Mastering Python Control Flow with Loops

✅Day 55 of #100DaysOfCode Solved LeetCode 3346 — Maximum Frequency of an Element After Performing Operations I (Accepted ✅ 635/635 cases) Thrilled to share I got an Accepted solution in Python — runtime: 289 ms (beats ~92%) and memory: 30 MB (beats ~95%) 🎉 What I did: • Sorted the array and used a sliding-window (two pointers) approach. • Kept a running sum of the window to compute how many operations are needed to make all elements equal to the current right-end value. • Grew the window while remaining within numOperations, otherwise moved the left pointer — the maximum window size is the answer. Why this works: sorting groups close values together, and the sliding window + prefix-sum-like maintenance lets us calculate required increments efficiently (O(n log n) for the sort, O(n) for the scan). Key takeaways: • Classic use of sort + two-pointer for "make equal with limited ops" problems. • Keep aggregate information (like sum) to avoid recomputing costs inside the window. • Small optimizations in Python (use integers, avoid heavy operations inside loops) help pass tight constraints. If you want, I can share the final Python snippet or a short walkthrough of the logic. Grateful for the practice — on to the next one! 🚀 #LeetCode #100DaysOfCode #Python #CompetitiveProgramming #ProblemSolving

🚀 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 4/100 — Cracked LeetCode 1611: Minimum One Bit Operations to Make Integers Zero 🔥 Today’s challenge was a deep dive into bit manipulation and recursion. LeetCode 1611 looked deceptively simple—but beneath the surface, it’s a clever twist on Gray code transformations. 🔍 Problem Summary Transform an integer n into 0 using two constrained bit-flipping operations. The trick? You can only flip the rightmost bit, or flip the i-th bit if the (i-1)th is 1 and all lower bits are 0. 🧠 Key Insight This problem maps beautifully to recursive Gray code logic. For any number n, we recursively reduce it by flipping the highest set bit and subtracting the operations needed for the remainder. 📈 What I Learned Bitwise recursion can be elegant and powerful. Understanding binary patterns unlocks optimization. Python’s bit_length() is a hidden gem for bit-level logic. 🔧 Next Steps I’ll be documenting more of these insights as part of my 100-day challenge. If you’re into algorithmic puzzles or want to collaborate on clean, modular solutions—let’s connect! #100DaysOfCode #LeetCode #Python #BitManipulation #GrayCode #CodingChallenge #TechJourney #ScarBuilds

Python Day-2 — Mastering Control Flow and operator” > Today’s focus was on control flow — making Python code think logically. 🔸 Practiced if, elif, and else statements and operator like arithmetic operator, comparison operator, logical operator, assignment operator and bitwise operators. 🔸 Built small loop programs (for and while) to process lists and calculate totals. 🔸 Understood how logic drives automation and data handling. Every line of code teaches you to think in steps — not just write syntax. #Python #Coding #LogicBuilding #DataScienceJourney

Hey everyone! 👋 As part of my new series “1% Smarter with Python Daily”, here’s Day 1. If you’re using print() statements for debugging and monitoring your Python code, you’re definitely not alone. But here’s the thing — for anything beyond quick one-time scripts, print() often falls short. That’s where the built-in logging module comes in. Here’s why using logging rather than print() can level up your code: With logging, you get severity levels (DEBUG, INFO, WARNING, ERROR, CRITICAL) — so you can distinguish between normal messages vs alerts. You can direct output not just to console, but to files, sockets, or external systems — much harder when you scatter print()s. Stack Overflow+1 You get contextual information automatically: timestamps, module name, line number — helps when debugging later rather than just in the moment. print() is fine for small throwaway scripts — but once your codebase grows, you’ll thank yourself for not relying exclusively on print(). Why this is better than print(): If you want to silence debug logs in production you just change the log level, without removing/changing calls in code. All logs go through the same centralized system; you can redirect to a file, filter by module, tag by severity. Helps you maintain code clarity, production readiness, and better observability. ✅ Takeaway Challenge: Instead of writing print("something happened:", x) when you’re debugging or logging events — try replacing it with logger.info() or logger.debug() (depending on severity) and configure your logging as shown. I challenge you: in your next script, swap out one print() and replace it with logging. See how it changes the flow, how you filter logs, and what it gives you. #Python #Coding #DeveloperTips #Logging #SoftwareEngineering #TechTips #PythonTips #CleanCode

