Valid Palindrome Solution with Python

🚀 Day 1/50 – LeetCode Challenge 🧩 Problem #14 – Longest Common Prefix Today’s problem was about finding the longest common prefix string amongst an array of strings. 📌 Problem Summary: Given a list of strings, we need to return the longest prefix that is common to all strings. If there is no common prefix, return an empty string. 🔍 Approach Used: Compared characters column-wise (vertical scanning method) Checked each character across all strings Stopped immediately when a mismatch was found ⏱️ Time Complexity: O(n × m) Where: n = number of strings m = length of the shortest string 💡 Key Learning: This problem strengthened my understanding of string manipulation and edge case handling (empty array, single string, no prefix). Consistency > Motivation 💪 On to the next one! 🔗 Problem Link: https://lnkd.in/gZYXMR7n #Day1 #50DaysOfLeetCode #LeetCode #DSA #python #ProblemSolving #CodingJourney #FutureAIEngineer #Consistency

Today’s practice was focused on String and List Manipulation using Slicing. This session helped me understand how powerful slicing is in Python and how it can simplify many problems without using loops. Programs practiced today include: 🔹 Reverse a given string using slicing 🔹 Print every alternate character from a string 🔹 Extract the middle three characters from a string 🔹 Reverse a list of integers using slicing (without loops or built-in reverse functions) 🔹 Remove the first and last characters from a string using slicing Key learnings from today’s session: • Understanding slicing syntax: start : stop : step • Using negative indexing for reversing sequences • How [::-1] works internally • Extracting specific portions of strings efficiently • Writing clean and shorter code without unnecessary loops • Strengthening fundamentals in string and list operations Today’s practice showed me how Python provides elegant solutions for common problems. Instead of writing long logic with loops, slicing makes the code simpler and more readable. Step by step, improving both logic and code efficiency. Grateful for the continuous guidance and support from the #10kcoders team. #Day32 #Python #StringManipulation #ListOperations #Slicing #LogicBuilding #ProgrammingJourney #Consistency

LeetCode #11 – Container With Most Water | Python Implementation I implemented a greedy two-pointer approach to find the maximum water a container can hold. Pointers start at opposite ends maximizing the width, and at each step the area is computed as the product of width and the shorter boundary. The critical greedy decision: always move the pointer with the smaller height inward, since keeping the shorter boundary can never yield a larger area with reduced width. This pattern directly applies to resource allocation optimization, load balancing systems, and capacity planning in distributed infrastructure. Key Takeaway: The greedy pointer movement is the core insight — moving the taller boundary inward is never beneficial since the area is always limited by the shorter side. This transforms an O(n²) brute-force enumeration into a clean O(n) single-pass solution by eliminating provably suboptimal states at every step. Time: O(n) | Space: O(1) #LeetCode #DataStructures #Python #TwoPointers #Greedy #CodingInterview #ProblemSolving #SoftwareEngineering

❄️ takeUforward ✅ Symmetric Pattern Problem | Logic Optimization | Python Today I worked on solving a complex symmetric star pattern problem using Python. I spent nearly 1 hour trying to simplify the logic and reduce the number of variables — a great learning experience in problem-solving and pattern recognition. 💡 Problem Approach • Generate a symmetric pattern using nested loops. • Control stars and spaces based on row position. • Handle increasing and decreasing pattern structure. • Focus on writing cleaner and shorter logic. ⏱ Complexity • Time: O(n²) • Space: O(1) 📌 Key Learning Improving logic clarity and reducing unnecessary variables makes code easier to understand and maintain. Struggling with optimization helps build strong problem-solving skills. 🚀 Day 34 completed — improving pattern logic and coding efficiency. #Python #DSA #Programming #ProblemSolving #CodingJourney #LearningInPublic #100DaysOfCode #DeveloperJourney

