This article surveys five notable open‑source 3D globe projects—echarts‑gl, Gio.js, three‑globe, Oclock, and starlink‑viz—detailing their core capabilities, technical stacks, usage examples, and ideal scenarios for data‑driven geographic visualizations.
The article reviews four open‑source projects—BuilderPulse, AutoProber, wx‑favorites‑report, and awesome‑claude‑design—that automate daily research, hardware probing, personal data visualization, and design system creation, helping indie developers cut hours of repetitive work each week.
The article explains why traditional report development has lagged behind AI advances, introduces the Ruankian Report Copilot that shifts interaction from graphical to language‑based interfaces, and details its rule‑engine architecture, practical command examples, advantages over generic LLMs, and its low‑cost, private‑deployment strategy.
The article showcases ten interactive 3D globe visualizations—ranging from a music‑driven world tour to real‑time earthquake maps—detailing their data sources, React‑based tech stacks, and design insights that help designers move beyond flat maps toward immersive, narrative‑driven data storytelling.
This guide walks you through designing five essential supply‑chain dashboards—order overview, material shortage alerts, production progress, inventory health, and supplier performance—detailing the questions to answer, required data, key metrics, and visual layout recommendations for quick, actionable insights.
The article explains how Obsidian users can overcome the hassle of external chart tools by installing the Vega Visualizations plugin, writing declarative JSON specifications, and rendering a variety of charts—simple and complex—right inside their markdown notes, complete with installation steps, examples, and troubleshooting tips.
This article presents a concise overview of ten popular open‑source data visualization tools—including Echarts, D3.js, Grafana, Plotly, Redash, Metabase, Superset, Kibana, AntV, and Pyecharts—highlighting their main features, typical use cases, and visual examples to help readers choose the right solution for their needs.
The article examines how traditional BI tools waste minutes on manual clicks, then details a step‑by‑step framework for selecting large models, designing memory‑aware architectures, mitigating security risks, and rolling out conversational analytics products that cut analysis time from days to minutes.
This article walks through using AntV Infographic together with Vibe Coding and Trae Solo to create an AI‑powered chart generator that turns natural‑language descriptions into beautiful infographics, covering configuration basics, prompt engineering, automated code generation, testing, and result analysis.
BettaFish is a Go‑based, AI‑driven multi‑agent opinion analysis platform that tackles information silos, overload, and bias by aggregating data from diverse sources, iteratively refining results through reflection loops, and delivering visualized, actionable reports for scientific decision‑making.
This guide explains ten common chart types—bar, line, area, numeric box, scatter, pie, tree, geographic map, table, and funnel—detailing their definitions, ideal use cases, related alternatives, and concrete best‑practice recommendations to help you choose the right visualization for your data story.
This guide walks you through installing Kibana, configuring its connection to Elasticsearch, exploring data via Discover, creating visualizations and dashboards, and monitoring cluster health, while also covering advanced query syntax, time filters, and practical tips for effective data analysis and visualization.
This comprehensive guide walks you through the fundamentals of Apache ECharts, showing how to set up the library, understand core concepts like option, series, axes, and datasets, and apply them in real‑world scenarios such as class performance analysis, subject distribution, correlation studies, and top‑student radar charts, while also covering interactivity, responsiveness, visual mapping, and animation techniques.
Tired of costly analytics dashboards, this guide showcases nine open‑source Python libraries—including Plotly Dash, Superset API client, Ibis, Lux, Redash‑API‑Py, Kibana‑API, Panel, Evidently, and Metabase‑Py—that let you create, automate, and monitor interactive visualizations and data pipelines for free.
This comprehensive guide explains what data visualization is, why it matters, how to choose the right chart type, preprocess data, design effective visuals, select appropriate Python tools, and provides numerous code examples for pie, bar, histogram, box, scatter, bubble, and deviation charts, concluding with best‑practice insights.
This article introduces ten Python data‑visualization libraries—ranging from the classic Matplotlib to newer tools like Gleam and Leather—detailing their main features, typical use cases, and where to find more information, helping readers choose the right tool for their projects.
