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.
This article details the architecture, core processes, and module designs of the JiShi BI platform, a self‑built data visualization and analysis system that integrates data ingestion, processing, enhanced AI‑driven analytics, and multi‑dimensional dashboard capabilities to support enterprise decision‑making.
Learn how to use Plotly in Python to build flexible subplot layouts combining bar, pie, and polar charts, with step-by-step code examples, troubleshooting tips, and best practices for arranging multiple visualizations on a single page.
This article introduces ten popular Python visualization and GUI libraries—Matplotlib, Seaborn, Plotly, Bokeh, Altair, Plotnine, VisPy, Pygame, Kivy, and PyQt/PySide—providing concise code examples and brief explanations of their typical use cases and strengths.
JetBrains' IntelliJ IDEA 2023.3 release brings full Java 21 support, a new Run‑to‑Cursor debugging option, floating editing toolbars, color‑coded editor tabs, refreshed macOS icons, enhanced Speed Search shortcuts, and integrates Lets‑Plot for no‑code data visualization, though the AI Assistant remains unavailable in mainland China.
Using Tencent's Hunyuan model, the tutorial walks through a Python workflow that scrapes a student‑score table from a web page, saves it as CSV and Excel, cleans missing values, computes total and average scores, and visualizes their distributions with matplotlib, illustrating how LLMs can accelerate data‑analysis coding while still needing human verification.
This article demonstrates how to build impressive data dashboards using VChart by configuring scene‑specific theme colors, implementing gradient fills, adding high‑customizable decorative marks and component decorations, and creating rich animation effects, all with detailed code examples and links to live demos.
This article explains the fundamentals of multi‑dimensional (pivot) tables, describes core BI concepts such as dimensions, hierarchies, members and indicators, and shows how VTable maps these concepts to configuration options, data analysis features, custom dimension trees, and real‑world use cases.
This article explains how to quickly build a data‑driven dashboard featuring a pseudo‑3D map, radar and line charts using layered flat maps, ECharts configuration, interaction handling, and visual enhancements such as click‑highlight and ripple effects.
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 with pandas, numpy, and matplotlib, including temperature, humidity, AQI, wind radar, and correlation plots.
This article details the design evolution and performance optimization of Vivo's Agile BI cross‑tab component—from a jQuery prototype to a React‑based solution—highlighting worker‑offloaded preprocessing, virtual scrolling, and micro‑frontend integration that enable sub‑second rendering of massive data sets and match or surpass competing BI tools.
VChart is an open‑source, multi‑terminal chart library built on VGrammar and VRender that provides a rich set of 2D and 3D visualizations, high‑performance rendering, extensive animation and interaction capabilities, and flexible layout and theming options for developers across web, mobile and mini‑program environments.
This article explains the concepts, analytical methods, visualizations, and SQL implementation of user crowd analysis—including distribution, metric, drill‑down, cross, and comparative analyses—while also covering trend monitoring, TGI calculation, and handling of array‑type tags in ClickHouse and Hive.
This guide walks you through installing Kibana, configuring its connection to Elasticsearch, creating index patterns, using Discover for searches, mastering Lucene‑based query syntax, building visualizations, assembling dashboards, and monitoring logs, all illustrated with clear screenshots and code examples.
This article demonstrates how to set up and use ECharts in a Vue 3 project with TypeScript, providing a reusable library, type definitions, a composable hook, and example components to render various chart types efficiently.
This tutorial explains how to use PHP to fetch JSON data and integrate Google Charts, providing step‑by‑step code examples for preparing the environment, retrieving data, generating a pie chart with JavaScript, and displaying it within an HTML page.
This article introduces four Python packages—Pivottablejs, PyGWalker, Qgrid, and Itables—that transform Pandas DataFrames into interactive tables within Jupyter Notebook, providing features such as pivoting, drag‑and‑drop filtering, visual exploration, and lightweight data inspection for enhanced data analysis.
This article explains how to use an open‑source CMDB to build customizable dashboards, covering five chart types, multi‑model and multi‑dimensional statistics, CI‑related metrics, and data filtering, with step‑by‑step configuration examples and visual illustrations.
The article explains the importance of data analysis for business insight, problem detection, decision support, operational optimization, forecasting, and competitiveness, and then presents ten practical Python code scenarios covering data loading, cleaning, filtering, aggregation, visualization, statistics, transformation, time‑series analysis, export, and machine‑learning applications.
