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.
This article introduces Plotly's offline plotting in Jupyter Notebook, explains the key parameters of plotly.offline.iplot, demonstrates how to create and combine traces, and shows how to customize layout elements such as fonts, titles, axes, legends, grids, and other figure properties using Python code.
Datav, Shopee’s internal low‑code data‑visualization platform, automates dashboard creation by providing a DSL‑driven designer, reusable chart components, and auto‑generated SQL, cutting project cycles from roughly 40 to 20 days and front‑end effort from ten to three person‑days while streamlining data‑source integration and permission management.
This article introduces ten Python packages that automate exploratory data analysis, explaining each library's capabilities, providing concise usage examples, and showing how they can generate comprehensive data summaries and visualizations with just a few lines of code.
This article introduces ten Python data‑visualization libraries—ranging from the classic matplotlib to modern interactive tools like Bokeh and Plotly—explaining their main features, typical use cases, and where to find more information.
This article explains the reasoning behind adopting JSX in the mobile visualization engine F2, comparing declarative JSON and imperative API approaches, and outlines JSX's programmability, extensibility, stable tree structure, complete description, and tooling advantages.
This guide explains how to leverage Seaborn's FacetGrid to create multiple coordinated plots, covering basic usage, parameter options, and customization techniques such as adding legends, titles, axis labels, figure size, and DPI settings, with complete Python code examples.
Sugar BI, Baidu Cloud’s AI‑powered business intelligence platform, lets users create professional, zero‑code dashboards in minutes by connecting to 30+ data sources, leveraging Apache ECharts, intelligent chart recommendation, and natural‑language voice interaction to deliver automated analysis, visualization, and predictive insights.
This article explains machine learning fundamentals using a house‑location classification example, illustrating how decision trees create split points from features like elevation and price, grow recursively, achieve high training accuracy, and reveal overfitting when evaluated on unseen test data.
This article details Sugar BI’s end‑to‑end technical architecture, covering its AI‑enhanced visual analytics, zero‑code report building, multi‑source data integration, smart chart recommendation, and voice interaction, while illustrating the underlying big‑data and cloud components that power the platform.
This article shares practical experiences and foundational concepts for creating data‑visualization dashboards, covering screen types, design principles, characteristics, audience analysis, and the broader role of visualization in turning massive data into actionable insights while enhancing human cognition.
This article explores Rust's rapid rise in front‑end infrastructure, data visualization, Node, Web3, and automotive software, highlighting its performance, safety, and growing industry adoption while acknowledging its steep learning curve and future importance for developers.
JSON Hero is a lightweight web‑based tool that provides multiple visualizations such as column, tree, and raw JSON views, along with editing, searching, and sharing features, and the article explains its purpose, installation steps, usage, and key capabilities.
This article surveys twelve widely used Python visualization libraries, dividing them into exploratory and interactive categories, and explains each library's strengths, typical use cases, and key features to help developers choose the right tool for their data analysis needs.
This article explores frontend data collection and monitoring systems, covering why they are needed, what they can achieve, existing implementation schemes, and future evolution directions including performance data reporting and interface data monitoring.
This article outlines a three-step approach to Python data visualization—defining the problem and selecting a chart type, transforming data and applying appropriate functions, and fine-tuning parameters—while introducing key libraries such as Matplotlib, Seaborn, Bokeh, and Pandas with code examples.
This article details the challenges, design choices, deployment steps, detection model creation, data processing, visualization, and future plans of JD Daojia's security operations platform, highlighting the use of Graylog, Elasticsearch, and MongoDB to achieve scalable, real‑time threat detection and response.
This guide demonstrates how to model a ten‑village water‑pipe network as a weighted graph, import the distance matrix into pandas, construct the graph with NetworkX, compute its minimum spanning tree to obtain the shortest connecting pipeline, and visualize the result.
This tutorial demonstrates how to use Python's ReportLab library to create PDF reports, covering installation, module imports, font registration, and a Graphs class with static methods for titles, paragraphs, tables, bar charts, and images, culminating in a complete script that assembles and builds the PDF.
Apache ECharts 5.3.3 has been released, introducing new stacking strategies for bar charts, tree focus support, boolean selectedMode for visualMap, and a series of bug fixes across line, tree, graph, radar, geo, tooltip, dataZoom, markLine, markArea, axis, graphic, decal, radialGradient, and type definitions.
This article introduces the concept of interpolation for discrete data, demonstrates how to use SciPy's interp1d function with various methods (linear, nearest, nearest‑up, zero, quadratic, cubic), visualizes the resulting curves alongside the original points, and provides complete Python code for reproducing the plots.
This article provides a comprehensive guide to Kibana, an open‑source data analysis and visualization platform for Elasticsearch, covering its installation, configuration, index pattern setup, data discovery, query syntax, visualization creation, dashboard building, and monitoring, with step‑by‑step instructions and screenshots.