📘 Python with DSA – Day 68 ✅ Topic: Bit Manipulation – LeetCode Practice Today’s focus was on applying bit manipulation concepts to solve coding problems efficiently using Python. 🔹 What I worked on: Understanding how XOR behaves with duplicate elements Using bitwise operations to optimize logic Practicing binary-based problem solving Improving time and space efficiency 🔹 LeetCode Practice: Solved a bit manipulation problem (Easy level) to strengthen fundamentals and build confidence. 🧠 Key Insight: Bit manipulation helps convert mathematical logic into constant-time operations, which is crucial for interviews. Step by step, building strong DSA foundations 🚀 #Python #DSA #BitManipulation #LeetCode #CodingJourney #DailyPractice #SDE#ProblemSolving #TechLearning

✅ Day 49 of 100 Days LeetCode Challenge Problem: 🔹 #1046 – Last Stone Weight 🔗 https://lnkd.in/gAeEMysb Learning Journey: 🔹 Today’s problem focused on repeatedly smashing the two heaviest stones until only one or none remains. 🔹 I used a max-heap approach by storing negative values in Python’s min-heap implementation. 🔹 Each step involved removing the two largest stones, calculating their difference, and pushing the remaining weight back into the heap if needed. 🔹 This ensured efficient retrieval of the heaviest stones at every iteration. Concepts Used: 🔹 Heap / Priority Queue 🔹 Max-Heap Simulation 🔹 Greedy Strategy 🔹 Simulation Key Insight: 🔹 Using negative values is a simple trick to simulate a max-heap using Python’s min-heap. 🔹 Heap structures are ideal when repeatedly accessing extreme values. 🔹 Simulation problems become efficient when paired with the right data structure. #LeetCode #DataStructures #Algorithms #CodingInterview #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #BackendDevelopment #InterviewPreparation #TechCommunity

LeetCode #15 – 3Sum | Python Implementation I implemented a sort-based two-pointer approach to find all unique triplets that sum to zero. Sorting the array upfront enables two key optimizations: duplicate skipping and directional pointer movement. For each element n as the fixed anchor, two pointers l and r converge inward adjusting based on whether the current sum is too small or too large. Duplicate anchors are skipped at the outer loop level, and after finding a valid triplet, the left pointer advances past any duplicates to ensure uniqueness. This pattern is foundational in computational geometry, collision detection systems, and financial portfolio balancing algorithms. Key Takeaway: Sorting transforms an O(n³) brute-force problem into O(n²) by enabling the two-pointer convergence strategy. The duplicate-skipping logic at both the anchor and left-pointer level is what guarantees unique triplets without using extra space like a HashSet. Time: O(n²) | Space: O(1) or O(n) (excluding output array) #LeetCode #DataStructures #Python #TwoPointers #Sorting #CodingInterview #ProblemSolving #SoftwareEngineering

Plenty of Python APIs exist, but most still require coding knowledge. What if you could automate your bridge design workflow with Python without writing a single line of code? With the MIDAS Python Plug-in, engineers can use ready-made plug-ins to streamline modeling, analysis, and reporting in just minutes. In the full video, you’ll learn: 1️⃣ An overview of automation in structural engineering 2️⃣ How Python fits into modeling, analysis, and reporting workflows 3️⃣ How to use Python plug-ins without writing complex code 4️⃣ Real project-based examples shared by MIDAS engineers 🔗 Watch Python Automation: https://hubs.ly/Q042qRmm0 #MidasIT #StructuralEngineering #EfficiencyExcellence #PythonAutomation #CivilEngineering #DigitalTransformation #EngineeringInnovation

Over the past year, I’ve had the privilege of representing MIDAS IT in Singapore, meeting many passionate partners and engineers who were always eager to learn and improve their workflows. A big part of my role was helping engineers fully utilise CIVIL NX, especially newer features like APIs and automation plug-ins. However, in reality, this often required some level of coding knowledge — which takes time and effort. And many engineers simply don’t have that flexibility due to ongoing projects and deadlines. As someone with limited structural engineering background, I constantly thought about how we could lower this barrier and make automation more accessible. That’s where the MIDAS Python Library really stands out. It allows engineers to use pre-built automation scripts that can be copied, pasted, and applied directly — no complex coding required. I highly recommend taking a few minutes to watch this video and see how practical Python automation can be in real engineering workflows. #MidasIT #StructuralEngineering #PythonAutomation #CivilEngineering #EngineeringWorkflow #DigitalTransformation #EfficiencyExcellence