Best of Data Visualization — 2024

  1. 1
    Article
    Avatar of communityCommunity Picks·1y

    Lightweight Charts™ library — TradingView

    TradingView's Lightweight Charts library offers a compact, feature-rich solution for interactive, high-performance charts. At just 45 KB, it uses HTML5 Canvas technology and supports streaming updates for custom data. The library is open-source, developer-friendly, and accessible, and is trusted by millions of traders and companies. It provides an easy-to-integrate charting solution with flexible styling options and is designed to be responsive and mobile-friendly.

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    Video
    Avatar of TechWithTimTech With Tim·2y

    Master Python With This ONE Project!

    This post guides you through building a personal finance tracker in Python, covering syntax, advanced features, and popular modules like Pandas and Matplotlib. The project involves tracking and logging transactions, organizing data, generating summaries of income and expenses, and visualizing the data with graphs. It also explains how to use CSV files for data storage and offers a quick demo followed by step-by-step instructions for implementation.

  3. 3
    Article
    Avatar of communityCommunity Picks·2y

    6 python libraries to make beautiful maps

    This post introduces 6 Python libraries for making informative and stylish maps. The libraries mentioned include Cartopy, Folium, Plotly, ipyleaflet, geemap, and ridgemap. They offer various features and capabilities for static and interactive map visualizations.

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    Article
    Avatar of cyberdevelopersCyber Devs·1y

    D3 / ThreeJS Network Topology Simulation

    Building a 3D Knowledge Graph for endpoint forensics can yield impressive results but organizing the endpoints clearly is challenging. Suggestions for clearer presentation methods, potentially using RTS game techniques or isometric gridding, are being sought.

  5. 5
    Article
    Avatar of communityCommunity Picks·2y

    The admin panel you don't have to build

    Basedash is an AI-generated admin panel that allows users to visualize, edit, and explore their data. It offers features such as creating gallery views for images, collaboration with team members, a SQL AI assistant, integration with external APIs, creating visualizations and charts, and tracking activity history. Using Basedash can save users 100x less time building internal tools compared to coding or drag-and-drop UI builders.

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    Article
    Avatar of communityCommunity Picks·2y

    fx – command-line tool for JSON

    A command-line tool called fx allows users to interactively visualize and explore JSON data.

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    Article
    Avatar of kdnuggetsKDnuggets·2y

    Building Data Science Pipelines Using Pandas

    Learn to build end-to-end data science pipelines using the Pandas pipe method. This method enhances code readability, enables function chaining, and improves code organization. The tutorial includes transforming code into a pipeline structure that handles data ingestion, cleaning, analysis, and visualization, demonstrating a comparison between pipeline and non-pipeline approaches.

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    Article
    Avatar of kdnuggetsKDnuggets·2y

    Project Ideas to Master Data Engineering

    To effectively learn data engineering, working on projects is essential. Key skills to focus on include data transformation, data visualization, building data pipelines, and implementing data storage solutions like data lakes and data warehouses. The post suggests six project ideas to cover these aspects: building an end-to-end data pipeline, transforming data sets, implementing a data lake, creating a data warehouse, processing real-time data, and visualizing data with dashboards.

  9. 9
    Article
    Avatar of metabaseMetabase·1y

    How to visualize time-series data: best practices

    Learn the best practices for visualizing time-series data, including selecting the right chart types and structuring your data for clear and impactful visualizations. Charts such as line, bar, area, trend, and waterfall are discussed, along with techniques like using offsets for comparisons. A time-series cheat sheet dashboard is also available for easy reference.

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    Article
    Avatar of communityCommunity Picks·1y

    Piling.js

    Piling.js supports a variety of dynamic mouse interactions for managing groups of items in a visualization. Users can utilize drag and drop, multi-select, lasso, in-place browsing, preview browsing, dispersive browsing, hierarchical browsing, pile dispersion, and pile scaling to manipulate piles of data intuitively.

  11. 11
    Article
    Avatar of communityCommunity Picks·1y

    One Million Screenshots

    Explore over a million rendered homepages from the web in an interactive manner, allowing you to zoom, pan, and click similar to Google Maps. This visual dataset could help you find websites you've been looking for or discover new ones. Check out the FAQ for more details and learn about the Screenshot API if you're interested in the data.

  12. 12
    Article
    Avatar of mlmMachine Learning Mastery·1y

    5 Tools for Visualizing Machine Learning Models

    Machine learning models require specialized tools to visualize their structure, performance, and behavior. Five useful tools for this purpose include TensorBoard for neural network models, SHAP for model prediction explanations, Yellowbrick for Python-based model diagnostics, Netron for deep learning model architecture visualization, and LIME for intuitive model explanations. These tools cater to various model types and use cases, helping users understand complex ML models better.

  13. 13
    Article
    Avatar of lobstersLobsters·2y

    State of HTML 2023

    The State of HTML survey covers a wide range of topics including accessibility, web components, and more. The report includes improved chart customization features.

