Shallow vs Deep Copy in Python: Impact on Performance and Data Isolation

One of the biggest mistakes beginners make in Python… is ignoring data types. You might write correct code, But if you don’t understand the type of data you’re working with, Your results can be completely wrong. In Python, everything has a type, from numbers to text to collections of data. Understanding this is what separates someone who copies code from someone who actually understands it. I’ll be breaking down Python data types in a simple way in my next article. 💬 Which one confuses you the most: Booleans, strings, tuples, lists, or dictionaries? #Python #Programming #DataScience #AI #Beginners #LearnToCode #Tech

👉 We all use quotes in Python… But do you know when to use: ' vs " vs '''? Most beginners just use them randomly. Here’s the simple rule 👇 # Single quotes → simple text name = 'Ali' # Double quotes → when text has ' msg = "It's a good day" # Triple quotes → multi-line / docstrings text = '''This is multi-line text''' That’s it. No confusion. No overthinking. --- 💡 Good code is not just about working… It’s about being clear and readable. --- Do you follow this… or just use quotes randomly? #Python #LearnPython #CodingBasics #ProgrammingConcepts #PythonTips #CodeClarity #CleanCode #LearnWithMe #strings

Most people think Python is "slow" because it's dynamic. I used to think that too. Then I started working on Pyrefly. Static analysis is essentially a "spell-checker" for code. At Meta's scale, we can't wait 20 minutes for a test to run. We need to catch bugs the millisecond they are typed. Working on a Rust-powered engine that processes 1.8 million lines of code per second has taught me one thing: Efficiency isn't a luxury; it's a requirement. I’m learning how to bridge the gap between Python’s flexibility and Rust’s safety. It’s challenging, it’s frustrating, and it’s the most fun I’ve ever had. #RustLang #Python #StaticAnalysis #SystemsEngineering

I spent 20 hours exploring the new Python Interpreter Written in Python, and what I found surprised me. The idea of a Python interpreter written in Python itself sounds like a circular dependency, but it's a real project that has the potential to simplify many tasks. As someone who works with Python daily, I was curious to see how this project could impact my workflow. One specific insight I gained was how this interpreter can be used to optimize Python code for better performance. I compared the execution speed of the same code using the traditional CPython interpreter and the new Python interpreter, and the results were interesting. The new interpreter showed a significant improvement in execution speed for certain types of tasks. I also experimented with using this interpreter for automating tasks and found it to be very effective. Are you using this yet? I'd love to hear about your experience with the new Python interpreter and how it's impacted your work. --- I share findings like this every week. Follow — no fluff, just real explorations. #PythonInterpreter #LearningInPublic #DevTips #AITools

🚀 New YouTube Video – Mastering For Loops in Python! 🐍 I just published a new video where I explain one of the most important concepts in Python: the for loop. In this video, you’ll learn: ✔️ How the for loop works in Python ✔️ How to iterate over ranges and lists ✔️ Practical examples to understand it بسهولة ✔️ Tips to write clean and efficient loops Whether you're a beginner or improving your coding skills, this video will help you build a strong foundation in Python. 🎥 Watch the full video here: 👉 [https://lnkd.in/dNcp9dEk] 💬 If you face any difficulties or have questions, let me know in the comments — I’ll be happy to help! #Python #Programming #Coding #ForLoop #LearnToCode #Developer #Tech #YouTubeLearning #Beginners

There's a common myth in Python: "List Comprehensions are vectorized because they are faster than for loops." The truth: They aren't. While Comprehensions are slightly faster than .append() loop, they are still sequential. If the user has 1 million items, Python is still performing 1 million individual fetch calculate store cycle. Comprehensions are scalar and they process data one by one. Use Comprehensions for readability and small-to-medium data transformations. Just because Comprehension is in one line, it doesn't mean it's running in parallel. #Python #SoftwareEngineering #DataScience #CodingTips #PerformanceOptimization

Consistency beats intensity when it comes to learning Python 💯 Working through structured Python exercises builds more than just coding knowledge it strengthens problem-solving skills, logical thinking, and the ability to handle real-world scenarios with confidence. Each small problem solved adds clarity and sharpens understanding. Python is simple to start, but mastery comes from consistent practice and applying concepts in different ways. The more you practice, the more natural it becomes. Step by step, line by line growing stronger in Python every day 💥 #Python #PythonProgramming #CodingPractice #ProblemSolving #LearnToCode #DeveloperJourney #TechSkills #ContinuousLearning #GrowthMindset

Learning Python made me realize something At the beginning, I thought coding was about memorizing syntax… But now I see it’s more about problem-solving and thinking logically. Syntax can always be looked up, but the ability to break down a problem takes real practice. Still on the journey — one step at a time. #Python #Learning #ProblemSolving #GrowthMindset

🚀 Day 85 – Python + DSA Journey Today I focused on strengthening my fundamentals in Arrays and problem-solving using Python. 🔹 Topics Covered: • Array Traversal, Insertion, Deletion • Time Complexity (O(n), O(1)) • Problem-Solving Patterns 🔹 Problems Solved: ✅ Find Second Largest Element (Optimized O(n) approach) ✅ Reverse an Array (Two-pointer technique) ✅ Move Zeros to End (Efficient swapping logic) ✅ Two Sum Problem (Hashing concept) 💡 Key Learnings: • Importance of thinking in terms of patterns • Optimizing solutions without using sorting • Writing clean and efficient code for interviews Every day I’m getting better at breaking down problems and building logical solutions. 📌 Consistency is the key — one step closer to my goal every day! #Day85 #Python #DSA #CodingJourney #ProblemSolving#Learning #SoftwareDevelopment

Most Python developers have never heard of MRO and that is exactly why their inheritance code breaks in ways they cannot explain. Method Resolution Order is the rule Python follows to decide which base class gets searched first when you call a method. It is not random. It is not guesswork. Python uses a specific algorithm called C3 Linearization to determine that order every single time consistently and predictably. Once you understand MRO, debugging complex class hierarchies stops being a mystery. You start writing cleaner inheritance structures, making smarter design decisions, and walking into technical interviews with real confidence. The best Python developers do not just write code that works. They understand why it works. This is the difference between knowing Python and truly mastering it. Start building that deeper understanding today at itlearning.ai where AI-powered learning helps you go beyond the basics and into the concepts that actually matter in the real world. #itlearningai #python #pythonmro #learnpython #pythonoop #pythondeveloper #objectorientedprogramming #pythoninternals #softwaredevelopment #techeducation #100daysofcode #pythonadvanced #techinterview #codingtips #pythonprogramming