Python in Dataverse: Running Code in VS Code

🐍 What is Python? Python is a high-level, interpreted, and easy-to-learn programming language. It is used to build websites, apps, games, AI systems, automation tools, and more. ⭐ Features of Python Easy to Learn & Read – simple English-like syntax Interpreted Language – no need to compile Cross Platform – works on Windows, Mac, Linux Open Source & Free Large Library Support – NumPy, Pandas, Django, TensorFlow Object-Oriented – supports OOP concepts Dynamic Typing – no need to declare variable types 🔧 Uses of Python 🌐 Web Development (Django, Flask) 🤖 Artificial Intelligence & Machine Learning 📊 Data Science & Analytics 🎮 Game Development ⚙️ Automation & Scripting 🧪 Scientific Computing 📱 Desktop Applications 💻 Installing Python & IDE Step 1: Install Python Go to python.org Download latest version Run installer ✔ Tick Add Python to PATH Click Install Step 2: Install IDE Option 1: VS Code Download from code.visualstudio.com Install Open Extensions Install Python Extension Option 2: PyCharm Download from jetbrains.com/pycharm Choose Community Edition Install and open ✨ Python Syntax (Basics) Python # Print print("Hello Python") # Variables name = "Ravi" age = 20 # If Condition if age >= 18: print("Adult") # Loop for i in range(5): print(i) # Function def greet(): print("Welcome to Python") greet() what's app channel link-: https://lnkd.in/gsjuwjF6 https://lnkd.in/gW3bBXBh

🚀 Ready to level up in Python? There is a huge difference between writing a quick, functional Python script and architecting a robust, scalable application. To bridge that gap, I’ve just wrapped up an intensive deep dive into Advanced Python Programming! 🐍 To solidify everything I learned, I built an interactive Jupyter Notebook repository that breaks down complex programming paradigms into hands-on, executable code. Here is what I focused on mastering: 🏗️ Deep-Dive OOP: Moving beyond basic classes to truly understand inheritance, polymorphism, and data encapsulation. ⚙️ Method Mechanics: Demystifying exactly when to use instance methods, @classmethod, and @staticmethod. 📁 Robust Data Handling: Safe file I/O operations using context managers (with statements) and implementing persistent logging. 🛡️ Resilience: Advanced error and exception handling so the application doesn't just crash, but fails gracefully. ⚡ Memory Efficiency: Leveraging comprehensions, iterators, and generators for optimized performance. If you are transitioning from a Python beginner to an intermediate/advanced developer, or just want to brush up on your Object-Oriented Programming concepts, check out the code and diagrams in my repository! 👇 🔗 GitHub Repo: https://lnkd.in/dHu36_n4 Python devs: What was your biggest "Aha!" moment when you first learned Object-Oriented Programming? Let's chat in the comments! 💬 #Python #SoftwareEngineering #OOP #DataStructures #Coding #DeveloperJourney #BackendDevelopment

Exploring some powerful Python libraries and their real-world applications: 🔹 NumPy – Numerical computing 🔹 Pandas – Data analysis 🔹 Matplotlib – Data visualization 🔹 Requests – API integration 🔹 Tkinter – GUI application development Building strong fundamentals step by step 🚀 #Python #Programming #Learning #Tech #DSA

FinModeler 💚 Dataverse 💙 Python. For generating financial models in our app, this is a major leap forward. The raw model of your FinModeler business plan is stored in Dataverse tables still. But when it comes to the financial model, we moved model computation out of Excel and into an in-memory Python engine. Excel is now the delivery format, not the runtime. Previously, Excel generation would often take several minutes. Today, with Azure Functions and Python, it's a matter of seconds. The same result: an investor-ready Excel workbook, with dynamic formulas. Plus everything available in Dataverse for iteration of the model. Nuno Nogueira, the mastermind behind our app logic, puts it like this: "We’re calling this new approach financial models in memory. The time from idea to output is no longer measured in hours or days, but in seconds." Check out more details from the blog post below.👇 And sign up for a free trial of the FinModeler app to experience the speed yourself!