This guide walks through creating a dynamic, theme‑switchable big‑screen dashboard using Echarts on the front end and a lightweight Java backend, covering requirements, architecture, configuration, code structure, and how to obtain the source code.
This article presents a comprehensive collection of practical data visualization methods—including KS plots, SHAP explanations, Q‑Q plots, cumulative variance, Gini vs Entropy, bias‑variance tradeoff, ROC and precision‑recall curves, and elbow analysis—each illustrated with Python code and clear explanations to help analysts and non‑experts quickly interpret complex datasets.
This article explains the three essential steps for Python data visualization, introduces key libraries such as Matplotlib, Seaborn, Bokeh and Pandas, and provides detailed examples of creating, customizing, and saving plots with code snippets and practical tips.
This guide reviews six popular Snowflake‑compatible tools—covering data preparation, visualization, integration/ETL, business intelligence, and governance—that can dramatically boost productivity for data professionals.
This article introduces ten Python visualization libraries—from the classic Matplotlib to the interactive Bokeh and Plotly—detailing their origins, strengths, typical use cases, and where to find more information, helping readers choose the right tool for their data projects.
The article details how iQIYI's Magic Mirror platform evolved from a simple single‑table reporting tool to a multi‑engine, self‑service big data analysis system that improves data access speed, reduces operational costs, and supports comprehensive business analytics across the company.
DataEase is a Chinese-developed open-source BI tool offering one‑command deployment, drag‑and‑drop dashboard creation, default configurations, dataset association, and a template market, making it more user‑friendly for Chinese users than foreign tools like Superset and Metabase.
This article provides a detailed tutorial on using Python’s Matplotlib library for creating high‑quality 2D and 3D visualizations, covering basic functions, advanced features like LaTeX support, color palettes, integration with IPython, and practical installation tips, all illustrated with code snippets and images.
The article details JDV, JD.com’s internal visual‑big‑screen data platform, describing its architecture, the demanding real‑time, cross‑midnight, and high‑stability requirements during the 618 promotion, the technical challenges faced, and the innovative solutions—including request state control, heartbeat monitoring, video recording, orchestration tools, precise stop handling, and proxy data sources—that ensured reliable large‑scale screen deployment.
Using Python's Selenium library, this tutorial demonstrates how to scrape real-time box office data from Maoyan's regular page, extract movie names, total and incremental earnings, process the data with pandas, export to Excel, and create visual analyses of top‑10 films' revenues and market shares.
This tutorial demonstrates how to use Python's Seaborn library to create a variety of attractive statistical charts—including bar, scatter, line, box, histogram, heatmap, violin, and KDE plots—by importing the library, setting styles, and executing concise code snippets for each chart type.
This article demonstrates how to use the Python Altair library to create a variety of visualizations—including scatter, line, bar, box, area, and pie charts—by loading sample datasets and applying concise, consistent API calls, while also highlighting customization options and interactive capabilities.
Apache ECharts 5.4.3, the open‑source JavaScript visualization library, introduces new features such as axisPointer triggerEmphasis, Sankey trajectory for emphasis, and numerous bug fixes across components like Sankey, Sunburst, Graph, Label, Axis, Pie, Custom, Legend, Util, API, i18n, and Type handling.
OpenAI’s newly opened Code Interpreter for ChatGPT Plus users lets the model execute Python code, enabling data upload, analysis, visualization, and file conversion, which many see as a game‑changing tool that could democratize data analytics while still facing limitations like paywalls and no internet access.
This article walks through reading Excel air‑quality data with pandas and creating four‑panel visualizations of AQI, pollutants, temperature and humidity using Matplotlib, providing complete code and tips for handling common plotting pitfalls.
This article explains the difference between information and scientific visualization, outlines common chart types such as funnel, heatmap, Sankey, Venn, force‑directed, rose, user‑experience, chord, swimlane and sunburst charts, and provides a step‑by‑step methodology for data preparation, mental‑model alignment, visual metaphor selection and storytelling to create effective visualizations.
This article reviews a decade of data‑science growth, defines data science as a multidisciplinary field, outlines its four high‑level and fourteen low‑level work processes, categorises nine distinct data‑science roles, and discusses how these insights should shape the next generation of data‑visualisation and analysis tools.
This article presents a complete guide to building a data‑visualization big‑screen dashboard, covering effect screenshots, requirement specifications, overall architecture, development environment setup, JSON library configuration, code structure, startup commands, browser access, and source‑code acquisition.
