Increment Integer Array by One on LeetCode

Handling Time Data: Logic over Strings. Today I worked on a common challenge: comparing time values like '7:15' and '10:30' stored in a list.The Problem: Standard string comparison can be unreliable (e.g., '7:15' vs '10:30'), and using float numbers leads to mathematical inaccuracies.The Solution: I converted all time entries into a single unit — Total Minutes from the start of the day (hours * 60 + minutes).This transformation turns time-strings into simple integers, creating a robust and scalable logic for sorting and filtering. A solid foundation is everything, whether it's infrastructure or code. 🛡️🦾#Python #Coding #ProblemSolving #SoftwareEngineering #Backend #Summerson

🚀 Day 74/100 of #100DaysOfDSA Today’s problem was one of those that really tests your understanding of tree traversals. It wasn’t just coding — it required visualizing how different traversals relate to each other. 🔹 Construct Binary Tree from Preorder and Inorder Traversal Approach Used: Initially, it was a bit confusing how to rebuild the tree using two traversals. Key observations helped: • Preorder gives the root node • Inorder helps split the tree into left and right subtrees Used a HashMap to store inorder indices for quick lookup. Recursively built the tree by identifying the root from preorder and dividing the inorder range accordingly. The tricky part was calculating the correct index for the right subtree in preorder — once that clicked, the solution became clear. ⏱️ Time Complexity: O(n) 🧠 Space Complexity: O(n) 📌 Key Learning: Tree construction problems are all about understanding traversal relationships. Once you visualize how the tree is formed, recursion does the rest. 📌 Follow my journey as I learn, struggle, visualize, and grow — 100 days, one concept each day. Let’s learn together! #100DaysOfDSA #DSA #DataStructures #Algorithms #BinaryTree #Recursion #TreeTraversal #ProblemSolving #CodingJourney #LearnInPublic #Java #InterviewPreparation #DailyCoding #Consistency #TechCommunity

𝗗𝗮𝘆 𝟲𝟴/𝟳𝟱 | 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝟳𝟱 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: 136. Single Number 𝗗𝗶𝗳𝗳𝗶𝗰𝘂𝗹𝘁𝘆: Easy 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘂𝗺𝗺𝗮𝗿𝘆: Given a non-empty array where every element appears twice except for one, find the element that appears only once. Constraints: • Linear time complexity required • Constant extra space 𝗠𝘆 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: This problem is efficiently solved using Bit Manipulation (XOR operation). • Key Properties of XOR: – a ^ a = 0 – a ^ 0 = a – XOR is commutative and associative • Logic: – XOR all elements in the array – Duplicate numbers cancel each other out – The remaining value is the unique element • Implementation: – Initialize a variable (e.g., result = 0) – Traverse the array and XOR each element with result – Final result holds the single number This works because pairs eliminate themselves, leaving only the non-repeating number. 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: • Time Complexity: O(n) • Space Complexity: O(1) 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Bit manipulation can simplify problems that seem to require extra space. XOR is especially powerful when dealing with pairs or duplicates. 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗟𝗶𝗻𝗸: https://lnkd.in/guP-fJnC #Day68of75 #LeetCode75 #DSA #Java #Python #BitManipulation #Algorithms #MachineLearning #DataScience #ML #DataAnalyst #LearningInPublic #TechJourney #LeetCode

Some problems are not about moving elements, but about understanding structure. Day 22/100 — Data Structures & Algorithms Journey Today’s Problem: Rotate List This problem helped me understand how linked lists behave when we manipulate their structure efficiently. Approach: Instead of rotating the list step by step (which is inefficient), I first calculated the length of the list. Then, I connected the tail to the head to form a circular linked list. By finding the correct breaking point using k % length, I was able to determine the new head and tail of the rotated list. At each step: Convert list into a circular structure Find the new tail position Break the circle to form the rotated list Key Takeaways: Understanding structure is more important than brute force Modulo operation helps avoid unnecessary rotations Linked list problems often become easier when visualized as cycles Efficient thinking leads to cleaner and faster solutions This problem strengthened my understanding of linked list manipulation and optimization techniques. #DSA #LeetCode #LinkedList #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

🚀 Day 49 - #geekstreak60 Today’s problem: Gray Code Generation 🔢 Generating Gray Code was an interesting mix of bit manipulation and pattern observation. The goal is simple but elegant — create a sequence where only one bit changes between consecutive numbers. 💡 Key Insight: Instead of manually flipping bits, we can use a powerful formula: 👉 Gray(i) = i ^ (i >> 1) This ensures every adjacent number differs by just one bit — making the solution both efficient and clean. 🧠 What I Learned: ✔️ How XOR helps in bit transitions ✔️ Importance of pattern-based thinking ✔️ Writing optimized solutions using bit manipulation ⚡ Approach: Iterate from 0 to 2^n - 1 Apply the Gray Code formula Convert results into binary strings with leading zeros 📌 Example (n = 3): 000 → 001 → 011 → 010 → 110 → 111 → 101 → 100 Each step differs by just one bit — that’s the beauty of Gray Code! ✨ 💻 Code: class Solution: def graycode(self, n): return [format(i ^ (i >> 1), f'0{n}b') for i in range(1 << n)] 🔥 Another step forward in mastering Data Structures & Algorithms! #Day49 #100DaysOfCode #DSA #Python #CodingJourney #BitManipulation #ProblemSolving #LearningEveryday GeeksforGeeks National Payments Corporation Of India (NPCI)

