map(), filter(), sort() with Lambda Functions In Python! ⚙️ ✨I explored how map, filter, and sort work with lambda functions that results in 2-3 code lines, sped up coding, and code redeability 👉The best part about lambda functions is that they can easily shrink 4-5 lines of code into just 2-3, crazy right? 🤓Also, lambda functions are interesting in a way that they collaborate seamlessly with map, filter, and sort to provide you with same results that a big chunk of code would ‼️Important: My python functions repo has been updated for Lambda Functions with map, filter, and sort HAPPY CODING! 😉 ---------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc 📁Python OOP: https://lnkd.in/eJFupCiK ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #lambdafunctions #pythonprogramming #pythonforbeginners #lambdafunctionsinpython #pythonlanguage

#Day21 of #120DaysChallenge Python Functions Today, I learned about functions in Python — one of the most powerful features that help make code reusable, organized, and easier to understand. Here’s what I covered: What functions are and why we use them The basic syntax using the def keyword How to pass parameters to a function How to use the return statement to send back results Here’s a simple example I practiced def add(a,b): print(a+b) add(1,2) output: 3 ex2 : def calculate():   a=   print('Sum is',a+b)   print('Difference is',a-b)   calculate() calculate() def calculate():   a=   print('Sum is',a+b)   print('Difference is',a-b)   calculate() calculate() ''' ''' def add():   a=input('enter fname:')   b=input('enter lname:')   print(a+b) add()    ''' ''' def add():   a=input('enter fname:')   b=input('enter lname:')   print((a+''+b).title()) add()   ''' #using While loop while True:     def cal():         a=int(input('enter a value:'))         b=int(input('enter b value:'))         print('The sume is',a+b)         cal() # a function call itself its recursive     cal() #recursive function : a function call itself def cal():     a=int(input('enter a value:'))     b=int(input('enter b value:'))     print('The sume is',a+b)     cal() # a function call itself its recursive cal() #return def mul(a,b):     return a*b print(mul(1,3)) ''' #Difference Between Print and return ->Print just show the human user output in a console ->return is used to terminate the function and gives back a value from function ''' Key takeaway: Functions help break big problems into smaller, manageable pieces — and make code cleaner and reusable! Excited to keep learning and building step by step. #Python #LearningJourney #Coding #Functions #100DaysOfCode Pooja Chinthakayala Day 22 Challenge done Mam

Great news for Python developers! No need to use negative lists for maxheaps. With Python 3.14 (Oct 2025) the heapq module now offers native max-heap functions : heapify_max() heappush_max() heappop_max() Check the update here: https://lnkd.in/duid6Y3k #Python #DataStructures #DevUpdate

Finding Symmetric Difference In Python Sets (3 Ways) 🧮 ✨Since i was brusing up my logic building for python collections today, i understood how we can find symmetric difference in sets using three ways 🤓Use .symmetric_difference() 🤓Use XOR (^) Operator 🤓Use difference between union ( | ) and intersection (&) 🤔 I found all of them easy depends what is ur personal favorite or standard method to find symmetric difference in python sets ---------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists & Tuples: https://lnkd.in/eZ8KiQNs 📁Python Dictionaries & Sets: https://lnkd.in/eDmgj7pc 📁Python OOP: https://lnkd.in/eJFupCiK ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #pythonlogicbuilding #pythoncollections #pythonsets #symmetricdifference #pythonforbeginners

Python Tuple Packing and Unpacking 🐍 In Python, tuples are more than just immutable lists. They are powerful tools that make your code cleaner, more readable, and incredibly Pythonic. And the concepts of tuple packing and unpacking are at the heart of writing elegant Python code. 🔹 Tuple Packing: Packing means grouping multiple values into a single tuple variable. Python makes this seamless: my_tuple = 1, 2, 3, 4, 5 👉Values are packed into a tuple 👉This allows you to store multiple values in a single variable, return multiple values from a function, or pass collections around without extra boilerplate. 🔹 Tuple Unpacking: 👉Unpacking is the reverse: extracting tuple elements into individual variables in a single, readable line. a, b, c = (10, 20, 30) print(a, b, c) 👉Output: 10 20 30 💡 Why Tuple Packing & Unpacking Matters? 1. Makes code more readable than indexing elements manually. 2. Enables returning multiple values from functions effortlessly. 3. Works beautifully with loops, function arguments, and nested data structures. ✨ Pro Tip: Tuple unpacking is especially powerful when swapping variables without a temporary placeholder: x, y = y, x No extra line, no temp variable—just clean, Pythonic magic. -------------------------- 🤓 Check Out More About Tuple Packing and Unpacking in my Python Lists Repo down below! -------------------------- ☺️ Here are Python (Beginner to Intermediate) GitHub Repos for you: 📁Python Variables: https://lnkd.in/e9rjz-_D 📁Python Operators: https://lnkd.in/e6hzgHSn 📁Python Conditionals: https://lnkd.in/egQNGZBF 📁Python Loops: https://lnkd.in/eezUg_-y 📁Python Functions: https://lnkd.in/eKdU6nex 📁Python Lists: https://lnkd.in/eZ8KiQNs ------------------------- ⚡ Follow my learning journey: 📎 GitHub: https://lnkd.in/ehu8wX85 🔗GitLab: https://lnkd.in/eiiQP2gw 💬 Feedback: I’d love your thoughts and tips! 🤝 Collab: If you’re also exploring Python, DM me! Let’s grow together! -------------------------- 📞Book A Call With Me: https://lnkd.in/e23BtnR9 -------------------------- #pythontuples #tuplepackingandunpacking #pythonforbeginners #pythonlanguage #pythonfordatascience

