Python Ecosystem Updates: Daggr, Streamlit, jstark & pip 26.0

🐍 Python for Everything — A Simple Map of Powerful Libraries Python continues to dominate the tech world because of its incredible ecosystem of libraries. 🙏 Credit: Python Python Developers Python DevelopersCommunity (moderated) Shared by Shiva Indokumar Whether you're working in data analysis, AI, automation, APIs, or web development, Python has a powerful tool ready for it. 🚀 Here’s a quick breakdown from this visual guide 👇 📊 Data Analysis 🐼 Pandas → Data cleaning, transformation, and analysis. 📈 Data Visualization 📉 Matplotlib → Core plotting library 🎨 Seaborn → Advanced statistical visualizations. 🧠 Machine Learning & AI 🤖 TensorFlow → Deep learning and neural network development. 🌐 Web Scraping & Automation 🔎 BeautifulSoup → Extract data from websites 🖱 Selenium → Browser automation and testing. ⚡ API & Backend Development 🚀 FastAPI → High-performance APIs 🗄 SQLAlchemy → Database access and ORM. 🌍 Web Development 🪶 Flask → Lightweight web apps 🏗 Django → Large scalable web platforms. 👁 Computer Vision 📷 OpenCV → Image processing and computer vision applications. 💡 The real strength of Python is its ecosystem. From data science → AI → automation → backend systems, Python powers modern development everywhere. 🐎 Follow Swarnava Ghosh for insights on Technology, AI, Business Analysis, and Data. #Python #PythonProgramming #DataScience #MachineLearning #ArtificialIntelligence #Automation #WebDevelopment #FastAPI #Django #Flask #ComputerVision #DataVisualization

🚀 Python for Everything: One Language, Endless Possibilities. Python’s real strength lies in its powerful ecosystem of libraries and frameworks. With the right tools, Python can be applied across almost every technology domain — from data science and AI to web development and automation. Here are some examples of how Python transforms into different superpowers when paired with the right libraries: 🔹 Python + Pandas → Data Manipulation Clean, analyze, and transform large datasets efficiently. 🔹 Python + TensorFlow → Deep Learning Build intelligent AI systems and neural networks. 🔹 Python + Matplotlib → Data Visualization Convert raw data into meaningful visual insights. 🔹 Python + Seaborn → Advanced Charts Create beautiful and informative statistical graphics. 🔹 Python + BeautifulSoup → Web Scraping Extract and analyze valuable information from websites. 🔹 Python + Selenium → Browser Automation Automate testing, workflows, and repetitive tasks. 🔹 Python + FastAPI → High-Performance APIs Develop modern, fast, and scalable backend services. 🔹 Python + SQLAlchemy → Database Management Interact with databases efficiently using powerful ORM tools. 🔹 Python + Flask → Lightweight Web Applications Ideal for building small to medium-scale web apps quickly. 🔹 Python + Django → Scalable Web Platforms Create secure and large-scale web applications. 🔹 Python + OpenCV → Computer Vision Enable applications like face detection, object recognition, and intelligent visual systems. 💡 One language. Multiple domains. Unlimited innovation. #Python #AI #MachineLearning #DataScience #WebDevelopment #Automation #DeepLearning #Programming #Tech

🌶️ 💪 Modern API workloads aren’t “one user, one request” anymore—they’re bursts of concurrent traffic, mixed fast/slow calls, and unforgiving tail-latency expectations. That’s why I’m excited to share our new post on Select AI for Python 1.3 and a major step forward for production-grade concurrency: connection pooling. https://lnkd.in/eze4sUCb With 1.3, developers can now pool connections using: select_ai.create_pool() select_ai.create_pool_async() In the blog, learn what changed from standalone connections, what we measured by integrating pooling into a FastAPI service, and how to think about choosing a pool size that fits your workload. The results: better throughput, improved p95/p99 latency, and more predictable behavior under load—exactly what matters in real-world services. If you’re running (or planning) concurrent Python services with Select AI, this is one of the simplest, highest-impact upgrades you can make. #Oracle #Database #SelectAI #OracleAI #Python #FastAPI #Concurrency #ConnectionPooling

🚀 Boosting AI app concurrency with smarter database connections The latest update to Oracle Autonomous Database Select AI for Python shows how connection pooling can significantly improve concurrency and throughput for AI-powered applications. Instead of opening a new database connection for every AI request, connection pools reuse a small set of connections, reducing overhead and enabling many concurrent AI calls from Python apps. Why it matters: ⚡ Higher concurrency for AI workloads 🔁 Reused connections reduce latency and overhead 🧠 Better performance for NL2SQL, RAG, and generative AI apps built on Autonomous Database For developers building AI-driven data apps in Python, this means more scalable, responsive AI pipelines with minimal code changes. #AI #Python #Databases #GenAI #AutonomousDatabase

Following the release of our Cognitive3D R package last week, the data science team has now pushed a Python package for working with XR analytics data. The new Python package makes it easier to pull session metrics, events, objectives, and survey responses directly from the Cognitive3D Analytics API into analysis-ready DataFrames. Instead of wrangling raw JSON or handling pagination manually, analysts can start exploring XR behavioural data in just a few lines of Python. For teams already using Python for data science, dashboards, or automated reporting, this provides a faster way to integrate XR analytics into existing workflows. Read the blog to learn more 👇

Data visualization using dash #machinelearning #datascience #datavisualization #pythonlibrary #dash The intersection of data science and web development is shown by Dash, a cutting-edge Python Data Visualization Library developed by Plotly. This library enables the creation of interactive online apps using Python, eliminating the requirement for knowledge of HTML, CSS, or JavaScript. The seamless integration of Dash with Plotly allows data scientists to effortlessly convert their insights into easily shared dashboards https://lnkd.in/gaMrhF6C

Spent the last few hours mastering web scraping and automation with Python. Here's what I learned: 🔹 Beautiful Soup 4 for parsing HTML & extracting data 🔹 Selenium for automating browser interactions 🔹 Building efficient Python scripts for intelligent data collection 🔹 Best practices for ethical web scraping & AI-ready data pipelines In today's AI-driven world, quality data is everything. These automation tools are essential for: ✨ Feeding machine learning models with real-time data ✨ Building intelligent automation workflows ✨ Extracting insights at scale for AI applications ✨ Creating data pipelines for advanced analytics If you're looking to automate repetitive web tasks, extract valuable data for AI/ML projects, or build intelligent automation systems, these tools are absolute game-changers! 💡 Certificate verified! Check my credentials section for full details. Who else is combining Python automation with AI? Let me know in the comments! 👇 #Python #WebScraping #Automation #AI #MachineLearning #DataScience #ArtificialIntelligence #DataExtraction #UdemyCertified #TechSkills #Learning #FutureOfWork

If you're curious about APIs, this is for you 👇 A simple guide to understanding APIs and building one with FastAPI. Feedback is always welcome! #FastAPI #Learning #BackendDevelopment #FastAPI  #APIs #Python #AI

Running Prophet forecasting inside a Node.js event loop? That's how you kill your server. When building SensorCore — AI-native analytics via MCP — we hit a wall: our ML tools (Isolation Forest, Decision Trees, Prophet) need heavy computation that doesn't belong in a JavaScript runtime. The solution: a dedicated Python microservice. Our analytics engine is a standalone FastAPI app running on Uvicorn. It owns ALL the heavy ML work: - 13 REST endpoints - 11 Python analyzers - Direct ClickHouse access for data - Python 3.11 + pandas + scikit-learn + Prophet + ruptures Why Python, not Node.js for ML? - scikit-learn, Prophet, ruptures — mature, battle-tested Python libraries - NumPy/pandas for vectorized data processing - No equivalent ecosystem exists in JavaScript - Python's data science stack is 10+ years ahead Why a separate service, not embedded? - Node.js stays fast — zero ML overhead on the API server - Independent scaling — scale ML horizontally without touching the API - Crash isolation — a Prophet OOM doesn't take down your ingestion pipeline - Independent deployments — update ML models without restarting the API The stack in one line: FastAPI + Uvicorn + pandas + scikit-learn + Prophet + ruptures + ClickHouse Packaged in a python:3.11-slim Docker container. One docker-compose up and it's running. The Node.js server handles 1000+ req/s for log ingestion. The Python engine crunches millions of rows for ML. Each does what it's best at. Do you separate your ML workloads from your API server? Or run everything in one process?

🐍 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐄𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 – 𝐓𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐁𝐞𝐡𝐢𝐧𝐝 𝐌𝐨𝐝𝐞𝐫𝐧 𝐓𝐞𝐜𝐡 One of the biggest reasons behind Python’s popularity is its versatility. With the right libraries and frameworks, Python can power everything from web apps to deep learning systems. 🚀 Python Certification Course :- https://lnkd.in/dzDmvcVZ Here’s how Python combines with different tools to solve real-world problems: 🔹 Python + Django → Web Applications Using the powerful framework Django, developers can build secure, scalable, and high-performance web applications quickly. 🔹 Python + NumPy → Numeric Computing The library NumPy provides efficient array operations and mathematical functions, making complex numerical computations faster and easier. 🔹 Python + Pandas → Data Manipulation With pandas, analysts can clean, transform, and analyze large datasets efficiently—making it a core tool in data science workflows. 🔹 Python + Matplotlib → Data Visualization The visualization library Matplotlib helps turn raw data into meaningful charts and graphs that reveal insights. 🔹 Python + BeautifulSoup → Web Scraping Using Beautiful Soup, developers can extract and parse data from websites for automation and data collection. 🔹 Python + PyTorch → Deep Learning Frameworks like PyTorch enable developers and researchers to build advanced AI and deep learning models. 🔹 Python + Flask → APIs With lightweight frameworks such as Flask, developers can quickly create REST APIs and backend services. 🔹 Python + Pygame → Game Development Libraries like Pygame make it possible to build simple games and interactive applications. 💡 The key takeaway: Python is not just a programming language—it’s an entire ecosystem that powers web development, data science, AI, automation, and much more.