Introducing TNG Python: automated test generation for Python

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

I’ve been reading and experimenting with Python 3.14 lately and kinda it’s a important update. Most Python updates give us a few syntax improvements or standard library tweaks. But this one? It changes how Python itself runs. While testing some concurrency-heavy scripts, I came across multiple interpreters (PEP 734) a new feature that lets you create independent Python interpreters inside the same process. Each interpreter has its own GIL, meaning for the first time, we can run Python code truly in parallel. no multiprocessing hacks, no GIL fights. Combine that with the new tail-call interpreter (a low-level CPython optimization that improves speed), and you can feel Python’s architecture evolving toward something far more scalable. It’s a glimpse of Python’s future more modular, more concurrent, and ready for multi-core systems. I wrote a deep dive about it with examples, diagrams, and how to get started here: https://lnkd.in/ehN43ECw #Python #Python314 #Concurrency #Performance #SoftwareEngineering #Developers #Programming

💡 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

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

Ever Wonder How Python Knows Your Code Is Wrong Instantly? How Python instantly throw “SyntaxError” or “IndentationError” the moment you hit run — even before your logic executes? Here’s what actually happens behind the scenes : 1️⃣Lexical Analysis (Tokenizer) Python first breaks your code into tokens — keywords, operators, variables, etc. If something doesn’t fit Python’s grammar rules (like missing a colon or wrong indent), it fails right here. 2️⃣Parsing (Syntax Tree) The parser then builds an Abstract Syntax Tree (AST) — a structural map of your code. If a token is out of place (like a stray bracket or unclosed string), Python raises a SyntaxError immediately. 3️⃣Bytecode Compilation Once syntax is valid, Python compiles your code into bytecode (.pyc) before executing. Runtime issues like TypeError or NameError are only caught after this stage when the code actually runs. So the next time you see: SyntaxError: invalid syntax Remember — Python’s parser is doing its job before execution.

🧩 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

Learn how to use the ES|QL query builder, a new Python Elasticsearch client feature that makes it easier to construct ES|QL queries using a familiar Python syntax: https://gag.gl/SLMca8 #ElasticSearchLabs

Learn how to use the ES|QL query builder, a new Python Elasticsearch client feature that makes it easier to construct ES|QL queries using a familiar Python syntax: https://gag.gl/SLMca8 #ElasticSearchLabs

Learn how to use the ES|QL query builder, a new Python Elasticsearch client feature that makes it easier to construct ES|QL queries using a familiar Python syntax: https://gag.gl/SLMca8 #ElasticSearchLabs

Learn how to use the ES|QL query builder, a new Python Elasticsearch client feature that makes it easier to construct ES|QL queries using a familiar Python syntax: https://gag.gl/SLMca8 #ElasticSearchLabs