Python 3.14: New asyncio introspection tools for debugging

⚡ 5 Ways to Make Your Python Code Run Faster Python is one of the most flexible and readable languages out there but it’s often called slow. In reality, Python just needs the right execution strategy for the right type of workload. Here are some techniques that can dramatically improve performance 👇 🧠 1️⃣ Synchronous (Default) Code runs line by line. Simple and reliable, but slow for tasks like file I/O or API calls. 💡 Best for: small scripts and quick automation. ⚙️ 2️⃣ Multithreading Runs multiple threads in the same process. Great for I/O-bound tasks like web scraping, API requests, or reading files. 💡 Try: ThreadPoolExecutor for simple and powerful parallelism. 🔥 3️⃣ Multiprocessing Spawns separate processes each with its own Python interpreter. Perfect for CPU-bound work like data processing, math, or image tasks. 💡 Try: multiprocessing.Pool to run CPU-heavy functions in parallel. 🌐 4️⃣ Asyncio Executes asynchronous code with async / await. Excellent for handling thousands of concurrent network operations efficiently. 💡 Try: aiohttp or asyncio.gather() for high-performance network I/O. 📊 5️⃣ tqdm A tiny but powerful library for progress bars. Adds instant visibility into long-running loops or downloads. 🚀 Key Takeaway Python isn’t “slow” it’s just synchronous by default. Once you understand when to use threads, processes, or async, you can unlock serious performance gains with just a few lines of code. 💬 What’s your go-to method for speeding up Python scripts? Let’s share tips and tools that make Python even more powerful! #Python #Performance #Asyncio #Multithreading #Multiprocessing #tqdm #SoftwareEngineering #LearningInPublic

Understanding Python Asyncio — Beyond async/await Just finished a fantastic Dev.to article that expertly explain the use of async await , coroutines and exception handling of it. Coroutines are the recipes, and Tasks are what actually cook them on the event loop. asyncio.create_task() starts execution immediately — don’t just define, schedule! Always name your tasks (name=) — it makes debugging way easier. Keep references to background tasks; otherwise, they might get garbage-collected. asyncio.gather() runs coroutines concurrently and preserves order; wait() gives more control. as_completed() lets you handle results as soon as they’re ready. TaskGroup (Python 3.11+) brings structured concurrency — cancel all when one fails. Handle timeouts (wait_for()), cancellations (task.cancel()), and ensure proper cleanup (CancelledError). Choose wisely: create_task() → control gather() → simplicity wait() → flexibility TaskGroup() → safety Async/await isn’t just about making code non-blocking — it’s about orchestrating multiple concurrent operations, managing their lifecycles, handling failures gracefully, and ensuring reliability even when things go wrong. Article : https://lnkd.in/gd7mFvQJ

🚀 Day 21 — Advanced Exception Handling & Error Management in Python Writing code is easy — making it error-proof is an art. Today’s focus: how to make your Python programs gracefully survive chaos. ⚙️ 🧠 What I Learned: 1️⃣ Introduction to Exceptions — Runtime errors that interrupt program flow, derived from BaseException. 2️⃣ Exception Hierarchy — Understand BaseException → Exception → Specific Errors. 3️⃣ try-except — Catch and handle errors without breaking execution. 4️⃣ else Block — Executes only when no exceptions occur. 5️⃣ finally Block — Always runs (perfect for cleanup tasks). 6️⃣ Nested try-except — Control errors at multiple levels. 7️⃣ Catching Multiple Exceptions — Handle several error types in one go. 8️⃣ Raising Exceptions — Trigger custom errors using raise. 9️⃣ Custom Exceptions — Create your own for domain-specific control. 🔟 Exception Chaining — Link original and new errors for easier debugging. 1️⃣1️⃣ Best Practices — ✅ Be specific ✅ Avoid bare except ✅ Always clean up ✅ Log errors ✅ Use custom exceptions wisely - 💡 Pro Tip: Robust code doesn’t just run — it recovers. - 🔥 If you’ve ever faced unexpected Python errors, this post is your sign to master exception handling today! - - #Python #PythonProgramming #LearnPython #PythonDeveloper #CodeNewbies #100DaysOfCode #WomenWhoCode #Developers #Tech #ProgrammingTips #SoftwareDevelopment #CodeBetter #DevCommunity #CleanCode #ErrorHandling #CodingTips #PythonLearning #MachineLearning #AI #DataScience - SAI PRASANNA SIRISHA KALISETTI Vamsi Enduri 10000 Coders -

