Python heapq for Priority Queues and Efficient Data Management

Python bisect module keeping lists sorted the smart way: Normally, when you insert new items into a list, you’d have to sort it again to keep order. That’s inefficient, especially with large datasets. The bisect module solves this by finding the right position for insertion automatically, so your list stays sorted without the overhead of full re‑sorting. Why developers love it: Saves time and memory. Perfect for maintaining ordered sequences like leaderboards, logs, or priority lists. Built‑in and easy to use no extra libraries needed. At IT Learning AI, we break down these hidden gems so you can write cleaner, faster, and more efficient code. Want to master Python’s built‑in modules? Explore tutorials, guides, and practical coding insights at www.itlearningai.ai Learn. Apply. Grow. With IT Learning AI. #itlearningai #pythonprogramming #learnpython #pythontips #pythonbasics #pythonforbeginners #codesmarter #pythonbisect #sortedlists #efficientcoding #pythonperformance #advancedpython #pythondevelopers

𝗠𝗼𝘃𝗲 𝘁𝗵𝗲 𝘀𝗵𝗼𝗿𝘁𝗲𝗿 𝘀𝗶𝗱𝗲. 𝗔𝗹𝘄𝗮𝘆𝘀. Day 41 of #1000DaysOfLearning Solved 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝟭𝟭 — Container With Most Water (𝗠𝗲𝗱𝗶𝘂𝗺) today. No hints, no AI, figured it out on my own. The problem: given an array of heights, find two lines that hold the 𝗺𝗮𝘅𝗶𝗺𝘂𝗺 𝘄𝗮𝘁𝗲𝗿. Water level is capped by the shorter line, width is the gap between them. The 𝘁𝘄𝗼-𝗽𝗼𝗶𝗻𝘁𝗲𝗿 approach clicks once you ask which pointer do I move? Moving the taller line inward is pointless — width shrinks and height can only stay the same or drop. Moving the 𝘀𝗵𝗼𝗿𝘁𝗲𝗿 𝗼𝗻𝗲 at least gives you a chance at something better. That one observation takes it from 𝗢(𝗻²) brute force to 𝗢(𝗻) cleanly. Came in at 𝟯𝟳𝗺𝘀 — beats 𝟵𝟳.𝟰𝟱% of Python submissions. What actually felt good wasn't the result. It was that the 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 came from me, not from reading someone else's solution first. That's the only thing worth building — the ability to 𝘁𝗵𝗶𝗻𝗸 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗶𝘁 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳. 🧠 #DSA #LeetCode #Python #TwoPointers #LearningInPublic

A 10-line python file just scored 100% on SWE-bench Verified. It didn't fix a single bug. It just told pytest to stop reporting failures. UC Berkeley did this across 8 benchmarks. Zero LLM calls. Zero tasks solved. Near-perfect scores everywhere. One benchmark's answer-checking code was imported but never called. Dead code. 890 tasks. Sending {} got full marks. I've been writing tests for years. The optimal strategy was apparently "overwrite the test runner." #AI #LLMs #AIBenchmarks #SoftwareEngineering

🚀 You’ve probably used Python’s print() hundreds of times… But do you really know what it can do? 👀 Most developers only use it for basic debugging — but print() comes with 4 powerful parameters: 👉 sep — control how values are separated 👉 end — control how output ends 👉 file — redirect output anywhere 👉 flush — force real-time output These small features can actually: ✔ Improve your code readability ✔ Replace messy string formatting ✔ Help in logging & ML workflows I recently wrote a complete guide on Medium covering all of this with real examples and practical use cases 👇 🔗 https://lnkd.in/gtW_W8Ry A huge thank you to Javier Armando Jimenez Villafaña and Swapneel Solanki for supporting me throughout this article — for checking the content, verifying the code examples, and providing valuable feedback that helped shape the final version. Really appreciate it! 🙌 I am also on this learning journey myself — exploring Python, AI, and ML one topic at a time. If you found this useful, have questions, or just want to discuss Python, AI, or ML — drop a comment below or DM me directly. I would love to connect and learn together! #Python #Programming #MachineLearning #DataScience #SoftwareDevelopment

