Testing Data Models with a 3-Layer Strategy

🚀 Day 38 – LeetCode Journey Today’s problem: Gray Code ✔️ Generated sequence using bit manipulation ✔️ Applied formula: "i ^ (i >> 1)" ✔️ Ensured only one bit changes between consecutive numbers 💡 Key Insight: Gray Code is useful in minimizing errors in digital communication, as only one bit changes at a time. Using bitwise operations makes the solution both elegant and efficient. This problem improved my understanding of bit manipulation and binary patterns. Exploring deeper into low-level concepts 🔥💪 #LeetCode #Day38 #BitManipulation #Binary #Python #ProblemSolving #CodingJourney #100DaysOfCode

Day 26 of 50 days of LeetCode The Problem: Best Time to Buy and Sell Stock. The Mistake: My initial logic used global max() and min() functions. This failed because it didn't account for time—you can't sell a stock before you've actually bought it! The Correction: I refactored the code to a single-pass approach. By tracking the min_price seen so far and updating the max_profit dynamically, I ensured the "buy" always happens before the "sell" while keeping the efficiency at O(n). #LeetCode #Python #ProblemSolving #CodingJourney #CSDesign

I got tired of scrolling through messy file names… so I fixed it with a small Python script. While reading One Piece manga PDFs, the file names were all over the place: chapter-1112, one-piece-chapter-1222, onepiece-1123, OP-Chapter-1123… Finding the correct order every time was annoying. So I wrote a simple script that: Extracts the chapter number from any format Renames files into a consistent structure Automatically arranges them in readable order Nothing fancy just solving a small personal problem and saving time. This reminded me: You don’t always need big projects. Even small scripts that remove friction from your daily life are worth building. Clean input → Clean output → Peace of mind 😌 #Python #LearningByDoing #Automation #OnePiece #Coding

Looping Through Logic 🔄 This infographic illustrates the lifecycle of a Python for loop, transforming a block of code into a clear, step-by-step physical process. By visualizing the list as a circular path, it's easier to see how Python "visits" every element without you having to write individual lines for each one. The Breakdown • Code: The simple syntax that tells Python what to do. • Flowchart: The logic gate that decides whether to keep going or stop. • Iteration: The actual journey each item takes from the list to your console. #PythonProgramming #Coding101 #DataScience #SoftwareDevelopment #LearnToCode #PythonLoops #ProgrammingLogic #TechEducation #CodeNewbie #Automation

🚀 Just solved the “Valid Number” problem on LeetCode! This problem looks simple at first glance—but handling edge cases like decimals, signs, and exponents makes it a great test of attention to detail and logical thinking. ✅ Key takeaways: Careful handling of edge cases is crucial Validating input step-by-step can simplify complex parsing problems Writing clean, readable logic beats overcomplicated solutions 💡 Performance: ⚡ Runtime: 3 ms 🧠 Efficient space usage ✅ All test cases passed Problems like this remind me that consistency in practice is what builds strong problem-solving skills. On to the next one! 🔥 #LeetCode #Coding #Python #ProblemSolving #SoftwareEngineering #AIEngineerJourney link of #Solution :- https://lnkd.in/ga9b5pVb

I built a pricing scraper on n8n that hits 100+ regions and pushes everything into Notion. Ran it on Railway for months. Loved it. Then I looked at the bill. Turns out processing thousands of rows per run adds up fast on these platforms. So I rewrote the whole thing in Python. Now it runs on my machine for free. The funny part is that n8n is what taught me enough about APIs and data flows to eventually not need it. Which makes me wonder how many people are running automations that would be cheaper as a script they just haven't gotten around to writing yet...

Just solved “Second Largest Digit in a String” on LeetCode — and here’s the simple approach I followed 👇 Instead of overcomplicating it, I focused on clean thinking + Python basics: 🔹 Converted the string into a set → removes duplicates instantly 🔹 Filtered only digits using isdigit() 🔹 Stored them as integers in a list 🔹 Sorted the list → easy access to largest & second largest 🔹 Edge case check: if less than 2 digits → return -1 💡 Key takeaway: Sometimes the most optimal solution isn’t about complex algorithms — it’s about using the right built-in tools smartly. 🚀 What I’m improving with each problem: • Writing cleaner logic • Thinking in steps instead of rushing • Handling edge cases early Consistency > Complexity. #LeetCode #DSA #Python #ProblemSolving #CodingJourney #100DaysOfCode

🚀 Day 3/100: Mastering Logic Flow & Decision Making! 🏝️ The #100DaysOfCode journey is heating up! Today was all about Control Flow and Conditional Logic in Python. I built a "Treasure Island" text adventure game to practice: ✅ Nested if/elif/else statements ✅ Complex logical operators (AND / OR) ✅ Managing user input edge cases Understanding branching logic is a massive step toward building robust automation scripts and handling real-world data scenarios. ⚔️ Check out my code here: 🔗 https://lnkd.in/gxyRjpGh Onward to Day 4! 🚀 #Python #100DaysOfCode #LogicBuilding #Programming #DevLife #GrowthMindset #CodeNewbie

🚀 Day 75 of #100DaysOfCode 🔥 LeetCode 179 – Largest Number 💡 Problem: Given a list of non-negative integers, arrange them such that they form the largest possible number. 🧠 Key Insight: Normal sorting won't work here ❌ We need a custom comparator based on string concatenation. 👉 Compare: - ""a + b"" vs ""b + a"" - Whichever gives a larger value should come first. ⚙️ Approach: 1. Convert numbers to strings 2. Sort using custom comparison logic 3. Join the result 4. Handle edge case (like "[0,0] → "0"") ⚡ Complexity: - Time: O(n log n) - Space: O(n) 🎯 Result: ✅ Accepted ⚡ Runtime: 0 ms (100%) 📌 Lesson Learned: Sometimes sorting logic depends on combination, not value. #LeetCode #Python #CodingJourney #DSA #100DaysOfCode #Sorting #ProblemSolving

Top K Frequent: Bucket Sort Beats Heap with O(n) Time Heap-based solution costs O(n log k). Bucket sort achieves O(n) by leveraging frequency bounds — frequencies can't exceed array length. Index = frequency, value = list of elements with that frequency. Traverse buckets high-to-low, collecting k elements. Bucket Sort Advantage: When value range is bounded and known (frequencies ≤ n), bucket sort beats comparison-based O(n log k). Exploiting constraints transforms complexity. Time: O(n) | Space: O(n) #BucketSort #TopK #FrequencyAnalysis #ComplexityReduction #Python #AlgorithmOptimization #SoftwareEngineering