This article explains the background of China's 211 university project, lists the 116 institutions across six tiers, and provides a complete Python script that uses geopandas, matplotlib, and animation to create an interactive map showing the geographic distribution of these top universities.
This article surveys eight popular Python visualization libraries—Matplotlib, Seaborn, Pandas, ggplot, Bokeh, Plotly, Pygal, and NetworkX—explaining their strengths, weaknesses, typical use‑cases, and providing concrete code examples to help readers choose the right tool for exploratory analysis or presentation.
This article details JD Logistics’ challenges in handling petabyte‑level data, outlines their existing data architecture, and explains how they adopted Apache Doris for faster, scalable analytics, covering table management, data import workflows, visualization tools, and future roadmap for data engineering.
This guide walks you through building a Python web‑scraper that extracts NBA player information from the Hupu website, cleans and visualizes the data, and automatically creates Excel files with embedded line charts, covering URL navigation, data parsing with requests and BeautifulSoup, and chart generation with xlsxwriter.
This article explains how Streamlit, a free open‑source Python framework, simplifies the creation of interactive machine‑learning applications by letting developers write pure Python scripts that automatically rerun on each interaction, cache results, and integrate seamlessly with existing tools and workflows.
This article explains Apache Zeppelin's features, architecture, and how to deploy and customize it on Kubernetes to achieve scalability, resource isolation, multi‑tenant support, and persistent storage for interactive big‑data analytics.
The article details DiDi’s development of a real‑time, large‑screen data‑visualization system built on a custom WebGL‑based map framework, describing how they tackled one‑time map loading, massive data compression, and high‑performance rendering of trajectories, bubbles, heatmaps, flight lines and animated scatter points, supported by interactive debugging tools.
Learn how to install the Python wordcloud module, generate custom word clouds from text, customize fonts, colors, backgrounds, and stopwords, and integrate with Pillow and matplotlib, all illustrated with code snippets and images, to visualize text data effectively.
This tutorial walks through installing Plotly Express in a Python 3.7 Jupyter environment and demonstrates how to create a wide range of visualizations—including line, scatter, area, geographic, violin, and polar charts—using concise one‑line commands and built‑in datasets.
This article walks you through creating impressive big‑screen visual dashboards using FineReport, covering tool selection, data preparation, decision‑report setup, dataset creation, layout design, visual polishing, dynamic effects, and final preview to help you deliver compelling data visualizations efficiently.
This tutorial walks you through creating a variety of Python visualizations—line plots, bar charts, histograms, density plots, scatter matrices, and faceted grids—using pandas, matplotlib, and seaborn, with complete code examples and tips for customizing appearance.
This article introduces fifteen essential Python packages—ranging from data visualization and game development to web frameworks, JSON handling, and home automation—explaining their core features, typical use cases, and why they are valuable tools for Python developers.
This article compares four popular data‑visualization platforms—Alibaba Cloud DataV, JimuReport, Baidu Sugar, and FanRuan—highlighting their features, registration processes, strengths, and drawbacks, to help you choose the most suitable solution for turning exported database data into clear, decision‑making charts.
This tutorial walks data analysts through transitioning from Excel and SQL to Python, covering environment setup, data import with pandas, web scraping, cleaning, renaming, type conversion, filtering, grouping, merging, and visualization using Jupyter Notebook and popular libraries.
This article presents a collection of practical techniques—including Pandas Profiling, Cufflinks/Plotly visualizations, IPython magic commands, Jupyter formatting, keyboard shortcuts, multiple-output cells, and RISE slide creation—to streamline and speed up everyday data analysis workflows in Jupyter notebooks.
This tutorial shows how to generate eye‑catching dynamic bar‑chart‑race visualisations in Python using the bar_chart_race library, covering installation challenges, basic usage, GIF/MP4 output, and a wide range of customisation options such as orientation, sorting, colour maps, labels and Chinese text support.
The article guides front‑end developers from basic 2D charting to immersive 3D data visualizations, explaining WebGL’s rendering pipeline, comparing libraries such as Three.js and Babylon.js, detailing camera, lighting, model handling techniques, and showing how Draco compression dramatically shrinks GLB assets while preserving quality.
