Building PlanetDiskHardDrive: A Python Simulation of Data Storage

“Strong systems are built on basics done right, not shortcuts taken early” Spent time today strengthening my Python fundamentals, with a focused deep dive into data types and type conversion — building the base that every reliable system depends on. The learning covered int, float, str, and bool, along with hands-on practice in arithmetic operations, string handling, logical evaluation, and explicit type casting. Beyond syntax, the focus was on understanding how data behaves in execution and why correct type usage matters in production-level code. 🎯 What I’m taking forward: Technical Foundation: Stronger grasp of Python’s core data types and their practical applications Code Discipline: Writing predictable, readable, and type-aware code Problem Solving: Using logic and conditions to control program flow effectively Engineering Approach: Fundamentals first, complexity later Consistency: Showing up daily compounds faster than rushing milestones #Python #DataTypes #ProgrammingFundamentals #SoftwareDevelopment #LearningJourney #Discipline #SkillBuilding

Day 4/30: Game Theory & Data Mapping in Python 🐍💧🔫 For Day 4 of my #30DaysOfPython challenge, I built a classic: Snake, Water, Gun. While it looks like a simple game, the underlying logic is a masterclass in Data Mapping and Conditional Matrices. The Analytics Breakdown: ✅ Dictionary Mapping: I used Python Dictionaries to translate user input ("s", "w", "g") into numerical values. This is exactly how we "encode" categorical data for machine learning models! ✅ Reverse Mapping: I created a second dictionary to "decode" computer results back into human-readable labels—a key part of data storytelling. ✅ Nested Logic: Managing multiple outcomes (Win, Loss, Draw) using structured if-elif-else blocks. Building this helped me understand how to handle user-generated strings and map them to backend logic seamlessly. Next stop: Day 5! Full code below! 👇 #Python #CodingChallenge #GameTheory #Logic #DataScience #LearningInPublic #Day4

🚀 Python Cheat sheet – From Basics to Advanced! 💡 Master Python with this quick guide covering key concepts: ✅ Basics: Printing, variables, data types ✅ Operators & Conditions: Arithmetic, comparison, ternary ✅ Loops: For, while, list comprehension ✅ Data Structures: Lists, tuples, dicts, sets ✅ Functions: Regular & lambda functions ✅ File Handling: Read/write files ✅ Exception Handling: Try-except ✅ OOP: Classes, objects ✅ Libraries: NumPy, Pandas, Flask, Requests ✅ Advanced: Slicing, decorators, multithreading 🔥 Master these Python concepts & level up your coding skills! 🚀 🔔 Follow Supriya Darisa for more Python & Data Engineering insights! 👍 Like & Save for quick reference! 🔁 Repost to help fellow programmers! 💬 Comment your favorite Python trick! 📤 Tag someone who needs this! #Python #Coding #PythonProgramming #SoftwareDevelopment #DataScience #Automation #WebDevelopment #DataEngineering #DataAnalytics

I analyzed live admission lead data using Python and ML-style logic 📊 With only Name and Contact details , I applied feature engineering to classify leads into High Interested and Low Interested categories. This project proves that strong logic and analytical thinking matter more than data size. 🎥 I’m sharing a short screen recording of my Jupyter Notebook analysis. 🔗 GitHub Project Link: https://lnkd.in/ds-2Upqk 💬 I’d love your feedback — how would you improve this analysis with limited data? #Python #DataAnalytics #MachineLearning #JupyterNotebook #FeatureEngineering #LearningByDoing

🎄 Advent of Code 2025 – Day 11 Complete Wrapped up Day 11 of Advent of Code. This one was an elegant demonstration of how memoization transforms exponential problems into tractable ones. 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 The input describes a directed graph where each node points to its children, ultimately leading to an 'out' node. 𝗣𝗮𝗿𝘁 1: Count all possible paths from a starting node ('you') to the destination ('out'). 𝗣𝗮𝗿𝘁 2: Count only the paths that pass through specific required nodes ('dac' AND 'fft') before reaching the destination. 𝗪𝗵𝗮𝘁 𝗠𝗮𝗱𝗲 𝗜𝘁 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 Part 1 is a classic recursive path counting problem. The naive approach recalculates the same subproblems repeatedly, leading to exponential time complexity. Adding @cache (memoization) transforms it into a dynamic programming solution that computes each node's result exactly once. Part 2 adds a twist: constraint tracking. We need to count only paths that satisfy certain conditions. The solution tracks which required nodes have been visited using a frozenset as part of the state space. This allows us to maintain memoization while ensuring we only count valid paths. The key insight: state space design matters. By using (node, visited_required_nodes) as our state, we keep the cache size manageable at O(nodes × 2^k) where k is the number of required nodes. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: • Memoization: exponential → polynomial time complexity • State space design is crucial for efficient DP • Python's @cache decorator makes memoization trivial • Adding constraints requires careful state tracking • Frozensets enable hashable collections for caching 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗜𝗺𝗽𝗮𝗰𝘁: Without memoization: Would timeout on large graphs With memoization: Instant results ⚡ This problem perfectly illustrates why dynamic programming is one of the most powerful optimization techniques in computer science. 🔗 Code Repository: https://lnkd.in/eYRvn-mk #AdventOfCode #DynamicProgramming #Algorithms #GraphTheory #Python #Memoization #ComputationalThinking #SoftwareEngineering #ProblemSolving

