LeetCode #15: 3Sum Python Implementation with Sorting

LeetCode Problem 1143: "Longest Common Subsequence": Given two strings text1 and text2, return the length of their longest common subsequence. If there is no common subsequence, return 0. A subsequence of a string is a new string generated from the original string with some characters (can be none) deleted without changing the relative order of the remaining characters. For example, "ace" is a subsequence of "abcde". A common subsequence of two strings is a subsequence that is common to both strings. The below implementation in Python resolves this problem in O(m*n) time and space complexity using the dynamic programming approach. A dp array is created whose cells store the value of longest common subsequence upto a specific length of text1 and text2. At the last cell we get the value of "longest common subsequence" for the given two strings. #Python #LeetCode #DynamicProgramming #Algorithms #DataStructures #CompetitiveProgramming #CodingChallenge #ProblemSolving

LeetCode Problem 83: Given the head of a sorted linked list, delete all duplicates such that each element appears only once. Return the linked list sorted as well. The below implementation in Python successfully resolves this in time complexity of O(n) where n is length of the list with constant space complexity. The approach is simple, handle the base case first like list having 0 or 1 number of nodes and then write the logic of handling lists of length greater than one. Maintain two pointers, one to keep track of present node and other to keep track of previous node. Check if the value of both nodes match or not, if match update the pointing of previous node, point its next to the present.next. #LeetCode #LinkedList #Python #CompetitiveProgramming #Algorithms #DataStructures #ProblemSolving

LeetCode #76 – Minimum Window Substring | Python Implementation I implemented a sliding window approach with two HashMaps to find the smallest substring containing all characters from t. The countT map stores the required character frequencies, while window tracks the current window's frequencies. Two counters have and need track how many unique characters have met their required counts. The right pointer expands the window until all requirements are satisfied, then the left pointer contracts to minimize the window size while maintaining validity. This pattern is critical in text search engines, log parsers, and bioinformatics for pattern matching in genomic sequences. Key Takeaway: The have vs need tracking elegantly reduces the problem to counting satisfied unique characters rather than checking all frequencies repeatedly. The inner while loop aggressively shrinks the window once valid, ensuring we capture the smallest possible substring before expanding again. Time: O(n + m) where n = len(s), m = len(t) | Space: O(m) #LeetCode #DataStructures #Python #SlidingWindow #HashMap #CodingInterview #ProblemSolving #SoftwareEngineering

✅ 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

🧠 Python Concept: List Comprehension Write powerful loops in one clean line. ❌ Traditional Way squares = [] for i in range(5): squares.append(i * i) print(squares) Output [0, 1, 4, 9, 16] ✅ Pythonic Way squares = [i * i for i in range(5)] print(squares) Same result, less code. ⚡ With Condition even_squares = [i * i for i in range(10) if i % 2 == 0] print(even_squares) Output [0, 4, 16, 36, 64] 🧒 Simple Explanation Imagine telling a robot: 👉 “Give me squares of numbers from 0–4.” 👉 Instead of repeating instructions, you give one rule. 👉 That rule = list comprehension. 💡 Why This Matters ✔ Shorter code ✔ Faster execution ✔ More readable loops ✔ Very Pythonic 🐍 Python often replaces multiple lines with a single elegant expression 🐍 List comprehensions are one of the most powerful examples of that philosophy. #Python #PythonTips #PythonTricks #AdvancedPython #List #ListComprehension #Tech #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