I gave my Python code a Rust Engine. (Day 8) 🚀 Day 8 of building a SOTA Tokenizer from scratch. Yesterday, I made my tokenizer fast (Parallel Rust). Today, I made it usable. The Problem: Rust is blazing fast, but my entire AI stack (PyTorch, Datasets) lives in Python. I didn't want to run a separate .exe file or deal with slow subprocess calls. I wanted native performance inside my Python scripts. The Solution: I used PyO3 0.23 and Maturin to compile my Rust code directly into a Python library. This wasn't just a "hello world" test—I had to refactor my code to use the new Bound API to support the latest Python 3.13. PyO3: Allows Rust structs to look exactly like Python classes. Maturin: Compiles the Rust code into a binary wheel (.whl) that installs just like pip install numpy. The Result: I can now do this in my Python scripts: import rust_tok # This runs optimized multi-core Rust code instantly tokenizer = rust_tok.UnigramTok.load("model.json") tokens = tokenizer.encode("Hello world") No context switching. No subprocess overhead. Just raw speed. Why this matters: This is the same architecture used by libraries like Pydantic, Polars, and HuggingFace Tokenizers. Today, I learned how to build it myself. Next Up In day 9 we will be Integrating this tokenizer into a real Data Pipeline to prepare 1GB of training data for my Tiny SLM! 🏗️ #RustLang #Python #PyO3 #MachineLearning #100DaysOfCode #SystemDesign

What can’t we do with Python? 🤔 Every time I think I’ve explored enough… Python casually unlocks another door. This week, I stumbled upon a library called FreeSimpleGUI — a lightweight way to build desktop applications without diving into heavy frameworks. Curiosity did what it always does. I couldn’t ignore it. So instead of just reading the docs, I built something simple: 👉 A To-Do List desktop application. Nothing fancy. Just: Add tasks Edit tasks Mark complete Clean, minimal UI And honestly? The result was way better than I expected. No complex boilerplate. No overwhelming setup. Just pure Python doing what it does best — making developers feel powerful. Sometimes we think: Python is for data science. Python is for automation. Python is for ML. Python is for backend. But then it quietly whispers: "Hey, I can build desktop apps too." 😄 The best part? You can prototype a working desktop app in hours, not days. 🎥 I’ve attached a short demo video below. 💻 GitHub link is in the comments if you'd like to explore the code: https://lnkd.in/g4nUBw2m If you’re a Python developer and haven’t explored GUI development yet — this might be your sign. What’s the most unexpected thing you’ve built with Python? #Python #OpenSource #DesktopApp #100DaysOfCode #Learning #Developers #FreeSimpleGUI

Most people say they "know Python." Very few are actually job-ready. Being job-ready means understanding the fundamentals that power real applications: • Data Structures — Lists, Dictionaries, Sets • Object-Oriented Programming — Classes, Inheritance • Algorithms & Time Complexity — Writing efficient code • Error Handling — Building reliable systems • File Handling & Modules — Working with real data • Web Frameworks — Django / Flask for real-world apps • Data Libraries — Pandas, NumPy, Scikit-learn Learning syntax is easy. Building problem-solving ability and production-ready skills is the real challenge. Currently deepening my journey with Python and Django, focusing on mastering the concepts that turn a developer into a professional. #Python #PythonDeveloper #Django #Programming #SoftwareDevelopment #CodingJourney

I recently explored 5 useful Python libraries and their real-world use cases, and compiled my learnings into a blog on Medium. Through this, I gained a better understanding of how different libraries simplify tasks in areas like data handling, automation, and development. This activity helped me strengthen my practical knowledge and documentation skills as part of my learning journey in programming. You can read my blog here:

The potential of Python and Go! In the ever-evolving landscape of programming languages, choosing the right one for your project can be a game-changer. Python and Go are two popular choices, each with its own strengths and ideal use cases. 🔍 Dive into our latest article to explore: - Performance comparisons between Python and Go - Detailed syntax differences - Practical use cases for each language Whether you're optimizing for speed or ease of use, understanding these differences can guide you to the best choice for your next project. Python vs Go learn more here for A Detailed Comparison: https://lnkd.in/gHemacrS #Python #Go #Programming #SoftwareDevelopment #TechTrends

Python is no longer just a “nice-to-have” skill. It’s widely used in web development, data analytics, and automation — making it one of the most relevant programming languages across industries today. Here are 3 reasons why Python continues to be in high demand, especially for professionals building future-ready skills. 💡 🔗 Learn more: https://lnkd.in/gf2jPBsq Follow us for more tech insights 💻