Arsh Singhal’s Post

Learning Python has involved a lot of long hours in front of the screen. Some concepts that look simple on the screen take hours to truly understand. Sometimes, a small error in the code is enough to stop everything from running, and in those moments, I just pause and wonder: “Will I ever really get comfortable with this?” But I’m beginning to understand something important about learning difficult skills. Progress rarely feels smooth. It often looks like confusion, repeated attempts, and going back to the same concepts more than once. Yet, even in those moments, something is building quietly. The familiarity, understanding, and also patience. So for anyone currently learning something challenging, whether it’s coding, research, or any new skill, the frustration you feel might actually be part of the process, not a sign that you’re failing. Sometimes, the most important progress is simply deciding to continue. For those who have learned Python or any difficult skill, did you also experience moments where the learning process felt overwhelming? #LearningPython #CodingJourney #ResearchJourney

Most people start learning Python… but quit halfway. Python isn’t difficult — the real problem is unstructured learning. I came across a structured 15-day Python roadmap and it perfectly explains why most learners struggle. 💡 It’s not about doing more — it’s about doing it right. Instead of jumping between random tutorials, this roadmap focuses on: Strong fundamentals (variables, loops, functions — Day 2–4) Consistent problem-solving practice every single day (seen across all pages) Real-world concepts like file handling, OOP, and data analysis (Day 8–12) Even advanced topics like Machine Learning basics (Day 15) 👉 That’s how learning actually sticks. Here’s what changed for me: I stopped rushing. I started building my own notes + solving daily problems. And suddenly… things started making sense. 📌 Truth is: You don’t need 10 courses. You need one structured path + consistency. From automation to AI, Python opens doors everywhere — but only if you stay long enough to master it. 🚀 If you're starting Python: Focus on clarity Practice daily Build small projects Stay consistent Because in the long run… consistency beats intensity. 💬 Are you currently learning Python? Drop “PYTHON” below — let’s grow together! #Python #Coding #Programming #LearnToCode #PythonNotes #Developers #Tech #100DaysOfCode #CareerGrowth #SoftwareDevelopment

Most beginners memorize Python… but never actually think like a programmer. Day 9 of my Python journey changed that. ⚡ So far, I’ve been building consistency—showing up daily, learning step by step. But today’s deep dive into dictionaries & sets flipped a switch in my brain. Here’s what clicked: 1️⃣ Dictionaries = Real-world thinking Stop seeing them as syntax. Start seeing them as relationships. Keys → Questions Values → Answers 2️⃣ Methods aren’t “extra” — they’re power tools🔧 `.get()` saved me from errors `.keys()` & `.values()` helped me think in structure, not chaos 3️⃣ Sets = Clean data, fast decisions No duplicates. No noise. Just clarity. That’s how real systems think. 💭 The challenge? At first, everything felt confusing—too many methods, too many rules. But instead of jumping ahead, I slowed down… practiced… broke things… fixed them. That’s when learning turned into understanding. 🚀 If you're learning to code, remember: Don’t rush to finish Python. Master how it thinks. 👇 Drop a comment: Are you still memorizing… or starting to understand? Let’s grow together. 💡

Today learning : Oops… today’s learning came from working with multiple classes in Python. I thought I had structured everything correctly—separate classes, clear responsibilities, neat logic. But when I tried to connect them all, things didn’t behave the way I expected. Methods weren’t communicating properly. Objects weren’t passing data as intended. And suddenly, my “clean design” felt confusing. That “oops” moment taught me more than when things work perfectly. Here’s what I realized today: • Just creating multiple classes isn’t good design—how they interact matters more • Understanding relationships (composition, inheritance) is key to writing scalable code • Naming, structure, and clarity make a huge difference when code grows • Testing each class individually before integrating saves a lot of time • Debugging teaches you more about your code than writing it I also noticed how easy it is to overcomplicate things in Python. Sometimes, a simpler approach with fewer classes would have done the job better. Today’s reminder: 👉 Don’t design for complexity—design for clarity 👉 Don’t just write code—understand how it flows Every small “oops” in coding is actually a step toward becoming a better developer. What’s a recent coding mistake that taught you something valuable? #Python #CodingLife #LearningByDoing #SoftwareDevelopment #GrowthMindset Lokesh V

“Learning to code is hard.” No — it’s not. What’s actually hard is: ❌ Not knowing where to start ❌ Jumping between random tutorials ❌ Giving up after 2 days That’s exactly why we built this 🚀 We launched a FREE Python course on YouTube – SP Learning Labs 🎯 💡 What makes it different? ✔️ Step-by-step roadmap (no confusion) ✔️ Real coding (not just theory) ✔️ Beginner-friendly explanations If you’ve been delaying learning Python, this is your moment. ⏳ Because while you’re thinking… Someone else is already building skills. 📺 Start here: 👉 https://lnkd.in/gcTcvpZ5 @JP Informatics Private Limited 💬 Comment “START” and I’ll help you with your learning path. Let’s grow together 💪 #Python #LearnToCode #CareerGrowth #Developers #TechSkills #Upskill #Programming #Python3 #AI #FutureSkills #Pythoncareer #Tech #Learning #Course #Free