🚀 Day 50 of My Python Journey Today I solved Complement of Base 10 Integer on LeetCode. 🔍 Problem Overview: The task is to find the bitwise complement of a given base-10 integer. The complement is obtained by flipping all bits in its binary representation — changing every 0 to 1 and every 1 to 0. 🧠 Approach: 1️⃣ Convert the integer into its binary representation. 2️⃣ Traverse the binary string and flip each bit (1 → 0, 0 → 1). 3️⃣ Convert the resulting binary string back to a decimal integer. ⚡ Key Learnings: • Practiced binary representation and bit manipulation • Improved understanding of number systems (binary ↔ decimal) • Strengthened string manipulation and logical thinking in Python 📊 Complexity: • Time Complexity: O(n) • Space Complexity: O(n) Under the Guidance of : Rudra Sravan kumar and Manoj Kumar Reddy Parlapalli #Day50 #Python #LeetCode #DataStructures #Algorithms #CodingJourney #ProblemSolving #100DaysOfCode 🚀

Scale vector search to millions without rewriting your prototype code ⚡ Building semantic search typically starts with storing vectors in Python lists and computing cosine similarity manually. But brute-force comparison scales linearly with your dataset, making every query slower as your data grows. Qdrant is a vector search engine built in Rust that indexes your vectors for fast retrieval. Key features: • In-memory mode for local prototyping with no server setup • Seamlessly scale to millions of vectors in production with the same Python API • Built-in support for cosine, dot product, and Euclidean distance • Sub-second query times even for millions of vectors ☕️ Run this code: https://bit.ly/4cCI76w #VectorDatabase #Python #SemanticSearch #DataScience

🚀 Day 3/100 — Enabling Repetition with Loops 🧠 “Efficiency emerges when logic learns to repeat itself.” Scalable systems rely on structured repetition to process continuous operations. Today, I explored how loops in Python enable efficient execution of repeated logic without redundancy. ⚙️ 🔧 Today’s focus areas: 🔁 for Loops — Iterating over structured sequences 🔄 while Loops — Executing logic based on conditions 🎯 Loop Control — Using break and continue 🧩 Iterative Thinking — Designing efficient execution flows 🎯 The objective was to build scalable execution patterns that reduce redundancy and improve efficiency. ✅ Day 3 complete: Iterative execution enabled. ▶️ Day 4: Structuring and organizing data using collections. Step by step. The system evolves. 🏗️ #100DaysOfCode #Python #DeveloperJourney #BackendDevelopment #SoftwareEngineering

🚀 Day 2 – DSA Series Remove Duplicates from Sorted Array Solved LeetCode 26 – Remove Duplicates from Sorted Array using Python. 🧠 Problem Summary Given a sorted array nums, remove duplicates in-place such that each unique element appears only once. Return the number of unique elements k, where: • The first k elements of nums contain the unique values • The remaining elements beyond k don’t matter • No extra array allowed (O(1) space) Example: Input: [1, 1, 2] Output: k = 2 → Modified array: [1, 2, _] 💡 Approach Used (Two Pointer Technique) Since the array is sorted, duplicates are adjacent. ✔️ Initialized k = 1 (first element always unique) ✔️ Iterated from index 1 ✔️ Compared current element with previous element ✔️ When a new unique element is found: • Placed it at index k • Incremented k This ensures in-place modification without extra space. ⏱ Complexity Analysis Time Complexity: O(n) – single pass Space Complexity: O(1) – no additional data structures 🔎 Key Takeaway This problem strengthens: • Two Pointer pattern • In-place array manipulation • Understanding problem constraints carefully Continuing the DSA series — one problem at a time. 🚀 #DSA #LeetCode #Python #Algorithms #TwoPointers #ProblemSolving #CodingJourney

Python Tip: With Statement The 'with Statement' Is More Than Syntax Most developers see 'with' as a shortcut for opening files. It’s not. It’s Python’s way of guaranteeing clean resource management, automatically and safely. No forgotten .close() No hidden leaks No fragile cleanup logic That’s the real shift from old to modern Python: Old mindset -> “Remember to clean up.” Modern mindset -> “Design so cleanup is automatic.” 'with' isn’t just cleaner code. It’s safer architecture. FOLLOW FOR MORE PYHON TIPS & INSIGHTS #Python #CleanCode #SoftwareEngineering #ProgrammingTips