This tutorial explains how to build a real-time data visualization feature by setting up a PHP WebSocket server with Ratchet and displaying live data on the client side using Highcharts, covering environment setup, server and client code, and deployment considerations.
This tutorial details how to create a dynamic, real-time updating data visualization dashboard using Echarts for the frontend and a Java Web backend, covering requirement analysis, architecture design, environment setup, code structure, deployment commands, and source code acquisition.
This article outlines Alibaba Cloud’s DataV journey, the industry‑wide Data Visualization Service Capability Grading Requirements, the three maturity stages, and the team’s award‑winning history, illustrating how standardized tools and processes elevate data‑driven decision making.
This article explains how to build a feature‑rich, responsive curve chart from scratch using HTML5 canvas, covering layer separation, adaptive sizing, custom gradient fills, Bezier curve calculations, point‑on‑path detection, label handling, animation masking, and a complete configuration object with code examples.
This article demonstrates how to create a Python-based tool that crawls click‑event data from an e‑commerce site, processes and visualizes the information to verify event‑tracking code, covering preparation, data collection, analysis, and visualization steps with complete source code.
This article demonstrates how to use Python, together with the openpyxl and xmind‑sdk libraries, to read a multi‑level Excel test case file, transform its hierarchical data into XMind topics, and generate a visual mind‑map representation of the test cases.
This article demonstrates how to create a dynamic, real‑time data visualization dashboard using the Echarts library on the front end and a Java‑based backend, covering requirements analysis, architecture design, environment setup, code structure, deployment, and source‑code acquisition.
This article presents a step‑by‑step technical guide for creating a real‑time, dynamic data‑visualization dashboard using Echarts on the front end and a Java‑based backend, covering design screenshots, requirement analysis, architecture, configuration, code structure, deployment commands, and source‑code acquisition.
The article reviews DataEase, a Chinese open‑source business‑intelligence platform that offers a low‑learning‑curve interface, extensive data‑source support, built‑in template marketplace, and Docker‑based one‑command installation, making data visualization and dashboard creation accessible to a broad range of users.
This guide walks you through creating a dynamic, real‑time data visualization big‑screen dashboard using Echarts on the front end and a Java‑based backend, covering requirements, architecture, configuration, code structure, and deployment steps.
This article provides a step‑by‑step tutorial on creating a dynamic, real‑time data visualization dashboard using the Echarts front‑end library and a Java Web backend, covering requirements, architecture, configuration, code structure, deployment, and source‑code acquisition.
This interview chronicles Wang Zhiwei’s journey from a university student experimenting with GitHub to the CTO of AntV, highlighting how his 100‑line Echarts‑for‑React component launched a thriving open‑source career, shaped Ant Group’s culture, and offers practical advice for developers eager to contribute to open source.
This article introduces the Python package Pynimate, explains how to install it from PyPI, prepares pandas‑style time‑series data, uses the Barplot function with interpolation parameters to generate smooth animated bar charts, and shows how to export the result as GIF or MP4.
Explore a curated list of popular pure-Python libraries—including Streamlit, Gradio, Dash, Voila, and PyWebIO—that let you quickly transform data scripts into attractive, shareable web interfaces, complete with usage stats, recent updates, and visual examples.
This article provides an in-depth exploration of ZRender, a lightweight 2D graphics rendering engine, and its relationship with ECharts, covering installation, basic usage, drawing shapes, interactive effects, and practical applications.
This guide shows how to collect nationwide weather data for the Qingming Festival via the free QWeather API, process it with Python to generate a CSV file, and visualize the rainfall distribution across China using a desktop data‑visualization tool.
The article details Zhongyuan Bank's end‑to‑end agile BI platform construction, covering business goals, a step‑by‑step development timeline, core architecture, eight key functionalities, low‑code data processing, real‑time streaming, visualization dashboards, intelligent Q&A, and future directions for platform intelligence and openness.
This article demonstrates how to create a dynamic, real‑time data visualization dashboard using Echarts on the front‑end and a Java Web backend, covering requirements, architecture, configuration, code structure, deployment, and source‑code acquisition, with screenshots and a Maven dependency example.
In 2022 Baidu’s open‑source ecosystem surged past 1,000 GitHub projects and 20,000 contributors, with flagship initiatives like PaddlePaddle, Apollo 8.0, XuperCore, Apache Doris, bRPC, Teaclave, BFE, Baetyl, HugeGraph and amis achieving major releases, graduations, and industry recognitions.
