Python 3.14 introduces native max-heaps with heapq module

𝗣𝘆𝘁𝗵𝗼𝗻 𝟯.𝟭𝟰’𝘀 “𝗡𝗼-𝗚𝗜𝗟” 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻...𝗪𝗵𝗮𝘁 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲’𝘀 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 🚀 For more than 30 years, Python’s Global Interpreter Lock (GIL) has been both a blessing and a curse. It made Python’s internals simple and safe, but it also blocked true multithreading, keeping CPU-bound programs from using all cores efficiently. 𝗧𝗵𝗮𝘁’𝘀 𝗳𝗶𝗻𝗮𝗹𝗹𝘆 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴... With Python 3.14, the long-awaited “no-GIL” (free-threaded) mode arrives, allowing threads to run truly in parallel, which isn’t just a performance tweak, it’s a paradigm shift unlocking true parallelism for Python developers, while preserving the language’s simplicity. E.g. benchmark:  • Python 3.10 (with GIL): 3.94 seconds  • Python 3.14 (no-GIL): 𝟭.𝟭𝟵 𝘀𝗲𝗰𝗼𝗻𝗱𝘀 => 𝟯.𝟯𝘅 𝗳𝗮𝘀𝘁𝗲𝗿, with zero code changes! 𝗦𝗼𝘂𝗻𝗱𝘀 𝗚𝗼𝗼𝗱...., 𝗪𝗲𝗹𝗹... 𝗻𝗼𝘁 𝗾𝘂𝗶𝘁𝗲. Here’s what most people aren’t talking about 𝟭. 𝗡𝗼-𝗚𝗜𝗟 𝗜𝘀 𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹. (𝗙𝗼𝗿 𝗡𝗼𝘄) Python 3.14 introduces the free-threaded build as an alternative, not the default. You’ll install it separately (python3.14t), and existing projects will continue using the traditional GIL version, giving the ecosystem time to adapt. 𝟮. 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗮𝗿𝗲 𝗟𝗲𝗳𝘁 𝗕𝗲𝗵𝗶𝗻𝗱. (𝗙𝗼𝗿 𝗡𝗼𝘄) Most high-performance Python packages (NumPy, Pandas, TensorFlow, etc.) are written in C or C++. They heavily relied on GIL for protecting shared memory.  With no-GIL, they need to:  • Recompile for thread safety  • Add atomic operations (thread-safe increments/decrements)  • Introduce per-object synchronization (mini-locks per object) Until that happens, many libraries won’t benefit fully from no-GIL mode. 𝟯. 𝗘𝘅𝗽𝗲𝗰𝘁 𝗦𝗺𝗮𝗹𝗹 𝗧𝗿𝗮𝗱𝗲𝗼𝗳𝗳𝘀  Removing the GIL isn’t “free”:  • Single-threaded scripts may see a tiny slowdown (few percent)  • Memory usage can increase slightly (due to atomic refcounts)  • Debuggers, profilers, and tools must adapt to the new model 𝗧𝗵𝗲 𝗕𝗶𝗴𝗴𝗲𝗿 𝗦𝗵𝗶𝗳𝘁: 𝗙𝗿𝗼𝗺 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝘁𝗼 𝗧𝗿𝘂𝗲 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺 With no-GIL:  • Threads can finally use multiple cores simultaneously  • CPU-heavy code (like AI preprocessing, cryptography, simulations) runs dramatically faster  • Multi-threaded Python servers and pipelines become far more efficient This bridges the gap between Python’s simplicity and the performance of C++ or Rust. 𝗦𝗼 𝗪𝗵𝗮𝘁𝘀 𝗡𝗲𝘅𝘁... We’re entering a new era for Python. As for now, 3.14 No-GIL (free-threaded) build is introduced as optional. It would take more or less 1-2 years for the entire ecosystem (libraries, tools, frameworks) to adapt. After which No-GIL could become the default build. If you work in AI, data science, or high-performance computing, keep a close eye on this, as your Python code might soon be running 3–4x faster without changing a single line. #Python #NoGIL #Programming #Multithreading #AI #DataScience #SoftwareEngineering #OpenSource #Python3_14

#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

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

Python Iteration Using iter() and next() 🤔What is Iteration in Python? 👉 Iteration in Python is the process of accessing elements of a collection one by one 🌀 When using iter() and next(): 👉iter() → creates an iterator object from an iterable (like a list, tuple, or string). 👉next() → retrieves the next element from the iterator, one at a time. ‼️When the iterator has no more elements, next() raises StopIteration, signaling the end. ------------------------- ☺️ 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 📁Python DSAs: https://lnkd.in/ebR3rjkt ------------------------- ⚡ 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 -------------------------- #iteration #pythonprogramming #iterationinpython #pythonforbeginners

