Dynamic vCores in Microsoft Fabric Python Notebooks

📊 Quick data snapshot with Python (Pandas) Today I worked on a small Python notebook to understand how I can summarize system-level metrics fast—without overthinking it. Using Pandas, I created a simple dataset with values like: CPU usage across different servers Memory usage across different servers Then I used df.describe() to generate a descriptive statistics view—including: mean / standard deviation min / max percentiles (25%, 50%, 75%) ✅ The goal: make it easier to spot trends early—before deeper analysis (visualizations, anomaly detection, forecasting, etc.). If you’re working with operational metrics, this kind of lightweight EDA is a great habit. Small steps = faster insights. #Python #Pandas #DataAnalysis #DataScience #MachineLearning #EDA #Analytics

An exercise to help build the right mental model for Python data. - Solution: https://lnkd.in/e55Pr8nJ - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW The “Solution” link visualizes execution and reveals what’s actually happening using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵: https://lnkd.in/e3sUM7wG

An exercise to help build the right mental model for Python data. - Solution: https://lnkd.in/eEi7JZ7t - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW The “Solution” link visualizes execution and reveals what’s actually happening using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵: https://lnkd.in/e3sUM7wG

An exercise to help build the right mental model for Python data. - Solution: https://lnkd.in/e5XVx7Y8 - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW The “Solution” link visualizes execution and reveals what’s actually happening using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵: https://lnkd.in/e3sUM7wG

💡I recently came across a post and wanted to share a small takeaway: different languages handle data differently. 💻In Python, variables reference objects, while in base R, copy-on-modify makes it feel like objects are independent. This extends to transitions between any two languages. Understanding what is happening under the hood really helps avoid subtle bugs and shapes how you write and think about code.

Stateful UDFs just changed how Python scales. With @daft.cls, you can turn any Python class into a distributed operator that initialises once per worker and reuses state across every row. That means models, API clients, and database connections no longer get rebuilt on every call. The mental model stays simple: write normal Python classes, add a decorator, and Daft handles execution, scheduling, and parallelism. Find out more: https://lnkd.in/e79SePbN #PythonScaling #DaftCls #DistributedComputing #PythonClasses

Real world data is messy. So I built my own 100-record JSON dataset to practice cleaning it with Python. The dataset included: • Duplicate entries • Missing values • Ratings in mixed formats like "five", "4", and "3.5" • Different types of customer feedback • Inconsistent formatting Using Python, I cleaned the data, removed duplicates, standardised ratings, and generated basic insights. Big takeaway: data cleaning is just as important as analysis. GitHub Repo: https://lnkd.in/gYr-4kkm #Python #DataScience #DataAnalytics #Projects #Coding

Python Data Types — One Post Cheat Sheet Understanding data types is fundamental to writing efficient Python code. Here’s a quick overview: 🔢Numeric int → 10 float → 10.5 complex → 2+3j 🔤 String (str) Ordered & immutable Example: "Hello Python" 📋 List Ordered, mutable, allows duplicates Example: [10, 20, 30] 📦 Tuple Ordered, immutable Example: (10, 20, 30) 🔁 Set Unordered, no duplicates Example: {10, 20, 30} 📖 Dictionary Key–value pairs, mutable Example: {"name": "Maha", "age": 25} 🧠 Boolean True / False Used in conditions 🔍 Check Type type(variable) Choosing the right data type improves performance, readability, and data handling. #Python #DataTypes #PythonBasics #Programming #LearnPython #Coding #DataAnalytics #PythonForBeginners

🚀 Web Scraper Project Built a Python-based web scraper to extract product details like names, prices, and ratings from a website and store them in a structured CSV file. Tech Used: Python, BeautifulSoup, Pandas 📊 Output: products.csv for data analysis This project helped me understand real-world data extraction and handling. #Python #WebScraping #DataScience #SkillCraftTechnology #LearningJourney

📊 Stop struggling with massive spreadsheets! Pandas is your supercharged Excel in Python, making it easy to analyze millions of rows with just a few lines of code. Data manipulation with pandas in Python Data cleansing with pd. Pandas: The backbone of any good Data Pipeline! 🐼 Raw data is almost always messy, incomplete, and inconsistent. Here’s how I use Pandas to go from chaos to clean in minutes #python #pandas #DataCleansing #DataHandling