorder traversal Maintain: Previous value Current count Maximum frequency Update result list whenever a new maximum frequency is found Key Learning This problem highlights how leveraging tree properties can help optimize solutions. Instead of using extra space (like hashmaps), we used traversal behavior to achieve an efficient solution. Conclusion Understanding the underlying structure of data (like BST properties) is often more powerful than brute-force approaches. Smart traversal choices can significantly reduce space complexity and improve performance. #Day22 #100DaysOfCode #DSA #BinarySearchTree #Python #CodingJourney #LeetCode #ProblemSolving

I was cleaning a dataset — filtering rows, transforming values, the usual. My 5-line for loop worked fine. But I wanted to be "Pythonic." So I compressed it into a one-liner. Then I added another layer. The next morning I stared at it for two full minutes trying to decode my own logic. If I couldn't read it, my future teammates had no chance. This carousel breaks down: → The mental model that makes list comprehensions click instantly → The reading order most beginners get backwards → The exact rule for when to stop using them and write a real loop What's the longest you've stared at your own code before realizing you had no idea what it does? #Python #DataAnalytics #DataAnalyst #PythonTips #LearnInPublic #AHAMoments #DataAnalystJourney

✅ Day 97 of 100 Days LeetCode Challenge Problem: 🔹 #1281 – Subtract the Product and Sum of Digits of an Integer 🔗 https://lnkd.in/gxTAZc6U Learning Journey: 🔹 Today’s problem involved extracting digits of a number and performing two operations simultaneously. 🔹 I initialized two variables: one for product (pr) and one for sum (sm). 🔹 Using a while loop, I extracted each digit using n % 10. 🔹 Updated the product by multiplying the digit and updated the sum by adding it. 🔹 Reduced the number using integer division (n //= 10) after each step. 🔹 Finally returned the difference between product and sum. Concepts Used: 🔹 Digit Extraction 🔹 While Loop 🔹 Arithmetic Operations 🔹 Number Manipulation Key Insight: 🔹 Both product and sum can be computed in a single traversal of digits. 🔹 Efficient use of modulus and division avoids converting the number to a string. Complexity: 🔹 Time: O(d) 🔹 Space: O(1) #LeetCode #Algorithms #DataStructures #CodingInterview #100DaysOfCode #Python #ProblemSolving #LearningInPublic #TechCareers

Stop digging through logs. Start fixing the problem. 🛠️ During a technical incident, every second counts. I built the Automated Technical Log Analyzer to handle the heavy lifting. What it does: It takes raw logs from multiple sources and uses native Python logic to strip away the noise. The Brain: I implemented an agentic workflow using GPT-4o-mini to identify root causes and recommend actionable recovery steps. The ROI: This pipeline is designed to reduce Mean Time To Repair (MTTR) by approximately 70%. Check out the repo: https://lnkd.in/dDPyJDNH #Python #DevOps #AI #AgenticWorkflows #GPT4

🚀 Day 45 of My Learning Journey – NumPy Shape & Reshape Today, I explored how to work with array dimensions using NumPy, focusing on shape and reshape. 🔹 Key Learnings: ✔️ shape Helps to identify the dimensions of an array Example: (3, 2) → 3 rows and 2 columns ✔️ Modifying shape We can directly change the structure of an array Useful when reorganizing data ✔️ reshape() Creates a new array with a different shape Does NOT modify the original array Very helpful in data preprocessing 🔹 Hands-on Task Completed: Converted a list of 9 elements into a 3×3 matrix using NumPy. 💡 Takeaway: Understanding how to manipulate array dimensions is essential for data analysis, machine learning, and efficient problem-solving. 📌 Every small concept builds a stronger foundation! #Day45 #Python #NumPy #LearningJourney #DataScience #Coding #StudentLife

Data collection series · Post 07 Imputation strategies — beyond filling with the mean "Mean imputation is fast. It's also wrong in most cases. Here are 4 better strategies and exactly when to use each." Filling missing values with the mean is fast. It's also quietly wrong in most cases. Here are 4 better strategies — and exactly when to use each. ▼ Mean imputation is the default. Everyone learns it first. It's one line of code. It ships fast. But it has a serious flaw: It collapses variance. Replace 500 missing values with the mean — and your distribution gets an artificial spike right in the middle. Your correlations weaken. Your model learns a distorted world. There are better options. Here's the practical guide. --- #Python #DataScience #DataQuality #DataCleaning #Analytics #DataAnalyst #DataAnalytics #DataEngineering #Imputationstrategies

Just wrapped up my first PBL (Project-Based Learning) project and it genuinely felt like my first real step into applying concepts beyond just coursework. The project focuses on analyzing blood spatter patterns from images to extract meaningful insights, something that has real-world relevance in areas like forensic investigation, where understanding patterns can help reconstruct events. Instead of treating it as just an implementation task, I approached it as a problem: how can visual data be processed in a way that meaningful patterns can be identified and interpreted systematically? 🔍 What I worked on: 🩸 Image preprocessing and pattern analysis 🩸 Extracting meaningful features from visual data 🩸 Building a pipeline combining frontend interaction with backend processing 📄 Project Report: https://lnkd.in/dQntEkDP 💻 Code: https://lnkd.in/dsicCw_7 This project pushed me to think more about why things work, not just how to implement them and how to apply that thinking to solve real problems. #Projects #ComputerVision #Python #Learning #PBL #Forensics #DigitalForensics