Jeffrey Arzu’s Post

Most people think a “simple project” is just about using basic tools. But here’s what I realized while building my Quiz App using Streamlit, Python, and PostgreSQL 👇 Yes, the tech stack looks simple on the surface: * Streamlit for frontend * Python for logic * PostgreSQL for backend But the real value came from applying deeper concepts behind the scenes: 🔹 Designed structured data models instead of dumping raw data 🔹 Applied data warehousing principles to organize quiz data efficiently 🔹 Thought about data governance — consistency, validation, and reliability 🔹 Built scalable data flows instead of one-time scripts 🔹 Focused on clean data transformations for accurate visualizations 🔹 Created meaningful insights instead of just displaying numbers What started as a small app turned into a hands-on exercise in: Data Engineering + Analytics + Product Thinking This project reminded me: It’s not about how complex your tools are It’s about how deeply you understand what you’re building Next step: Enhancing it with user analytics, personalization, and maybe even an AI-powered quiz generator 🚀 #DataEngineering #Python #PostgreSQL #Streamlit #LearningInPublic #Analytics #Projects

🏗️ Building the Backbone: From Python Classes to Persistent Data "The best way to learn is to build, break, and fix. 🛠️ Over the last few days, I’ve been architecting the backend for my FastAPI TodoApp. It’s been a journey of connecting the dots: Defining the Schema: Using SQLAlchemy to turn Python classes into database tables. Establishing the Link: Setting up the engine and SessionLocal to bridge the gap between my app and SQLite. Overcoming Hurdles: Navigating Windows environment variables and mastering the SQLite3 CLI to verify data integrity. The foundation is now officially set. By calling models.Base.metadata.create_all(engine), my app now automatically generates its own database environment on startup. Next stop: Developing the CRUD API endpoints to bring this data to life! 🚀" #Python #FastAPI #SQLAlchemy #BackendDevelopment #CleanCode #SoftwareEngineering

Built a small Python utility to refresh core fundamentals and structure. Built a CLI-based Contact Management system with: • Full CRUD operations • JSON-based persistent storage • Modular function design The idea wasn’t complexity, but to reinforce clean logic, data handling, and building systems from scratch without relying on frameworks. Planning to extend this further towards API-based and AI-driven workflows. GitHub: https://lnkd.in/gW8BXbKP #Python #SoftwareDevelopment #Backend #ContinuousLearning

You don't need to be a developer to use Python. You just need 5 scripts that eliminate the boring parts of your work. [swipe for all 5 →] After 11 years in Data Field closely working with operation team, here are the 5 automations every ops person should learn: 1️⃣ Automated Email Reports — pandas + smtplib 2️⃣ Data Cleaning & Merging — pandas + openpyxl 3️⃣ Inventory Reconciliation — pandas + sqlalchemy 4️⃣ Competitive Price Tracking — requests + BeautifulSoup 5️⃣ KPI Breach Alerts — slack_sdk + schedule Learning path: Week 1-2: Python basics + pandas Week 3: Connect to databases Week 4: Build your first automated report Total: 2-4 weeks of weekend learning → 100+ hours saved every quarter. Which of these would save you the most time? 👇 #Python #Automation #Operations #DataAnalytics #Productivity #CareerGrowth

📅 Day 73 of #100DaysOfCode — and today the data told a story I didn't expect! Today's focus: data visualization with Matplotlib using real StackOverflow data on programming language popularity from 2008 to 2020. Here's what I worked through today: 🔧 Renamed DataFrame columns using the names parameter in read_csv() for cleaner, more readable data 📅 Converted messy datetime strings into proper pandas datetime objects — a crucial data cleaning step before any time series analysis 🔍 Used groupby() + sum() + idxmax() to identify the most popular programming language of all time by total posts (spoiler: JavaScript 👑) 📊 Filtered DataFrames using boolean indexing to isolate specific languages for visualization 📈 Plotted time series data with Matplotlib — first a single language, then overlaid two languages on the same chart The most compelling insight? The chart says it all: 🔵 Java peaked around 2013-2014 and has been declining ever since 🟠 Python has been on a relentless rise — and by 2020, it's not even close The numbers don't lie. If you're wondering whether to learn Python, the StackOverflow community already voted with their questions. Onward to Day 74! 💪 #Python #Pandas #Matplotlib #DataVisualization #100DaysOfCode #DataScience #ContinuousLearning #MicrosoftFabric

✨ Turning boring tables into stunning visuals just got easier! If you work with data, you know that presentation matters just as much as analysis. Great Tables by Posit PBC isn’t just another tool, it redefines how data tables should look. Here’s why it stands out: 🚀 Beautiful by default No more plain, hard-to-read tables. You get clean, polished outputs with minimal effort. 🎯 Highly customizable From styling to formatting, you can fine-tune everything to match your needs. 🐍 Works seamlessly with Python Perfect for data workflows, especially if you're already using pandas. 📈 Built for storytelling Great tables help you highlight insights, not just display data. Whether you're building reports, dashboards, or sharing insights with stakeholders, this tool helps your data speak clearly and look great doing it. Definitely worth checking out if you care about both data and design 👇 https://lnkd.in/eShM4gZu #DataScience #Python #DataVisualization #Analytics #OpenSource #DataEngineering

🚀 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

Most people learn Python for Data Analysis like this: Watch tutorials. Copy code. Feel productive. But when it’s time to analyze real data… they get stuck. Because tools ≠ understanding. Two libraries change everything when used right: Pandas and Matplotlib Here’s the simple way to think about them: → Pandas = Thinking in tables You clean data, filter it, group it, and actually understand what’s going on → Matplotlib = Seeing patterns You turn numbers into visuals so insights become obvious You need to ask: “What question am I trying to answer?” Because real Data Analysis isn’t about code. It’s about: → Asking better questions → Exploring data step by step → Communicating insights clearly Pandas helps you explore. Matplotlib helps you explain. Together, they turn raw data into decisions. Quick question: What kind of data would you actually like to analyze? 👇

Most small businesses lose hours every week updating data manually. ⏳ I recently built a reliable Python pipeline that handles the heavy lifting: ✅ Fetches data directly from APIs ✅ Cleans data & removes duplicates ✅ Stores everything in a structured PostgreSQL database ✅ Updates automatically every day No more manual copy-paste. No more messy spreadsheets. 🚫📊 This is a game-changer if you deal with: • Growing Excel files that crash constantly • API data that needs daily manual updates • Repetitive, boring reporting tasks If this sounds familiar, I can help you automate your workflow and reclaim your time. 🚀 Check out the Demo & Code here: 👇 https://lnkd.in/dyXCXSPk #DataAutomation #Python #ETL #SmallBusiness #Automation