Python in Microsoft Fabric Execution Options and Scaling at SQLBits

I always heard: “NumPy is faster than Python lists.” But today, I tested it myself 👇 Day 8 of my Data Science Journey 🚀: I added 1,000,000 elements using: 🔹 Python lists 🔹 NumPy arrays 📊 Result? NumPy was significantly faster. 💡 Why this happens: NumPy uses vectorized operations and runs on optimized C code, avoiding slow Python loops. 👉 This is why NumPy is the backbone of Data Science & Machine Learning. Small step today, but building real understanding. #DataScience #Python #NumPy #LearningInPublic #Day8

🐍 Exploring NumPy Basics in Python Today I practiced core NumPy operations to understand how numerical computing works in Python. ✔ Converted Python lists into NumPy arrays ✔ Created arrays using np.array() ✔ Generated sequences with np.arange() and np.linspace() ✔ Built matrices using np.zeros(), np.ones(), and np.eye() ✔ Worked with random numbers using np.random.rand() and np.random.randint() ✔ Performed basic array operations like max(), min(), and reshape() 💡 Key takeaway: NumPy is powerful for handling large datasets and is the foundation for Data Science and Machine Learning in Python. 📌 Full code available here: 👉https://lnkd.in/dCMhYQey Next step: I will explore array indexing, slicing, and basic statistical operations. #Python #NumPy #DataScience #MachineLearning #100DaysOfCode #LearningJourney

🚀 Day by Day Learning Data Analysis with Python & Pandas Today’s practice session included: ✔️ Understanding DataFrame structure ✔️ Checking column data types ✔️ Using df.info() ✔️ Learning iloc[] indexing ✔️ Finding missing values with isnull().sum() Small daily practice = Big future skills 💻📊 From confusion to confidence… slowly becoming better in Python & Data Analysis every day. 🔥 #Python #Pandas #DataAnalysis #LearningPython #CodingJourney #JupyterNotebook #DataScience #BeginnerToPro #100DaysOfCode #ProgrammerLife

I don’t just learn tools — I build systems around them. I’ve launched a GitHub repository focused on one of the most overlooked foundations in data science: data cleaning and preprocessing. Using Python, I systematically work through real datasets with: 🐼Pandas for structured data transformation 🔢NumPy for efficient numerical operations 📊Matplotlib for quick, clear data insights This is not a tutorial collection. It’s a working log of how raw data becomes usable intelligence. 📌 Clean data is not optional — it is the product before the product. 🔗 GitHub: https://lnkd.in/dHYsrekE This will evolve as I refine my approach. #DataScience #Python #MachineLearning #DataEngineering #BuildInPublic

🚀 Just dropped a new video in my Pandas Series! If you’ve ever felt confused about what Pandas actually is, this one is for you. In this video, I break it down in a simple way:
 📊 What Pandas is
 📊 What a DataFrame means
 📊 A real Python code example
 📊 And how it’s used in real-life data scenarios Most beginners struggle because they jump straight into code without understanding the why. This video fixes that. Whether you're a beginner in data science or brushing up Python for interviews, this is a must-watch. 🎥 Watch it here: https://lnkd.in/gFtVuiJ2 Let me know in the comments:
Have you used Pandas before or are you just getting started? #Python #Pandas #DataScience #MachineLearning #DataAnalysis #Programming #LearnPython #Coding #TechLearning

The data landscape evolves rapidly, but the fundamentals remain the same. Whether you are performing data engineering tasks, running a statistical analysis, or training predictive models, having a seamless command over Python's core libraries makes all the difference. This quick cheat sheet covers the essential building blocks for: ➡️ Handling complex dataframes efficiently ➡️ Running numerical operations Visualizing key insights ➡️ Integrating Python with relational databases Keeping these core functions in your toolkit is a great way to ensure your code stays clean, efficient, and ready for production. Check out the breakdown below! 👇 #Python #DataAnalytics #DataValue #Coding #ContinuousLearning #DataProfessionals

🚀 Clean data = powerful decisions. Just revised the essentials of data cleaning using Python & Pandas — from handling missing values to removing duplicates, standardizing text, and dealing with outliers. Every dataset tells a story… but only after you clean it. 🧹📊 🔹 Missing Values 🔹 Duplicates Removal 🔹 Data Type Conversion 🔹 Outlier Handling 🔹 Text Standardization Consistency in data → clarity in insights → smarter decisions. #Python #Pandas #DataCleaning #DataAnalytics #DataScience #LearningJourney #TechSkills

Learn Python for data science with this comprehensive guide, covering basics, advanced techniques, and expert insights for becoming a proficient data scientist https://lnkd.in/gJikYqmK #PythonForDataScience Read the full article https://lnkd.in/gJikYqmK

After a short pause, I’m back to sharing my Data Science journey. Over the past few weeks, I revisited Python fundamentals and realized that strong basics make everything easier. Instead of rushing into advanced topics, I focused on: • Writing clean and readable Python code. • Understanding data structures deeply. • Practicing small problems consistently. Next stop: Deep dive into Python operators for data analysis and data science. If you're on a similar path, let’s learn together. 🚀 #DataScienceJourney #Python #Consistency #Learning

🚀 Day 21/30 – Matplotlib for Data Visualization! Today I learned about Matplotlib, one of the most widely used Python libraries for data visualization 🐍📊 It was exciting to see how raw data can be transformed into meaningful charts and graphs that make analysis easier to understand. 🔍 What I explored: ☑️ Introduction to Matplotlib ☑️ Creating basic charts and graphs ☑️ Visualizing data using Python ☑️ Understanding how visuals improve data storytelling This session helped me realize how important visualization is in presenting insights clearly and effectively 💡 Excited to create more visualizations and improve my data storytelling skills! #Day21 #DataAnalytics #Python #Matplotlib #DataVisualization #LearningJourney #Upskilling