Alexander Goida’s Post

Introducing Python 3.14! Python 3.14 is a serious evolution—less about flashy syntax, more about performance, scalability, and tooling maturity. Key highlights: 🔹 Deferred Evaluation of Annotations (PEP 649 & 749) Annotations are no longer eagerly evaluated. This improves performance, removes the need for string-based forward references, and introduces the new annotationlib for safer, more flexible introspection. 🔹 Multiple Interpreters in the Standard Library (PEP 734) concurrent.interpreters brings true parallelism inside a single process. Think process-level isolation with thread-level efficiency—unlocking new concurrency models and better multi-core utilization. 🔹 Template String Literals – t-strings (PEP 750) A new string primitive that separates static text from interpolated values at runtime. Enables safer SQL, HTML, shell commands, logging, and even lightweight DSLs. 🔹 Free-Threaded Python Is Officially Supported (PEP 779) No longer experimental. Major performance gains, cleaner APIs, and the adaptive interpreter now work in free-threaded mode. Still optional—but now production-grade. 🔹 Incremental Garbage Collection GC pauses are dramatically reduced for large heaps. Fewer stop-the-world moments, better latency profiles. 🔹 Zero-Overhead Remote Debugging (PEP 768) Attach debuggers and profilers to live Python processes—without restarts or performance penalties. A big win for production observability. 🔹 Asyncio Gets Introspection Superpowers New CLI tools (python -m asyncio ps / pstree) visualize running async tasks and await graphs—finally making complex async systems debuggable in real time. 🔹 Zstandard Compression in the Stdlib (PEP 784) Native compression.zstd support plus integration with tarfile, zipfile, and shutil. Faster compression, better ratios, modern defaults. 🔹 Syntax Highlighting & Autocomplete in the REPL The default shell now highlights syntax and supports import autocompletion—small change, big daily productivity boost. 🔹 Performance & Platform Advances • Experimental JIT included in Windows/macOS binaries • New tail-call interpreter (opt-in) • Emscripten officially supported (Tier 3) • Android binaries now published 🔹 Cleaner Errors, Cleaner APIs More actionable error messages, safer warnings in concurrent code, many long-deprecated APIs finally removed. 💡 Bottom line: Python 3.14 isn’t just “another release”—it’s laying infrastructure for Python’s post-GIL, multi-core, production-first future. 👇My takeaway: Python 3.14 feels less like a language update and more like infrastructure for the next decade—multi-core, production-heavy, introspectable, and safer by default. For the full details, the official docs are here: 👉 https://lnkd.in/eHs-y5us Curious what others think: which of these changes actually affects how you write or deploy Python today?

Context managers — Python’s most unappreciated feature. If you’ve written Python, you’ve probably used this: with open("file.txt", "r") as f: data = f.read() But do you actually know what "with" is doing? In simple terms, "with" makes sure something is properly cleaned up after you’re done using it. Without "with" , you would write: f = open("file.txt", "r") data = f.read() f.close() Now imagine you forget f.close()… Or your program crashes before it runs. That’s where with shines. When you use "with", Python automatically: • Sets things up • Runs your code • Cleans everything up — even if errors happen It’s not just for files. You can use with for: - Database connections - Locks in multithreading - Opening network connections - Even capturing print output And here’s something many beginners don’t realize: You can create your own custom context managers. Yes — you can teach Python how to automatically handle setup and cleanup for your own logic. That means: Start something → Use it → Safely finish it All enforced by the language itself. For beginners, this is powerful. It makes your code safer. It reduces hidden bugs. And it builds strong engineering habits early. The with statement isn’t complicated. It’s just disciplined coding — made simple. If you're learning Python and treating with like magic syntax, dig deeper. It’s one of the cleanest tools Python gives you. #Python #Programming #SoftwareEngineering #CleanCode