🐍Python Day 3 Each day, the logic gets tougher, but so does my clarity. 🔹 What I Tried: I wanted to take yesterday’s logic and make it work across multiple numbers instead of just one. 🔹 What I Built: A Python program that: ✅ Generates numbers within a range ✅ Checks if they’re even, odd, positive, or negative ✅ Detects prime numbers efficiently using the √n trick 🔹 What I Learned: Even a small piece of code can teach how to think systematically — to break a big problem into smaller parts and optimize as you go. Coding truly shapes how you feel, not just what you type. 🔹 What Was Challenging: Getting the prime number logic right without unnecessary loops. It took a few test runs (and print statements), but it finally clicked. Each day, a new script. Each script is a new insight. On to the next challenge! #PythonJourney #100DaysOfCode #LearnToCode #WomenWhoCode #CodingCommunity #PythonForBeginners #ProblemSolving #TechLearning #CodeNewbie #LogicBuilding #KeepLearning 

𝗣𝘆𝘁𝗵𝗼𝗻 𝗷𝘂𝘀𝘁 𝗵𝗮𝗱 𝗶𝘁𝘀 “𝗖𝗮𝗿𝗴𝗼 𝗺𝗼𝗺𝗲𝗻𝘁.” When I first started working with Rust, the thing that genuinely impressed me was 𝗰𝗮𝗿𝗴𝗼. A single tool that felt fast, cohesive, and thoughtful. I always wished Python had something similar. In the last few months, I began seeing 𝘂𝘃 mentioned more and more. I tried it. It didn’t just work well — it changed how Python feels to use. 🧱 The Old Python Workflow (What We Usually Juggle) 🐍 pip Installs packages 🎪 venv / virtualenv Manages environments 📦 pipx Runs isolated CLI tools 🔢 pyenv Manages Python versions 🎼 poetry / pip-tools Dependency resolution & lockfiles 🚀 What uv Brings Instead (This Is the “Cargo” Feeling) ⚡ Speed → Rust-powered and incredibly fast 🎛️ Unified → One mental model across tasks 🔁 Reproducible → Lockfiles and deterministic builds 🧩 Seamless → No patchwork of commands 🧠 Low overhead → Less “remembering how to set things up” Python has always had the parts. 𝘂𝘃 is the first time the experience feels whole. I’m excited to see where the community takes this. #Python #uv #Rust #Cargo #DeveloperExperience #SoftwareEngineering #OpenSource #DevTools #PythonDevelopers #Programming

Debug Smarter: How Unit Tests Help You Code Like a Pro Every professional developer knows, debugging isn’t about fixing errors, it’s about preventing them before they happen. That’s where unit tests come in. By writing small, focused tests for each part of your code, you: Catch bugs early, before they snowball into major issues Improve code reliability and performance Gain confidence every time you refactor or add new features In our Python Data Structures and Algorithms: Complete Guide, you’ll learn to write and run unit tests that make your code not just work, but last. Because real professionals don’t just code fast. They code smart. #LearnProgrammingAcademy #TimBuchalka #PythonCourse #UnitTesting #CleanCode #PythonDevelopers #ProgrammingTips #CodeQuality #softwareengineering

I’ve seen teams argue for hours about tabs vs spaces, but skip the basics that actually make code easier to read and maintain. Here’s what really moves the needle for Python projects: 1) Write code that explains itself. Clear names and small functions solve half the pain. 2) Treat PEP 8 as a baseline, not a religion. Consistency matters more than strictness. 3) Add type hints. They save time, catch silly mistakes, and make the code easier for teammates and tools to reason about. 4) Keep functions focused. If it’s hard to describe what it does in one line, it’s trying to do too much. 5) Handle errors thoughtfully. Catch what you expect and log what you need. 6) Document the “why,” not the obvious. 7) Clean imports, meaningful tests, and no random magic values sprinkled around. These simple habits make Python code kinder to whoever reads it next -including future you. #python #codingstandards #codequality #cleancode #bestpractices #programmingtips Follow Sneha Vijaykumar for more... 😊