This article reviews eight popular Python visualization packages—Matplotlib, Seaborn, Pandas, ggplot, Bokeh, Plotly, Pygal, and NetworkX—detailing their strengths, typical use cases, and providing code examples so readers can choose the most suitable tool for their data‑visualization needs.
This article introduces fifteen essential Python packages, explaining their core features and use cases—from data visualization with Dash to web services with Flask—providing beginners with a concise guide to the most useful libraries in the Python ecosystem.
This article introduces the Python D3Blocks library and shows how to quickly build interactive network and particle graphs using a few lines of code, including data preparation, visualization settings, and example outputs.
Version 6.0 of the open‑source ECharts library introduces a sweeping visual redesign, dynamic theme switching, intelligent scatter jitter, new chord diagrams, axis breakpoints, matrix coordinate systems, label overflow protection, upgraded mark layers, and stronger custom series, making both new and existing projects more powerful and easier to develop.
This article provides a detailed overview of DBeaver, covering its core features, cross‑platform support, powerful SQL editor, visual data management, step‑by‑step usage instructions, and key advantages such as being free, open‑source, and highly stable for developers and DBAs.
This article walks through building a single‑page tourism dashboard using Vue 3 and ECharts, covering project setup, technology stack, map rendering with geojson, layered map effects, custom numeric fonts, carousel implementation, and future plans for a Node‑based backend management system.
This tutorial walks you through a complete, zero‑code workflow using Tongyi Lingma to import, clean, visualize, and automatically generate a data analysis report—and even create AI‑driven content based on the insights.
This article details the design and implementation of a high‑performance data‑screen platform, covering its background, functional matrix, cloud‑native architecture, caching strategies, resource isolation, load testing, and monitoring, and demonstrates how it supports over 500 concurrent screens with sub‑second response times for enterprise decision‑making.
Apache ECharts 6.0 beta introduces twelve major upgrades—including a new default theme, dynamic theme switching, dark‑mode support, new chart types like chord and beeswarm, a matrix coordinate system, reusable custom series, and axis‑label optimizations—while providing migration guidance and breaking‑change notes for developers.
This article details how to create a Python‑based weather data analysis and visualization platform using Flask for the backend, PyEcharts for interactive charts, and web scraping to fetch live meteorological data, covering architecture, core features, and deployment steps.
This article walks through a follower's Pyecharts map visualization problem, explains why the original code failed, and provides a corrected Python script that successfully renders COVID‑19 data maps, complete with screenshots and full code examples.
This article demonstrates how to integrate the open‑source ECharts library with a PHP backend to dynamically generate a labeled, legend‑enabled statistical chart, covering resource inclusion, data preparation in JSON, HTML container setup, and JavaScript configuration with init() and setOption() calls.
This article demystifies Principal Component Analysis by explaining its intuition, the role of variance, step‑by‑step visual analogies, the mathematical foundation, and a complete Python implementation using scikit‑learn, including data generation, scaling, fitting, scree plot visualization, component interpretation, and dimensionality reduction to two principal components.
This tutorial demonstrates how to add Apache POI dependencies via Maven and use Java code to create an Excel workbook containing a multi‑series temperature line chart, including custom font sizes for titles and axes, and finally export the file to disk.
This tutorial explains how to retrieve Amap traffic‑life‑circle data via its public API, decode district IDs, and use Python with the Folium library to visualize 30‑minute reachable areas on an interactive map, providing a practical workflow for geospatial data analysis.
This tutorial demonstrates how to generate a wide range of 2D and 3D charts—including line, bar, scatter, pie, map, word cloud, gauge, funnel, tree, parallel, sankey, geo, timeline, and 3D visualizations—using the Python library pyecharts, providing complete code snippets and rendering instructions for each chart type.
This article introduces Python's Matplotlib and Seaborn libraries for data visualization, covering basic and advanced statistical charts, common plot types, customization techniques, and multi‑plot layouts with clear code examples and a comparative summary of each library's strengths.
