Python System Monitor Built with Python Automation

Been building a small Python toolkit over the past few days to automate the stuff I kept doing manually on my machine monitors CPU/RAM/disk in real time, sorts files automatically, runs scheduled jobs in the background, backs up folders to zips, and plots a live stats chart all from one CLI menu nothing groundbreaking, just scratching my own itch and figured I'd clean it up and open source it repo's up if you want to check it out or use it https://lnkd.in/dSyts_Gx built under Litends we're slowly open sourcing more of our internal tooling #Python #OpenSource #Automation #Litends

🚀 Day 6 – Python Automation Journey Today I built a File Organizer using Python. This script automatically organizes files into folders based on their file type. 📂 Images (.jpg, .png) → Images folder 📄 Documents (.pdf, .txt) → Documents folder 🎵 Audio (.mp3) → Audio folder Using Python libraries like os and shutil, the script scans files and moves them into the correct folders automatically. This project helped me understand file handling and automation using Python. 🔧 Tech Used: Python, OS module, Shutil module #Python #Automation #PythonProjects #CodingJourney

💾 Python Automation: Disk Space Monitor ```python import shutil total, used, free = shutil.disk_usage("/") print("Total:", total // (2**30), "GB") print("Used:", used // (2**30), "GB") print("Free:", free // (2**30), "GB") if free // (2**30) < 5: print("⚠️ Low Disk Space Alert") ``` 💡 Use case: ✔ Monitor server storage ✔ Prevent system crashes #Python #Automation #CloudSupport

🚀 Built a Scientific Calculator using Python! I recently created a GUI-based Scientific Calculator using Python with tkinter. Features: • Basic operations (+, -, *, /) • Scientific functions (sin, cos, tan, log, sqrt) • Backspace & clear functionality • User-friendly interface 🧠 What I learned: • GUI development using tkinter • Handling user input dynamically • Using Python’s math module for advanced calculations Check out my project here 👇 GitHub Repo:https://lnkd.in/gBkt2hKM

Python more efficient than C++? Never gonna happen! Even using the C API. In this video, we dive into this topic by recreating the String split function. https://lnkd.in/dW5QFXWz

I posted a new Microsoft Fabric idea for Pure Python notebooks: https://lnkd.in/dmCsfsa9 A useful improvement would be support for: - configurable session timeout - explicit session termination / kill session This would improve capacity efficiency and make Pure Python notebooks much more practical for automated and production workloads. If you see the same gap, feel free to support the idea. #MicrosoftFabric #Python #DataEngineering #FabricNotebook

Day 3/30 – Python Challenge 🐍 Today I built: File Search Tool 🔍 🔹 What it does: Searches for files in the system by name and returns their location instantly. 🔹 What I learned: Working with file systems using os module Traversing directories using os.walk() Handling user input and search logic 🔹 Challenge: Optimizing search for large directories and handling different file paths 👉 GitHub link: https://lnkd.in/dj_FEm5j Building real-world tools step by step 🚀 #Python #CodingChallenge #LearnInPublic #100DaysOfCode #Automation #DeveloperJourney

Mac Scraper: Python Based System Scraper built for managing storage on MacOS and finding files quickly based on extension to emulate Global Discovery and Disk Sweeping on a high-level GUI

Built a simple File Reader CLI in Python today. This project takes a file path as input, opens the file, reads its contents, and prints everything directly in the terminal. What I learned while building it: • Taking user input with input() • Opening files using open() • Reading file content with read() • Using with for safe file handling • Adding error handling with try/except Small projects like this are helping me strengthen my Python fundamentals and get more comfortable with writing clean, practical code. GitHub Repository: https://lnkd.in/gud495tr #Python #PythonProjects #CLI #CodingJourney #Programming #LearningInPublic

Most data engines serialize data across process boundaries every time a Python User-Defined Function runs. Think Spark shipping rows to a separate Python process and back. Long ago, we eliminated that boundary entirely in Sail. Rust + PyO3 + Arrow, same process, zero copy.