🚀 Understanding super() in Python (with a simple clarity boost!) Many learners get confused about one question: 👉 “If we are using parent methods and variables, why don’t we always use super()?” Let’s clear this up 👇 🔷 Key Idea You use super() only when you override a method and still want to use the parent version. 🔷 Case 1: No Overriding → No super() needed class Vehicle: def show(self): print("Vehicle details") class Car(Vehicle): pass car = Car() car.show() ✅ Output: Vehicle details 👉 Python automatically checks the parent class if the method is not found in the child. 👉 This is inheritance working by default. 🔷 Case 2: Overriding → super() becomes useful class Car(Vehicle): def show(self): super().show() print("This is a car") ✅ Output: Vehicle details This is a car 👉 Now we are extending the parent method, not replacing it. 🔷 Case 3: Overriding __init__ → super() is important class Car(Vehicle): def __init__(self): super().__init__() 👉 Without super(), parent variables won’t be initialized ❌ 🔷 Simple Rule to Remember ✔ No override → Python uses parent automatically ✔ Override → Parent is hidden ✔ Use super() → To bring parent behavior back 💡 One-line takeaway super() is not for inheritance — it’s for accessing the parent implementation when you override it. This small concept makes a big difference when teaching or writing clean OOP code in Python 💻✨ #Python #OOP #Coding #Programming #Developers #Learning #TechEducation

I keep meeting people who are learning to code in 2026 and starting with Python 101. Meanwhile the actual builders in the space are just describing what they want in plain English and letting Claude write it. The gap isn't closing. It's growing. This isn't a hot take against learning. It's a question about what you're learning FOR. If you're learning to code because you love it — amazing, go deep. If you're learning to code because you think it's the only path to building — you're solving the wrong problem. The constraint isn't "can you write the function." It's "can you describe what the function should do, review what was built, and iterate fast." That's a completely different skill set. And most people are still training for the old one.

*Day 3 of my 21-day consistency challenge 🚀* *One thing I struggled with while learning Python…* I am still learning Python, and honestly… it humbled me 😅 At first, I thought it was just about writing simple lines of code. But when I started, I quickly realized it was more than that. My biggest struggles? 👉 Understanding the syntax 👉 And importing libraries I’d see things like: import pandas as pd import numpy as np And I’d wonder… “What exactly am I importing?” “Why do I need this?” Then came the errors… Sometimes I’d miss a small detail—like a colon, indentation, or bracket—and everything would stop working. Other times, I’d forget to import a library, and my code wouldn’t run at all. It was frustrating. There were moments I just stared at my screen thinking, “What am I doing wrong?” But over time, I started to understand something: Python isn’t just about typing code… It’s about thinking logically and paying attention to details. And libraries? They’re like tools that make your work easier and more powerful—you just have to learn how to use them. So I changed my approach: – I started practicing small codes – I paid attention to error messages – I focused on understanding, not rushing I’m still a beginner. Still making mistakes. But I’m no longer afraid of errors or confused by libraries. Because now I know—it’s all part of the process. If you’re learning Python and it feels hard, you’re not alone. What’s something that confused you when you were starting out? Yabatech Digital Technology Academy Ibraheem Adedotun Abdul #Day3 #Python #DataAnalytics #LearningJourney #GrowthMindset #LearningInPublic

🚨 Stop scrolling if you're learning Python… I just found a goldmine 💎 👉 71 Python Projects with source code + references And honestly… this is better than most paid courses. Here’s what you can build 👇 💡 Beginner to Advanced Projects: Sentiment Analyzer (ML) Weather App 🌦️ Password Manager 🔐 Contact Book App 📱 Typing Speed Tester ⌨️ Quiz App 🧠 Tic Tac Toe 🎮 Alarm Clock ⏰ 🤖 Machine Learning Projects: Iris Flower Classification Flight Fare Prediction ✈️ Credit Card Fraud Detection IPL Score Predictor 🏏 🛠️ Real-world Tools: File Renaming Tool Notification App Desktop GUI apps (Tkinter) 💥 Why this is powerful: - Hands-on learning > Theory Build portfolio projects -Perfect for beginners + intermediate devs - Includes references & tutorials 📌 If you're serious about Python: Don’t just watch tutorials… 👉 Start building projects 💬 Comment "PYTHON" and I’ll share the full resource 🔁 Repost to help others #Python #Coding #Programming #MachineLearning #Developers #100DaysOfCode #TechCareers #AI #Learning #Students

Introducing CodeLens, a free visual Python learning platform built for school students who struggle with understanding and writing code. I've seen how intimidating code can feel for beginners. A blank editor, cryptic error messages, no idea what's happening "inside" the program. That's exactly the problem CodeLens tries to solve. 🎯 What it does: → Write Python code in a clean editor → Hit Run, and watch it execute line by line, step by step → See every variable change in real time as the program runs → Auto-generate a flowchart of your code's logic → Get an AI explanation of what your code does, in plain English (In Progress) → Practice with built-in exercises, from beginner to advanced No setup. No installation. Just open the link and start learning. 🔗 Try it live → https://lnkd.in/gS5NwTE6 🛠️ Currently supports Python. JavaScript and more languages are coming in the next version. 📖 Open source is coming soon, I'll be releasing the full source code so the community can build on it, improve it, and make it better for students everywhere. Contributions, feedback, and suggestions are very much appreciated. If you're a developer, educator, or student, I'd love to hear what you think and what features would make this more useful in a real classroom. Drop a comment, share with someone who's learning to code, or reach out directly. Let's make programming more accessible for the next generation. 🚀 #Python #EdTech #OpenSource #CodingForBeginners #LearnToCode #WebDev #Education #StudentDevelopers #Programming #100DaysOfCode