This article introduces the open‑source Plotly library for Python, showing how to install it, use the cufflinks wrapper with Pandas, and create a variety of interactive visualizations—from simple bar and box plots to scatter matrices, time‑series charts, heatmaps, and themed 3D figures—using just one or two lines of code.
This article provides a comprehensive guide to rendering and animating lines in the Canvas API for data visualization, detailing the mathematical foundations of polylines and cubic Bezier curves, segment-based drawing algorithms, spline interpolation methods, and optimized animation synchronization techniques.
This case study describes how the Luffy platform was built from scratch to replace cumbersome Excel workflows with a minimalist, component‑driven design that visualizes data securely, improves team collaboration, and accelerates product iteration for operations and product teams.
Learn how to quickly generate beautiful SVG visualizations in Python using the Pygal library, covering installation, data loading with pandas, and creating line, bar, pie, radar, box, scatter, funnel, and gauge charts with various built‑in styles, all with minimal code.
This week’s developer newsletter highlights Google’s new TReCS framework for text‑image translation, OpenAI’s massive 7,500‑node Kubernetes scaling for machine learning, Apache ECharts 5 release, WebRTC becoming an official standard, China’s autonomous "长安链" blockchain, JD’s open‑source FaceX‑Zoo toolkit, and two AAAI 2021 papers on graph diffusion traffic forecasting and label‑confusion‑enhanced text classification.
This guide provides a comprehensive walkthrough of Kibana, covering installation, connection to Elasticsearch, data discovery, query syntax, visualizations, dashboards, and monitoring, with step‑by‑step instructions and screenshots to help users effectively explore and visualize their Elasticsearch data.
This roundup highlights Glassdoor’s 2020 job ranking with Java developers leading, Firefox’s decision to drop desktop PWA support, Edge’s new enterprise PWA features, Google’s MicroDroid minimal Android project, Twitter’s open‑source iOS text‑editor API, and Apache Superset’s rise as a premier data‑visualization platform.
Learn step‑by‑step how to scrape Tencent’s location data via its API, process the coordinates with pandas, deduplicate and filter them, and finally visualize regional foot‑traffic as an interactive heatmap using Folium, complete with code snippets and practical tips.
This article provides an in‑depth overview of Apache Superset, covering its recent graduation to a top‑level Apache project, key visualization features, extensive data source support, step‑by‑step installation via pip or Docker, PostgreSQL connection, role‑based permission model, and practical usage insights.
In this article we demonstrate how to use Python’s requests and BeautifulSoup to scrape a static webpage listing the world’s top 100 most beautiful women, process the data with pandas and pyecharts for continent, nationality and profession statistics, and apply Baidu’s face‑recognition API to score each celebrity’s attractiveness.
This guide reviews fifteen essential Python packages—from data‑visualisation with Dash and image handling with Pillow to web requests, JSON parsing, and home automation—explaining their key features, typical use‑cases, and why they’re valuable tools for developers.
This article explains D3.js fundamentals—including its selection, join, and scale modules—then walks through a step‑by‑step implementation of a fully customized bar chart using SVG, demonstrating how to bind data, create axes, and add interactive tooltips.
This article presents a comprehensive methodology for designing clear and aesthetically pleasing charts in B‑end platforms, covering visual hierarchy, element layering, size rules, legend placement, and data color palette techniques to improve information readability and visual impact.
This article examines how the Google Maps team spent a year redesigning their color system, reducing over 700 legacy colors to just 25, to improve map usability, accuracy, and visual consistency while preserving geographic detail.
This article explains the design, features, and technical implementation of Youzan’s BI platform, covering its target users, visualization workflow, supported chart types, filtering, permissions, drill‑down, calculated fields, SQL generation logic, and future development directions.
This talk explores how to conduct user behavior analysis on massive data sets, compares existing analytics tools, and presents Alibaba Dataworks' end‑to‑end solution—including funnel and link visualizations, a big‑data processing architecture, and future intelligent link capabilities—to uncover and resolve user‑experience issues efficiently.