𝗔 𝗣𝘆𝘁𝗵𝗼𝗻 𝗴𝗼𝘁𝗰𝗵𝗮 𝘁𝗵𝗮𝘁 𝗯𝗶𝘁𝗲𝘀 𝗲𝘃𝗲𝗿𝘆 𝗯𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝗼𝗻𝗰𝗲. >>> 10 / 2 5.0 Wait... 5.0? Not 5? Python's division operator 𝘢𝘭𝘸𝘢𝘺𝘴 returns a float. Even when the result is a whole number. This seems like a minor detail until you try to use that result as a list index: → my_list[10 / 2] # TypeError! The fix is floor division: → 10 // 2 returns 5 (an integer) But here's another trap: floor division rounds toward 𝘯𝘦𝘨𝘢𝘵𝘪𝘷𝘦 𝘪𝘯𝘧𝘪𝘯𝘪𝘵𝘺, not toward zero. → -7 // 2 returns -4, not -3 Small details. Big consequences. The developers who know these quirks aren't necessarily smarter—they've just been burned by them before and learned. Now you know without the burn. I'm writing "Zero to AI Engineer: Python Foundations" in public. Follow along on Substack for behind-the-scenes updates and excerpts (link in comments). What Python gotcha caught you by surprise? #Python #Programming #SoftwareEngineering #TechCareers #LearnToCode

While drafting a Python script to automate retaining wall design and drawing generation, an unexpected challenge emerged: How to evaluate an equation — nested inside a function — across a range of values? The solution turned out to be surprisingly elegant: a lambda expression. It’s one of those moments where something learned early on — but never quite understood where to apply — suddenly clicks. The equation was wrapped in a lambda, mapped across a list, and the results flowed. Simple, expressive, and effective. What fascinates me is how functional tools reveal their power in real-world design logic. In C#, I’ve also relied on anonymous lambda functions — especially in my ASP.NET Core projects — so the idea isn’t unique to Python. But here, working out the structural analysis logic, Python felt like the right tool at the right time: lightweight, flexible, and candy‑sweet for rapid prototyping. Still learning. Still surprised. Still enjoying the pinch. 🐍 #ChangmarinLearns #MotleyPython #ThePythonPinch #Python #EngineeringAutomation #RetainingWallDesign #FunctionalProgramming

🚀 Day 45 of #100DaysOfCode — Finding the Index of an Element in an Array Hey everyone! 👋 Today’s challenge focused on searching for an element in an array and returning its index. If the element doesn’t exist, we return -1 — simple but fundamental logic 💡 👨💻 What I practiced today: ✅ Implementing a linear search ✅ Iterating through arrays using loops ✅ Handling “not found” cases gracefully ✅ Writing clean and readable Python functions 📌 Today's Task: ✔ Create a function findIndex(arr, element) ✔ Return the index of the given element ✔ Return -1 if the element is not present 🧠 Example: Input: [1, 2, 3], 2 Output: 1 Input: ["a", "b", "c"], "c" Output: 2 Input: [5, 8, 10], 3 Output: -1 💡 Key Takeaway: A simple loop can solve many problems efficiently. Linear search is easy to understand and works well for small to medium datasets. #100DaysOfCode #Python #Arrays #Programming #ProblemSolving #LearningToCode #CodingJourney #Day45

🚀 Day 45 of #100DaysOfCode — Finding the Index of an Element in an Array Hey everyone! 👋 Today’s challenge focused on searching for an element in an array and returning its index. If the element doesn’t exist, we return -1 — simple but fundamental logic 💡 👨💻 What I practiced today: ✅ Implementing a linear search ✅ Iterating through arrays using loops ✅ Handling “not found” cases gracefully ✅ Writing clean and readable Python functions 📌 Today's Task: ✔ Create a function findIndex(arr, element) ✔ Return the index of the given element ✔ Return -1 if the element is not present 🧠 Example: Input: [1, 2, 3], 2 Output: 1 Input: ["a", "b", "c"], "c" Output: 2 Input: [5, 8, 10], 3 Output: -1 💡 Key Takeaway: A simple loop can solve many problems efficiently. Linear search is easy to understand and works well for small to medium datasets. #100DaysOfCode #Python #Arrays #Programming #ProblemSolving #LearningToCode #CodingJourney #Day45