This article demonstrates how to use Python's requests and BeautifulSoup libraries to scrape current and 14‑day weather data from China Weather, store the results in CSV files, and perform comprehensive visual analysis—including temperature, humidity, AQI, wind direction, and forecast charts—using pandas, numpy, and matplotlib.
This article walks through a common Python matplotlib visualization issue, showing how to read parameter data, generate multiple gamma distribution curves, add labels and legends, and produce polished plots, with complete code examples and explanations to help readers replicate the solution.
The Tianji team built a no‑code, component‑based visualization platform using Vue3 and Echarts, where a DSL defines reusable chart cards, tables and other widgets that users drag‑and‑drop onto a 24‑column grid, enabling real‑time, permission‑controlled, personalized dashboards that have already produced over 300 dashboards and 6,000 components for growth operations.
This tutorial explains how to use PHP together with the Baidu Map API to prepare heatmap data, generate the heatmap image, and display it on a web page, providing step‑by‑step code examples for each stage of the process.
This article explains how to create and display heatmaps in a PHP project by integrating the Baidu Map API, covering prerequisite setup, library inclusion, data preparation, heatmap generation using the HeatMap class, and rendering the map with JavaScript on a web page.
This article guides Java developers through a complete Chinese sentiment‑analysis dataset exploration, covering label distribution, sentence length statistics, vocabulary counts, adjective extraction, and visual word‑cloud generation using Python libraries such as pandas, seaborn, jieba, and wordcloud.
This tutorial explains how to use a PHP interface together with the ECharts JavaScript library and a MySQL database to fetch data, process it on the server, and render dynamic, interactive bar charts in a web page for effective data visualization.
This tutorial provides 17 practical Python plotting examples—from basic line and bar charts to 3D visualizations and real‑time updates—complete with ready‑to‑run code snippets using Matplotlib, Seaborn, and Plotly, helping newcomers quickly master data visualization techniques.
This comprehensive guide walks you through DeepSeek's features, installation on Windows, macOS, and Linux, configuration details, basic commands for data import, querying, and visualization, as well as advanced cleaning, analysis, plugin extensions, troubleshooting tips, and a handy command cheat sheet.
This article introduces the open‑source Python project Covid‑19‑analysis (installable via the covidify package), which automatically fetches the latest pandemic data, generates daily updated visual reports, and provides features such as country filtering, Excel export, and comprehensive charting of confirmed, death, and recovered cases.
This article presents a concise tutorial on essential Python data science libraries, covering data cleaning with Pandas, numerical analysis with NumPy and SciPy, visualization with Matplotlib and Seaborn, machine learning with scikit‑learn, NLP with NLTK and spaCy, time‑series modeling, image processing, database access, and parallel computing, each illustrated with ready‑to‑run code examples.
This article details the correlation between system and business metrics, introduces three generic business‑monitoring platforms (UMP, PFinder, Taishan), defines a unified log format, provides Log4j and Java logging code, and explains alert rule configurations, visualizations, and real‑world incident case studies to improve operational reliability.
This article explains how system‑level metric anomalies relate to business‑level metrics, describes the three internal business‑monitoring platforms (UMP, PFinder, Taishan), details unified log formats and Log4j configurations, and shares best‑practice case studies for alert rules, data visualization, and incident handling to improve operational reliability.
This article provides an overview of widely used Python libraries spanning web development, GUI programming, web scraping, game development, multimedia processing, security, cloud computing, data visualization, version control, parallel computing, natural language processing, and IoT, highlighting each library's primary purpose and typical use cases.
This article presents practical examples and code snippets for using Python libraries such as NumPy, Pandas, SciPy, Statsmodels, Dask, Vaex, Modin, CuPy, Scikit‑learn, TensorFlow, PyTorch, XGBoost, LightGBM, and various visualization tools to perform efficient numerical computation, data processing, machine‑learning modeling, and interactive visual analytics.
This article provides a detailed tutorial on using Python's matplotlib library for creating 2D and 3D visualizations, covering installation, library architecture, basic plotting, line charts, bar charts, scatter plots, contour maps, image handling, and customizations with complete code examples.
