Started learning Python with a simple Band Name Generator.

In today's article, I shared what I'm learning about Python's time management capabilities! 🐍 ⏰ I'm learning these concepts as I write. I walked through some practical ways to handle time, schedule tasks, and launch programs in Python. Here's what I covered: # Quick example: import time from datetime import datetime start = time.time() print(f"Current time: {datetime.now().strftime('%H:%M:%S')}") time.sleep(2) # Wait 2 seconds print(f"Time elapsed: {time.time() - start} seconds") I show you how to: • Track time with the `time` module 🕒 • Work with dates using `datetime` 📅 • Schedule tasks with the `schedule` library ✅ • Launch programs via `subprocess` 🚀 I included real working code examples that you can try right now! Here's another cool trick: # Schedule multiple tasks easily schedule.every().day.at("10:00").do(morning_task) schedule.every().friday.do(weekly_report) I'm still learning new things about Python every day, and I'd love to hear about your experiences with these time management tools! What will you automate first? 🤔 Let's keep learning together! Drop a comment with your questions or share what you're working on. #PythonProgramming #Automation #CodingTogether Post: https://lnkd.in/eKxiq6bD

Ever had a “perfect” Python function quietly break in production — and wondered why your tests didn’t catch it? I did. I thought my unit tests covered all cases until Hypothesis came along and threw hundreds of unexpected inputs at my code. Suddenly, edge cases I never imagined popped up, exposing bugs hidden deep inside. Hypothesis flips testing on its head: instead of writing examples, you describe the properties your code should always follow. Then it generates countless inputs, hunting for the exact one that makes your code fail — and it even shrinks that input so debugging feels less like detective work and more like solving a puzzle. From checking that encoding and decoding always round-trip correctly, to revealing bugs in cache eviction policies, or validating optimized functions against simple references — this tool makes your code bulletproof in ways traditional tests can’t. 🛠 Catch weird edge cases without brainstorming endless inputs ⚡ Test complex data models or stateful classes efficiently 🔎 Find the exact failing example to debug faster What if your next bug isn’t a mystery but a challenge waiting for Hypothesis to uncover? Ever let your tests surprise you? What hidden bugs has property-based testing caught for you? #Python #DataScience #SoftwareTesting #Hypothesis #CodingTips #QualityAssurance #DeveloperLife

Lesson: Be old-school — double-check with tables. I had the opportunity to test a program. At my suggestion, my task was to write an independent implementation that should return the same result — within reasonable limits. I received the test program’s output and input data. First, I wrote the script in Python — the Type I error value was different. Since R is also part of my toolkit, I implemented it there as well. Same result as in Python. I took the risk and rewrote it in VBA. Again, the same result. Then I implemented a quick Beta function integration to calculate the F-distribution value — and yes, that also matched. Did I eliminate the possibility that I was wrong by testing three methods? No — I only validated my own mistake. (prior about 0,1122) When I ran out of ideas on how to find the error in my reasoning or knowledge, I remembered the old-school step: I checked the tabulated values. They matched. I suggested that the issue might be with the program's handling of the degrees of freedom, and it turned out I was right. My assumption turned out to be correct, and after the fix, everything lined up perfectly. Lessons learned: Doubt — and investigate. Independent implementations are gold. Different languages, different algorithms — same truth. Checking against standard or tabulated values may feel old-school, but it’s often the fastest way to uncover errors. Did I waste time writing the same thing in three languages? From my perspective — no. Was it worth it? Absolutely — it ruled out implementation errors. Should I trust myself more? No. Never. I’m not infallible — and I don’t want to be. Maybe your code also hides a bug you’re not aware of yet. Would you want to know? Old-school verification never goes out of style. Check out my other posts for more insights! #DataValidation #Programming #Python #RStats #ExcelVBA #Statistics #QualityByDesign #SoftwareTesting #DataScience #OldSchool #IndependentExpert

Day 2 — Building Logic: If, Else, and Loops 🧠 Today’s focus was on helping Python think and decide. I explored conditional statements (if, elif, else) and loops (for, while), the foundations that make code dynamic and responsive. Here’s what I practiced today: 🔹 Creating a simple “Student Grade Evaluation” program using if–else 🔹 Using loops to process multiple inputs 🔹 Writing my first list comprehension to generate squared numbers 💡 It’s fascinating how programming logic mirrors real-world data thinking, we set conditions, look for patterns, and act based on results. For this challenge, I’ve been letting AI guide me through daily tasks. It’s like having a personal mentor who designs the perfect learning flow each day. 🤖✨ Can’t wait to dive into Day 3 tomorrow! #Day2 #Python #DataAnalytics #LearningJourney #30DaysChallenge #WomenInTech

𝗧𝘆𝗽𝗲 𝗪𝗧𝗙 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲? You know, that “magic” that lets avantgarde-languages like #fsharp or #haskell guess your types without you writing them everywhere — unlike #csharp or #typescript? ——— I’m creating a 𝘃𝗶𝗱𝗲𝗼 𝘀𝗲𝗿𝗶𝗲𝘀 that tears down the mystery and rebuilds it from 𝗳𝗶𝗿𝘀𝘁 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀. 𝗧𝗵𝗲 𝗚𝗼𝗮𝗹: Build a language that’s as clean as Python, but as safe as TypeScript by making 𝘁𝘆𝗽𝗲 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 the foundation, not an afterthought. ——— 𝗧𝗵𝗶𝘀 𝗳𝗶𝗿𝘀𝘁 𝗲𝗽𝗶𝘀𝗼𝗱𝗲 𝗰𝗼𝘃𝗲𝗿𝘀: • We invent a small expression-based core syntax that's capable for a lot of different text syntaxes • Why type inference isn’t just a “nice feature” you can bolt on later • How to turn typing rules into data structures (constraints) • The three-phase algorithm that powers our type systems • Interactive visualization of the solver at work (see screenshot) ——— No hand-waving. No “it just works.” We’ll walk through the AST, generate constraints from typing rules, and watch the unification algorithm 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗲 𝗽𝘂𝘇𝘇𝗹𝗲 step-by-step. After the video, you’ll understand the theory 𝗮𝗻𝗱 have working code to play with. Where this will go? We'll see :) ——— If you’ve ever been curious about what happens behind the scenes when your IDE shows you type errors before you even run your code — 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂. 𝗩𝗶𝗱𝗲𝗼 𝗱𝗿𝗼𝗽𝘀 𝘀𝗼𝗼𝗻. Check out my YT channel. #TypeInference #ProgrammingLanguages #CompilerDesign #TypeSystems #SoftwareEngineering