Python 3.11 TaskGroup Ensures Safe Structured Concurrency

I recently contributed to keon/algorithms, one of the most popular algorithm repositories on GitHub that helps developers worldwide learn data structures and algorithms through clean Python implementations. 🔍 My Contribution I implemented a Generalized Binary Search algorithm that works over a numeric range using a monotonic boolean predicate, instead of searching for a fixed value in an array. 💡 Why this enhancement matters: • Flexible Input – Operates on a range [low, high] with a predicate function f(x) • Smart Output – Finds the smallest value x where f(x) becomes True • Optimal Performance – O(log N) time, O(1) space • Broader Applications – Ideal for optimization problems, boundary detection, and non-array search spaces • Backward Compatible – Existing implementations remain untouched This abstraction makes binary search far more powerful and reusable, enabling real-world problem solving beyond traditional array searches. 🔗 Repository: https://lnkd.in/daS_m-dM #OpenSource #GitHub #Algorithms #BinarySearch #Python #DataStructures #SoftwareEngineering #Coding #TechCommunity #OpenSourceContribution

#PythonCoding 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝘁𝗮𝘁𝗲𝗺𝗲𝗻𝘁: 𝗥𝗲𝗺𝗼𝘃𝗲 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀 𝗳𝗿𝗼𝗺 𝗮 𝗟𝗶𝘀𝘁 𝗪𝗶𝘁𝗵𝗼𝘂𝘁 𝗨𝘀𝗶𝗻𝗴 𝗣𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻 Write a Python function that takes a list as input and returns a new list with duplicate elements removed, while maintaining the original order of elements. 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀: The function should work for both numbers and strings. The function should not use built-in functions like set(), dict.fromkeys(), or collections.OrderedDict. The function should preserve the original order of elements. 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 1.Create an empty list called unique_list to store only unique elements. 2.Iterate through the original list using a loop. 3.Check if the element is already in unique_list: -->If not, add it to unique_list. -->If it already exists, skip it. 4.Return the unique_list, which now contains elements without duplicates while preserving the original order. #Python #PythonProgramming #LearnPython #PythonDeveloper #CodingLife #PySpark #MachineLearning #Automation

In Python, tuple unpacking (also known as iterable unpacking) allows you to take a collection of data and assign it to individual variables in a single, readable line. Instead of doing this: data = (1, 2, 3) a = data[0] You can do this: a, b, c = (1, 2, 3) Why it matters: Readability: It’s immediately clear what the data represents. Efficiency: It reduces boilerplate code. Pythonic: It’s the "intended" way to handle multiple return values from functions. Are you using unpacking in your scripts, or are you still stuck in the index[0] world? Let's discuss below! 👇 #PythonProgramming #CleanCode #DataScience #SoftwareEngineering

How async/await turns 30 seconds into 3 seconds (without changing the server)? TL;DR: By letting the program handle multiple requests while waiting using async and await! What does AWAIT do? When code hits await, it tells Python: "This will take time, go do something else meanwhile." Python's event loop then switches to another task, keeping the CPU busy instead of idle. What's Actually Happening? -> async def marks a function as asynchronous -> await marks points where the program can pause and switch tasks Note that this works for I/O bound tasks only, CPU bound tasks need multiprocessing. Takeaway: -> async/await is perfect for I/O bound concurrent operations -> 10x performance improvements are common -> The learning curve is worth it for any I/O-heavy application I'm diving deep into Python concurrency. Follow along and share your bottleneck stories in comments! #Python #Concurrency #SoftwareEngineering #BackendDevelopment #Performance

 Why Python remembers things after a function is done (code screenshot below) db_connector has finished execution. Its stack frame is gone. Yet connect still remembers host and port. That preserved state is a closure — created automatically when an inner function captures outer scope. You don’t “use closures” explicitly. You design around them. Why this matters: • avoids globals   • keeps config scoped   • cleaner APIs   • safer state Closures aren’t a trick. They’re how Python naturally models state + behavior. Once you notice this, patterns such as DB clients, API wrappers, and rate limiters become obvious. #Python   #SoftwareEngineering   #BackendDevelopment  

🚀 Recently, I came across the @property decorator in Python and it’s such a neat feature! It allows you to define a method that can be accessed like a normal variable, which is super handy for checking derived status or computed values without changing your API. Example: class Order: def __init__(self, shipped): self._shipped = shipped @property def status(self): return "Shipped" if self._shipped else "Pending" order = Order(True) print(order.status) # Access like a normal variable ✨ You don’t call order.status()—you just use it like any other attribute, and it dynamically returns the computed value! 💡 Makes your code cleaner, more readable, and Pythonic. #Python #PythonTips #Programming #CodeOptimization #SoftwareEngineering #CleanCode #Developers #PythonProgramming #TechTrends #CodingLife

You don’t always need async to get concurrency in Python. Async is powerful, but it has a problem: it needs async-compatible libraries for everything. TL;DR: Threading works with regular blocking libraries - no async migration needed! Threading solves the same I/O bound problem as async, but works with regular blocking libraries too. How It Works? 1. Instead of one event loop switching between tasks, threading creates multiple OS threads. 2. Each thread can make blocking calls independently. When one thread waits for I/O, others keep running. Where Threading Wins? -> Working with libraries that don't have async versions -> Existing codebase with blocking code Threading and async give similar performance for I/O bound tasks! Trade off - Threads have more overhead than async coroutines. For thousands of concurrent operations, async is lighter. I'm diving deep into Python concurrency. Follow along and share your bottleneck stories in comments! #Python #Concurrency #SoftwareEngineering #BackendDevelopment #Performance

Python 3.15 introduces PEP 747 (TypeForm). On the surface, it looks like a small typing improvement. It isn’t. It formalizes something deeper: the ability to operate not just on classes, but on type annotations themselves. Most teams won’t need this immediately. But if your product builds: - validation layers - API schema systems - dependency injection frameworks - custom abstractions over typing these shifts compound over time. Small changes in the type system often signal larger architectural evolution. And architecture rarely breaks loudly. It degrades quietly. Curious how teams working with typing-heavy systems see this direction.

That `InitVar` in a dataclass… do you actually know what it does? It looks like a field. It’s declared like a field. But it’s not really a field. In today’s video, I walk through 7 interesting things you can do with Python dataclasses. From automatic class registration and lightweight validation systems to cached derived values, self-building CLI parsers, and even using dataclasses as context managers. Most developers treat dataclasses like “nicer structs.” But they’re just normal Python classes with less boilerplate. And once you realize that, they become a design tool, not just a convenience. 👉 Watch the full video here: https://lnkd.in/e5pkc7Ae. #python #dataclasses #softwaredesign #cleancode #developers #arjancodes