Solved Pascal's Triangle II with Python and DP

🔥 Day 24 String Patterns & Prefix Power 💪 Today’s DSA session was all about smart scanning and pattern precision decoding strings from both ends like a pro 🧠 🔹 LeetCode 1903 Largest Odd Number in a String We learned to scan from right to left to grab the largest possible odd substring mastering substring slicing and number logic in one go. A simple yet powerful trick in string + math hybrid problems ⚡ 🔹 LeetCode 14 Longest Common Prefix We revisited a classic finding the common thread across multiple strings! Perfect practice for pattern alignment and character-wise comparisons 🧩 💡 Strings may look simple but they hide some of the most elegant problem-solving patterns in all of coding. What’s your go-to trick when solving string questions slicing, pointers, or brute force? 👇 #Day24 #100DaysOfCode #LeetCode #StringProblems #CodingJourney #ProblemSolving #DSA #LearnToCode #TechCommunity #Python #Programming #CodingChallenge #DSA90WithSUUMIT #DSA90 #FullStack #Strings #DEV

Today’s task was about applying conditional statements in Python to check loan eligibility based on income and credit score. Here’s the simple program I wrote 👇 def loan_eligiblity(income, creditscore): if income > 50000 and creditscore > 750: print('He is eligible for a loan') else: print('He is not eligible for a loan') loan_eligiblity(50000, 750) 🧠 What I learned: How to use if-else statements in Python How logical operators like and work The importance of testing boundary conditions (e.g., >= vs >) Every small step counts in becoming better at coding 🚀 #Python #CodingJourney #100DaysOfCode #LearningEveryday #PythonBeginner#Battula Venkata Narayana #1000coders

🚀 Day 54 of #100DaysOfCode Solved LeetCode Problem 2011. Final Value of Variable After Performing Operations ✅ This problem tests simple yet essential programming logic — understanding how pre/post increment and decrement operations affect variable states. Given a list of operations like ["--X", "X++", "++X"], the goal is to compute the final value of X after applying all updates sequentially. 💡 Key Insight: Each operation (++X, X++) increases the value by 1, while (--X, X--) decreases it by 1. The implementation can be efficiently handled in O(n) time by iterating through the operations once. ⚙️ Result: Runtime: 0 ms ⚡ Beats 100% of Python submissions Memory Usage: 17.76 MB (Beats 60.70%) Another step forward in improving my algorithmic problem-solving and code optimization skills 💪 #LeetCode #Python #100DaysOfCode #CodingJourney #ProblemSolving #DailyPractice #TechLearning #MythylyCodes

🚀 Day 41 of #100DaysOfDSA Solved LeetCode Problem #70 – Climbing Stairs 🪜✨ 📌 Problem Insight: You are climbing a staircase with n steps, and you can take either 1 step or 2 steps at a time. The task is to find the number of distinct ways to reach the top. A classic problem that beautifully introduces Dynamic Programming concepts! 💡 Key Learnings: Understood how this problem relates to the Fibonacci sequence. Practiced iterative DP optimization — using only two variables instead of an array. Learned how to recognize recurrence relations in real problems. Time complexity: O(n) Space complexity: O(1) 📌 Approach (short): Each step can be reached either from (n−1) or (n−2) → ways(n) = ways(n-1) + ways(n-2) 👉 Dynamic Programming problems show that thinking ahead pays off — one step at a time 🧠💪 #LeetCode #DSA #ProblemSolving #100DaysChallenge #Day41 #CodingJourney #Python #DynamicProgramming

🗓️ Day 48 / 100 – House Robber (LeetCode #198) 💡 Today’s Challenge Today’s problem was House Robber, another classic Dynamic Programming problem that builds on logical decision-making. The goal: find the maximum amount of money you can rob without robbing two adjacent houses. This problem perfectly shows how DP helps in balancing choices and consequences — you decide whether to rob the current house (and skip the previous one) or skip it to include the next. 🔍 Key Learnings Dynamic Programming is about optimizing decisions step-by-step. The “non-adjacent” rule introduces real-world constraints into logic. Storing sub-results saves time and simplifies complex problems. 💭 Thought of the Day Life — like coding — is about smart choices. Sometimes, skipping an immediate gain (a nearby house 🏠) can lead to greater rewards later. Problem-solving isn’t always about doing more — it’s about doing smarter. 🔗 Problem Link:https://lnkd.in/gMCjampn #100DaysOfCode #Day48 #LeetCode #Python #DynamicProgramming #ProblemSolving #Algorithms #CodingChallenge #PythonProgramming #CodeEveryday #TechGrowth #LearningJourney #SmartDecisions #HouseRobber

