Python Performance Optimization: Understanding CPython Internals

𝗣𝘆𝘁𝗵𝗼𝗻 𝗹𝗼𝗼𝗸𝘀 𝗳𝗮𝘀𝘁. Until you realize C++ is the one carrying it. --- Python is the Ferrari everyone brags about. But behind every production ML pipeline, game engine, and real-time system? C++ is the flatbed truck doing the actual heavy lifting. → NumPy? C under the hood. → PyTorch? C++ core. → CPython itself? Written in C. Python gives you the syntax. C++ gives it the speed to exist. Most devs never look under the hood. The ones who do? They build differently. --- I'm not saying ditch Python. I'm saying: know what's actually running your code. The abstraction is the feature. But ignorance of what's beneath it is a liability. 𝗪𝗵𝗮𝘁'𝘀 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘀 𝗱𝗲𝘃𝘀 𝗻𝗲𝘃𝗲𝗿 𝗯𝗼𝘁𝗵𝗲𝗿 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲𝗶𝗿 𝘀𝘁𝗮𝗰𝗸? Drop it below 👇 #Python #CPlusPlus #SoftwareEngineering #SystemsThinking #Developer

Stop waiting for pip. Start using uv. ⚡ If you’re still using traditional tools to manage Python environments, you’re losing hours to loading bars. I’ve been diving into uv lately, and it’s a total game-changer for Python development. It’s a single tool (written in Rust) that replaces pip, venv, pip-tools, and pyenv. Why I’m switching: Insane Speed: It’s 10x–100x faster than pip. No hyperbole—it’s nearly instant. All-in-One: It manages your Python versions, virtual environments, and packages in one place. Reliability: It creates a uv.lock file by default, making deployments to VPS or containers 100% reproducible. Modern Features: It can run single-file scripts with inline dependencies automatically. Whether you're building FastAPI bots or complex data pipelines, your time is too valuable to spend watching packages install. Have you made the switch to uv yet, or are you sticking with the classic pip? #Python #SoftwareEngineering #Rust #DeveloperTools #OpenSource #Programming

𝗧𝗵𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 "𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲" 𝗧𝗿𝗮𝗽 I noticed something interesting today : changed a value inside a function, and it reflected outside too I didn’t return anything. I didn’t re-assign the variable ➡️ 𝗧𝗵𝗲 𝗖𝗮𝘁𝗰𝗵 : Python functions don't always create a new "𝗰𝗼𝗽𝘆" of your data Instead, they often work with 𝗮 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 to the original object ▪️𝗠𝘂𝘁𝗮𝗯𝗹𝗲 𝗼𝗯𝗷𝗲𝗰𝘁𝘀 (Lists, Dicts, Sets) are modified in place. Any change inside the function affects the original data directly. ▪️𝗜𝗺𝗺𝘂𝘁𝗮𝗯𝗹𝗲 𝗼𝗯𝗷𝗲𝗰𝘁𝘀  (Integers, Strings,Tuples) are safe because they can't be changed in place. ➡️ 𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 : If you’re working with Lists or Dictionaries and want to keep your original data safe, you must be explicit: update(my_list.copy()) Small detail, but missing it can lead to hours of debugging bugs #Python #30DaysOfCode #SoftwareEngineering #LearningInPublic #Day19

𝐃𝐚𝐲 𝟔/𝟑𝟎: 𝐇𝐨𝐰 𝐝𝐨 𝐏𝐲𝐭𝐡𝐨𝐧 𝐝𝐞𝐯𝐬 𝐡𝐚𝐧𝐝𝐥𝐞 𝐭𝐡𝐢𝐬? 🐍 Coming from C++, I’m used to the compiler being my safety net. If I try to add a string to an int there, the code won’t even run. ➡️ But yesterday I realized Python is... a different world! def add(a, b): return a + b add(10, "5")💣 Boom! Runtime Error In a tiny script, this is a 2-second fix.. But in a massive codebase with thousands of functions? This feels like a 𝐡𝐢𝐝𝐝𝐞𝐧 𝐫𝐢𝐬𝐤 𝐰𝐚𝐢𝐭𝐢𝐧𝐠 𝐭𝐨 𝐡𝐚𝐩𝐩𝐞𝐧. So, the real question for the experts here: How do you guys stop these "sneaky" errors before they hit production? Is it: A) Just remember everything B) Test everything thoroughly (Unit tests for every single edge case) C) Use Python type hints (a: int, b: int) (👀but do they actually stop the crash?) D) Something else(MyPy? Pylint?) I’ve been digging into this 𝐜𝐥𝐚𝐬𝐡 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐟𝐥𝐞𝐱𝐢𝐛𝐢𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐬𝐚𝐟𝐞𝐭𝐲 𝐭𝐨𝐝𝐚𝐲. I’m hunting for a way to make Python feel just as safe as C++. I'll share what I find tomorrow! Drop your choice below 👇 What’s the standard industry workflow for catching these? #Python #Cpp #LearningInPublic #30DaysOfCode #SoftwareEngineering #Programming

𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 𝗳𝗲𝗲𝗹𝘀 𝘀𝗹𝗼𝘄 𝗱𝗲𝘀𝗽𝗶𝘁𝗲 𝘂𝘀𝗶𝗻𝗴 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗵𝗿𝗲𝗮𝗱𝘀 ? The secret lies in how Python handles execution. I’ve put together a 12-slide deep dive into Python Concurrency, moving from absolute basics to the future of Python 3.13. What’s inside? ✅ Synchronous vs. Async: Why "𝘄𝗮𝗶𝘁𝗶𝗻𝗴" is the biggest bottleneck. ✅ The Event Loop: How 𝗮𝘀𝘆𝗻𝗰𝗶𝗼 manages thousands of tasks on a single thread. ✅ The 𝗚𝗜𝗟 (𝗚𝗹𝗼𝗯𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗲𝗿 𝗟𝗼𝗰𝗸): Why traditional Python threading isn't always "parallel." ✅ The 𝗙𝘂𝘁𝘂𝗿𝗲 (𝗙𝗿𝗲𝗲-𝗧𝗵𝗿𝗲𝗮𝗱𝗶𝗻𝗴): How Python 3.13+ finally enables true multi-core parallelism. 🟪 𝗧𝗵𝗲 "𝗞𝗶𝘁𝗰𝗵𝗲𝗻" 𝗔𝗻𝗮𝗹𝗼𝗴𝘆: Think of a single cook (Thread) multitasking between a gas stove (I/O) and a cutting board. That’s Async. Now imagine a kitchen with multiple cooks and multiple gas stoves. That’s Modern Free-Threading. Whether you're building 𝘄𝗲𝗯 𝘀𝗰𝗿𝗮𝗽𝗲𝗿𝘀 (𝗜/𝗢-𝗯𝗼𝘂𝗻𝗱) or 𝗵𝗲𝗮𝘃𝘆 𝗱𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 (𝗖𝗣𝗨-𝗯𝗼𝘂𝗻𝗱), choosing the right model is key to performance. Check out the slides below! #Python #Programming #SoftwareEngineering #Concurrency #AsyncIO #Multithreading #Python313 #TechLearning

Step beyond natural-language prompts and explore how the Code Interpreter unlocks deterministic, Python-driven logic inside Copilot Studio. This session walks through how agents can generate and execute Python code in a secure sandbox to create reliable outputs such as charts, enriched Excel or PDF files, and Dataverse insights. You’ll see how to enable the feature at the environment, agent, and prompt levels, and how to analyze and visualize data using both design-time instructions and Python generated on the fly by your agents. We’ll look at prompt-design best practices, key considerations, and how to make the most of the Python output inside your agent workflows. Download the invite here: https://lnkd.in/ecNM3-wc #Microsoft #PowerPlatform #PartnerCommunity #TechTalks

𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗹𝗲𝗮𝗿𝗻 𝗣𝘆𝘁𝗵𝗼𝗻... But very few actually master it. The reason? They focus on syntax, not structure. If you want to move from beginner to advanced, you need to follow a clear path. Here’s a simple roadmap I’ve been following: 🔹 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 Build strong logic with reusable code 🔹 𝗢𝗢𝗣 𝗙𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹𝘀 Start thinking in systems, not just scripts 🔹 𝗢𝗢𝗣 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 Encapsulation, Inheritance, Polymorphism, Abstraction 🔹 𝗘𝗿𝗿𝗼𝗿 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 Write code that doesn’t break in real-world scenarios 🔹 𝗙𝗶𝗹𝗲 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 Work with real data and build practical applications Most developers stop at basics. But real growth starts when you go deeper. 𝗠𝗮𝘀𝘁𝗲𝗿 𝘁𝗵𝗲𝘀𝗲 𝟱 𝘀𝘁𝗮𝗴𝗲𝘀, 𝗮𝗻𝗱 𝘆𝗼𝘂 𝘄𝗼𝗻’𝘁 𝗷𝘂𝘀𝘁 𝘄𝗿𝗶𝘁𝗲 𝗰𝗼𝗱𝗲 — 𝘆𝗼𝘂’𝗹𝗹 𝗯𝘂𝗶𝗹𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. #Python #Programming #Developers #Coding #SoftwareEngineering #LearnPython

𝗬𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱𝗻'𝘁 𝗵𝗮𝘃𝗲 𝘁𝗼 𝗰𝗵𝗼𝗼𝘀𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘆𝗼𝘂𝗿 𝗳𝗮𝘃𝗼𝘂𝗿𝗶𝘁𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗹𝗶𝗯𝗿𝗮𝗿𝘆 𝗮𝗻𝗱 𝗮 𝗴𝗿𝗲𝗮𝘁 𝘀𝗼𝗹𝘃𝗲𝗿. 🧩🚀 #Python optimization modeling ecosystem is rich: PuLP, Pyomo, Linopy, PyOptInterface, CVXPY, ... — each with its own style and strengths. With #FICO #Xpress as the backend, you get enterprise-grade LP, QP, and MIP solving where supported, along with many advanced features. In our latest "𝗫𝗽𝗿𝗲𝘀𝘀 𝗘𝘃𝗲𝗿𝘆𝘄𝗵𝗲𝗿𝗲" blog (Part 2), Francesco Cavaliere and I solve the same #portfolio #optimization problem five different ways, highlighting how each library exposes Xpress features like warm starts, SOS constraints, solver parameters, and solver callbacks. https://lnkd.in/eyAS-68r 🗂️ 𝗪𝗲 𝗮𝗹𝘀𝗼 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄 𝗚𝗶𝘁𝗛𝘂𝗯 𝗿𝗲𝗽𝗼 with complete, runnable examples for all integrations — including OR-Tools from Part 1. You can even run them on GitHub Codespaces with zero local setup, Xpress #Community #License pre-configured. 📖 𝗥𝗲𝗮𝗱 𝗣𝗮𝗿𝘁 𝟭 (OR-Tools, with Daniel Junglas): https://lnkd.in/eqdCZwCH 📖 𝗥𝗲𝗮𝗱 𝗣𝗮𝗿𝘁 𝟮 (Python ecosystem): https://lnkd.in/eyAS-68r 💻 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 #GitHub 𝗿𝗲𝗽𝗼: https://lnkd.in/ea-UcFMB

I’ve been spending my recent free time in building an Event-Driven Backtesting Engine from scratch for Options. Backtesting complex option strategies requires processing massive amounts of market data, calculating Greeks, and tracking portfolio metrics simultaneously. To handle this without latency bottlenecks, I decided to architect the entire core engine in C++. for now I have mostly tried to make it very flexible like modular commission and slippage and ability to write custom strategies instead of editing the core engine itself I completely decoupled most of the core things so The entire C++ backend is compiled as a standalone library. I am also trying to Integrate a python bridge using pybind11 exposing this compiled library directly to Python. The goal for this is to make the engine to do all the computation in the background, allowing anyone to write, test, and plug in custom strategies dynamically using simple Python scripts without ever needing to modify the core engine files. Getting the C++ event loop to work good with Python scripting is proving to be a little complicated right now! I'll be pushing a final README and some sample strategies once I get the bindings fully stabilized. You guys can check out the code here : https://lnkd.in/gRSgd4gs #quantfinance #cpp #python #algorithmictrading #options #pybind11 #derivatives

Just shipped LLMPrice 🚀 A lightweight Python + TypeScript library for LLM pricing lookup. - 2500+ models - Offline-first - Search / compare / CLI - Auto-synced pricing data Built for developers who want pricing data only, without pulling in a heavy LLM stack. Links in first comment. #Python #TypeScript #LLM #OpenSource #AIEngineering #DeveloperTools