Python lists can do more than most people realize. Lists go much deeper than append, remove, and sort: - Slice assignment lets you replace, insert, or delete entire sections of a list in one operation - Comprehensions with conditions can filter and transform data in a single line - Nested lists give you 2D grids, matrices, and game boards — but creating them wrong means every row shares the same memory - Shallow copies only go one level deep — if your list contains other lists, you need deepcopy or you'll get bugs that make no sense - Membership testing on a list is O(n) — convert to a set and it drops to O(1) #Python lists are simple enough to learn in an hour and deep enough to keep teaching you things years later. That's the beauty of Python's design. https://lnkd.in/g2QWtv3H #tutorials #code #programming

Python 3.14 keeps raising the bar for developer productivity : clearer errors, smarter templates(t-strings) and serious progress toward parallel execution. Small changes, big long-term impact.

Python Coding Tip Use pathlib for Cleaner and Smarter Directory Management If you’re still using os.path() for handling file paths in Python, it’s time to upgrade your approach. Modern Python provides a more powerful and Pythonic solution: pathlib. Instead of treating file paths like plain strings and passing them through multiple os.path functions, pathlib treats paths as objects. This object-oriented approach makes your code cleaner, more readable, and easier to maintain. With pathlib, you can: • Join paths using the intuitive / operator • Check if files or directories exist with simple methods • Read and write files directly from the path object • Build cross-platform applications without worrying about OS-specific separators Unlike os.path(), which feels procedural and fragmented, pathlib keeps everything structured and expressive. For backend systems, AI pipelines, data engineering workflows, or production-ready applications, proper directory management is critical. Using pathlib reduces bugs, improves portability, and aligns your code with modern Python best practices. Clean code is not just about solving the problem. It’s about solving it in a way that is readable, scalable, and professional. If you're serious about writing production-level Python, start using pathlib. Follow for my YouTube Channel Code with Felix where I make tutorial on AI Multi-Agent system. https://lnkd.in/dTk7YJ-x

FOSDEM, room 'Python': 1. PEP 810: Lazy Imports. One of the most anticipated Python proposals targeting Python 3.15. PEP 810 introduces a new "lazy" keyword that defers module loading until first use, instead of at import time. This can cut startup time by 50-70% for CLI tools and applications with large dependency graphs. The Python Steering Council has already greenlighted it :D. Some caveats to keep in mind though: import-time side effects shift to first use, type checkers need updates, and missing dependencies are only discovered at runtime. The recommendations: "Keep eager imports for always-used packages". "Explicit is better than implicit". 2. The GIL and API Performance: Past, Present, and Free-Threaded Future. A deep dive into Python's Global Interpreter Lock, the mechanism that has limited true multi-threaded parallelism for almost three decades. Multiple past attempts to remove it failed because the trade-offs in single-threaded performance were too steep. But with Python 3.14, free-threaded mode is now officially supported (no longer experimental), with only about 5-10% overhead on single-threaded code. You can try it today with uv run -p 3.14t. For API developers, this means real parallel request processing using threads instead of workarounds like multiprocessing. I hope you find this useful for future projects. Let Python go brrrrrrr

Python Functions: A function is a block of organized, reusable code and that us used perform a single used and multiple tasks. Syntax: def calculate(a,b): Recurstion:In Python, recursion refers to a function calling itself to solve a problem. It involves two critical components: Base case: This is the condition that terminates the recursion. Difference between print and Return: print just stored the human user output in a control. Return:-is keyword and return used to terminate the function and gives that a value the function. Return only one time call the function because terminate the value. Keyword and positional arguments: number of values and keyword accepted to jumbling order also accepted. Default arguments: already existed non default followed. *arguments: Is used to unpack the elements only should pass single * arguments. Variable length arguments of automatically stored in tuple and used * arguments. **keywards of automatically stored in dictionary and used * arguments. Global and Local variables: A variable inside and outside function is called global and local variables. Usage of global Keywords: When user want to access the global variable inside the function directly and carry farward the updated value given outside the function then we need to used global keyword. Generators: No tuples comprehension in above cases if we remove don't spaces and key parameter than out coming generators. A generators is also a function which can be used as an loops by producing group of values, variable used yield keywords. Yield/Return: Return keyword terminate the function where as yield can pass function and go on the every function iteration Pooja Chinthakayala Mam,Saketh Kallepu Sir,Uppugundla Sairam Sir.