The article surveys ten Python visualization libraries—from the classic matplotlib to newer tools like Plotly and Gleam—detailing each library’s main features, typical use cases, developer information, and where to find further documentation, helping readers choose the right tool for their data projects.
This tutorial explains how to count word frequencies using Python's collections.Counter from static, random, or file‑based text sources and then visualize the results with the WordCloud library, covering code examples, parameter settings, and available colormaps.
This article outlines the step‑by‑step approach to constructing a big data platform—starting with script toolization, evolving through tool services, platformization, and productization, comparing business‑scenario and generic‑component construction methods, and detailing the Lambda architecture for data collection, processing, and visualization to drive business operations.
This guide introduces Plotnine, a Python implementation of ggplot2, covering installation, data preparation, quick plotting with qplot, building layered graphics, customizing colors, sizes, gradients, creating bar and line charts, and links to further documentation.
This curated collection highlights recent essential reads for front‑end developers, covering the deprecation of LibSass, using service workers to turn single‑page apps into polyfills, career growth insights, comprehensive HTTP knowledge, overcoming technical and business bottlenecks, performance optimization tips, Node.js sandbox security, Facebook’s Recoil state‑management library, and the opportunities and challenges of enterprise‑level data visualization.
The talk outlines why enterprise data visualization is essential for extracting value from massive, multi‑dimensional data, describes design and development challenges, presents AntV's comprehensive frontend visualization solutions, and predicts future trends such as intelligent, democratized, and decision‑integrated visual analytics.
This tutorial explains how to create a visually striking, interactive 3D globe using three.js, covering texture mapping, GLSL shader programming, click interaction logic, flight‑line effects, performance considerations, and a complete example of vertex and fragment shaders with accompanying JavaScript code.
From the early days of static web pages to today's data‑driven, AI‑enhanced interfaces, this article explores how the big‑data boom and artificial‑intelligence advances since 2010 have transformed front‑end technologies, driving innovations in data visualization, web‑based software, and diverse user interactions.
Xianyu transformed its slow, manual data‑analysis workflow— plagued by BI bottlenecks, slow SQL, and cumbersome front‑end integration—into a three‑layer, serverless architecture that abstracts SQL into reusable atoms, automates data pipelines, and delivers smart, seconds‑level visual dashboards, slashing development effort from five days to half a day.
From the early days of static web pages to today’s data‑driven, AI‑enhanced interfaces, this article explores how the rise of big data platforms like Alibaba Cloud’s Feitian has transformed front‑end development through advanced visualization, software‑Web convergence, and diverse new interactions.
This article introduces the cutecharts Python library that generates hand‑drawn style visualizations, explains how to install it, describes common chart parameters, and provides detailed API references and code demos for Bar, Line, Pie, Radar, and Scatter charts, complete with example images.
This article compares eight popular Python visualization libraries, outlining their strengths, weaknesses, and ideal use‑cases while providing clear code examples to help developers pick the right tool for their data‑storytelling needs.
The article chronicles a front‑end engineer’s eleven‑year evolution at Alibaba, detailing a six‑layer technical‑leadership pyramid, personal missteps, the shift to data‑visualisation, and key reflections on career growth, teamwork, and strategic vision for long‑term success.
Apache ECharts 4.9.0, an open‑source JavaScript charting library incubated by Apache, introduces new features such as polygon graph connections and horizontal funnel orientation, adds tooltip text shadow and brush style options, and resolves numerous bugs across components like toolbox, tooltip, and bar charts.
This article compares five popular Python visualization libraries—Matplotlib, Pyecharts, Seaborn, Plotly, and Bokeh—by drawing identical multi‑series bar charts, evaluating interactivity, aesthetics, code complexity, documentation quality, and assigning a subjective score to each.
This article explores Alibaba's Data Experience Technology team's comprehensive architecture for SQL editors, BI platforms, data visualization, low‑code solutions, heterogeneous rendering, and data security, highlighting design principles, performance optimizations, and future directions across the data lifecycle.