This article explains how to build a reusable Vue 3 component that draws customizable ring charts on a canvas, using TypeScript, props for size, stroke width, and a ratio list to render multiple colored arcs dynamically.
This article explains how to create and display heatmaps using the Baidu Map API in a PHP project, covering preparation, API integration, data preparation, heatmap generation with the HeatMap class, and rendering the map with JavaScript on the web page.
This tutorial teaches how to use Dask for handling large‑scale CSV data, covering data loading, exploration, cleaning, filtering, aggregation, visualization with pandas, and saving the processed results, all illustrated with complete Python code examples.
This article introduces four innovative Python chart types—Radar Chart, Treemap, Ternary Plot, and Joy Plot—explaining their concepts, typical applications such as performance evaluation and market analysis, and providing complete code examples to help data analysts create compelling visualizations.
This tutorial demonstrates how to use Matplotlib for basic data visualization, including creating line charts, scatter plots, and bar charts with practical Python code examples.
Discover eight underrated Python packages—including Missingno, Tabulate, Wikipedia, Wget, Faker, Numerizer, Emoji, and PyAztro—that simplify data visualization, table formatting, web content retrieval, file downloading, fake data generation, numeric conversion, terminal output embellishment, and horoscope fetching, helping developers save time and effort.
This tutorial demonstrates how to integrate the ECharts JavaScript library with a PHP backend to generate interactive statistical charts featuring labels and legends, covering resource inclusion, data preparation, HTML container setup, and JavaScript configuration with code examples.
This article provides a comprehensive overview of 20 essential Pandas data processing methods with detailed code examples covering statistics, data cleaning, transformation, filtering, merging, grouping, sorting, reshaping, aggregation, window functions, time series analysis, conditional selection, indexing, slicing, visualization, type conversion, data filling, filtering, renaming, and import/export operations.
This article demonstrates how to use a PHP interface to retrieve data from a MySQL database and employ the ECharts library to create dynamic visual statistical charts, guiding developers through preparation, data extraction, and chart generation steps, including code examples for a bar chart.
This guide walks you through installing Grafana via Helm, configuring its values, deploying the service, verifying the deployment, and adding a Prometheus data source, enabling a fully functional monitoring dashboard for servers and networks.
This guide explains why data analysis is essential, breaks it into three core stages—data collection, data mining, and data visualization—offers practical tool recommendations, and presents principles for efficient learning and skill development.
This article walks through a Python community Q&A where a user’s xlwings code fails to insert a chart image into Excel, explains that using an absolute file path or passing the figure object directly resolves the error, and shares the final working solution.
Bilibili’s front‑end team built a custom “mirror” SDK and full‑stack monitoring platform that captures JavaScript and resource errors, detects white‑screens, logs user behavior offline, routes data through Kafka‑ClickHouse pipelines to visual dashboards, and provides one‑click alerts, now serving over 1,700 projects across 85% of business lines.
This tutorial explains how to integrate the open‑source ECharts library with a PHP backend to produce interactive statistical charts that include custom labels and legends, providing step‑by‑step code examples for data preparation, HTML markup, and JavaScript configuration.
This guide introduces JSON Hero, a lightweight JSON viewer with multiple viewing modes, explains how to install it locally or use it online, and highlights its key features such as content preview, search, shareable links, and VS Code integration, helping developers work with JSON more efficiently.
This tutorial explains how to encrypt sensitive data with PHP's OpenSSL functions, decrypt it, and then safely feed the decrypted results to ECharts to render interactive statistical charts, providing complete code examples for each step.
This tutorial explains ten crucial visualizations for linear regression—scatter plot, trend line, residual plot, normal probability plot, learning curve, bias‑variance tradeoff, residuals vs fitted, partial regression, leverage, and Cook's distance—each illustrated with clear Python code using scikit‑learn, matplotlib, seaborn, and statsmodels.
This guide provides ready‑to‑run Python scripts for creating 3D scatter, line, surface, bar, and contour charts with Matplotlib, plus interactive 3D visualizations using Plotly, and includes pip installation commands for all required libraries.
