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

I used to think NumPy was just another Python library… until I understood this 👇 NumPy is all about working with arrays efficiently. Instead of using normal Python lists, NumPy lets you handle data faster and smarter. Think of it like this: A Python list = normal road 🚶♂️ NumPy array = highway 🚀 For example: If you want to add 10 to every number In Python list: You loop through each element In NumPy: 👉 It happens in one line That’s the power. NumPy is heavily used in: - Data Science - Machine Learning - Data Engineering If you're working with data, learning NumPy is not optional. It makes your code faster, cleaner, and more efficient. What confused you the most when you started NumPy? #NumPy #Python #DataScience #MachineLearning #DataEngineering #CodingJourney #TechLearning

🐍 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

One thing that completely changed how I think about data 👇 👉 Writing code vs Designing for scale In Python: You solve problems on a single machine In Spark: You solve problems across a cluster of machines Same problem. Totally different thinking. Example: - Python → Loop through list and calculate sum - Spark → Use distributed transformations like "map" and "reduce" The real shift is: ❌ “How do I write this function?” ✅ “How will this run across multiple nodes efficiently?” This is where many developers struggle when moving to Big Data. It’s not about syntax. It’s about distributed thinking. Learning this the hard way, but enjoying the process 🚀 #DataEngineering #BigData #Spark #LearningInPublic

If you’re stepping into data analytics in 2026, these Python libraries are your real toolkit 🚀 From Pandas & NumPy for data handling to Streamlit & Dash for building dashboards — this stack covers everything from raw data to real insights. The best part? You don’t need all 20 at once… just start, build, and grow. Which one is your go-to library? 👇 #DataAnalytics #Python #DataScience #Learning #CareerGrowth

📊 Mastering Pandas — Part 4: Data Visualization with Matplotlib & Seaborn is now live! In this article, you'll learn: ✅ Matplotlib — the core engine behind all Python charts ✅ Seaborn — beautiful statistical visualizations with minimal code ✅ When to use each tool (and how to combine them) ✅ 30+ chart types explained with clean, practical examples 🔗 Read the full article on Medium: https://lnkd.in/dxyhPhPv 📁 Full reference & code on GitHub: https://lnkd.in/dXr4itRw This is Part 4 — the final article in the Mastering Pandas series. If you missed the earlier parts, check out the GitHub repo for all references. #Python #Pandas #DataVisualization #Matplotlib #Seaborn #DataScience #MachineLearning #Programming

🚀 My Data Science Journey Today, I focused on strengthening my Python fundamentals 🐍 — the backbone of every Data Science & ML workflow. 📚 Here’s what I covered: 🔹 Python Basics & Syntax 🔹 Operators, If-Else Conditions & Loops 🔹 String Handling in Python 🔹 Practicing real-world string-based problems 💡 Key Learnings: - Writing clean and readable code is more important than just making it work - Loops + conditions = powerful logic building - Strings are everywhere — mastering them is a must! ⏳ Consistency is the real game changer. Small steps every day lead to big results. #Python #DataScience #MachineLearning #CodingJourney #CampusX #100DaysOfCode #LearnInPublic

I decided to go all in on Python for data engineering. 🐍 Here's everything I've learned in just the first week: → Data types, variables & expressions → Lists, tuples, sets, and dictionaries → Conditionals, branching, and loops And in the coming week, I'll be starting the fun part — functions, classes, pandas, NumPy, and working with APIs. I used to think coding was for "technical" people. Turns out it's just logic + practice. What's one Python concept you wish you'd learned sooner? Drop it below — I'm taking notes. 👇 #Python #DataEngineering #LearningInPublic #TechCareer

Most beginners treat int64 and Int64 as the same. They’re not. 🔍 Quick insight: • int64 → NumPy type ❌ Does NOT support missing values • Int64 → Pandas nullable type ✅ Handles NaN in real-world datasets 💡 Why this matters: Real data is messy. Choosing the wrong data type can break your entire pipeline. Small detail. Big impact. #DataAnalytics #Python #Pandas #DataCleaning #LearningInPublic

Today’s learning session was all about exploring the power of Pandas and visualizing data in Python using Jupyter Notebook. We worked on handling datasets, cleaning data, and understanding how to organize information efficiently with Pandas. Alongside that, we also created simple graphical views to better understand data patterns and insights. It’s exciting to see how raw data can turn into meaningful visuals with just a few lines of code. Step by step, building strong foundations in data analysis. #Python #Pandas #DataAnalysis #JupyterNotebook #LearningJourney #DataVisualization YouExcel Training