The article reviews Vue 3’s mid‑2020 status with three new SFC syntax‑sugar features, debates the future of front‑end/back‑end integration, evaluates popular data‑visualization frameworks, highlights security risks in Node.js’s legacy URL API, and showcases a DevOps‑based productivity tool and high‑performance mini‑program practices.
This article analyzes 13,966 Chinese operations‑engineer job postings scraped from 51job, cleaning the data with Python and Pandas, then visualizing industry demand, city concentration, salary ranges, education requirements, company size distribution, and keyword trends to guide job seekers and recruiters.
This tutorial walks backend engineers through real‑world scenarios of log collection, parsing, and analysis using the ELK stack—Logstash, Elasticsearch, and Kibana—showing configuration examples, Grok patterns, RESTful API queries, aggregations, and visualizations to boost operational efficiency.
This tutorial explains how to use Python's Matplotlib library to embed a zoomed inset subplot, configure its size and position, plot the same data inside it, set custom axis limits, and connect the inset to the main figure with visual cues.
QQ Music’s data team tackled massive PB‑scale, real‑time analytics challenges by migrating from Hive to a ClickHouse‑based OLAP platform integrated with Tencent Cloud EMR and Superset, achieving low‑latency, high‑availability data processing, self‑service visualization, and efficient read/write scaling for billions of daily events.
This guide demonstrates how to install PyEcharts, import necessary modules, and use the Liquid chart class to create customizable waterball charts in Python, including setting colors, shapes such as rectangle, diamond, round‑rect, triangle, and arrow, and rendering them to HTML files.
This article introduces DevUI’s fully configurable quadrant diagram component for Angular, detailing its features such as draggable labels, three display modes, customizable styles, comprehensive configuration interfaces, axis settings, and drag‑and‑drop integration, along with code examples for quick implementation.
This tutorial walks you through using Python to crawl the TIOBE index from 2001 to the present, clean the data into a nested dictionary, and create dynamic visualizations that reveal how programming language popularity has shifted over two decades.
Grafana 7.0 introduces a revamped user experience, a unified data model, a new plugin platform, Jaeger tracing support, powerful data transformations, AWS CloudWatch Logs integration, and enterprise usage analytics, offering enhanced visualization and monitoring capabilities across major data sources.
This article presents a data-driven analysis of programming language popularity and salary distribution in the Chinese job market, featuring a detailed table of average and median wages, headcount, and percentage share for each language, along with a pie‑chart visualization.
This article examines Douban ratings and 500 user comments for the Chinese web drama "Dragon Tomb Cave", visualizes rating distribution, comment timing, geographic origins, and compares sentiment with other series using Python web‑scraping and data‑visualization techniques.
This case study demonstrates how to clean, explore, and model a large restaurant dataset using Python and QGIS, derive a price‑performance metric, rank food categories, and identify the most suitable locations for opening a dessert shop through visualisation and scoring formulas.
This article explains how to install the PrettyTable library, create and populate tables with rows or columns, import data from CSV, databases, or HTML, customize output formats and styles, and selectively display or sort table content for Python command‑line tools.
This article details the technical process of creating a 3D visualization component for city management systems, covering HTML structure, CSS animations, and mathematical calculations for distributing elements on a circular path.
This tutorial demonstrates how to install Plotly, load COVID‑19 case data from an Excel file using pandas, and generate an interactive US choropleth map that visualizes cumulative confirmed cases by state, concluding with exporting the map to an HTML file.
This article introduces the G2 JavaScript visualization engine, highlights its ease of creating interactive, data‑driven charts, and provides three complete code examples—including an app user distribution chart, an enterprise cash‑flow chart, and a dynamic bar chart—demonstrating how to render Canvas or SVG graphics with minimal effort.
This guide shows how to set up Elastic Stack (Elasticsearch, Logstash, Kibana) on a Vagrant CentOS‑8 VM, import COVID‑19 data from DXY and WHO sources, customize mappings, enrich fields with painless scripts, and create interactive visualizations and a dashboard to monitor the pandemic in real time.
This comprehensive Matplotlib tutorial walks you through installing the library, importing pyplot, creating basic and advanced plot types—including line, contour, histogram, bar, and stream plots—customizing fonts, axes, and labels, saving figures, and using subplots to compose multi‑panel visualizations, all with clear code examples.