This tutorial explains how to merge multiple graphs into a single figure using the patchwork package in R and the patchworklib library in Python, providing installation steps, code examples for arranging plots side‑by‑side and in grids, and visual results.
This tutorial demonstrates how to use Python's Matplotlib library to create five essential data‑visualization charts—simple bar, pie, donut, stacked bar, and Pareto—by installing the library, explaining each chart’s purpose, and providing ready‑to‑run code examples.
This article walks through a Python community member's question about configuring a Matplotlib contour plot's colorbar to display values from 0 to 17, presents two code attempts, and provides a working solution using a custom ScalarMappable with defined tick levels.
This article explains how to create and display heatmaps in a PHP project by integrating the Baidu Map API, covering preparation, library inclusion, data preparation, heatmap generation, and rendering the map with JavaScript, accompanied by complete code examples.
This article explains the fundamentals of Principal Component Analysis (PCA), demonstrates its application on the Breast Cancer Wisconsin dataset using Python code, and shows how scaling, PCA transformation, scree plots, and feature-group comparisons can reveal data structure and improve predictive modeling.
This tutorial demonstrates how to create an interactive Echarts map that supports drill‑down to county level and back navigation using Vue 3, vue‑echarts, and Vite, covering data acquisition, option configuration, event handling, and full component code.
This tutorial demonstrates how to create a desktop GUI using Python's Tkinter library to fetch per‑capita GDP data from the World Bank via pandas_datareader, process it with pandas, and display interactive line charts with Matplotlib, providing a user‑friendly interface for non‑technical users.
This article introduces how to automate Excel chart generation using Python libraries like pandas and openpyxl, enabling efficient data visualization and analysis.
This guide explains how to install, configure, and leverage the Grafana Alibaba Cloud OpenAPI plugin to visualize cloud resources, create alerts, and accelerate prototype validation in fast‑paced development environments.
This guide walks you through installing the Alibaba Cloud OpenAPI data source plugin for Grafana, configuring authentication and connection parameters, building queries and visual panels, and using the built‑in alert system to monitor cloud resources efficiently.
This article reviews the ten most valuable JavaScript libraries for 2024—including React, Redux, D3.js, TensorFlow.js, Angular, Node.js, Vue, Svelte, Three.js, and Lodash—detailing their core features, performance benefits, typical use cases, and why they remain essential tools for modern frontend development.
This article presents six practical Python techniques—including exception handling, list comprehensions, regular expressions, web scraping with BeautifulSoup, lambda functions, and simple data visualizations using matplotlib—to help developers write cleaner, more efficient code and enhance their programming workflow.
This guide walks you through creating a dynamic, real‑time data visualization dashboard—covering design mockups, requirement planning, architecture using Echarts for the front‑end and Java for the back‑end, JSON data handling, deployment without installation, configuration steps, code structure, and source acquisition.
Learn how to install, configure, and connect Kibana with Elasticsearch, explore data using Discover, craft powerful queries with Lucene syntax, create visualizations and dashboards, and monitor your logs—all illustrated with step‑by‑step screenshots and practical tips for effective data analysis.
This guide introduces DataEase, an open‑source BI platform built with SpringBoot, Apache Doris, and Kettle, walks through its architecture, provides step‑by‑step Docker‑based installation, and demonstrates how to create datasets, visualizations, and dashboards from Excel and MySQL sources.
The article explains how dimensionality reduction techniques—such as PCA, LDA, and t‑SNE—combined with graph theory can transform high‑dimensional data into simpler, low‑dimensional representations, enabling clearer analysis of complex systems like neural networks and image data, and enhancing machine‑learning efficiency.
This tutorial explains how to use the Baidu Map API together with PHP to prepare heat‑map data, create a HeatMap instance, add weighted points, generate the heat‑map image and finally display it on a web page using JavaScript.