The article details HuoLala’s internally built YunTai BI platform, covering its motivation, system architecture, data source integration, zero‑code modeling, visual report and dashboard creation, performance optimizations, and future plans for stability and code design, illustrating a comprehensive big‑data visualization solution.
The article introduces d3blocks, a Python library that extends D3.js's interactive visualization capabilities to Python, demonstrating how to create interactive scatter, network, Sankey, and image slider charts with just a few lines of code and offering examples with code snippets and GIF illustrations.
This article introduces Plotly, a powerful Python library for creating interactive line, scatter, and bar charts, explains how to install and use it in offline mode within Jupyter notebooks, and provides complete code examples for each chart type along with visual results.
Streamlit is a free, open‑source Python framework that lets machine‑learning engineers quickly turn scripts into interactive apps, offering features such as top‑down script execution, widget‑as‑variable handling, caching, GPU support, and seamless integration with version‑control tools, all without requiring separate frontend development.
This article introduces data visualization as a scientific discipline, explains its evolving concepts and basic ideas, outlines its relationship to related fields, and provides a curated list of free software tools for visualizing complex multidimensional data.
This article demonstrates how to create a dynamic, real‑time data visualization dashboard using Echarts on the front end and a Java‑based backend, covering requirements, architecture, configuration, code structure, deployment, and source‑code acquisition.
From the 19th‑century origins of Business Intelligence through BI 1.0’s data warehouses, BI 2.0’s self‑service dashboards, and today’s emerging BI 2.5 intelligent era, Sugar BI’s DI predictive module offers low‑code, AI‑driven chart recommendations, automatic analysis, voice interaction, and customizable machine‑learning models for non‑technical business users.
This article introduces the wrn-echarts project, detailing its background, feasibility analysis, architecture, implementation of SVG and Skia rendering modes, touch‑event handling, solved issues, usage steps, performance comparison with react-native-echarts-pro, conclusions, and future plans.
This article introduces the Python library plottable, which builds highly customizable tables on top of Matplotlib, and provides a complete example showing data loading, column styling, and rendering of a styled table along with additional recommended reading links.
This article reviews the evolution of data visualization from early charts to modern interactive graphics, analyzes major visualization categories and schools of thought, discusses technical challenges and emerging web and graphics technologies, and highlights the role of visualization in big‑data analytics and BI solutions.
This article traces the evolution of Business Intelligence from its 19th‑century origins through BI 1.0, 2.0 and the emerging intelligent BI era, explains AI‑augmented analytics, compares predictive‑analysis platforms, and showcases Sugar BI’s DI module that enables code‑free forecasting and smart decision‑making.
This article presents a comprehensive overview of the 58 Agile BI platform called Starfire, covering its background, technical architecture, core permission and query engine challenges, MPP cache acceleration, visualization resource library, developer services, and future development directions.
This article explains how designers can select appropriate chart types, analyze data relationships and features, enhance visual hierarchy, and apply responsive design techniques to create effective B‑end data visualizations that improve information transmission and user understanding.
This article walks through a Python word‑cloud issue where Chinese characters were not displayed, shows how to suppress irrelevant warnings, and provides the exact Matplotlib font settings and font‑installation steps needed to render the characters correctly.
This tutorial introduces five non‑traditional Plotly visualization techniques—animation, sunburst, parallel categories, parallel coordinates, and gauge/indicator charts—showing how to create dynamic, interactive, and aesthetically appealing graphics that enhance data storytelling and communication.
This article explains the motivation, architecture, technology stack, adaptive layout techniques, component development workflow, drag‑and‑drop enhancements, and real‑time state management used to build a low‑code, front‑end‑focused big‑screen data visualization platform.
This guide demonstrates how to generate a fractal tree in Python using Matplotlib, covering library imports, functions for rotation, distance calculation, split-point determination, and a recursive drawing algorithm that visualizes branching structures.
This article explains why ECharts is a popular choice for large‑screen dashboards, introduces the configuration‑driven mindset, demonstrates graphic and animation techniques, and walks through building a reusable Vue 3 base component with dynamic resizing, theming, and empty‑state handling.
This article presents a step‑by‑step tutorial for creating a dynamic, real‑time data visualization dashboard using Echarts on the front end and a Java Web backend, covering design, deployment, architecture, configuration, code structure, and source acquisition.
