I spent a weekend building a tool I actually needed : A PDF-to-Flashcard pipeline that runs 100% locally. The Win: No subscriptions, no data exposure, and zero latency. Just Python and local intelligence. The Stack: → PyMuPDF : Clean text extraction → Ollama to run Llama 3 locally:: High-performance local LLM. → Streamlit for the interface (and Sithara Hayavadana — the standalone local UI is genuinely great for this kind of project) → Pandas: Instant Anki-compatible CSV exports. The Biggest Learning: Data preparation beats model size every time. I found that chunking strategy mattered more than prompt engineering or model choice. The stack is entirely free — and yes, Keming Wang, free and open source tools were enough to buIld this 😁 I have shared the full article and technical breakdown in the comments below! 👇 Have you experimented with Ollama for your local workflows yet?

🚀 I just published my first Python library on PyPI! As a self-taught developer learning Data Science, I faced a simple but annoying problem every day: ❌ print(df) → boring console output ❌ Hard to read 3500+ rows in terminal ❌ No visual info about nulls, duplicates So I built the solution myself. 💡 ✅ Introducing ViewTable — A beautiful GUI table viewer for Pandas DataFrames! 📦 pip install viewtb 🔥 What it does: → Opens a beautiful dark-mode GUI table → Shows null cells in Blue → Shows duplicate rows in Red → Sidebar with dataset info — rows, columns, memory → Just ONE line of code! 💻 Usage: ——————————————— from viewtb import ViewTable df = pd.read_csv('data.csv') df.dropna(inplace=True) ViewTable(df, info=True) ✨ ——————————————— Built with: 🐍 Python 🐼 Pandas 🎨 Tkinter Canvas + CustomTkinter This is Day 1 of my Data Science journey. Small library. Big learning. 🙏 👇 Check it out: 🔗 GitHub: [your link] 📦 PyPI: [your link] #Python #DataScience #OpenSource #MachineLearning #100DaysOfCode #Programming #buildinpublic

🚀 I just published my first Python library on PyPI! As a self-taught developer learning Data Science, I faced a simple but annoying problem every day: ❌ print(df) → boring console output ❌ Hard to read 3500+ rows in terminal ❌ No visual info about nulls, duplicates So I built the solution myself. 💡 ✅ Introducing ViewTable — A beautiful GUI table viewer for Pandas DataFrames! 📦 pip install viewtb 🔥 What it does: → Opens a beautiful dark-mode GUI table → Shows null cells in Blue → Shows duplicate rows in Red → Sidebar with dataset info — rows, columns, memory → Just ONE line of code! 💻 Usage: ——————————————— from viewtb import ViewTable df = pd.read_csv('data.csv') df.dropna(inplace=True) ViewTable(df, info=True) ✨ ——————————————— Built with: 🐍 Python 🐼 Pandas 🎨 Tkinter Canvas + CustomTkinter This is Day 1 of my Data Science journey. Small library. Big learning. 🙏 👇 Check it out: 🔗 GitHub: [your link] 📦 PyPI: [your link] #Python #DataScience #OpenSource #MachineLearning #100DaysOfCode #Programming #buildinpublic

https://lnkd.in/dS2cnrxZ Welcome to this step-by-step beginner’s guide on installing and using Jupyter Notebook and JupyterLab. In this video, you’ll learn how to set up Python, install Jupyter tools, and launch your working environment. Whether you're starting your first program or preparing a workspace for data science, visualization, or GIS automation, this tutorial gives you exactly what you need to begin. You’ll also learn the key differences between Jupyter Notebook and JupyterLab, how to navigate their interfaces, manage files, and run Python code cells. By the end of this video, you’ll be ready to create, execute, and share your own projects with confidence. በዚህ ቪዲዮ የPython ፕሮግራምን እንዴት እንደሚያስተካክሉ፣ የJupyter መገልገያዎችን እንደሚጭኑ እና የስራ አካባቢዎን (workspace) እንዴት እንደሚጀምሩ ይማራሉ። በተጨማሪም በJupyter Notebook እና በJupyterLab መካከል ያሉትን ዋና ዋና ልዩነቶች፣ ገፅታቸውን (interface) እንዴት መጠቀም እንደሚቻል፣ ፋይሎችን ማስተዳደር እና የPython ኮድ ሴሎችን (code cells) እንዴት create ማድረግ እንደሚችሉ በዝርዝር ያያሉ።