Day 24 of 100 Completed Today reinforced cycle detection patterns and continued working with real-world data through EDA. • #141 - Linked List Cycle (Easy) - solved • Continued EDA on dataset 🔎 Focus Areas • Fast-slow pointer technique for cycle detection • Recognizing repeated patterns across different problem types • Going deeper into data understanding and cleaning 💡 Key Takeaways (DSA) 📌 #141 Linked List Cycle This is a classic application of Floyd’s Cycle Detection: use slow and fast pointers if they meet → cycle exists no extra space needed, efficient and elegant Key insight: cycle detection isn’t limited to numbers - it applies to linked structures as well. 🚀 Python + EDA Continued working on EDA and exploring the dataset further. 💡 Key Takeaways (Python) • Better understanding of missing values and distributions • More confidence in using Pandas for exploration • Visualization is helping uncover patterns in data ⚡ Honest Reflection This was a steady day. Not very difficult, but important for reinforcing patterns. Cycle detection is now clearly a recurring concept across problems, which makes it easier to recognize. EDA still needs depth, especially in drawing meaningful insights instead of just running operations. Consistency is holding. Progress is gradual but real. Patterns recognized: Fast-Slow Pointers | Cycle Detection | Linked Lists | Data Cleaning | EDA | Pattern Recognition #100DaysOfCode #DSA #Python #EDA #LinkedList #LeetCode #BuildInPublic #CodingJourney #Consistency

Some problems are not about counting frequency, but about optimizing how we track it. Day 21/100 — Data Structures & Algorithms Journey Today’s Problem: Single Number II This problem pushed me to think beyond basic approaches and focus on writing efficient code with minimal space. Approach: Initially, a simple way is to use a dictionary to count occurrences. But the challenge was to solve it in O(n) time and O(1) space. So I used bit manipulation to track numbers appearing once and twice. By maintaining two variables (ones and twos), I could efficiently simulate counting. When a number appears the third time, it automatically gets removed. At each step: ones stores numbers appearing once twos stores numbers appearing twice Key Takeaways: Optimized solutions often require thinking beyond brute force Bit manipulation is a powerful tool for reducing space complexity Understanding patterns can simplify complex problems Writing efficient code is as important as writing correct code This problem improved my understanding of bitwise operations and optimization techniques. #DSA #LeetCode #BitManipulation #ProblemSolving #SoftwareEngineering #CodingJourney #100DaysOfCode #TechLearning #DeveloperJourney #Programming #Python #InterviewPreparation #CodingSkills #ComputerScience #FutureEngineer #TechCareers #SoftwareDeveloper #LearnInPublic #OpenToWork

Day 09 of #50DaysOfLeetCode Challenge Just tackled the "Combination Sum" problem! This was a fantastic exercise in backtracking. The challenge is to find all unique combinations of numbers that sum up to a specific target, with the twist that you can use the same number multiple times. Key Insights: Backtracking Power: It’s all about exploring every possible path and "backtracking" as soon as the sum exceeds the target. State Space Tree: Visualizing how the recursion branches out helped me understand how to avoid duplicate combinations while allowing multiple uses of the same element. Decision Making: Learning when to include an element and when to move to the next index is crucial for optimizing the search. Each day, the logic gets sharper and the problems get more interesting! #DataStructures #Algorithms #CodingJourney #Java #Backtracking #LeetCode #ProblemSolving #SoftwareEngineering

📅 Date: April 25, 2026 Day 4 of my LeetCode Journey 🚀 ✅ Problem Solved: 9. Palindrome Number 🧠 Approach & Smart Solution: While it's easy to solve this by converting the integer to a string and reversing it, that takes extra memory! I opted for a highly optimized mathematical approach. I reversed the integer by peeling off its digits one by one using modulo and division, keeping the space complexity to an absolute minimum. • Pseudo-code: Handle edge cases: If the number is less than 0, it can't be a palindrome, so return false. Store the original number in a temporary variable for final comparison. Initialize a 'reverse' variable to 0. Loop while the number is greater than 0: Extract the last digit: digit = x % 10. Add it to the reversed number: reverse = (reverse * 10) + digit. Remove the last digit from the original number: x = x / 10. Finally, return true if the original number matches the reversed number. This math-based solution avoids expensive string conversions and runs instantly! ⏱️ Time Complexity: O(log₁₀(n)) (We only iterate through the digits of the number) 📦 Space Complexity: O(1) (Only using a few integer variables) 📊 Progress Update: • Streak: 3 Days 🔥 • Difficulty: Easy • Pattern: Math / Digit Extraction 🔗 LeetCode Profile: https://lnkd.in/gBcDQwtb (@Hari312004) Choosing mathematical operations over string manipulations is a great way to optimize basic algorithms! 💡 #LeetCode #DSA #Math #Java #CodingJourney #ProblemSolving #InterviewPrep #Consistency #BackendDevelopment

Built a Python-based Directory Sync Tool to compare and synchronize files between two directories with reliability and control. Instead of relying only on file names or timestamps, the tool uses a combination of metadata and SHA-256 hashing to accurately detect new, modified, and missing files. Key highlights: • Recursive directory scanning with structured metadata (name, extensions, size, hash) • Efficient change detection using size-first filtering followed by hash comparison • Memory-efficient hashing using chunk-based file reading (handles large files) • Synchronization support with metadata preservation using shutil.copy2 • Safe cleanup by optionally removing extra files from the destination While building this, I focused on moving beyond a basic script and treating it like a real tool, structuring the code into clear components, improving output readability, and adding validation and error handling to make it more reliable in real use. GitHub:https://lnkd.in/gt-Ec3rF #Python #CLI #GitHubProjects #SoftwareDevelopment #LearningByBuilding #SystemsThinking