Writing tests shouldn't be harder than writing code. Last week we launched TNG Python: automated test generation powered by Rust-based AST analysis, and wrote an article about this. ⏱️ Sub-100ms code parsing 🎯 Framework-aware context for Django, FastAPI, PyTorch, and more 🖥️ Interactive terminal UI to select exactly which tests you want Read the full breakdown → https://lnkd.in/gVSTSAVS pip install tng-python 📦 #Python #Django #FastAPI #BackendDevelopment #AI #LLM #DevTools

🚀 Just published Part 2 of my Python learning journey: Functions, Inputs & Conditionals. In this post I dive into writing dynamic functions, accepting user input, using if/else logic — and I even built a mini-calculator to bring it all together. If you’re new to Python (or coming from JavaScript like me), I hope you’ll find this helpful. Let’s keep learning together! 👉 Read it here: https://lnkd.in/deMwxiqh

🔍 Python's Hidden Gem: Short-Circuit Evaluation with Operand Return Ever wondered why Python's "or" and "and" operators are more powerful than they seem? Unlike Java or C++, they don't just return True/False – they return the actual values! Short-circuit evaluation means that logical operators like and and or stop evaluating as soon as the result of the expression is known. In Python, these operators don’t just return True or False; they actually return the operand that determined the result — that’s what is meant by operand return. Python's logical operators ("or" and "and") return one of the actual operands, not necessarily True or False. The "or" Operator - - Returns the first truthy value it encounters - If all values are false, returns the last value The "and" Operator in Python - - Returns the first false value it encounters - If all values are truthy, returns the last value ``` # OR examples print(None or [1, 2, 3])   # [1, 2, 3] - returns first truthy print("hello" or "world")   # "hello" - returns first truthy print(False or 0 or [] or {}) # {} - all falsy, returns last print(42 or False)       # 42 - returns first truthy # AND examples   print([1, 2] and "text")    # "text" - all truthy, returns last print("text" and None)     # None - returns first falsy print(5 and 10 and 20)     # 20 - all truthy, returns last print(0 and [1, 2, 3])     # 0 - returns first falsy # Check if user exists AND has valid credentials OR is admin user = get_user(username) access_granted = (user and user.password == password) or is_admin # Returns: user object if password matches, False if not, or True if admin ``` False Values in Python (the complete list) : False None 0 (integer zero) 0.0 (float zero) 0j (complex zero) "" (empty string) [ ] (empty list) ( ) (empty tuple) { } (empty dict) set( ) (empty set) range(0) (empty range) Truth Values in Python (everything else!) : True Any non-zero number: 1, -1, 3.14, 2+3j Any non-empty string: "hello", "0", " " (even a space!) Any non-empty collection: [1, 2], (1,), {"a": 1} Any object or instance (by default) Functions, classes, modules ⚡ Why This Matters: - Reduces code complexity - Eliminates nested if-else pyramids - Makes default value handling elegant - Improves code readability dramatically #Python #CleanCode #CodingTips #SoftwareDevelopment

