Visualizing Data with Pygal

🚫 𝗦𝘁𝗼𝗽 𝗺𝗮𝗻𝘂𝗮𝗹 𝘀𝗰𝗿𝗮𝗽𝗶𝗻𝗴. 𝗨𝘀𝗲 𝘁𝗵𝗲 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗔𝗣𝗜. I just published a simple guide on Medium about fetching and visualizing YouTube data using Python. 𝗪𝗵𝗮𝘁'𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: - Getting your API key. - Fetching channel stats. - Visualizing data with Python. - Exporting to Excel. Read the full guide here: https://lnkd.in/gkRijvnS #Python #YouTubeAPI #DataScience #Automation LinkedIn YouTube

As Stack Overflow decreases in popularity, companies move to their own communities on Discourse. For RAG and Agentic workflows to be effective, they need a way to access that data. Our colleague Mauk Muller built Discourse Reader: a simple, yet powerfull Python package to bridge the gap between these communities and your AI pipelines. Check it out on Hacker News and GitHub: 👉 Hacker News: https://lnkd.in/e2WKZaBj 👉 GitHub: https://lnkd.in/eusnecqq #OpenSource #AI #RAG #Discourse #Python #ElNiño

Just wrapped an energizing session teaching data loaders for local LLMs in Python! We mapped out Text Loaders pulling PDFs, JSONs, CSVs, and TXTs into strings or key-value trees via #LangChain, Image Loaders converting to binary formats, plus #OCR magic on pure-image #PPTX files using Python’s pptx loader—and Pandas crushing CSV/XLS flows. Total game-changer for building rock-solid AI pipelines! Tell me how do you think about texts on whiteboard? #Python #AI #LocalLLM #whiteboardKnowledge

Real data is never clean. Save this cheat sheet for the next time you are dealing with messy text columns. 12 pandas string functions. Grouped by what they actually do. Clean it. Find it. Transform it. Validate it. Follow Everyday Data People for a cheat sheet every day. #Python #Pandas #DataCleaning #DataScience #DataAnalytics #EverydayDataPeople

Day7 of #30DayChartChallenge Theme: Multiscale Category: Distributions Tool: Python Data Source: python scikit-learn Datasets I worked with a few features from a machine learning dataset and plotted their distributions. At first, everything sits on different ranges. One stretches far, another stays tight, another somewhere in between. It looks fine, but comparing them like that is off. After scaling, they fall into the same range. Now the comparison actually makes sense. It’s a small step in most workflows, but seeing it visually makes the difference clearer. #30DayChartChallenge #python #Dataviz #Datascience

Day 5 Consistency is key! 🚀 I’ve been dedicating time to strengthening my Python fundamentals, specifically diving deep into how to work with data sequences. From understanding immutability to mastering indexing and slicing techniques, I’m building a solid foundation to handle data manipulation more effectively. It’s rewarding to see how these concepts translate into cleaner, more efficient. Today I’ve been practicing advanced sequence manipulation in Python. Key takeaways from my study session: Immutability: Understanding why certain data types (like strings) cannot be changed in place. Slicing Syntax: Mastering [start:stop] and how to omit indices for cleaner, faster code. Negative Indexing: Leveraging indexing from the end to make my code more dynamic. There is always something new to learn when it comes to optimizing data extraction! 💡 #PythonProgramming #SoftwareDevelopment #LearningToCode #DataManipulation #CodingTips #Python #CodingJourney #ContinuousLearning #DataHandling #SelfDevelopment #TechSkills

Workflow Experiment Tracking using traintool #machinelearning #datascience #workflowexperimenttracking #traintool traintool is the easiest Python library for applied machine learning. It allows you to train off-the-shelf models with minimum code: Just give your data and the model name, and traintool takes care of the rest. https://lnkd.in/g4BhbTpw

Most people default to Pandas. Works fine… until your data scales. That’s where Polars wins: > Similar syntax for most operations > Faster execution > Lazy evaluation (big performance boost) Don’t ditch Pandas. But ignoring Polars now? That’s a mistake. Learn both. Use what fits. Found Insightful? ♻️ Repost in your network and follow Sahil Alam for more. #DataEngineering #Python #Pandas #Polars #BigData #DataAnalyticsSahil Alam for more.

Finally moved Python & AI project from local to live! 🎈 I’ve been experimenting with Python lately and finished building a simple Personal Assistant for my morning routines and productivity. It was fun to step outside .NET and try out some new tools: ⚡ Groq : Really impressed with how fast the responses are. 🐍 Python: Getting more comfortable with the syntax every day. 🖥️ Streamlit: Made it super easy to put a clean UI on top. It’s work in progress, but you can check out V1 here: https://lnkd.in/gwyuaGZt #Python #Streamlit #PersonalProject #BuildingInPublic #WomenInCode

Most Python workflows rely on heuristics. They’re quick, intuitive, but usually not optimal. A simple greedy approach might get you a solution, but it often leaves efficiency, performance, and cost savings on the table. GAMSPy brings algebraic modeling into Python, so you can express constraints and objectives directly and solve for a true optimum. At PyConDE & PyData 2026, Justine Broihan and Muhammet Soyturk will walk through this using a classic operations example, and then extend it into machine learning. They'll cover: 🔸 How optimization compares to rule-based heuristics and 🔸 How it can be used to test ML models (e.g. minimal changes needed to trigger misclassification) 🔸 The Art of the Optimal: A Pythonic Approach to Complex Decision-Making 📍 April 14 · 16:30 📍 Platinum (2nd Floor) If you're building decision-making systems in Python, this is worth a look. More details 👉 https://lnkd.in/dyifGdVi #PyConDE #PyData #Optimization #GAMSPy #GAMS #Python