JSON Hero is a lightweight, feature‑rich JSON utility that offers multiple viewing modes, easy installation via Git and npm, drag‑and‑drop file handling, searchable tree structures, and shareable links, making JSON inspection and editing more intuitive for developers.
This tutorial walks you through installing Matplotlib, importing it alongside NumPy, and creating a variety of common charts—including line, scatter, bar, histogram, pie, and box plots—detailing the required code, parameters, and customization options for each visualization.
Learn how to perform one-dimensional interpolation in Python using SciPy's interp1d function, explore various interpolation methods such as linear, nearest, quadratic, and cubic, and visualize the results with Matplotlib to compare their fitting behaviors on sample data.
This guide outlines the essential concepts of big data, the roles of a front‑end data team, practical workflow steps, platform architecture, industry benchmarks, and actionable strategies for small teams to improve efficiency, visualization capabilities, and digital operations.
The article examines the open‑source Perspective library—originating from J.P. Morgan’s FinOS—and its multi‑language architecture that combines C++/WASM, Rust, JavaScript, and Python to deliver high‑performance, framework‑free data visualizations for real‑time financial analytics, highlighting its use of Apache Arrow, Web Components, and Jupyter integration.
This article presents the overall design, technology selection, architecture, and core implementation details—including adaptive layout, component development workflow, drag‑and‑drop handling, and state push mechanisms—of a low‑code, reusable big‑screen data visualization platform built with React and modern front‑end visualization libraries.
This guide demonstrates how to create a dynamic, real‑time data visualization dashboard using Echarts for the frontend and a Java‑based backend, covering requirement analysis, architecture design, environment setup, code structure, and deployment instructions.
Matplotlib does not render Chinese text by default, showing squares instead, but you can fix this by setting a Chinese font directly in code, adjusting rcParams, or editing the config file; the example demonstrates loading a local font file and applying it to axis labels and titles.
This article introduces fifteen of the most useful Python packages—ranging from data visualization and game development to HTTP requests and home automation—explaining their core features, typical use cases, and why they are valuable tools for Python programmers.
This guide reviews the most popular data visualization tools of 2022, explaining their key features, pricing plans, and how they help organizations turn complex data into clear, actionable insights for better decision‑making.
The paper presents a low‑code, schema‑driven big‑screen visualization platform built with React, ECharts, D3 and related libraries, detailing its four‑module architecture, adaptive layout techniques, reusable component workflow, drag‑and‑drop enhancements, and WebSocket‑based state management, and outlines future extensions such as a component marketplace and 3D rendering.
This article introduces the powerful open‑source Plotly library for Python, showing how a single line of code can create interactive charts such as bar, box, scatter, time‑series, and advanced visualizations, while also covering installation, theme customization, and integration with Jupyter Notebook and Plotly Chart Studio.
This guide walks you through six practical steps—from choosing the right chart style and fonts to defining colors, element details, and decorative touches—so you can create tailored, professional charts that fit any business or reporting context.
The article guides readers through practical Excel functions—such as REPT, SUMPRODUCT, FREQUENCY, and text extraction—and advanced chart types like grouped bar, butterfly, and dual‑pie charts, while offering visualization best practices that prioritize clarity, efficient data processing, and effective communication of insights.
TransBigData is a Python library that streamlines the preprocessing, gridding, visualization, and OD extraction of transportation spatiotemporal datasets such as taxi GPS, bike sharing, and bus data, offering concise, efficient functions for data cleaning, rasterization, interactive mapping, and analytical workflows.
This article presents a step‑by‑step guide to creating a real‑time data‑visualization dashboard using Echarts on the front end and a Java Web backend, covering requirement analysis, architecture design, environment setup, code structure, deployment, and how to obtain the source code.
This article outlines five practical principles—show the data, reduce clutter, combine text and graphics, avoid spaghetti charts, and start from gray—to help creators design clear, audience‑focused visualizations for reports, blogs, and social media.
This article demonstrates how to analyze and visualize time‑series sensor data in Python using libraries such as NumPy, Pandas, Matplotlib, Seaborn and Scikit‑learn, covering data import, preprocessing, mean‑std plots, boxplots, Fourier transforms, entropy calculation, PCA dimensionality reduction and autocorrelation analysis.
This article introduces four advanced Python data‑visualization methods—heat map, 2D density plot, spider (radar) plot, and hierarchical tree diagram—explaining their concepts, practical use cases, and providing complete seaborn, matplotlib, and SciPy code examples for each.
