Pydantic vs Dataclass: Debugging Nested Data

✅Day 12 of #DSAPrep > Problem: Product of Array Except Self > Platform: LeetCode > Concept: Prefix Sum + Suffix Product Solved using an optimized approach by calculating the product of elements on the left and right side separately. First pass stores left products, second pass multiplies right products — without using division. > Time Complexity: O(n) > Space Complexity: O(1) #DataStructures #Algorithms #Python #ProblemSolving #CodingJourney

𝗧𝗵𝗿𝗲𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀. 𝗢𝗻𝗲 𝗽𝗮𝘀𝘀. 𝗢𝗻𝗲 𝗴𝗵𝗼𝘀𝘁 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲. Day 28 of #1000DaysOfLearning Today's problem: 𝗦𝗼𝗿𝘁𝗲𝗱 𝗦𝘂𝗯𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲 𝗼𝗳 𝗦𝗶𝘇𝗲 𝟯 (GFG Medium) Find arr[i] < arr[j] < arr[k] where i < j < k — in a single pass. Track the smallest first, then the smallest second after it. The moment you see x > second, you've got your triplet. The tricky part: if first gets updated 𝗮𝗳𝘁𝗲𝗿 second is already set, it's no longer positionally before second. So you keep a prevFirst — 𝘁𝗵𝗲 𝘃𝗮𝗹𝘂𝗲 first 𝗵𝗲𝗹𝗱 𝘄𝗵𝗲𝗻 second 𝘄𝗮𝘀 𝗹𝗮𝘀𝘁 𝘂𝗽𝗱𝗮𝘁𝗲𝗱. Even if first moves to something smaller later, prevFirst still holds the element that actually appeared before second in the array. That's what makes the returned triplet 𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻𝗮𝗹𝗹𝘆 𝘃𝗮𝗹𝗶𝗱, not just value-valid. 𝗢(𝗻) time. 𝗢(𝟭) space. #DSA #Python #DataScience #GFG #1000DaysOfLearning #LearningInPublic

✅ Today I Solved: Count Pairs with Given Sum 💡 Problem: Count all pairs in an array whose sum equals a target. 🔑 Approach: HashMap (Frequency Method) For each element x, check if target - x already exists. ⚡ Key Insight: count += freq[target - x] → then update freq[x] 🚀 Complexity: Time: O(n) Space: O(n) 🔥 Learned: Small optimization in thinking can turn a common problem into an efficient one. #DSA #ProblemSolving #CodingJourney #Python

Working with messy real-world datasets taught me one thing: The cleaning step takes longer than the actual analysis. So I spent the last few weeks building dfdoctor - an open-source Python library that audits your DataFrame, tells you what’s wrong, and helps you fix it systematically instead of manually. It helps you quickly understand what’s broken and what to fix first. The part I'm most proud of: 5 correlation methods (including Kendall τ and Phi-k) implemented from scratch in pure numpy - no scipy dependency anywhere. 164 tests. CI passing across Python 3.9–3.12. Try it: pip install dfdoctor https://lnkd.in/e-ChV6mE #Python #OpenSource #DataEngineering #Pandas #EDA #DataScience

🚀 Today I Solved: Pair Sum Closest to Target Practiced the Two Pointer technique to efficiently find a pair whose sum is closest to a given target. 💡 Key Insight: Sort the array → use left & right pointers → track the minimum difference. Handled the tricky part as well: if multiple pairs have the same closest sum, choose the one with maximum difference 🔥 ⏱ Time: O(n log n) | 📦 Space: O(1) Small steps daily, big progress over time 📈 #DSA #Python #Coding #ProblemSolving #100DaysOfCode

DSA Tip: Binary Trees (Part 2) Building a tree is one thing… traversing it is where the real power comes in. In Binary Trees, we don’t just store data, we visit nodes in a specific order. The 3 main ways: Inorder (Left -> Root -> Right) Preorder (Root -> Left -> Right) Postorder (Left -> Right -> Root) Each traversal gives a different perspective of the same data. Insight: How you traverse data can be just as important as how you store it. Quick Challenge: Given this tree: 5 / \ 3 7 What is the Inorder traversal? Drop your answer, I’ll review the best ones. FOLLOW FOR MORE DSA TIPS & INSIGHTS #DSA #BinaryTree #Python #CodingTips #LearnToCode

🚀 Day 9 of #100DaysOfCode Today’s problem: Valid Anagram ✅ 🔍 What I learned: How to check if two strings are anagrams Importance of sorting vs frequency counting Strengthened my understanding of strings & hashing concepts 💡 Approach: I used a simple and clean method: Sort both strings Compare them If equal → Anagram ✔️ 📊 Result: ✅ All test cases passed (54/54) ⏱️ Runtime: 19 ms 🔥 Key takeaway: Sometimes a simple solution is enough, but there’s always room to optimize using hash maps for better performance. Consistency > Perfection 💯 Let’s keep going! #LeetCode #DSA #CodingJourney #Day9 #Python #ProblemSolving

Day 3 of my Build in Public journey 🚀 Today’s focus: Advanced Python concepts and aptitude practice. Here’s what I worked on today 👇 💻 Python • Explored lambda functions, map(), filter(), and reduce() • Practiced recursion (tail and non-tail) • Learned about decorators and how functions act as first-class objects 🧠 Aptitude • Solved questions to improve logical thinking ⚠️ Note: Didn’t work on development today — focusing on strengthening fundamentals. 💡 Lesson: Understanding concepts deeply takes time, but it makes everything easier later. Back again tomorrow to keep building. #BuildInPublic #100DaysOfCode #LearningInPublic #TechJourney

Day 7 of my Python learning journey: Today I focused on classic array and string patterns, and tried to keep solutions clean and efficient. What I solved: Two Sum using brute-force and hash map Valid Palindrome with two pointers Move Zeros with an in-place two-pointer approach Container With Most Water in O(n) Big takeaway: Correctness first, clarity second, optimization third. Small design choices, like in-place updates vs extra arrays, really affect code quality. GitHub link: https://lnkd.in/gGPw8_js #Python #ProblemSolving #Algorithms #TwoPointers #LearningInPublic #CleanCode

🚀 Day 51/200 – LeetCode Challenge Today’s problem: Island Perimeter 🌊🏝️ Focused on understanding how to efficiently calculate boundaries in a grid using a simple yet powerful observation: 👉 Each land cell contributes 4 edges 👉 Shared edges reduce the total perimeter 🔍 Key Takeaways: Learned to optimize by avoiding unnecessary checks Strengthened grid traversal and pattern recognition skills Practiced writing clean and efficient code 💡 Result: ✅ Accepted ⚡ Runtime: 38 ms 📊 Beat ~65% of submissions Consistency is the real game changer. Small improvements every day lead to big results over time. #Day51 #200DaysOfCode #LeetCode #Python #DataStructures #Algorithms #CodingJourney #Consistency