Implementing Majority Element with Boyer-Moore Voting Algorithm in Python

Day 64 of my #100DaysOfCode challenge 🚀 Today I worked on generating all combinations of a string using Python’s itertools module. This is an important concept in combinatorics and widely used in problem-solving and interviews. What the program does: • Takes a string as input • Generates all possible combinations (subsets) • Uses itertools.combinations() • Prints combinations of all lengths Example (Input: "abc"): All combinations: a b c ab ac bc abc How the logic works: Step-by-step: 1. Loop from length = 1 to n 2. Generate combinations of each length 3. Join characters into strings 4. Store and print results It systematically builds all possible subsets ✔️ Why this is important: – Core concept in combinatorics – Used in problems like: Subset generation Feature selection Probability problems – Frequently asked in interviews – Helps understand power set logic Time Complexity: O(2ⁿ) Space Complexity: O(2ⁿ) Key Takeaways: – Understanding combinations vs permutations – Using Python’s powerful itertools – Generating subsets efficiently – Clean and readable implementation #100DaysOfCode #Day64 #Python #Programming #Combinations #Itertools #Algorithms #DSA #CodingPractice #ProblemSolving #InterviewPrep #LearnByDoing #DeveloperJourney #Consistency #BTech #CSE #AIandML #VITBhopal #TechJourney

🚀 Built a Symptom Risk Analyzer using Python & Streamlit Healthcare decisions are often delayed due to a lack of quick and reliable guidance. To explore this problem, I developed a simple health-tech application that analyzes user symptoms and classifies them into risk levels. 🔍 Key Features: • Symptom-based risk classification (Low / Moderate / High) • Risk scoring system with visual indicators • Clean and user-friendly interface • Instant feedback with actionable guidance 🛠 Tech Stack: Python, Streamlit This project helped me understand how simple logic combined with good UI can create meaningful user impact. 📸 Demo and code: https://lnkd.in/diE4_UMP #Python #Streamlit #HealthTech #AI #MachineLearning #WebApp #SoftwareDevelopment #Coding #Developer #TechProject #BuildInPublic #GitHub #Innovation #DataScience #ProblemSolving

Early in my career, I thought "Senior" meant writing the most complex Python scripts possible. I wanted every pipeline to be a masterpiece of custom logic. Now? I aim for "boring" code. If it can be done in simple, modular SQL (dbt), do it there. If a Stakeholder says they need "Real-time," ask them if 15 minutes is enough (it usually is). If you can’t explain the model to a non-technical person in 2 minutes, it’s too complex. The goal isn't to show off how smart we are. The goal is to build something that doesn't wake the team up at 3 AM. Keep it simple. Keep it testable. Keep it scalable. #Analytics #DataStrategy #WorkingInTech #Engineering

𝐃𝐚𝐲 𝟏𝟏: 𝐒𝐭𝐨𝐩 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠, 𝐒𝐭𝐚𝐫𝐭 𝐅𝐢𝐧𝐝𝐢𝐧𝐠  I’ve noticed as I move deeper into Python, is that how we 𝐬𝐭𝐨𝐫𝐞 𝐝𝐚𝐭𝐚 is just as important as the focusing on the code logic. Up until now, I’ve been reaching for a 𝐋𝐢𝐬𝐭 for almost everything. It’s the go-to structure when you start. But as my data grows,I realized why that can be a trap. 👉 If you have 1,000 items, a List forces you to check every single one until you find a match. • With a 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲, I’m not just piling data into a box; I’m giving every piece of data a unique Key. 👉𝐓𝐡𝐞 𝐋𝐢𝐬𝐭 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 (𝐦𝐲𝐥𝐢𝐬𝐭) : Iterating through the stack until you hit "Bob." 👉𝐓𝐡𝐞 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 (𝐦𝐲𝐝𝐢𝐜𝐭) : Direct access via a unique Key. Instant results✅ A Dictionary is a completely different, and much more efficient, tool for the job. It’s a fundamental shift in how we handle data retrieval. #Python #30DaysOfCode #Day11 #LearningInPublic #SoftwareEngineering

A.I. without knowing Python = DANGER I built a Valuation Dashboard. It took me a few hours to build a client-ready valuation dashboard. Under the hood: - 4,100 lines of Python code - yet to be debugged - So many libraries used and functions defined - Not written by me - So many functionalities which should be dynamic but are static - Dashboard working fine right now but may break big time when used for practical purposes Leaders to take note: Can someone impress us showing this? - Yes Is it deployable - Not until it is debugged and tested thoroughly But can't another A.I. debug these 4,100 lines of code? Yes, but who validates the validator? A year back I thought what's the point to knowing/teaching Python when A.I. is here doing all the coding. Now I feel knowing a programming language (specially Python) is more important than ever before if you want to reap the benefits of A.I. for complex tasks. Have you experienced a similar situation before? #Python #AI #Valuation #Claude #Anthropic