🧠 Day 44 / 100 – Product of Array Except Self (LeetCode #238) Today’s challenge was all about computing an array where each element is the product of all numbers except itself — without using division. This one teaches prefix and suffix logic beautifully. The trick is to build two running products: One from the left (prefix) One from the right (suffix) Then combine both to get the final result. No division needed, no nested loops — just clean, efficient logic. 🔍 Key Learnings Use prefix and suffix multiplications for O(n) time complexity. Avoid division to handle zero cases efficiently. Think about how to reuse partial results rather than recomputing them. 💭 Thought of the Day Efficiency comes from rethinking the obvious. Instead of brute-forcing every combination, using prefix and suffix logic shows how planning ahead can simplify everything. Each day, I’m learning to design solutions, not just write them. 🔗 Problem Link: https://lnkd.in/gbGfa4dd #100DaysOfCode #Day44 #LeetCode #Python #Arrays #PrefixSum #ProblemSolving #Algorithms #DataStructures #EfficientCoding #CodeEveryday #LearningJourney #TechGrowth #CleanCode

🗓 Day 10 / 100 – #100DaysOfLeetCode 📌 Problem 3228: Maximum Number of Operations to Move Ones to the End The task was to determine the maximum number of operations needed to move all '1's in a binary string to the end, given specific operation rules. 🧠 My Approach: Traversed the string while keeping track of the total count of '1's seen so far. Whenever a '10' pattern appeared, added the current count of '1's to the total operations. This ensured that each move was counted optimally without unnecessary shifts or recomputation. ⏱ Time Complexity: O(n) 💾 Space Complexity: O(1) 💡 Key Learning: This problem reinforced the value of pattern-based logic and prefix counting — powerful techniques for problems involving strings or sequences. It also highlighted how thinking in terms of transitions (1→0) can simplify seemingly tricky problems. Ten days down, ninety to go 🚀 #100DaysOfLeetCode #LeetCodeChallenge #Python #ProblemSolving #Strings #LogicBuilding #DataStructures #Algorithms #DSA #CodingJourney #CompetitiveProgramming #SoftwareEngineering #LearningInPublic #DeveloperJourney #TechStudent #CodingCommunity #CareerGrowth #CodeEveryday #Optimization #KeepLearning #Programming

LeetCode 3668: Restore Finishing Order Question: You’re given two arrays: order → the finishing order of all race participants friends → your friends’ IDs (sorted in increasing order) The goal is to return your friends’ IDs in their finishing order based on the order array. Approach: We iterate through the order list and add each element to the result if it’s in friends. This ensures we maintain the finishing order naturally from left to right. Topics Covered: Array iteration Conditional filtering Python basics and logic building Complexity: Time: O(n × m) — can be optimized to O(n) using a set for lookups Space: O(n) Watch the original version here: https://lnkd.in/eWgEdQCv If you enjoyed this video, subscribe to my channel for more Python and LeetCode walkthroughs I post new content regularly! #LeetCode #LeetCode3668 #RestoreFinishingOrder #Python #ProblemSolving #CodingInterview #DataStructures #Algorithms #Programming #SoftwareEngineering #100DaysOfCode #DeveloperCommunity #PythonCoding #InterviewPrep #LearnToCode #CodingChallenge #CodeNewbie #PythonDeveloper #TechJourney

🚀 LeetCode Daily Challenge 📘 Problem: Implement Stack using Queue (LeetCode #225) Today, I implemented a stack (LIFO) using a single queue (FIFO) — a classic problem that strengthens understanding of data structure manipulation. 💪 Key learning points: Efficiently rotating elements in a queue to simulate stack behavior Mastering core operations: push, pop, top, and empty Reinforcing how data structure constraints lead to creative solutions #LeetCode #DataStructures #Python #DSA #Learning #Programming #TechJourney #Queue #Stack