1. Swapping Two Variables Without a Temp Variable Python Trick: You can easily swap two variables in Python without needing an extra temporary variable. a, b = 5, 10 a, b = b, a print(a, b) # Output: 10 5 2. Using List Comprehensions for More Readable Code Python Trick: List comprehensions are a concise way to create lists and perform operations in a single line of code. numbers = [1, 2, 3, 4, 5] squares = [x**2 for x in numbers] print(squares) # Output: [1, 4, 9, 16, 25] 3. Using zip() to Combine Lists Python Trick: zip() allows you to combine two or more iterables (e.g., lists or tuples) into a single iterable names = ['Alice', 'Bob', 'Charlie'] ages = [24, 27, 22] combined = list(zip(names, ages)) print(combined) # Output: [('Alice', 24), ('Bob', 27), ('Charlie', 22)]

🧩 5 Hidden Python Built-ins That Even Pros Forget Exist Most Python devs know zip() and enumerate(). But Python hides some real treasures — tools that can save hours once you discover them. Here are 5 deep built-ins that deserve your attention 👇 1️⃣ vars() — The instant object explorer: Returns an object’s __dict__ — basically, its attributes and values. Perfect for debugging, serialization, or logging class data cleanly. 2️⃣ callable() — To check if something can be called: Ever got an error calling a non-function? callable() safely checks whether an object is a function, method, or callable class before you run it. 3️⃣ id() — For memory-level debugging: Every object in Python has a unique memory identity. id() helps you detect aliasing bugs, reference issues, and subtle logic errors when working with mutable data. 4️⃣ hash() — For immutability & performance: Want to know if an object can be used in a set or dict key? If it’s hashable — you can. hash() tells you instantly. It’s also used under the hood in Python’s data structures. 5️⃣ import() — The dynamic import trick: Yes, you can import modules at runtime using this. It’s the secret behind dynamic workflows, plugin systems, and meta-programming tools. 💡 Bonus: Try pairing getattr() and setattr() for dynamic variable or method access — pure Python magic. 💡 Knowing these isn’t just “clever” — it’s what makes your code meta-programming ready. 👉 Which of these blew your mind the most? #Python #PythonTips #PythonTricks #CodeNewbie #LearnPython #DeveloperLife #HiddenGems #SmartCoding #BuildInPublic #100DaysOfCode #DidYouKnow #HiddenGems #MindBlownCode #CodeMagic #SmartCoding #GeekThings #TechReel #CodingReel #BuildInPublic #100DaysOfCode

A couple of days ago, I dived deep into Python’s concurrency model — and honestly, it cleared up a lot of confusion I had around parallelism vs concurrency, back when I was trying to understand the same concepts via projects. Here’s what I learned: 🧠 Multiprocessing: Runs multiple processes in parallel, each with its own Python interpreter. Great for CPU-heavy tasks since it bypasses the Global Interpreter Lock (GIL) by using multiple cores. 🧵 Threading: Multiple threads run within a single process. Works well for I/O-heavy tasks like file downloads, but is still limited by the GIL for CPU-bound operations. 🌐 AsyncIO: Perfect for handling many I/O-bound tasks concurrently using a single thread, ideal for tasks like API calls or handling multiple connections simultaneously. 🔒 Global Interpreter Lock (GIL): This was fascinating to learn about. It exists due to historical reasons, mainly to make integrating C extensions easier back when Python was being ported from C, aka CPython. Essentially, it allows only one thread to execute Python bytecode at a time, simplifying memory management but limiting true parallelism. And guess what? I already applied these concepts a few years ago in a side project: a YouTube Playlist Downloader that uses thread-based parallelism for faster downloads. 🖥️ Check it out here: https://lnkd.in/gsSKQV_X It was such a fun project to work on, and it made all these concurrency concepts finally click in practice. I am revisiting these concurrency concepts just to brush up on the Python concepts. #python #learning

🧠 Advanced Functions: Going Deeper into Python’s Power Functions in Python are more than blocks of reusable code. They are flexible objects that can be passed around, stored in variables and used to create powerful patterns. Mastering advanced function concepts helps you write cleaner and more expressive programs. Here are six ideas every intermediate developer should understand 👇 1️⃣ Higher Order Functions A higher order function is one that accepts another function as a parameter or returns one. This is common in filtering, mapping or wrapping behavior. 2️⃣ Lambdas Lambda functions are small anonymous functions. They are great when you need quick logic without defining a whole function. Example: square = lambda x: x * x 3️⃣ Closures A closure is created when an inner function remembers variables from the outer function, even after the outer one finishes. def make_counter(): count = 0 def increment(): nonlocal count count += 1 return count return increment 4️⃣ Args and Kwargs These let your function accept a variable number of arguments. *args receives positional arguments and **kwargs receives named arguments. 5️⃣ Using Functions as Data Because functions are first class citizens, you can store them in lists, dictionaries or even return them dynamically. This opens space for elegant patterns. 6️⃣ Why This Matters Advanced function patterns let you reduce duplication, simplify logic and write code that adapts to different situations naturally. 📌 Conclusion Understanding these ideas helps you think more clearly about how Python really works. With practice, you will see that many problems can be solved in a much simple way. 👉 Which of these concepts you use more often in your code?