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

🐍 Cracking the Python Concurrency Code: Threading vs. Multiprocessing & the GIL Navigating concurrency in Python can be tricky, primarily due to the infamous Global Interpreter Lock (GIL). Understanding whether to use the threading or multiprocessing module is crucial for writing efficient code. Here is a quick guide to help you decide: 🧵 Threading: The "Concurrent" Approach Threads share the same memory space, but due to the GIL, only one thread runs Python bytecode at a time within a single process. It’s about concurrency (rapid switching), not true parallelism. ✅ Best for I/O-Bound Tasks: Tasks where your program spends time waiting for external operations. Examples: Network requests (APIs, web scraping), file I/O, database queries. The GIL is released during these wait times, allowing other threads to run. ❌ Not for CPU-Bound Tasks: Won't speed up heavy math or data analysis because the GIL prevents true parallel execution on multiple cores. 💻 Multiprocessing: The "Parallel" Approach Multiprocessing creates entirely separate Python processes, each with its own memory space and its own GIL. ✅ Best for CPU-Bound Tasks: Tasks that rely heavily on computation and can use multiple CPU cores simultaneously. Examples: Data science computations, video rendering, heavy mathematical simulations. ❌ Higher Overhead: Slower to start and use more memory than threads. Communication between processes is more complex (via queues/pipes). 🤔 The Lock Confusion: Shared Memory & The GIL Threading allows memory sharing, but the GIL keeps the internal interpreter state safe. You still need locks for your own application logic to avoid race conditions. Multiprocessing uses isolated memory (no locks needed by default). If you intentionally use shared memory objects, then locks are required for safety. 💡 Key Takeaway: A lock only creates a temporary, synchronous bottleneck (a "critical section") to protect shared data. It doesn't make your entire application synchronous; the rest of your code still runs concurrently/in parallel. If you want to speed up your Python code: 👉 Use threading for latency hiding (I/O). 👉 Use multiprocessing for pure computation (CPU). #Python #Concurrency #Threading #Multiprocessing #GIL #PythonProgramming #CodeTips #WeekendLearning #Learning #Revision #MultipleThreading #ArtificialIntelligence #GenerativeAI

#AI #ML #Post8🧠 Invoke Python from C# using Python.Runtime ✅️Python.Runtime is the core library of Python.NET, a bridge between C# (.NET) and Python. It allows your C# code to embed the Python interpreter, run Python code, and interact with Python objects just like native C# objects. Think of it as a “translator” between C# and Python. --- ⚙️ Installation You can install it via NuGet: ✅️dotnet add package Python.Runtime --- 🧩 Key Components of Python.Runtime Let’s break down the main classes and methods you’ll use: 1. PythonEngine This is the core interface for controlling the embedded Python interpreter. Common methods: ✅️PythonEngine.Initialize() Starts the Python interpreter ✅️PythonEngine.Shutdown() Stops the interpreter and frees resources ✅️PythonEngine.Exec(string code) Executes a block of Python code ✅️PythonEngine.RunString(string code) Runs a single Python expression and returns the result ✅️PythonEngine.ImportModule("module_name") Imports a Python module --- 2. Py.GIL() In Python, the Global Interpreter Lock (GIL) ensures only one thread executes Python bytecode at a time. ✅ Example: using (Py.GIL()) { // Interact with Python here } When the using block ends, the GIL is automatically released. --- 3. PyObject A wrapper around Python objects. You can use PyObject to reference, call, or convert Python data. --- 4. Py.Import() Used to import Python modules from C# just like import in Python. --- 🧠 Example: Calling a Python function from C# Let’s say you have this Python file (myscript.py): # myscript.py def multiply(a, b): return a * b Here’s the C# code that uses Python.Runtime to call it: using System; using Python.Runtime; class Program { static void Main() { // Initialize Python interpreter PythonEngine.Initialize(); using (Py.GIL()) { // Import your Python script (make sure it's in the same folder or on sys.path) dynamic script = Py.Import("myscript"); // Call the function dynamic result = script.multiply(5, 10); Console.WriteLine($"Result from Python: {result}"); } PythonEngine.Shutdown(); } } --- ⚙️ How It Works Internally 1. PythonEngine.Initialize() loads the Python C API into your .NET process. 2. The embedded interpreter behaves exactly like a normal Python runtime (you can import any installed package). 3. Objects are wrapped as PyObject so you can use them in C#. 4. Python.NET automatically converts between basic .NET and Python types: int ↔ int double ↔ float string ↔ str List<T> ↔ list --- 💡 Tips & Notes ✅️Python must be installed on your system (and PYTHONHOME or PYTHONPATH should be correctly set if needed). ✅️You can specify a specific Python version with: Environment.SetEnvironmentVariable("PYTHONNET_PYDLL", @"C:\Python311\python311.dll");

🔍 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

🚀 Python Just Got 20x Faster with Mojo Integration For years, developers accepted a trade-off: Python gives us simplicity, but C/C++ deliver raw speed. That line just blurred. A recent test using Mojo, the new superset of Python, produced jaw-dropping results: 🔥 Mandelbrot Set Calculations • 20x faster than pure Python • 5x faster than NumPy • Zero compromise on readability ⚡ Sigmoid Function Processing • 12x faster than Python • 2x faster than NumPy • Same Python-like syntax we already love But here's the twist: NumPy still wins at some tasks. In numerical integration, its vectorized operations outperformed Mojo. This isn't a replacement-it's a new weapon in the toolkit. 💡 The secret sauce? Mojo lets you write high-performance code without leaving the Python ecosystem. Offload the heavy lifting to Mojo, keep the rest in Python, and you get the best of both worlds. 👉 Pro tip: Minimize Python↔Mojo crossings and stick to native Mojo types for peak performance. With 20x more speed at your fingertips, the question isn't if you'll use Mojo-it's what you'll build with it. #Python #Mojo #Programming #TechInnovation #Developers 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/d6uc43xX