This article details how to use Python to crawl user and article data from the Jianshu platform, then apply FineBI for business intelligence analysis and visualizations, covering author contracts, follower distribution, popular articles, and engagement metrics.
This guide shows data product managers how to master essential Excel features—including pivot tables, VLOOKUP, and the Text‑to‑Columns tool—so they can turn raw data into actionable insights and implement data‑driven product strategies efficiently.
The article explains how Python can serve as a backend for data visualization by demonstrating the implementation of PCA on the Iris dataset using sklearn, recommending Anaconda for beginners, and discussing performance‑boosting strategies such as integrating C modules.
This tutorial demonstrates how to add Baidu's ECharts JavaScript visualization library to a Spring Boot backend, covering project setup, Maven dependencies, controller creation, Thymeleaf template configuration, and rendering a line chart in the browser.
This article explains how the front‑end team built an internal visual dashboard framework called “Big” to replace manual coding and costly third‑party services, detailing its architecture, component design, editing workflow, code snippets, challenges, and future improvements.
This article presents CuteCharts, a Python package that generates hand‑drawn style charts such as bar, line, pie, radar, and scatter plots, provides installation instructions, and includes complete code examples for each chart type to help developers quickly create visually appealing graphics.
The document outlines AutoBI, a company‑wide one‑stop data visualization platform, detailing its background, overall architecture, key technical components such as real‑time/offline data switching and query processing, integration capabilities, and practical case studies, highlighting efficiency gains and future development plans.
This article explains how to use d3.js to create interactive resource topology diagrams, covering SVG basics, d3 selections, data binding, enter/update/exit patterns, force‑directed layout, path calculations, and various optimizations such as text centering, arrow markers, and intersection handling.
This article introduces Sketchify, a JavaScript tool built on RoughJS that transforms any SVG‑based chart into a hand‑drawn sketch style, explains its underlying algorithm, provides sample code, demonstrates its application to popular libraries such as ECharts, AntV G2, Highcharts, amCharts and xCharts, and discusses its limitations and resource considerations.
This article explains how machine‑learning engineers can create fully functional, interactive apps using only Python and the open‑source Streamlit framework, covering its core principles, widget handling, caching, GPU support, deployment workflow, and real‑world examples with code snippets and diagrams.
This article shares ten practical Python plotting tricks—covering subplots, axis labels, titles, annotations, color hues, point sizes, threshold lines, multiple Y‑axes, overlapping plots, and bar‑plot ordering—to help data scientists create clear, explanatory visualizations.
LADV, an AI‑powered visual design tool embedded in DataV, automatically recognizes hand‑drawn sketches, infographics, and screenshots, generating fully configurable visualizations in seconds, drastically reducing design costs and streamlining the workflow from concept to interactive dashboards.
Python’s ecosystem—Jupyter notebooks, Pandas for data manipulation, scikit-learn for machine learning, and matplotlib/Basemap for powerful visualizations—offers a streamlined, scriptable environment that outperforms traditional Java or PHP workflows, enabling researchers to write, run, and document code seamlessly in a single web interface.
Learn how to create wave interference simulations by generating data with Java, processing it in Python, and visualizing the results using Plotly's contour plot and heatmap features, including step‑by‑step code snippets and configuration details for producing clear interference patterns.
This guide shows how to use Python and Plotly to create a distplot—combining a histogram and density curve—to visualize API response time data read from a log file, complete with a ready‑to‑run script and sample output image.
This article reviews five common dashboard chart types—bar, bar‑horizontal, pie, line, and area—explaining their ideal use cases, key characteristics, and design tips to help B‑end designers choose the most effective visualization for their data.
This article presents a Python script that reads performance test log files, extracts timing data, and visualizes the results as violin charts using Plotly, providing a reusable utility for server performance analysis and reporting.
This article demonstrates how to use Selenium and Python to crawl over 6,000 Chinese tourist attractions from Qunar, extract ratings, popularity and sales data, and visualize the results with pandas, seaborn, matplotlib, and pyecharts, revealing the most visited sites and regional travel trends during the 2019 National Day holiday.