  14. 14
    Article
    Avatar of hnHacker News·1y

    anvaka/map-of-github: Inspirational Mapping

    This project maps over 400,000 GitHub projects, clustering them based on common stargazers. Using data from GitHub's public activity events, Jaccard Similarity, and AWS for processing, the map visualizes connections between repositories. The layout was computed with ngraph.forcelayout and rendered with maplibre. Clusters and labels were generated with the help of various tools, including ChatGPT. The project is open-source under the MIT license, with contributions welcome.

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    Article
    Avatar of uxplanetUX Planet·2y

    Line Chart UI Design Tips & Tricks

    Line charts are ideal for illustrating changes over time, comparisons, and trends. To create effective line charts, limit the number of data lines to avoid clutter. Use a neutral background and a minimal, distinct color palette to enhance readability. Employ shapes and textures alongside colors for better data communication and ensure the design meets WCAG AA contrast requirements. Provide enough space for labels and incorporate interactive elements like hover states and tooltips to enrich user experience.

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    Article
    Avatar of kdnuggetsKDnuggets·2y

    5 Common Data Science Mistakes and How to Avoid Them

    Data scientists often make five common mistakes that can negatively impact their projects: rushing into projects without clear objectives, overlooking foundational steps like data cleaning and statistics, choosing the wrong visualizations, neglecting feature engineering, and focusing more on accuracy than overall model performance. Understanding these pitfalls and how to avoid them is key to improving your workflow and becoming a more effective data scientist.

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    Article
    Avatar of communityCommunity Picks·2y

    Build a Chatbot for your SQL database in 20 lines of Python using Streamlit and Vanna

    Learn how to build a chatbot for your SQL database using Streamlit and Vanna in just 20 lines of Python. The guide walks through setting up the environment, connecting to a SQLite database, generating SQL queries with AI, and visualizing the results in tables and charts.

  18. 18
    Article
    Avatar of communityCommunity Picks·2y

    Top 5 Data Engineering Projects for Beginners 2024

    Get hands-on experience with five beginner-friendly data engineering projects that enhance skills in data processing, analytics, and visualization using tools like Apache Kafka, Python, and cloud platforms. These projects prepare you for real-world challenges and make your resume stand out.

  19. 19
    Article
    Avatar of communityCommunity Picks·2y

    Bring Postgres relationships to light

    Entity-relationship diagrams (ERDs) are invaluable for visualizing and managing complex Postgres databases. ERDs showcase entities, attributes, and relationships, making database structures more accessible, especially for non-technical team members. Tools like Outerbase simplify the creation and maintenance of ERDs by automatically generating and updating diagrams for Neon databases. This democratization of data aids developers in understanding, communicating, and scaling database schemas efficiently.

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    Article
    Avatar of ds_centralData Science Central·2y

    Building Professional Diagrams: LLM/RAG Example

    Creating professional diagrams for AI architecture can be challenging due to the complexity and bugs in tools like Mermaid. The author shares experiences of overcoming these challenges by experimenting with Mermaid and discusses the potential of using GenAI for faster, higher-quality diagrams. Several issues such as unpredictable layouts and rendering problems in Mermaid are highlighted, with potential solutions and alternatives like GraphViz and custom Python code suggested.

  21. 21
    Article
    Avatar of communityCommunity Picks·2y

    The Art of Data Visualization: Exploring D3.js

    Explore the power of D3.js, a JavaScript library for creating dynamic and interactive data visualizations. Learn about data binding, SVG manipulation, and data manipulation utilities provided by D3.js.

  22. 22
    Article
    Avatar of communityCommunity Picks·2y

    7 Best Python Visualization Libraries for 2024

    Discover the top Python libraries for data visualization in 2024.

  23. 23
    Article
    Avatar of hnHacker News·2y

    Data Visualization Framework

    Flitter is a high-performance data visualization framework for JavaScript that seamlessly integrates with React, Svelte, and more. It offers an elegant and efficient API inspired by Flutter, with 50+ widgets available for creating charts effortlessly.

  24. 24
    Article
    Avatar of hnHacker News·2y

    Openpanel-dev/openpanel: All the goodies from both Mixpanel and Plausible combined into one tool.

    Openpanel is an open-source analytics tool that combines the functionalities of Mixpanel and Plausible. It allows users to visualize data with various charts, access events and visitors' history, and own their own data. Openpanel is GDPR compliant, privacy-friendly, and offers predictable pricing. It supports Nextjs for the dashboard, Fastify for the event API, Postgres for storing basic information, Clickhouse for storing events, and Redis for cache layer and pub/sub.

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    Article
    Avatar of communityCommunity Picks·2y

    Introduction to D3.js

    D3.js is a JavaScript library used to create bespoke, interactive charts and maps on the web. It provides building blocks for custom charts or maps and requires experience with JavaScript, HTML, SVG, and CSS. D3's features include data-driven modification of HTML and SVG elements, scale functions, loading and transforming data, complex chart creation, transitions, and advanced interaction support.

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