Python CLI Rock Paper Scissors Project: Practicing Data Fundamentals

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

Python in Excel content only works if it respects how Excel users already think. I just posted a short review of a new book, Python in Excel: Unlocking Powerful Data Analysis and Automation Solutions by Liam Bastick and Kathryn Newitt, and why it stands out as a genuinely Excel-native approach. No more generic Python 101 with an Excel veneer! Just real workflows, clear constraints, and a progression that actually makes sense for Excel users. 🔗https://lnkd.in/ggsWnJXH

What can you build with a CSV file, some Python, and open data? Quite a lot, actually. With free public transport for under-18s across Melbourne, the question isn’t just where trains go — it’s what’s actually reachable from each station. By combining open datasets from Public Transport Victoria and OpenStreetMap, we used Python isochrones and a React interface to show what’s walkable in 15 minutes via real streets — not straight lines. This is the power of open data: turning public infrastructure into real opportunity. 👉 Read more here: https://lnkd.in/gDEeyN8E #OpenData #DataVisualisation #Python #UrbanTech #TransportData #Accessibility

🚀 Day 2/100 — Introducing Control Flow 🧠 “Logic transforms static code into a decision-making system.” Every functional system depends on its ability to evaluate conditions and respond accordingly. Today, I focused on understanding how Python enables programs to make decisions based on dynamic inputs and defined conditions. ⚙️ 🔧 Today’s focus areas: 🔀 Conditional Statements — Implementing if, elif, and else 🧩 Logical Operators — Combining multiple conditions 🎯 Decision Logic — Controlling execution flow based on evaluation 🖥️ Practical Exercises — Building programs with dynamic behavior 🎯 The objective was to enable programs to respond intelligently — forming the core of backend validations and control systems. ✅ Day 2 complete: Decision-making logic established. ▶️ Day 3: Structuring repeated execution using loops. Step by step. The system evolves. 🏗️ #100DaysOfCode #Python #FullStackDevelopment #SoftwareEngineering #BackendDevelopment #DeveloperJourney

🐍 Day 72 – NumPy Indexing, Slicing & Boolean Masking Code can be correct. Logic can be sound. And performance can still suffer — if you think one element at a time. Today, I focused on shifting how I work with data in NumPy — moving from loop-based thinking to true array-based computation. What I explored today: ✅ NumPy indexing for fast, direct access to data ✅ Array slicing that scales effortlessly across large datasets ✅ Boolean masking to filter data without explicit loops ✅ Vectorized operations outperform traditional Python patterns ✅ Thinking in arrays simplifies both code and logic Why this matters: ✅ Cleaner code with fewer loops and conditionals ✅ Massive performance gains on large datasets ✅ More expressive data transformations with less effort Key takeaway: NumPy isn’t just faster Python — it’s a different way of thinking. Stop processing values one by one. Start operating on the entire dataset at once. Python journey continues… onward and upward! #MyPythonJourney #NumPy #Python #DataAnalytics #LearningInPublic #AnalyticsJourney

🚀 Day 1/100 — Building the First Logic Layer 🧠 “Revisited the core — refining the foundation before scaling.” Every system begins by understanding how to store and handle data. Today, I focused on the foundational building blocks of Python — learning how programs store information, interact with users, and process basic inputs. ⚙️ 🔧 Today’s focus areas: 🔤 Variables — Assigning and managing data in memory 🔤 Data Types — Understanding strings, integers, floats, and booleans ⌨️ Input — Accepting dynamic data during runtime 🖥️ Output — Displaying and verifying program behavior 🎯 The objective was to understand how programs manage state and interact with the external environment — the first step toward building scalable backend systems. ✅ Day 1 complete: Core fundamentals established. ▶️ Day 2: Introducing control flow — enabling programs to make decisions. Step by step. The system evolves. 🏗️ #100DaysOfCode #Python #FullStackDevelopment #SoftwareEngineering #BackendDevelopment #DeveloperJourney

🎉 Just crushed my Data Structures and Algorithms course in Python! 🔥 Started with the fundamentals, then tackled linear powerhouses like Stacks, Queues, and Lists—mastering inserts, updates, deletes, and beyond. Now unlocking the magic of non-linear structures for smarter, faster solutions. This has supercharged my problem-solving for data analytics! What's your go-to data structure for real-world projects? Stack or Queue fan? Drop your tips below—I'd love to hear! 👇 #DataStructures #Algorithms #Python #Coding #DataAnalytics #TechTips

This is an integrated course covering Excel, Python, and AI—all in one programme. If you already have a solid understanding of financial modelling in Excel and want to start working with Python, this course provides the tools and guidance to help you enhance your models using this powerful language. To help you decide whether this course is right for you, we’re offering free access to the first module, giving you a preview of the content and approach. https://lnkd.in/etKsbgw3

🐍 PYTHON LEARNING ROADMAP 2026 For anyone asking "Where do I start with Python?" - here's your answer. This mindmap covers: 📦 Package Management (pip, conda, arrays) 🤖 Automation (GUI, web scraping, network automation) 🧪 Testing (unit, integration, end-to-end, load testing) 📊 Data Science (NumPy, Pandas, Matplotlib, Scikit-Learn) 🔧 Advanced (OOP, decorators, threading, magic methods) #PythonTutorial #LearnPython #TechEducation #DataScience #SoftwareEngineering"