Python Performance: Debunking the Slow Myth

🚀 Python for Everything @windshipdev From data analysis to machine learning, web development, automation, and even computer vision, Python powers some of the most important technologies in the world. Here’s a quick visual guide to some of the most useful Python libraries and what they’re commonly used for: 🐼 Pandas → Data manipulation 🧠 TensorFlow → Deep learning 📊 Matplotlib / Seaborn → Data visualization 🌐 BeautifulSoup / Selenium → Web scraping & automation ⚡ FastAPI → High-performance APIs 🗄️ SQLAlchemy → Database access 🧩 Flask / Django → Web development 👁️ OpenCV → Computer vision Python’s ecosystem is one of the main reasons it dominates fields like AI, data science, backend development, and automation. 💾 Save this image so you can come back to it whenever you need a quick Python reference. And if you found it useful, feel free to share it with someone learning Python 👨💻 Which Python library do you use the most? Learn python here: https://lnkd.in/esb9K794 #publi #Python #Programming #DataScience #MachineLearning #AI #BackendDevelopment #WebDevelopment #Coding #SoftwareEngineering

Most people still think Python is “just a programming language.” That’s a narrow view — and honestly, it’s outdated. Python is an ecosystem. Pair it with the right libraries and it becomes a tool for almost anything: • Pandas → Data manipulation • TensorFlow → Deep learning • Matplotlib / Seaborn → Data visualization • BeautifulSoup / Selenium → Web scraping & automation • FastAPI / Flask / Django → APIs & web platforms • SQLAlchemy → Database access • OpenCV → Computer vision & beyond The real leverage isn’t in learning Python syntax. It’s in understanding which stack solves which problem — and how to combine them efficiently. If you’re learning Python, stop collecting tutorials. Start building use-case stacks. That’s where the actual career advantage is. #Python #DataScience #MachineLearning #WebDevelopment #Automation #AI #Programming #TechCareers

ONE Language. Endless Possibilities. Why Python Dominates🐍 Ever noticed how Python shows up everywhere? That’s because it’s more than a programming language — it’s a powerful ecosystem. Here’s how Python connects directly to real-world impact: 📊 Data Analysis → Pandas 📈 Visualization → Matplotlib 🎨 Advanced Visuals → Seaborn 🤖 Machine Learning → TensorFlow 🌐 Web Scraping → BeautifulSoup ⚙️ Browser Automation → Selenium 🚀 High-Performance APIs → FastAPI 🗄️ Database Access → SQLAlchemy 🌍 Lightweight Web Apps → Flask 🏗️ Full Web Frameworks → Django 👁️ Computer Vision → OpenCV From data and AI to automation and web apps — Python scales with your ambition. If someone asks, “Is Python worth learning in 2026?” The better question is: What can’t you build with it? Tag someone who’s thinking about learning Python 👇 #Python #DataScience #MachineLearning #WebDevelopment #Automation #AI #Programming #TechCareers #iamuzairmehmood

35 seconds in Python. 700ms in Rust. Same result. Same precision. VarLiNGAM is the reference algorithm for causal discovery in time series — figuring out which variables cause what, and in what order. The Python implementation doesn't scale. Past 10 variables, it's unusable in production. I rewrote it from scratch in Rust. Pure Rust, zero Python dependencies. The result: 14 to 50x faster depending on problem size. 3 to 6x less memory. Less than 1% precision gap across all test cases vs ground truth. Drop-in replacement for Python via PyO3. Change the import, done. Open source, MIT + Apache 2.0. https://lnkd.in/e7BKxw_7 If you do causal discovery, finance, neuroscience, climate, I'm curious to see how it runs on your data.

𝗪𝗵𝘆 𝗣𝘆𝘁𝗵𝗼𝗻 𝗜𝘀 𝗦𝗹𝗼𝘄 𝗕𝘂𝘁 𝗦𝘁𝗶𝗹𝗹 𝗗𝗼𝗺𝗶𝗻𝗮𝘁𝗲𝘀 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 You might think Python is slow. But it dominates Machine Learning, Data Science, and AI. So why is Python everywhere in Machine Learning? Here are key reasons: - Python is dynamically typed - It runs on a virtual machine - Almost every operation has extra overhead For example, a for loop in Python usually runs much slower than the same loop in C. But here's the thing: Machine Learning does not rely on pure Python execution. Python acts as a high-level controller. Heavy computations run in optimized C/C++ and CUDA. Libraries like NumPy, TensorFlow, and PyTorch do the real work. Python gives a clean interface while computation runs at near C-level speed behind the scenes. This is the real secret. Python itself is not fast. But Python doesn't need to be fast. It delegates heavy work to C/C++ and GPU kernels using CUDA. This gives you the best of both worlds: easy-to-write Python code and high-performance numerical computation. Python allows you to move fast without sacrificing performance. You get simple and readable syntax, a huge ecosystem, faster experimentation and prototyping, automatic memory management, and optimized native code under the hood. Python lets you focus on solving problems, not fighting the language. Source: https://lnkd.in/g84u6yis

Why People Say Python is Slow — And Why That’s Misleading 🐍 When I started learning Python for AI/ML, one statement kept coming up: “Python is slow.” But the reality is more nuanced. 🧠 Why Python is called slow? 1. Interpreted Language - Python code is executed line-by-line by the CPython interpreter, unlike C/C++ which are compiled directly to machine code. 2. Dynamic Typing Overhead - Types are resolved at runtime. This flexibility adds execution overhead. 3. Global Interpreter Lock - In CPython, only one thread executes Python bytecode at a time — limiting CPU-bound multi-threading. 4. High-Level Abstractions - Everything in Python is an object. Object handling adds memory and performance cost. ⚡ Then Why is Python Dominating AI/ML? Because: ✔️ NumPy runs on optimized C ✔️ TensorFlow / PyTorch use CUDA + C++ backend ✔️ Vectorized operations bypass Python loops ✔️ Heavy computation happens outside the interpreter 📊 When is Python Actually Slow? ❌ Tight loops in pure Python ❌ CPU-bound multi-threaded tasks ❌ Real-time low-latency systems (e.g., trading engines, game engines) 🚀 When is Python Fast? ✔️Data analysis (NumPy, Pandas) ✔️Machine learning pipelines ✔️Automation scripts ✔️Backend APIs ✔️Prototyping high-performance systems quickly 🎯 My Learning Insight Python is slow if you misuse it. Python is powerful if you understand where performance actually happens. As I go deeper into AI/ML, I'm realizing: 💟 The ecosystem matters more than raw language speed. #AIML #machinelearning #python #linkedinpost #DataScience #MachineLearning #ArtificialIntelligence

Python has a huge ecosystem, and sometimes it’s hard to decide which tools are really important to know. In the article, I go through some of the libraries that are most commonly used in real data science work — from data processing and visualization to machine learning and explainability. I also touch on a problem many of us face: modern data science often requires combining many different tools and libraries. https://lnkd.in/dRm4uPCn #autoML #mljar #python

Why NumPy and Pandas Are Essential for Every Python Learner When people talk about Python in data science, two libraries always stand at the core: NumPy and Pandas. NumPy is the foundation for numerical computing. It allows us to work with large, multi-dimensional arrays and perform complex mathematical operations efficiently. Instead of writing long loops, NumPy helps process data faster with optimized functions. Pandas builds on that power and makes data handling simple and intuitive. It introduces DataFrames — structured tables that allow us to clean, filter, analyze, and transform data with just a few lines of code. Together, they help us: • Handle large datasets with ease • Perform fast mathematical computations • Clean and organize messy real-world data • Prepare data for Machine Learning and analytics • Make analysis more readable and efficient In short, NumPy gives Python speed, and Pandas gives it structure. For anyone stepping into data analysis, AI, or research, mastering these two libraries is not optional — it’s the starting point. #Python #NumPy #Pandas #snsdesignthinkers #designthinking #snsinstitutions

Processing 1.54 billion pixels with Python multiprocessing taught me why distributed systems are hard. Talked about serialization overhead, data skew, the straggler problem, and when parallelism actually helps here:  https://lnkd.in/edufD9GW #Python #DistributedSystems #Multiprocessing