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𝗚𝗶𝘁 𝘃𝘀 𝗚𝗶𝘁𝗛𝘂𝗯: What's the Difference Ever mixed up Git and GitHub? You’re not alone—they’re related but serve distinct purposes! 𝐆𝐢𝐭: A powerful version control system that tracks changes in your code. It’s your local toolkit for managing versions, rolling back changes, and collaborating. 𝐆𝐢𝐭𝐇𝐮𝐛: A cloud-based platform that hosts Git repositories online. It enhances collaboration by letting you share, review, and manage code—think of it as a social network for developers. In short: Git = Local version control tool GitHub = Cloud-based hosting service for Git repositories Understanding the difference can significantly improve your workflow and collaboration in software development! Join for More https://whatsapp.com/channel/0029Va9pSbzBlHpU1fu6ff1Q https://telegram.me/sign2tech

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Python code To download Video from Youtube ⚙ from pytube import YouTube # Enter the YouTube video URL url = "https://www.youtube.com/watch?v=dQw4w9W" # Create a YouTube object with the URL yt = YouTube(url) # Select the highest resolution video video = yt.streams.get_highest_resolution() # Set the output directory and filename output_dir = "/storage/emulated/0/Documents/" filename = yt.title+".mp4" # Download the video video.download(output_dir, filename) print(f"Download complete: {filename}")

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Essential Python Libraries for Data Analytics 😄👇 1. NumPy: - Efficient numerical operations and array manipulation. 2. Pandas: - Data manipulation and analysis with powerful data structures (DataFrame, Series). 3. Matplotlib: - 2D plotting library for creating visualizations. 4. Scikit-learn: - Machine learning toolkit for classification, regression, clustering, etc. 5. TensorFlow: - Open-source machine learning framework for building and deploying ML models. 6. PyTorch: - Deep learning library, particularly popular for neural network research. 7. Django: - High-level web framework for building robust, scalable web applications. 8. Flask: - Lightweight web framework for building smaller web applications and APIs. 9. Requests: - HTTP library for making HTTP requests. 10. Beautiful Soup: - Web scraping library for pulling data out of HTML and XML files. As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. t.me/sign2tech

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