Python 3.14 Released: Performance Improvements and Better Error Messages

Today I learned about Lambda Functions in Python 🐍 A lambda function is a small, anonymous function written in a single line using the lambda keyword. It’s mainly used for: ✅ Short and simple operations ✅ Writing compact code ✅ Using inside functions like map(), filter(), and sorted() Example idea: Instead of writing a full function to square a number, we can do it in one line with a lambda function. Less code. Same logic. Cleaner style. Understanding these small tools really helps in writing more Pythonic and efficient code. Learning one concept every day 🚀 #Python #DataScience #LearningInPublic #Programming #100DaysOfCode #CareerSwitch

Today I worked on a Python logic exercise focused on list traversal, duplicate handling, and comparing two lists of different lengths using pure loops. 🔹 What this code does: Takes two lists with different lengths, both containing repeated numbers Iterates through them safely using index-based nested loops Collects common elements while preserving order Removes duplicate values manually, without using built-in shortcuts like set() 🔹 Why I approached it this way: Instead of relying on Python conveniences, I deliberately used: Explicit for loops Conditional logic Intermediate lists This forced me to think about: Boundary conditions when list sizes don’t match How duplicates are detected step by step Writing logic that doesn’t assume equal input sizes 🔹 Key takeaway: Understanding fundamentals—especially edge cases like unequal input lengths—builds stronger problem-solving skills than jumping straight to optimized one-liners. Consistent practice, steady improvement. 💻📈 #Python #Programming #LogicBuilding #DataStructures #ProblemSolving #CodingPractice

🚀 Day 23/100 | #100DaysOfCode with Python 🐍 Today I learned three super useful concepts that make Python code shorter, cleaner, and more powerful 👇 ✨ Lambda Functions Small, anonymous functions written in a single line. Perfect when the logic is simple and you don’t need a full function. 🔁 map() Function Used to apply the same operation to every element in a list or iterable. Great for transforming data quickly and efficiently. 🎯 filter() Function Helps extract only those values that match a condition. Super helpful when working with real-world data. What I loved today: Less code ✅ Better readability ✅ More confidence with Python ✅ Taking one step forward every day, no matter how small 💪 Consistency > Perfection 🚀 If you’re learning Python too, what did you practice today? Let’s share and grow 👇 #Day23 #PythonLearning #Lambda #MapFunction #FilterFunction #100DaysOfCode #CodingJourney #LearnToCode #DeveloperInMaking #DailyLearning

The coolest move in Python syntax: The "Moonwalk" loop. 🕺 Beginners often overcomplicate counting backwards in Python. I’ve seen clunky while loops, manually decrementing counters, or creating lists just to reverse them [::-1]. Stop working so hard. The "Pro" move is to unlock the often-ignored third parameter of the built-in range() function: the step. The syntax is: range(start, stop, step) By default, the step is positive (+1), moving you forward. But if you set the step to -1, you tell Python to slide backwards. In the visual below: Start at 10. Stop before 0 (Remember, the stop index is exclusive!). Step back by -1 each time. It’s clean, readable, and honestly, it just looks way cooler than a messy while loop. 😎 Want to learn a new Python trick every morning? We turn boring documentation into fun, bite-sized lessons. Get them delivered straight to your inbox daily. 👉 Join the PyDaily community here: https://lnkd.in/ducXvs-y #Python #CodingTips #LearnPython #SoftwareEngineering #Developer #PyDaily

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

Have you ever written a perfectly “correct” Python function… that still feels slow and clumsy? Not because the logic is bad, but because it keeps doing expensive work over and over again: • re-reading files • re-parsing data • recomputing values that never change In today’s video, I walk through 10 Python features hiding in the standard library that make your code faster, clearer, and easier to reason about. Things like caching expensive operations, expressing intent more clearly, managing resources safely, and writing logic that scales without turning into spaghetti. 👉 Watch the video here: https://lnkd.in/eXcxPAQe #Python #PythonTips #ArjanCodes #CleanCode #SoftwareDesign #Pythonic

Have you ever written a perfectly “correct” Python function… that still feels slow and clumsy? Not because the logic is bad, but because it keeps doing expensive work over and over again: • re-reading files • re-parsing data • recomputing values that never change In today’s video, I walk through 10 Python features hiding in the standard library that make your code faster, clearer, and easier to reason about. Things like caching expensive operations, expressing intent more clearly, managing resources safely, and writing logic that scales without turning into spaghetti. 👉 Watch the video here: https://lnkd.in/ePuhn3VB #Python #PythonTips #ArjanCodes #CleanCode #SoftwareDesign #Pythonic

Python Lists: When Single Values Aren’t Enough int, float, and string felt powerful, until I realized real programs work with collections. That’s where lists shine 👇 🔹 Store multiple values in one variable 🔹 Access items with indexing (starts at 0) 🔹 Use len() to count elements 🔹 Check existence with in 🔹 Slice lists just like strings (list[1:3]) 💡 Best part? If you understand strings, you already understand lists. Same rules. Same logic. More power. One concept learned → many doors unlocked #Python #LearningInPublic #ProgrammingBasics #VSCode #DeveloperJourney

Most Python problems don’t fail because of logic. They fail because of how we expect Python to behave. We ask Python to give us everything.... All rows. All values. All results ....right now. And Python quietly asks a better question: What if you only took what you need? That’s where a different way of thinking begins. Generators don’t rush. They don’t store. They don’t panic about size. They move forward, one step at a time. When data grows, when files get heavy, when performance starts to matter this mindset changes everything. Python doesn’t reward clever tricks. It rewards calm, intentional thinking. And the day you realise that, your code stops feeling busy and starts feeling clean. For those who enjoy learning concepts this way, I’ve shared my Python learning notes and resources on Topmate. https://lnkd.in/gasgBQ6k #Python

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™