Mastering Python: A Quick Tip on lambda and filter() 🐍💡 Ever wondered how to make your code cleaner and more Pythonic for simple list operations? The lambda and filter() functions are your best friends! They are especially useful for concise, one-off functions where defining a full def block might be overkill. Here’s a simple example of how to filter even numbers from a list: python nums = [1, 2, 4, 5, 6, 7, 8, 9, 10] # Using a lambda function with filter() to get even numbers evens = filter(lambda x: x % 2 == 0, nums) print(list(evens)) # Output: [2, 4, 6, 8, 10] Use code with caution. ✅ Why this is great: Concise: Achieves a specific task in a single, readable line. Efficient: filter() returns an iterator, which is memory efficient for large datasets. Functional: Showcases a core concept of functional programming in Python. #Python #LambdaFunctions #CleanCode #PythonProgramming #TechTips #DataScience# Abhishek kumar # Harsh Chalisgaonkar # SkillCircle™

🐍 Day 3 of My Python Journey: Variable Re-initialization Today I learned something fundamental yet powerful - variables in Python are incredibly flexible! Unlike some languages where you're locked into a data type, Python lets you reassign variables to completely different types: python x = 42 # I'm an integer x = "Hello" # Now I'm a string x = [1, 2, 3] # Now I'm a list Key takeaways: Variables are just labels pointing to objects in memory You can change what a variable points to at any time Python automatically handles the type conversion The old value gets garbage collected if nothing else references it Practical use case I tried: python user_input = input("Enter a number: ") # String user_input = int(user_input) # Now it's an integer result = user_input * 2 This flexibility makes Python beginner-friendly, but I'm learning to be mindful about keeping my code readable and maintaining consistent variable purposes. What's a Python concept that surprised you when you first learned it? #Python #100DaysOfCode #LearnPython #PythonProgramming #CodingJourney #TechLearning

What are the 33 words in Python? I thought Python was simple—until I learned this A lot of beginners ask: “Are there really only 33 words in Python?” Yes — Python has a small set of reserved keywords you can’t use as variable names. Here’s the simple way to remember them 👇 💡 Logic & flow: if, else, elif, for, while, break, continue 💡 Functions & classes: def, return, class, lambda 💡 Truth & logic: True, False, and, or, not, is 💡 Exceptions & context: try, except, finally, raise, with 💡 Misc essentials: import, from, as, pass, None, global, nonlocal, assert, del, yield That’s it. Master these—and Python suddenly feels way less scary. 🐍 Comment “Python” and I’ll DM you a beginner cheat sheet. #Python #LearnToCode #TechCareers #ProgrammingBasics #LinkedInLearning

🟢 DAY 9: List Methods in Python 📋🐍 Today I learned that Python gives us built-in tools to work with lists easily — called list methods 🛠️ They help us add, remove, count, and organize data without writing complex code. 👇 Examples in the image ➕ append() → adds an item to the list ❌ remove() → removes an item 🔢 len() → counts items 🔁 sort() → arranges items 💡 These small methods are used everywhere — from simple programs to real-world applications. Learning basics slowly, one day at a time 🌱 💬 Comment LIST if you’re learning Python 📌 Save this post for revision 👉 Follow for Day 10 🚀 #Python #PythonBasics #LearningInPublic #CodingJourney #BeginnerFriendly

How FINALLY keyword in Python can silently change your function’s behaviour? How does the try except flow works? 1. When Python enters a try block, it pushes a "cleanup" instruction onto the stack. 2. When you hit a return statement inside try, Python doesn't actually exit the function immediately, it just "saves" the return value. 3. If you return inside the finally block itself, the original return value is discarded. More worse - This same thing happens with exceptions too. If your try block raises a error, but your finally block has a return or a break, the error vanishes! Takeaway - 1. Never use return, break, or continue inside a finally block. It can lead to "silent failures" and unexpected bugs. 2. Finally is meant only for cleanup (closing files, releasing locks) and not logic. I’m deep-diving into Python internals. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

🚀 New YouTube Video: Python zip() Function Explained | From Basics to Internals 🐍🔗 The zip() function looks simple, but it plays a very important role when working with multiple iterables in Python—especially in clean, efficient code. In this video, I’ve explained the Python zip() function from scratch, including: ✅ What the zip() function does ✅ How zip() works with multiple iterables ✅ How iter() and next() work behind the scenes ✅ Why zip() stops silently ✅ Common mistakes beginners make If you’re learning Python or revising core concepts, this video will help you understand zip() with clarity and confidence 💪 🎥 Watch here: 👉 https://lnkd.in/gAEpQiUW If you find it helpful, don’t forget to like, share, and subscribe 🙌 Your feedback really motivates me to create more quality content. #Python #PythonProgramming #FileHandling #LearnPython #DataAnalytics #DataScience #ProgrammingBasics #SoftwareDevelopment #Coding #YouTubeEducation #datadenwithprashant #ddwpofficial If this helped you, drop a 👍 or comment “zip” 👇

What Really Happens When You Pass a Variable to a Function in Python? In Python, variables don’t hold values — they hold references to objects. When you pass a variable to a function: 👉 Python passes the reference, not a copy of the object. This model is often called “pass-by-object-reference” (or call by sharing). Why this confuses people? Immutable objects (int, str, tuple): Reassignment inside a function creates a new object → original stays unchanged. Mutable objects (list, dict, set): In-place modification changes the same object → caller sees the change. So it feels like: Immutable → pass by value Mutable → pass by reference But that’s just an illusion. The real rule Python always passes a reference to an object. What you do with that reference determines the outcome. #Python #ProgrammingConcepts #PythonInternals #Mutable #Immutable #CleanCode

Day 12 | Mini Python Insight A small Python realization that made learning easier 👇 Instead of asking: ❌ “How do I write perfect code?” Ask: ✔️ “Can I make the logic clear?” Clean logic > fancy code. When learning Python, focus on: writing readable code understanding the flow fixing mistakes instead of fearing them Errors are not failures. They’re feedback. This shift alone made Python feel less scary and more logical. If you’re learning Python — what part feels confusing right now? #Day12 #PythonTips #PythonLearning #DataScienceBasics #AIWithPython #CodingJourney #LearningInPublic #BeginnerToPro

While working with Python, I noticed something curious. When you assign a value to a variable, then change it, the object’s memory address changes. That’s expected. But if you later assign the same value again,Python gives you the exact same address as before. At first glance, this feels like Python is somehow “remembering” the old location. But that’s not what’s happening. What’s really going on? In CPython (the most common Python implementation), there is a mechanism called interning / caching. CPython pre-allocates and reuses certain immutable objects, most notably: Small integers in the range -5 to 256 Some short strings and identifiers So when you write: a = 10 b = 10 Both a and b usually point to the same object in memory. That’s why id(a) == id(b) is often True. Now compare that with larger integers: a = 10000 b = 10000 Here, you’ll often get different memory addresses. These values are not guaranteed to be cached, so Python may allocate new objects. Why does Python do this? This design has very practical benefits: Saves memory by reusing common immutable objects Reduces object allocations Lowers pressure on the garbage collector Improves performance for frequently used values Since integers and strings are immutable, sharing them is completely safe. #python #coding #LearningJourney #DeveloperJourney #Insights