Managing Package Risks with Astral's Exclude-Newer Option

[EN] This would have been extremely useful to me when something suddenly changed between versions of PowerPlatform-DataverseClient-Python! It’s a great feature to add during development. Additionally, if you’re working on a Python integration that reads from or writes to Dataverse, I recommend this library (or using the API directly).

Day 17 Learning | Python Sets & Dictionaries 🚀 Today, I explored Sets in Python, focusing on their core properties, operations, and built-in functions such as: Union, Intersection, Difference, Symmetric Difference I also practiced adding, removing, and updating elements, and understand how sets help manage unique data efficiently. To apply these concepts practically, I built a small Library Management System using Sets and Dictionaries, where I worked on: Managing unique book records Efficient data handling using set operations Structuring and accessing data effectively with dictionaries This hands-on project strengthened my understanding of data structures, logic building, and real-world problem-solving in Python. 🔗 GitHub Repository: https://lnkd.in/gPTy4_Xz Consistent learning and implementation are key steps toward becoming a better developer. Looking forward to learning more and building stronger projects. #Python #PythonLearning #DataStructures #SetsInPython #DictionariesInPython #LibraryManagementSystem #HandsOnLearning #CodingPractice #LogicBuilding #SoftwareDevelopment #ProgrammingJourney #LearningByDoing #CareerGrowth #SkillDevelopment #Consistency #TechCareers #FutureDeveloper

Chains worked perfectly for my product analyst agent when the workflow was simple and tool calling was linear: pull data from Sheets → generate charts with Python → return output. But the moment any conditional logic got added at runtime like “if sample size is low, switch to weekly aggregation” or if it couldn't find the data on it's first try, the flow failed. This is when a more structured framework becomes necessary, a framework that doesn't let the agent get thrown away by logic inside prompts that the tools weren't explicitly designed for, so it can reason, retry and dynamically reroute itself. This week’s breakdown: when simple chains are enough, and when something like LangGraph starts to make more sense. Next week: Building an MCP server hands on. Packaging the tools I designed for the product analyst agent into a locally run MCP server. #TuninginTuesdays #AgenticAI #LangGraph

🐼 pandas 3.0 is here - and it’s more than just another version bump. With a dedicated string dtype, copy-on-write behaviour, pd.col() expressions, and Arrow interoperability, this release marks one of the biggest API shifts in years. In our latest blog, we explore: - what’s changed under the hood - why performance is improving - migration risks to consider - Whether now is the time to upgrade If you work with Python data pipelines or analytics workflows, this is a must-read. https://lnkd.in/eNPgj_ea

🎬 Demo Munquet! Transform and prepare tabular datasets with ease — convert CSV/TSV to Parquet, merge files, rename headers, validate columns, all offline. Perfect for reducing file size and streamlining analytics workflows. GitLab & Flathub coming soon. #Python #PyArrow #GTK4 #Parquet #Adwaita #DataScience #Flatpak

I just shipped a small but complete Python CLI project: Rock, Paper, Scissors. Beyond the game, the goal was to practice fundamentals that map directly to analytics and data work: Input validation (data quality mindset) Deterministic decision logic (rule-based classification) Modular functions + clean entry point (reusable, maintainable code) Reproducible execution from the command line Next iteration: I plan to log outcomes to CSV and run a quick analysis on win rates across multiple simulations. GitHub: https://lnkd.in/ea3fxBbi #Python #DataScience #Analytics #LearningInPublic #GitHub #ProblemSolving

🚀 Mini Project: Incident → Runbook using Vector Database Built a small system where vague incidents like 👉 “Jenkins issue” are matched to the right runbook using a Vector Database (FAISS) — not keyword search. 🧠 How it works Incident text → Embeddings → Vector DB (FAISS) → Best runbook ✅ Semantic intent matching ✅ Persistent vector DB ✅ Confidence-based runbook suggestion 🔗 GitHub: https://lnkd.in/gKqcreQY Python · LangChain · FAISS · OpenAI Embeddings Learning how Vector DBs power RAG & AIOps systems in real production scenarios 🔥 #VectorDatabase #FAISS #DevOps #MLOps #LangChain #RAG #AIOps #LearningInPublic

Just discovered "Pydantic" today and it's a game-changer! If you're building APIs in Python or dealing with data management, this library makes life so much easier. What I learned: -> Automatic data validation - no more manual checks -> Works perfectly with FastAPI -> Clean settings and config management You get validation, error handling, and type safety automatically. If you work with APIs(FastAPI) or external data, definitely check it out. Perfect Source: Chai Aur Code-Pydantic Crash Course(YouTube) #Python #Pydantic #API

Docker for AI Agents is officially over.. Pydantic just dropped Monty. It's a python interpreter written in rust that lets agents run code safely in microseconds. no containers. no sandboxes. no latency. 100% open source. LINK: github.com/pydantic/monty

🚀 Do you use Pandas in your day-to-day programming? Pandas just hit a major milestone with release of pandas 3.0. If you work with data, here is a quick breakdown of what's new and what might break in your current code. 1. Arrow-backed data becomes the default 2. .append() is fully removed 3. Strict typing for string and mixed columns 4. Datetime & timezone rules are stricter 5. Copy‑on‑Write is now mandatory 6. Indexing is predictable and strict 7. No more implicit dtype coercion If you maintain a production pipeline/notebook now might be a time to modernize it. ✔ My 2 cents: Upgrade to Pandas 2.3 first to address any deprecation warnings before completely switching Pandas 3.x #Pandas #Python #Data