Rust outperforms Python in analytics workloads

Some scripts run. Some scale. That difference hit home while reading this 👇 🧠 “Professional code isn’t about cleverness. It’s about predictability.” For a long time, my Python scripts worked… but felt fragile. The shift wasn’t more syntax — it was better libraries and better thinking. 🔧 Tools like: pathlib → clean, cross-platform file handling loguru → real logging beyond print() A mindset of structure first, failures second, scale always Small choices. Big upgrade. From “it runs on my machine” to “this could ship.” 🚀 If you’re automating with Python, boring + predictable beats clever every time. #Python #Automation #SoftwareEngineering #CleanCode #DeveloperGrowth #LearningEveryday

As developers, our most valuable asset is Time. Why write 100 lines of code when a library can do it in 1 line? I curated 10 Python libraries that I use to build AI apps faster and better. Here are my top picks from the slides: Streamlit: Turned my Python scripts into web apps instantly. No HTML needed. Rich: Because staring at black-and-white terminal logs is boring. Icecream: The smartest way to debug (Goodbye print("test")) Typer: Building CLIs has never been this beautiful. (Swipe through the carousel to see all 10 gems 👉) 💡 Pro Tip: If you are building data apps, Streamlit is a non-negotiable tool in 2026. #Python #SoftwareEngineering #Productivity #AI #DataScience #CodingBestPractices #MuthukumarPl #CodeWithMK

🐍 5 Python Libraries Every Developer Should Learn in 2026 Stop writing everything from scratch. These libraries will 10x your productivity: 1️⃣ FastAPI → Build APIs in minutes, not hours 2️⃣ Pandas → Data manipulation made ridiculously easy 3️⃣ Pydantic → Data validation that actually makes sense 4️⃣ LangChain → Build AI apps without reinventing the wheel 5️⃣ Rich → Beautiful terminal output (yes, it matters!) Which one are you learning next? 👇 Save this for later 🔖 #TechTuesday #Python #PythonDeveloper #CodingTips #AI #MachineLearning #LearnToCode #AdvikaITSolutions #IndoreIT

Looking to master Python in 2026? Here is the blueprint. 📍 Python is versatile, but its vastness is exactly what makes it intimidating. I’ve put together/curated this roadmap to streamline the process. The Key Pillars: Foundations: Logic, Data Types, and Loops. Efficiency: Functional programming and List Comprehensions. Professional Grade: OOP, Decorators, and Testing. Specialization: [Insert your niche, e.g., FastAPI, Pandas, or AI]. Save this post if you're planning to level up your dev skills this year! 🚀 #SoftwareEngineering #PythonDeveloper #TechCommunity #LearningPath

Two numbers. Same math. Different answers. 🤯 Copy code Python x = 10**100 y = 10**100 + 1 print(x == y) Output: False Python handles this without blinking. No overflow. No precision loss. Now watch this: Python a = 1e16 b = a + 1 print(a == b) Output: True At this scale, adding 1 changes nothing. The value is already beyond what a float can accurately represent. Integers in Python grow as large as memory allows. Floats live within fixed precision and range boundaries. Same language. Same operator. Very different realities. Next time a number behaves “strangely”, remember — it’s not a bug, it’s a boundary. #Python #DataScience #ProgrammingInsights #NumericalComputing #TechThoughts

Publishing Pipeline - Refactoring ## From a One-Off Script to a Publishing Platform ### Three Weeks of Refactoring, Learning Python, and Building Something That Scales Three weeks ago, my publishing “pipeline” was exactly what many automation projects start as: a single Python script, built to solve a single problem, for a single platform. It worked — until it didn’t. Today, that script has evolved into a modular, extensible publishing platform that can target multiple services, reuse logic cleanly, and grow without collapsing under its own weight. Along the way, I learned more about Python, architecture, and disciplined automation than I had in the previous three years combined. #automation #devops #python Read more: https://lnkd.in/d-X3CtzD https://lnkd.in/d-X3CtzD

Excited to announce that #Python is now part of #Psicle’s ecosystem of industrialisation and automation! This unlocks the ability for businesses to harness Python scripts in a governed, auditable, and scalable way - bringing confidence and control to advanced modelling.

Strings are everywhere in Python, and mastering string methods makes your code cleaner, faster, and more efficient. This visual covers commonly used Python string methods like: Case conversion (upper(), lower(), title()) Searching & counting (find(), index(), count()) Validation checks (isdigit(), isalpha(), islower()) Formatting & alignment (format(), center(), ljust(), rjust()) Cleaning & splitting (strip(), replace(), split()) These methods are extremely useful in Data Analytics, Data Cleaning, and Text Processing. #Python #DataAnalytics #PythonProgramming #LearningPython #DataScience #Coding #Developer #Analytics

Day 20/30: Automating the Mundane with Python 🤖 I’m currently on Day 20 of my 30-day Python journey, and today’s project was all about Leverage. I built a Price Tracker & Automation Bot designed to monitor multiple URLs, clean incoming data (handling those tricky encoding bugs!), and log price history to a persistent CSV file. Key Learnings: - Data Integrity: Real-world web data is messy. Robust cleaning is the difference - between a broken script and a working tool. - Scalability: Moving from single-item tracking to multi-URL loops. - Automation: Why BeautifulSoup remains a staple for rapid tool-building. Project 23 of 30 is in the books. Seven more to go! Check out the source code here: https://lnkd.in/d3NmAchr #Python #SoftwareEngineering #Automation #WebScraping #BuildInPublic #LearningToCode