This article explains Elasticsearch X‑Pack's machine‑learning capabilities, covering supervised and unsupervised learning concepts, data preparation, task creation types, architecture components, data flow, result indices, and provides code examples for configuring and running ML jobs.
This article demonstrates how to generate a simple pie chart using Python's Plotly library, providing step‑by‑step code, explanation of the class structure, and a visual example for beginners; it also shares the resulting chart image and links to additional technical resources.
This article provides a thorough front‑end guide to data visualization, covering its definition, scientific and information branches, a step‑by‑step workflow, common pitfalls, and a curated list of tools and libraries such as AntV G2, F2, G6, L7 and others for various scenarios including general reports, mobile, large‑screen dashboards, graph editing, and geographic visualizations.
This comprehensive guide walks you through installing pyecharts, creating basic charts such as bar, line, scatter, boxplot, word cloud, and geographic maps, customizing themes, axes, data zoom, and styles, and mastering chart combinations like Grid, Overlap, Page, and Timeline to build sophisticated visualizations.
This tutorial walks you through loading historical stock data from Yahoo Finance with pandas, computing moving averages and returns, comparing multiple tech stocks, engineering features, training linear, quadratic, and K‑Nearest‑Neighbor models using scikit‑learn, evaluating their confidence scores, and visualizing short‑term price forecasts—all in under a dozen minutes of reading and coding.
This article reviews ten high‑performance JavaScript chart libraries—amCharts, AnyChart, Chart.js, Chartist.js, D3.js, FusionCharts, Google Charts, Highcharts, Plotly.js, and ZingChart—detailing their main features, pricing models, and where to find documentation, helping developers choose the best tool for their data‑visualisation needs.
This tutorial demonstrates how to log into WeChat via the itchat library, retrieve friends' region, gender, and signature information, store it as JSON, and generate visualizations such as gender distribution charts, province maps, and word clouds using Python.
This tutorial walks you through installing the WordCloud library, resolving common Windows compilation errors, handling Chinese font encoding issues, and creating both basic and image‑masked word clouds in Python, complete with code examples and visual results.
This article details a complete workflow for extracting nationwide tourist attraction data from Qunar, cleaning and enriching it with geographic coordinates, and performing multi‑level statistical analysis and visualizations—including sales rankings, popularity metrics, heatmaps, and word clouds—to reveal regional tourism patterns across China.
The article details JD's 2018 upgrade of its Business Intelligence platform, describing how unified offline OLAP with ClickHouse, Spark, and Scala, timeliness optimizations, and a React‑based visualization component library together improve data consistency, performance, and user experience for merchants.
This article surveys a wide range of data visualization tools—from basic spreadsheet solutions like Excel to advanced JavaScript libraries such as D3, GIS platforms like QGIS, and specialized charting suites—highlighting their key features, supported data sources, and typical use cases for effective big‑data analysis.
Improve your data visualizations by applying conditional formatting, adding trend lines, using rule‑based filters, building drill‑down hierarchies, sorting strategically, formatting numbers for clarity, incorporating comparative metrics, and crafting concise, relevant chart titles to convey clear, actionable insights.
This guide walks you through installing ECharts, creating a basic chart in a web page, and using the Python ECharts API, providing step‑by‑step instructions, code snippets, and visual examples for effective data visualization.
The article reviews twelve leading IoT platforms—such as Temboo, Ubidots, Cayenne, ThingSpeak, TheThings.io, Xively, Kaa, Google Cloud IoT, Azure IoT, IBM Watson IoT, Artik Cloud, and AWS IoT—highlighting their key services for data ingestion, device management, security, analytics, and integration to help developers build scalable, secure IoT projects.
These 20 free and open‑source data visualization tools—from JavaScript libraries like D3.js and Chartist.js to user‑friendly platforms such as Datawrapper, Google Data Studio, and Tableau Public—enable businesses and analysts to transform raw data into interactive charts, maps, timelines, and dashboards, improving insight, decision‑making, and profitability.
