Python Fundamentals: Tuples, Sets, Lambda Functions, File Handling

𝐏𝐲𝐭𝐡𝐨𝐧 𝐢𝐬 𝐭𝐡𝐞 𝐫𝐨𝐨𝐭 𝐨𝐟 𝐆𝐞𝐧 𝐀𝐈. Hello Data Points 👋 Today, we are revising all the data types in Python. These are my old digital short notes that I created during my learning journey. I am trying to make concepts simple so that anyone can understand the foundation before moving into Gen AI. 𝐂𝐨𝐯𝐞𝐫𝐞𝐝 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐫𝐞𝐯𝐢𝐬𝐢𝐨𝐧: • List • Tuple • String • Set • Dictionary • All core data types 𝐅𝐨𝐫 𝐞𝐚𝐜𝐡 𝐝𝐚𝐭𝐚 𝐭𝐲𝐩𝐞: • Creating • Accessing • Operations • Editing • Deleting • Important functions Strong basics build strong AI systems. Stay connected with data.... Repost ♻️ if this helps someone in their learning journey. #Python #GenAI #DataScience #MachineLearning #LearningInPublic #CodingJourney --- ID: 29 Project: Python Date: 12-02-2026 | 10: 57 IST

🎯 Exploring flight delay forecasting over 20 years of data taught me a lot about seasonality, feature engineering, and hyperparameter optimization. Key takeaways: Cyclical encoding and log transforms can sometimes matter more than the choice of algorithm, Bayesian optimization with Optuna saved me hours of manual tuning Statistical rigor (like testing diverse airport encoding strategies) really pays off. 🛠️ Stack: Python • CatBoost • Optuna • Pandas • Statistical Analysis 📁 Check out the full notebook: https://lnkd.in/dqS5aqq7 #DataScience #MachineLearning #Forecasting #Python #AirlineAnalytics

 Python is still the king of data + AI — but it’s not just about pandas anymore. These tools changed how I build in 2026: 🔸 Polars – 10× faster than pandas, lower RAM. Built on Rust. A no-brainer for big data. 🔸 FastAPI + Pydantic – Blazing fast APIs, auto-validating schemas, and async support. 🔸 Rich + Typer – Want beautiful CLIs? You need these. 🔸 Ruff + Black + Pyrefly – Lint, format, and type-check at warp speed. ⚡ Bonus: Tools like uv and RightTyper make env and typing management effortless. 👉 Python’s ecosystem isn’t just powerful — it’s lightning fast now. 💬 What’s one Python tool you discovered recently that you now can’t live without? #Python #DataScience #DevTools #FastAPI #OpenSource #Productivity

Twice per week, I send 2 tips to help you discover useful Python tools for data and AI. Here's what this week looked like: • uv - Switch Python versions without rebuilding your environment • Polars - Replace nested np.where() with readable condition chains • Granite Vision - Turn chart images into structured CSV data • PydanticAI - Disable OpenAI data retention in one setting If you want practical AI and Python tools delivered directly and easy to revisit, subscribe to the CodeCut newsletter: https://bit.ly/46fdOPl

𝗧𝗵𝗶𝘀 𝗦𝗶𝗺𝗽𝗹𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗵𝗲𝗰𝗸 𝗖𝗮𝗻 𝗦𝗮𝘃𝗲 𝗬𝗼𝘂 𝗛𝗼𝘂𝗿𝘀 Before blaming the model, check the data types. Numbers stored as text dates stored as strings categories treated as numbers Small datatype issues silently break analysis. Many “model problems” are actually data problems. Two minutes of checking can prevent hours of debugging later. #DataScience #MachineLearning #DataAnalytics #Python #AI #LearningInPublic

🧠 Building a Multi-Algorithm Pathfinding Visualizer with Python & Pygame I built an interactive tool that visualizes 6 different search algorithms in action! Watch how each algorithm "thinks" differently to find the path through the same grid. 🎯 The Algorithms: BFS - Explores level by level (shortest path guaranteed) DFS - Goes deep before wide (memory efficient) UCS - Handles weighted paths with diagonal costs DLS - Respects depth limits IDDFS - Combines DFS efficiency with BFS completeness Bidirectional - Searches from both ends simultaneously 📊 Real-time Stats: • Nodes explored • Time taken • Color-coded visualization (🟡 exploring, 🔴 explored, 🟣 final path) 🤯 Fascinating insight: Bidirectional search found the path in nearly half the time by expanding from both start AND target simultaneously! 🛠 Built with: Python • Pygame • Queues • Stacks • Heaps 🔗 GitHub: https://lnkd.in/dbHt9iHM #Python #Pygame #Algorithms #Pathfinding #BFS #DFS #Coding #ComputerScience #AI #DataStructures

Quick question 👇 When we use *args inside a function… Where are the passed values stored? 🤔 ✅ Answer: In a Tuple Why? Because *args collects any number of positional arguments and automatically stores them inside a tuple. Here’s a simple example: Python Copy code def my_function(*args): print(type(args)) print(args) my_function(1, 2, 3, 4) 🔎 Output: Copy code <class 'tuple'> (1, 2, 3, 4) 💡 Why Tuple instead of List? Tuples are immutable (cannot be modified) Slightly more memory-efficient Protect the data from accidental changes inside the function Understanding small details like this is what separates someone who writes code from someone who truly understands Python. #Python #Programming #AI #SoftwareDevelopment #LearningJourney #TechGrowth

This is how we built a Ai token counter from scratch — in Python. No fluff. Just code. Part 1: Building a token counting tool 🛠️ Here's what we used: tabulate → clean, readable tables rich → beautiful terminal output The tool compares token counts across different AI models. Side by side. In real time. This is how you stop guessing and start knowing your costs. Save this post. Share it with your team. Follow for Part 2 — where we go deeper. Tags & Keywords: #Python #BuildInPublic #LLMTools #TokenCounting #AIEngineering #RichLibrary #Tabulate #OpenAI #Claude #Anthropic #PythonDev #AITools #LLMOps #MachineLearning #DevTools #SoftwareEngineering #AIProducts #TechContent #PythonProgramming #Part1

Let’s test your understanding of Python 👇 a = [1, 2] b = a b.append(3) print(len(a)) What’s the output? 2 3 1 Error 🧠 Take 5 seconds before answering… ✅ Correct Answer: 3 Why? Because this line: b = a Does NOT create a copy. It makes b point to the same list in memory. So when we do: b.append(3) We modify the SAME object. Now the list becomes: [1, 2, 3] And since a and b reference the same object: len(a) = 3 🔥 Key Insight Lists in Python are mutable. Variables store references, not copies (for mutable objects). That’s why small misunderstandings like this cause real bugs in data & AI pipelines. Have you ever been surprised by Python references before? 👇 #Python #AI #DataScience #LearningInPublic #30DayChallenge

Just wrapped up the Longest Consecutive Sequence problem on LeetCode and wrote a short breakdown on Medium. It’s a fun example of how a problem that looks like it needs sorting can actually be solved in O(n) time using a set and a simple insight about sequence starts. Small optimization, big payoff. This also marks the end of the Arrays & Hashing section of NeetCode 150 for me. Two pointers are up next, and for the first time in a while, burnout feels like it’s finally backing off. If you’re learning DSA or prepping for interviews, this one is worth a look. Read more here: https://lnkd.in/gWZ4WQmP #LeetCode #DataStructures #Algorithms #Python