“Vibe coded” scripts can look impressive, but they often fall apart the moment something changes (environment, auth, paths, libraries). The core issue: no clear intent + no foundational structure = impossible to support or scale. Troubleshooting a user's script that wouldn’t run in ArcGIS Pro, I’ll be honest… outside of the import statements, I couldn’t tell what the script was supposed to do. I asked a simple question: “What is this script trying to accomplish?” Answer: download a zip file from an open data site. The twist? It was “vibe coded.” I didn’t try to reverse-engineer chaos line by line. I showed the user a section from a book I use to level up my Python skills, Advanced Python Scripting for ArcGIS Pro (2nd Edition) by Paul Zandbergen. It walks through a clean, simple example of downloading a file from the web: import requests url = "https://lnkd.in/gDC8aks7" file = "bikeways.zip" response = requests.get(url) open(file, "wb").write(response.content) Clear, readable, and scalable. That’s the difference. Start with fundamentals like this, and scaling up becomes a whole lot easier. 🥠

Day 9/120 – Today I learned something most beginners ignore… but pros don’t 😳🔥 Yesterday → Lists Today → CONTROL over data 👇 👉 Tuples & Sets in Python Here’s the problem 🤯 Lists can be changed anytime… But what if your data SHOULD NOT change? ❌ Example: Coordinates 📍 Dates 📅 Configurations ⚙️ That’s where TUPLES come in 👇 data = (10, 20, 30) ✔ Cannot be modified ✔ Safe & reliable Now comes something even more powerful 👇 👉 SETS nums = {1, 2, 2, 3, 3} Output? 😳 {1, 2, 3} ✔ No duplicates ✔ Clean data This is HUGE in Data Analytics 📊 Now I can: ✔ Protect data (Tuples) ✔ Clean data (Sets) This is getting serious now 🔥 Comment “DATA” if you're learning with me 💪 #Day9 #Python #DataAnalytics #LearningInPublic #CodingJourney #Consistency

Sharing my latest project : Automatic Certificate Automation System I developed a system that automates the entire certificate generation process — from data collection to PDF creation and email delivery. 🔹 Key Features: • Automated certificate generation (Completion & Experience) • Dynamic data processing from sheets • Email integration for instant delivery • Logging and status tracking system • User-friendly dashboard for management This project helped me strengthen my skills in Python, Flask, automation, and backend development. 🔗 GitHub: https://lnkd.in/ggW_mMxy #Python #Flask #Automation #Data Science#Project #BackendDevelopment #LearningJourney

Excited to share my project: CSV Data Analyzer App 📊 I built an interactive web application using Python and Streamlit that allows users to upload CSV files and instantly generate insights without writing code. This project focuses on simplifying Exploratory Data Analysis (EDA) for beginners and students. 🔍 Key Features: ✔ Upload CSV files easily ✔ View dataset overview (rows, columns, cells) ✔ Detect missing values ✔ Generate statistical insights ✔ Interactive and user-friendly interface 🛠️ Tech Stack: Python | Streamlit Live demo:https://lnkd.in/gSiGat8h 💻 GitHub Repository:https://lnkd.in/gQU_cK22 🎯 I’m continuously improving this project by adding visualizations and advanced analytics features. I would really appreciate your feedback! 😊 #Python #DataScience #Streamlit #Projects #OpenToWork #Learning #GitHub

8 R/command line tools to deal with excel, tsv and csv files 🧵 that makes your life easier R packages: * [readxl](https://lnkd.in/e5SnVG-D) * [tidyxl](https://lnkd.in/e37xrQ2a) * [janitor](https://lnkd.in/emtMdrmT) command line tools: * [VisiData](https://www.visidata.org/) is an interactive multitool for tabular data. It combines the clarity of a spreadsheet, the efficiency of the terminal, and the power of Python, into a lightweight utility which can handle millions of rows with ease. [csvkit](https://lnkd.in/eTXarcwJ) [csvtk](https://lnkd.in/eW-B3BfT) a cross-platform, efficient and practical CSV/TSV toolkit. [Miller](https://lnkd.in/eSRavm68) is a command-line tool for querying, shaping, and reformatting data files in various formats including CSV, TSV, JSON, and JSON Lines. [eBay's TSV Utilities](https://lnkd.in/emSecqGc) I hope you've found this post helpful. Follow me for more. Subscribe to my FREE newsletter https://lnkd.in/erw83Svn

Open Data Product Specification Knowledge Base updated. Structure has been cleaned up. You find what you need faster and see how topics connect. A Python library is now included. You can validate ODPS YAML and plug it into your development workflows. FAQ now covers product strategy and internal KPIs. This is where many teams struggle. Data products without strategy and measurement stay as outputs. Two new guides: 1. How to define a product strategy 2. How to define internal KPIs Explore Knowledge Base now https://lnkd.in/ghA3Xtin