Here’s a clean, professional LinkedIn post you can use: --- 🐍 Identifiers in Python – Explained Simply! In Python, identifiers are the names used to identify variables, functions, classes, modules, and other objects. They are the foundation of writing clean and readable code. ✅ Rules for Naming Identifiers: Must start with a letter (A–Z or a–z) or an underscore (_) Cannot start with a number Can contain letters, numbers, and underscores Are case-sensitive (name and Name are different) Cannot use reserved keywords (like if, for, class, etc.) --- 🌟 Features of Identifiers in Python 1️⃣ Case Sensitive Python treats uppercase and lowercase differently. Example: Age ≠ age 2️⃣ Unlimited Length Identifiers can be as long as needed (though shorter, meaningful names are recommended). 3️⃣ Supports Unicode Python allows Unicode characters in identifiers (Python 3+). 4️⃣ Keyword Restrictions Reserved words cannot be used as identifiers. --- 🚀 Advantages of Proper Identifier Usage ✔️ Improves code readability ✔️ Enhances maintainability ✔️ Makes debugging easier ✔️ Encourages clean coding practices ✔️ Helps in writing self-documenting code --- 💡 Pro Tip: Follow naming conventions like: snake_case for variables and functions PascalCase for classes _single_leading_underscore for internal use Following proper identifier naming makes your Python code professional and industry-ready! #Python #Coding #Programming #SoftwareDevelopment #Learning #TechTips

Variables hold your data. Operators act on it. Every calculation, comparison, decision, and transformation in a Python program is driven by an operator. They're the verbs of your code -- and most beginners only learn half of them. Over on PythonCodeCrack, you can find a complete guide covering all 8 categories of Python operators: -- Arithmetic (including floor division and modulo gotchas) -- Comparison and logical operators -- Assignment and augmented assignment -- Membership (in / not in) -- Identity (is / is not -- and why it's not the same as ==) -- Bitwise operators for low-level work -- The walrus operator (:=) -- Operator precedence rules Quick example of something that trips people up: -7 // 2 returns -4, not -3. Python's floor division rounds toward negative infinity, not toward zero. Small details like this matter when your code needs to be correct. 13-minute read. Real code. Real explanations. https://lnkd.in/g8PtMy86 #Python #PythonProgramming #LearnPython #CodingForBeginners #Programming #PythonTutorial #SoftwareDevelopment

Python 3.11.9 on Windows 11 ARM64 / AArch64 https://lnkd.in/gmKE5ec8 PS C:\Users\khale> python --version Python 3.11.9 PS C:\Users\khale> pip list Package  Version ---------- ------- pip    24.0 setuptools 65.5.0 [notice] A new release of pip is available: 24.0 -> 26.0.1 [notice] To update, run: python.exe -m pip install --upgrade pip PS C:\Users\khale> python.exe -m pip install --upgrade pip Requirement already satisfied: pip in c:\users\khale\appdata\local\programs\python\python311\lib\site-packages (24.0) Collecting pip  Using cached pip-26.0.1-py3-none-any.whl.metadata (4.7 kB) Using cached pip-26.0.1-py3-none-any.whl (1.8 MB) Installing collected packages: pip  Attempting uninstall: pip   Found existing installation: pip 24.0   Uninstalling pip-24.0:    Successfully uninstalled pip-24.0 Successfully installed pip-26.0.1 pip list Package  Version ---------- ------- pip    26.0.1 setuptools 65.5.0