💡 Python Tip: Easy Dictionary Merging! 🐍 Did you know you have two straightforward ways to combine Python dictionaries? Merging dictionaries is a common task, and thankfully, Python offers two clean, readable methods to get the job done quickly. Here's how you can combine two dictionaries, name1 and name2, using both the merge operator (|) and the unpacking operator (**). Method 1: Using the Merge Operator (|) This is the newest, most concise way (available since Python 3.9). It's clean and direct! name1 = {"kelly": 23, "Derick": 14, "John": 7} name2 = {"Ravi": 45, "Mpho": 67} # Simply use the pipe operator to merge them! names = name1 | name2 print(names) # Output: {'kelly': 23, 'Derick': 14, 'John': 7, 'Ravi': 45, 'Mpho': 67} Method 2: Using the Unpacking Operator (**) A classic method that works across older Python versions (since 3.5). You just need to unpack both dictionaries inside new curly braces ({}). name1 = {"kelly": 23, "Derick": 14, "John": 7} name2 = {"Ravi": 45, "Mpho": 67} # Unpack both dictionaries into a new one names = {**name1, **name2} print(names) # Output: {'kelly': 23, 'Derick': 14, 'John': 7, 'Ravi': 45, 'Mpho': 67} Both methods yield the same result! Choose the one you find most readable or that aligns best with your project's Python version requirements. Which method do you prefer for merging dictionaries, and why? Let me know in the comments! 👇 #Python #CodingTips #Developer #Dictionary #Programming #Tech

Remember when the “free-threaded” build in Python 3.13 felt like a bold experiment? With Python 3.14, true multithreading — without the Global Interpreter Lock (GIL) is officially supported! And the difference? Breathtaking. ⚙️ Why This Matters For decades, Python’s GIL was the invisible ceiling that kept us from unlocking our CPUs’ full potential. No matter how many threads you launched — only one could run Python bytecode at a time. That’s like owning a 16-core beast of a machine… and letting 15 cores nap while one does all the work. 💤 Now, the handcuffs are off. 🔥 🖼️ Example scenario : Heavy Image Processing You’re processing 1,000 high-res images — resizing, filtering, watermarking. Python 3.12 (With GIL): Your 16-core machine sits mostly idle. One thread works, 15 watch. Execution time: ~45 minutes. You go make lunch. Python 3.14 (No GIL): All 16 cores light up like a Christmas tree. 🎄 The same script finishes in ~3 minutes. You don’t even have time to pick a coffee mug. ☕ 💡 The Takeaway This isn’t a performance tweak. This is Python becoming multi-core native — catching up to the likes of C++, Go, and Rust in CPU efficiency, while keeping its simplicity. - AI/ML workloads - Image & video processing - Large-scale data simulation - Real-time analytics All these pipelines are about to get orders of magnitude faster — without changing your code. ⚙️ The Road Ahead Yes, some libraries will need refactoring for thread safety. Yes, there will be growing pains. But this is the future of Python — a language no longer limited by its own design. The GIL was a safety net. Python 3.14 just learned to fly. 🦅

Ever made your Python code “concurrent” and still felt it’s running like a snail? Yeah… same. 😅 That’s because Python gives you multiple ways to do concurrency — but each one solves a different problem. Earlier I was using native threads but soon realized there is now a better way for coordation. Let’s make this simple 👇 🧵 1️⃣ ThreadPoolExecutor — When your code is mostly waiting Think of API calls, database queries, or file reads. Threads let your program handle multiple “waiting” tasks at once instead of blocking everything. 👉 It’s like calling 10 people and putting them all on hold together instead of one by one. ⚙️ 2️⃣ ProcessPoolExecutor — When your code is burning CPU This one actually uses multiple cores. Great for math-heavy or data processing tasks where the CPU is sweating. It’s like having 4 chefs in 4 kitchens instead of 1 chef doing everything alone. ⚡ 3️⃣ asyncio.create_task — When you want async I/O magic This is pure event-loop goodness — no threads, no processes. It just switches super fast between tasks that are waiting on I/O. 👉 It’s like cooking multiple dishes — while one simmers, you chop the veggies for the next. 🔄 4️⃣ asyncio.to_thread — When async meets blocking code Sometimes you’re in an async app but still need to run an old, blocking function. This lets you sneak it into a background thread without freezing everything. 👉 It’s like having someone else hold your place in line while you grab a coffee. ☕ 💡 Quick guide: 🧵 ThreadPoolExecutor → I/O bound (lots of waiting) ⚙️ ProcessPoolExecutor → CPU bound (heavy lifting) ⚡ asyncio.create_task → Async I/O (event loop magic) 🔄 asyncio.to_thread → Mix sync with async safely Threads handle waiting. Processes handle computation. Async handles coordination. Once you get this mental model — concurrency in Python finally clicks. #concurrency #python #asynchronous #learning