✨ The Python Story – Episode 8: The Global Interpreter Lock (GIL) ✨ If Episode 7 was about why Python is called “slow,” today we dive into one of the biggest reasons behind that reputation — and one of Python’s most misunderstood features: the Global Interpreter Lock, or simply, the GIL. 🧠 When Guido van Rossum created Python, his goal was to make a simple, safe, and readable language. But there was one tricky problem — memory management. Python uses reference counting to track how many variables point to an object in memory. When no references remain, that memory is freed. It’s clean and elegant — but not thread-safe. If two threads update those counts at the same time, chaos can occur — corrupted memory or random crashes. Guido’s fix? A simple yet powerful idea — the Global Interpreter Lock. 💡 What exactly is the GIL? The GIL is a mutex — a lock that allows only one thread to execute Python bytecode at a time in a process. Even if your computer has 8 cores, only one runs Python code at once; others wait. That sounds limiting — and it is, for CPU-heavy work — but it also made Python safe, stable, and easy to extend with C libraries. For Guido, simplicity and reliability mattered more than theoretical speed. ⚙️ So does that mean Python can’t do multi-threading? Not really. Threads in Python still handle I/O-bound tasks well. When one thread waits for input/output — reading files, APIs, databases — the GIL is released so another thread can run. That’s why frameworks like Flask, FastAPI, and Scrapy manage thousands of concurrent operations. But for CPU-bound tasks — image processing, ML, or crunching data — the GIL becomes a bottleneck. 🚀 Workarounds Developers learned to work around the GIL: 🔹 Use multiprocessing to run multiple processes instead of threads. 🔹 Offload heavy work to NumPy, Cython, or C extensions. 🔹 Use asyncio for efficient concurrency. Despite its limits, these techniques made Python incredibly versatile — from automation scripts to large-scale systems. 🧩 The road to “No GIL” Recently, Python’s community has been working toward a GIL-free future. Python 3.13 introduced an experimental “no-GIL” build, a huge leap for true multi-threaded Python. And in Python 3.14, it gets practical — you can now disable the GIL at runtime for the new free-threaded build! 🎉 python -X gil=0 your_script.py # or PYTHON_GIL=0 The same lock that once symbolized simplicity may soon be unlocked in the name of progress. 🔓 📌 Next Sunday – Episode 9: Memory & Garbage Collection in Python 🧹 How Python keeps programs clean behind the scenes — reference counting, garbage collection, and what really happens when you call del. ⚡ Fun Fact: Run a multi-threaded CPU task in Python and watch — only one core hits 100%, while the rest relax. 😅 #python #ThePythonStory #StoryOfPython #programming #developers #PythonInternals

🚀 My First Python Menu-Driven Project — Simple Calculator 🧮 Today, I’m excited to share my very first menu-driven Python project — a Simple Calculator! 🎉 This project helped me understand how to make interactive programs using loops, conditions, and exception handling. 💡 What I learned: ✅ Creating functions and reusing them effectively ✅ Using while loops to keep programs running until the user chooses to exit ✅ Handling user input safely with try and except blocks ✅ Implementing menu-driven logic for multiple operations ✅ Using Python’s eval() function carefully for BODMAS rule evaluation ✅ Formatting output using f-strings 🧩 Features of My Calculator: 1️⃣ Addition 2️⃣ Subtraction 3️⃣ Multiplication 4️⃣ Division (with ZeroDivisionError handling) 5️⃣ Percentage Calculation 6️⃣ Power Operation 7️⃣ BODMAS Expression Evaluation 8️⃣ Exit Option Here’s a small preview of my code 👇 def menu():   print('### Simple Calculator ###')   print('1. Addition\n2. Subtraction\n3. Multiplication\n4. Division\n5. Percentage\n6. Power\n7. BODMAS\n8. Exit') while True:   try:     menu()     choice = int(input('Enter the number (1-8): '))   except ValueError:     print('Invalid input! Enter a number.')     continue   if choice in {1,2,3,4,5,6}:     num1 = float(input('Enter number 1: '))     num2 = float(input('Enter number 2: '))   if choice == 1:     print('Addition:', num1 + num2)   elif choice == 2:     print('Subtraction:', num1 - num2)   elif choice == 3:     print('Multiplication:', num1 * num2)   elif choice == 4:     print('Division:', num1 / num2 if num2 != 0 else 'Cannot divide by zero!')   elif choice == 5:     print('Percentage of num1:', num1 / 100, '%')     print('Percentage of num2:', num2 / 100, '%')   elif choice == 6:     print('Power:', num1 ** num2)   elif choice == 7:     bodmas = input('Enter expression: ')     print('BODMAS Result:', eval(bodmas))   elif choice == 8:     print('Thank you for using my calculator!')     break   else:     print('please enter between 1 to 8 only!') 🎯 My Learning Goal: I’m continuously improving my Python skills to build a strong foundation for Data Science and Machine Learning. This small step motivates me to take on bigger projects like database systems, data visualization, and ML models soon! 💪 👩💻 If you’re also learning Python, let’s connect and share knowledge! 💬 Suggestions and feedback are always welcome. #Python #CodingJourney #BeginnerProject #MenuDrivenProgram #WomenInTech #LearningInPublic #DataScience #dataAnalyst #engineeringinkannada simple_output: