Post #2090

✅📚Python Libraries You Should Know 1. NumPy – Numerical computing - Arrays, matrices, broadcasting - Fast operations on large datasets - Useful in data science & ML 2. Pandas – Data analysis & manipulation - DataFrames and Series - Reading/writing CSV, Excel - GroupBy, filtering, merging 3. Matplotlib – Data visualization - Line, bar, pie, scatter plots - Custom styling & labels - Save plots as images 4. Seaborn – Statistical plotting - Built on Matplotlib - Heatmaps, histograms, violin plots - Great for EDA 5. Requests – HTTP library - Make GET, POST requests - Send headers, params, and JSON - Used in web scraping and APIs 6. BeautifulSoup – Web scraping - Parse HTML/XML easily - Find elements using tags, class - Navigate and extract data 7. Flask – Web development microframework - Lightweight and fast - Routes, templates, API building - Great for small to medium apps 8. Django – High-level web framework - Full-stack: ORM, templates, auth - Scalable and secure - Ideal for production-ready apps 9. SQLAlchemy – ORM for databases - Abstract SQL queries in Python - Connect to SQLite, PostgreSQL, etc. - Schema creation & query chaining 10. Pytest – Testing framework - Simple syntax for test cases - Fixtures, asserts, mocking - Supports plugins 11. Scikit-learn – Machine Learning - Preprocessing, classification, regression - Train/test split, pipelines - Built on NumPy & Pandas 12. TensorFlow / PyTorch – Deep learning - Neural networks, backpropagation - GPU support - Used in real AI projects 13. OpenCV – Computer vision - Image processing, face detection - Filters, contours, image transformations - Real-time video analysis 14. Tkinter – GUI development - Build desktop apps - Buttons, labels, input fields - Easy drag-and-drop interface Credits: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L/1885 ❤️ Double Tap for more ❤️