Day 8 of My Learning Challenge: Understanding Loops in Python Today, I explored one of the most powerful concepts in programming — loops. Loops allow us to execute a block of code repeatedly without writing the same code multiple times. This is especially useful when working with large datasets or automating repetitive tasks in machine learning. Types of Loops in Python 1. For Loop Used when you know the number of iterations. for i in range(5): print("Iteration:", i) 2. While Loop Runs as long as a condition is true. count = 0 while count < 5: print("Count is:", count) count += 1 Loop Control Statements break → stops the loop completely continue → skips the current iteration for i in range(5): if i == 3: break print(i) Why This Matters in AI/ML 🤖 Loops are essential when: Iterating through datasets Training models over multiple epochs Processing batches of data Automating repetitive computations Every day, I’m getting more comfortable writing efficient and structured code. The journey continues 🚀 #M4ACELearningChallenge #M4ACE #Day6 #Python #MachineLearning #AI #LearningJourney

Day 26/∞: Learning GenAI – Getting Started with Pydantic Today I explored Pydantic, a Python library that makes data validation and settings management much more reliable. One thing that stood out to me is how useful it is for GenAI development, where structured and predictable data matters a lot. Using BaseModel, I learned how to define clear data types, add constraints like character limits or value ranges, and handle optional fields in a clean way. I also saw how Pydantic supports nested models, lists, and reusable validators, which makes it a strong fit for building well-organised applications. For me, the biggest takeaway was that Pydantic helps bring more consistency and safety into Python projects. It feels especially valuable when building with frameworks like LangChain and LangGraph, where structured inputs and outputs can make the whole system more dependable. #Python #Pydantic #GenAI #LangChain #LangGraph #AIEngineering #Day26 #BuildingInPublic

--- Day 6 of My Learning Challenge: Understanding Loops in Python 🔁 Today, I explored one of the most powerful concepts in programming — loops. Loops allow us to execute a block of code repeatedly without writing the same code multiple times. This is especially useful when working with large datasets or automating repetitive tasks in machine learning. Types of Loops in Python 1. For Loop Used when you know the number of iterations. for i in range(5): print("Iteration:", i) 2. While Loop Runs as long as a condition is true. count = 0 while count < 5: print("Count is:", count) count += 1 Loop Control Statements break → stops the loop completely continue → skips the current iteration for i in range(5): if i == 3: break print(i) Why This Matters in AI/ML 🤖 Loops are essential when: Iterating through datasets Training models over multiple epochs Processing batches of data Automating repetitive computations --- Every day, I’m getting more comfortable writing efficient and structured code. The journey continues 🚀 #M4aceLearningChallenge #m4ace #Day6 #Python #MachineLearning #AI #LearningJourney

The bottleneck in AI-assisted coding isn't the model or your prompts. When an agent can't see your notebook state, it guesses. You're relaying error messages and stuck in the (endless) loop at every step. With marimo-pair, coding agents get a live view of your notebook. Variables, errors, UI elements - if you can interact with it, the agent can too. PS: you're also not paying per token to analyze your own CSV files. https://lnkd.in/gcBjKijm #python #AI #datascience #openSource #mlops

🚀 𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 & 𝐔𝐬𝐢𝐧𝐠 𝐏𝐲𝐭𝐡𝐨𝐧 𝐌𝐨𝐝𝐮𝐥𝐞𝐬 Another step forward in my Python learning journey 🐍 — exploring how to make code more efficient, reusable, and powerful using modules. 📚 𝐖𝐡𝐚𝐭 𝐈 𝐥𝐞𝐚𝐫𝐧𝐞𝐝: 📦 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐚 𝐌𝐨𝐝𝐮𝐥𝐞? • A file that contains functions, variables, and reusable code • Helps organize and simplify large programs ⚙️ 𝐈𝐦𝐩𝐨𝐫𝐭𝐢𝐧𝐠 𝐌𝐨𝐝𝐮𝐥𝐞𝐬 • import math → perform mathematical operations • from math import sqrt → import specific functions • Cleaner and more efficient coding 🧰 𝐂𝐨𝐦𝐦𝐨𝐧 𝐁𝐮𝐢𝐥𝐭-𝐢𝐧 𝐌𝐨𝐝𝐮𝐥𝐞𝐬 • math → calculations • random → random values • os → system operations 💡 𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Using modules allows us to avoid rewriting code and build scalable, professional applications. 📈 Step by step, learning these concepts is helping me move from basic coding to real-world problem solving. #Python #Programming #DataScience #AI #Coding #LearningJourney #TechSkills