Financial Data Visualization Tools

🎄🎁 Advent of Open Source – Day 22/24: tiller-streamlit 💰 (See my intro post: intro post (https://lnkd.in/gVNYBE9m) Tracking personal finances is crucial, especially when working towards financial independence (see my WenFIRE post on Day 4). Today's project is tiller-streamlit, a Streamlit app that visualizes your financial data from Tiller. I host it on my local network under money.local using Traefik for easy access. (🖼️ Screenshot blurred for privacy reasons) 📖 Origin Story • Used Intuit Mint, but when they shut down, I needed an alternative with full data control. • Tiller automatically populates your financial data into a Google Sheet that you own forever. • With 20+ accounts, manual updates are tedious – Tiller automates it. • Combined Tiller's data with Streamlit's ease of use, creating a personal finance dashboard. 🔧 Technical Highlights • Powered by Streamlit: Turns Python into a web app with minimal code. • Tiller Integration: Connects to Tiller's Google Sheets for automated data retrieval. • Interactive Visualizations: Uses Plotly and Altair for dynamic charts. • Customizable Analysis: Filter by categories, time periods, and more. • Net Worth Tracking: Calculates and visualizes net worth over time. • Dockerized Deployment: Easy deployment with Docker, and served locally with Traefik. 📊 Impact • Primarily for personal use, it has significantly improved my understanding of my finances. • Great way to learn Streamlit and explore its capabilities. • Visualizing spending patterns helps identify areas to cut back and make informed financial decisions on the path to financial independence. 🎯 Challenges and Solutions • Learning Streamlit: Excellent documentation and community support made it a smooth process. • Data Wrangling: Transforming Tiller's raw data into a format suitable for visualization. • Performance Optimization: Caching and query optimization improved responsiveness. 💡 Lessons Learned • Data Ownership Matters: Owning your financial data gives you control and flexibility. • Visualization is Key: Seeing your finances visually makes a huge difference. • Streamlit is Powerful: Simplifies web app development. • Automation is Essential: Tiller's automatic data population makes the whole process sustainable. • Docker and Traefik Simplify Local Deployment: Making it easy to access the app on my home network. 🔮 Future Plans • Budgeting Features: Integrate budget tracking and forecasting. • Goal Setting: Set financial goals and track progress. • Investment Tracking: Incorporate investment data for a complete picture. Want to visualize your own finances with Tiller and Streamlit? Check out tiller-streamlit on GitHub (https://lnkd.in/gY9TB7hS)! #OpenSource #Python #Streamlit #PersonalFinance #DataVisualization #Tiller #Traefik #Docker

📢 Power BI vs Tableau : Which Data Visualization Tool is Right for You? I worked in both power bi and tableau tools in different projects requirements. Power BI and Tableau stand as titans in the realm of business intelligence and data visualization, each offering distinct advantages tailored to different business needs. This comparison will help you decide which of these tools to use for your data science and analytics needs. The main differences between them are: Power BI, with its seamless integration with Microsoft products and user-friendly interface, proves advantageous for organizations heavily invested in the Microsoft ecosystem. On the other hand, Tableau boasts unparalleled data visualization capabilities and advanced analytics features, making it a preferred choice for data-driven enterprises requiring sophisticated insights. Power BI: ➡️ Developed by Microsoft ➡️ More affordable pricing options, with a free version and lower-cost Pro version ➡️ Strong integration with other Microsoft products, such as Excel and Azure ➡️ Emphasizes ease of use, with a user-friendly interface and simplified data modeling ➡️ Offers real-time collaboration features Tableau: ➡️ Developed by Tableau Software ➡️ Generally more expensive, with a free version (Public) but requiring more advanced licenses for enterprise use ➡️ Offers a wider range of advanced data visualization options and a more powerful data engine ➡️ Emphasizes data discovery and exploration, with robust data blending and data mapping capabilities ➡️ Offers robust mobile and web authoring options for easy sharing of insights and data visualizations. Happy Learning 😃 ! Any key points you would like to add? Let's discuss! Follow Nirav Prajapati for more posts related to #DataAnalytics and #DataScience. #powerbi #datavisualization #tableau #dataanalytics

Tutorial:- 'FinData Explorer: A Step-by-Step Tutorial Using BeautifulSoup, yfinance, matplotlib, ipywidgets, and fpdf for Financial Data Extraction, Interactive Visualization, and Dynamic PDF Report Generation' (Colab Notebook Included) In this tutorial, we will guide you through building an advanced financial data reporting tool on Google Colab by combining multiple Python libraries. You’ll learn how to scrape live financial data from web pages, retrieve historical stock data using yfinance, and visualize trends with matplotlib. Also, the tutorial demonstrates how to integrate an interactive UI using ipywidgets, culminating in a dynamic PDF report generated with FPDF..... Full Tutorial: https://lnkd.in/gZpyBzbc Colab Notebook: https://lnkd.in/g4knaGqS