Sugar BI, Baidu’s AI‑driven next‑generation business intelligence platform, evolves from the 2016 ShowX system into a zero‑code, multi‑source analytics suite that integrates over 30 data connectors, advanced semantic modeling, and the Wenxin‑powered Sugar Bot, which transforms natural‑language queries into optimized visualizations via intelligent chart recommendation, positioning it as a leading AI‑augmented BI solution.
Sugar BI, evolving from the internal ShowX platform to versions 2.0‑4.0, now offers a zero‑code, drag‑and‑drop visual editor, support for over 30 data sources, AI‑powered automatic analysis and the Sugar Bot Q&A module that transforms multi‑day data tasks into minutes, delivering containerized SaaS BI with intelligent chart recommendation and rapid, code‑free decision‑making for enterprises.
This article details the evolution of Baidu's Sugar BI platform, highlighting its AI‑driven analytics, extensive data source support, zero‑code visual design, smart chart recommendation, and the conversational Sugar Bot that transforms natural‑language queries into actionable visual insights.
This article presents an in‑depth overview of JD.com’s retail data visualization platform, detailing its product matrix—including EasyBI, a low‑code platform, and JDV large‑screen tool—its architectural layers, key capabilities, business case studies, challenges faced, and future development directions.
This article explains the concepts, formulas, and step‑by‑step implementation of a user‑retention analysis model, covering both Hive‑based offline processing and ClickHouse‑accelerated real‑time queries, complete with SQL examples, architecture diagrams, and practical optimization tips.
This article introduces the TransBigData Python package, explains how to install it, read mobile signaling data with pandas, preprocess and grid the data, identify stay and move events, determine home and work locations, and visualize individual user activity using built‑in functions.
This content introduces Mayavi, Matplotlib, and Plotly as Python libraries for 3D data visualization, providing code examples for creating surface plots, scatter plots, and interactive charts, with a focus on handling large-scale scientific datasets.
These 20 practical recommendations guide you in selecting appropriate chart types, setting correct axes, avoiding misleading designs, improving readability, applying suitable color schemes, ensuring accessibility, and choosing the right visualization libraries, helping you create clear, accurate, and insightful data visualizations.
This article introduces data visualization concepts and demonstrates how to create line charts, scatter plots, histograms, pie charts, and box plots using Pandas, while applying these techniques to real-world datasets such as the 2013 American Community Survey and the Boston Housing dataset.
This tutorial explains how to use Pandas for data visualization and exploratory analysis, covering line, scatter, histogram, bar, pie, box, and heatmap charts with code examples on the Iris, American Community Survey, and Boston Housing datasets.
This tutorial walks through essential seaborn techniques for visualizing data distributions—including univariate histograms, conditional histograms, KDE curves, ECDFs, boxplots, violin plots, bivariate histograms, and joint plots—providing code snippets, parameter explanations, and practical examples using the penguins dataset.
This article reviews the key concepts from the Bad Data Handbook, covering noise identification, data validation, human readability, web data restructuring, special domain challenges, and data quality analysis, while also presenting practical data visualization techniques, popular analysis tools, Python web‑scraping libraries, and a basic NLP workflow with code examples.
This article walks through a community member's Pandas data‑processing problem, shows the initial incorrect output, explains how removing a column and re‑exporting resolves the issue, and offers tips for asking effective data‑analysis questions in Python groups.
This article explains the concepts of pretraining and fine‑tuning for large language models, compares full‑parameter, LoRA and QLoRA approaches, discusses resource consumption, introduces the ModelScope SWIFT framework with code examples, and shows how fine‑tuning can improve data‑visualisation tasks while reducing token usage.
This article presents an in‑depth overview of JD's retail data visualization platform, detailing its product matrix (EasyBI, low‑code platform, JDV), real‑world business use cases, architectural challenges, future development strategies, and a Q&A session that highlights technical and operational insights.
This article introduces twenty data‑visualization tools—including entry‑level options like Excel, online libraries such as Google Chart API and D3, GUI‑control libraries, and advanced desktop applications like Processing, R, and Gephi—explaining their main features, supported formats, and typical use cases for creating charts, maps, and interactive graphics.
