Free Python Talks: Modern Dev, Data & AI Workflows

90% of Python devs only know context managers as "with open()". That's like buying a Swiss Army knife and only using the toothpick. Context managers can handle: → Database connections (auto commit/rollback) → Execution timing (profile any code block) → Temp file cleanup (zero manual deletion) → Lock management → API sessions Slide through for real code examples ↗️ Which one do you use most? Drop your favorite use case below 👇 #Python #SoftwareEngineering #CodingTips #PythonDev #CleanCode

Most data engines serialize data across process boundaries every time a Python User-Defined Function runs. Think Spark shipping rows to a separate Python process and back. Long ago, we eliminated that boundary entirely in Sail. Rust + PyO3 + Arrow, same process, zero copy.

Step beyond natural-language prompts and explore how the Code Interpreter unlocks deterministic, Python-driven logic inside Copilot Studio. This session walks through how agents can generate and execute Python code in a secure sandbox to create reliable outputs such as charts, enriched Excel or PDF files, and Dataverse insights. You’ll see how to enable the feature at the environment, agent, and prompt levels, and how to analyze and visualize data using both design-time instructions and Python generated on the fly by your agents. We’ll look at prompt-design best practices, key considerations, and how to make the most of the Python output inside your agent workflows. Download the invite here: https://lnkd.in/ecNM3-wc #Microsoft #PowerPlatform #PartnerCommunity #TechTalks

Choosing between Python and Rust isn’t about hype—it’s about business fit. Python wins when speed matters: rapid MVPs, fast iteration, and easier hiring. Rust shines when reliability matters: predictable performance, memory safety, and safer concurrency. Many teams go hybrid—Python for product velocity, Rust for critical services. What would you pick? #Python #Rust #SoftwareEngineering https://lnkd.in/drA87Bi7

If you're not automating your daily workflows in 2026, you're missing out on massive time savings. 🛑⏳ Python has always been the king of automation, but the libraries leading the charge this year are taking things to a whole new level. From simple task automation to complex orchestration, there’s a modern tool for exactly what you need. 🛠️✨ Curious about which Python libraries you should be adding to your tech stack right now? I found a great guide that ranks and reviews the best options available today. 🔗 The link is in the first comment! Let me know what tasks you are automating this year. #WorkflowAutomation #PythonCoding #TechTrends #ProductivityHacks #SoftwareEngineering #Python

Data visualization using glumpy #machinelearning #datascience #datavisualization #pythonlibrary #glumpy Glumpy is a python library for scientific visualization that is both fast, scalable and beautiful. Glumpy offers an intuitive interface between numpy and modern OpenGL. https://lnkd.in/gAJiqZd7

Imagine building a digital empire with just a few lines of code. That’s the power of mastering Python string operations. […] https://lnkd.in/gcmJgqfG

In the fast lane of entrepreneurship, mastering Python strings can be your secret weapon. From handling user inputs to crafting […] https://lnkd.in/g-nWEntu

🚀 Day 12/100: Mastering Python Loops & range() Today’s coding session was all about automating repetition! I dove deep into for loops and the range() function to control iterations efficiently. Key Takeaways: 🔹 Used range(start, stop, step) to generate precise number sequences. 🔹 Leveraged for loops to iterate through lists and strings. 🔹 Practiced break and continue to manage loop flow.  Loops are absolute game-changers for automating repetitive tasks and data processing. 💡  #100DaysOfCode #Python #LearningToCode #DataScience #Automation #ProgrammingBasicsCodegnan

HashMaps are the duct tape of software engineering. 🛠️ Need to count frequencies? HashMap. Need to optimize a nested loop? HashMap. Need O(1) lookups? HashMap. Mastering how hash collisions work under the hood (Chaining vs Open Addressing) is the difference between writing code that works and code that scales. #DataStructures #SoftwareDevelopment #Python