Your Python isn’t slow. Your data model is. Most developers chase faster libraries or rewrite code. But the real bottleneck? Invisible overhead between your code and the machine. I cut a batch job from 10 minutes -> 90 seconds without concurrency. Just by: - replacing a dict with a slots-based structure - pre-allocating a list Less memory churn. Fewer cache misses. CPU finally did real work. Two facts most people ignore: - A Python int isn’t just a number, it’s ~28 bytes of object overhead - A dict lookup is fast, but still far heavier than array-style access In tight loops, that overhead > actual computation. That’s why switching to typed arrays (or minimal C paths) feels like a massive speedup, same logic, different cost model. My rule: Don’t optimize algorithms first. Optimize how data moves. - reduce allocations - batch work - keep data contiguous Measure with real data. Then optimize where it actually hurts. #Python #Performance #Engineering #Optimization

Python is often called slow. Mostly because it’s an interpreted language. But here’s what gets missed. Speed at the language level is only one part of the system. In real applications, performance depends more on architecture, I O handling, and how workloads are designed. That’s why Python continues to power AI systems, data platforms, automation tools, and large-scale backends. Heavy computation is often offloaded to optimized libraries or handled outside the main runtime. The rest is about writing efficient, structured code. So yes, Python is slower at a low level. But that rarely becomes the bottleneck in real systems. Time to break the myth. #MadForCoding #Python #SoftwareEngineering #BackendDevelopment #TechMyths #SystemDesign

My Python code was working perfectly… But it was slow. And that’s a problem. Because in real systems: 👉 Slow code = bad performance 👉 Bad performance = poor user experience 💡 Today I learned performance optimization in Python And it completely changed how I write code. 📊 What I improved: • Replaced loops with vectorized operations • Reduced unnecessary calculations • Filtered data early • Used built-in optimized functions 💡 Simple example: Before 👇 Loop over thousands of rows ❌ After 👇 Single vectorized operation ✅ 👉 Same result… but much faster 💡 Biggest realization: Code that works is not enough… 👉 It must also be efficient 📌 Real-world truth: • Small inefficiencies → huge impact at scale • 1 second delay × millions of rows = serious issue 💬 Let’s discuss: Have you ever optimized a piece of code and seen a huge performance improvement? What did you change? #Python #PythonTutorial #Performance #Optimization #DataEngineering #PythonDeveloper #Pandas #CodingJourney #LearnInPublic #DevelopersIndia #Tech #100DaysOfCode #BuildInPublic Day 30 tomorrow: Final Project (complete portfolio-level build 🔥)

🚀 Day 21 of My Python Journey – Solved Search Insert Position! 💻✨ Today I worked on a problem that looks simple but really tests your understanding of Binary Search. 📌 Problem Statement Given a sorted array of distinct integers and a target value: Return the index if the target is found If not, return the index where it should be inserted Example: nums = [1,3,5,6] target = 2 ✅ Output: 1 🔍 My Approach I used Binary Search to solve this efficiently. Start with left and right pointers Find the middle element Compare with the target Adjust search space accordingly The interesting part is: 👉 Even if the element is not found, the left pointer ends at the correct insertion position That’s a really neat trick I learned today 💡 💡 Key Learning This problem helped me understand: ✔ Binary Search more deeply ✔ How to find insertion position without extra steps ✔ Writing clean and efficient logic ✔ Handling edge cases ⏱ Complexity Time Complexity: O(log n) Space Complexity: O(1) Small problems like this sharpen the fundamentals really well 🔥 #Python #DSA #BinarySearch

🚀 Day 65 of My Python & DSA Journey Today’s problem was Word Pattern (290) — a great exercise in understanding mapping and relationships between data. 🔍 Problem Solved: Given a pattern and a string, determine if the string follows the same pattern with a one-to-one mapping (bijection) between characters and words. 💡 Approach Used: • Split the string into words • Use two hashmaps (dictionaries): • One for character → word • One for word → character • Ensure consistency in both mappings while iterating ⚡ Key Learnings: • Concept of bijection (one-to-one mapping) • Using hashmaps for efficient lookups • Handling edge cases like unequal lengths • Writing clean validation logic 📊 Complexity Analysis: ✅ Time Complexity: O(n) We traverse the pattern and words once ✅ Space Complexity: O(n) For storing mappings 🎯 Why This Works? Using two dictionaries ensures no duplicates or conflicts, maintaining a strict one-to-one relationship. Another step closer to mastering problem-solving patterns! Under the Guidance of: Rudra Sravan kumar and Manoj Kumar Reddy Parlapalli #Day65 #Python #LeetCode #DSA #Algorithms #CodingJourney #100DaysOfCode #10000Coders 🚀