This guide shows how to download historical Kweichow Moutai stock data from NetEase Finance as a CSV file, handle encoding issues, and use Python's pandas and matplotlib libraries to filter the data and create both volume and OHLC line charts for a selected month.
This tutorial explains how to build a responsive, theme‑switchable real‑time data visualization dashboard using a Python Flask back‑end and ECharts front‑end, covering requirements, architecture, HTML/CSS grid layout, JavaScript chart logic, JSON data handling, and deployment steps.
This article describes how a metrics dashboard was refactored by standardizing chart and table data formats, leveraging Ant Design Charts, and building reusable React components such as ChartCardList and RankTable, thereby minimizing front‑end development effort and enabling rapid addition of new visualizations.
This guide walks you through installing Kibana, configuring its connection to Elasticsearch, exploring data with Discover, crafting queries using Lucene and DSL, creating visualizations and dashboards, and monitoring cluster health, providing step‑by‑step instructions, screenshots, and practical tips for effective data analysis.
This tutorial explains how to use Python's Matplotlib library to generate common visualizations such as line, histogram, bar, and pie charts, covering the basic workflow, code examples, and function signatures for quick data analysis and reporting.
This article teaches how to use Python's pandas library to create various exploratory visualizations—line, scatter, histogram, bar, pie, and box charts—on real datasets such as Iris, the 2013 American Community Survey, and the Boston Housing data, including code examples and interpretation of results.
Learn how to install Kibana, connect it to Elasticsearch, configure index patterns, explore data with Discover, craft queries using Lucene syntax, create visualizations and dashboards, and monitor your stack, all with step‑by‑step guidance and practical examples.
This article introduces PyScript, a framework that lets you embed and run Python code inside standard HTML, walks through a simple Hello World example and a data‑visualization demo, and shares practical insights on its current capabilities and limitations.
The article explains why properly interpreting modeling results is essential, covering reader comprehension, the role of visualizations, guiding key insights, and how explanation fosters reflection and improves the overall significance of the research.
This article introduces several open‑source JSON utilities—including visualization, command‑line manipulation, data querying, online storage, and dynamic form generation—explaining their features, typical use cases, and providing direct GitHub links for developers seeking practical JSON solutions.
This article demonstrates how to generate Gaussian noise images in Python using matplotlib, adding both vertical and horizontal colorbars with custom tick labels, and provides complete code snippets for creating the plots and visualizing the results.
This article explores various innovative metric visualizations and practices for agile teams, distinguishing process and value metrics, emphasizing accurate data collection, and offering numerous chart examples to deepen insight and drive continuous improvement.
This article provides an extensive overview of over 60 data visualization chart types—including bar, line, pie, heatmap, and specialized maps—explaining their purposes, advantages, drawbacks, and recommending popular tools such as D3, Tableau, Excel, and many open‑source libraries for creating each chart.
This tutorial shows how to download NASA GES DISC SO₂ data for the 2022 Hunga Tonga–Hunga Ha'apai eruption, extract relevant fields with H5py, build a Pandas DataFrame, and create both scatter‑plot and time‑varying heat‑map visualizations using Matplotlib, Seaborn and Folium.
This article introduces the Python library Folium, explains its installation, demonstrates basic map creation, marker and shape addition, and presents a practical case of visualizing parking lot geolocation data with interactive features and clustering, providing code snippets and usage guidance.
This article provides a detailed tutorial on plotnine, the Python implementation of the Grammar of Graphics, covering installation methods, data handling, aesthetic mappings, geometric objects, statistical transformations, scales, position adjustments, coordinate systems, faceting, themes, and multi‑panel plotting with code examples.
This article explores practical visualization design methods for data middle platforms, identifying common issues like unclear lists, weak brand perception, and poor scenario continuity, and presents case studies and step‑by‑step strategies to create clearer, more intuitive B‑end interfaces.
This guide walks you through analyzing Douyin's web pages, bypassing its anti‑scraping checks, constructing the proper API URLs, fetching comment data via Python, storing the results, and visualizing the comments as a dynamic dancing word cloud.
This tutorial introduces pyecharts, a Python wrapper for Echarts, covering installation (including Tsinghua mirror), Python 2 compatibility, and step‑by‑step code examples for creating Bar, Pie, Boxplot, Line, Radar, Scatter, Grid, and Overlap charts with rendering to HTML.
This tutorial demonstrates how to build an interactive fruit‑sales dashboard using Python's Dash framework together with Tailwind CSS for styling, covering data preparation with Pandas, chart creation with Plotly, layout design, and deployment on a local server.
ByteDance’s data platform front‑end team surveyed academic, commercial, and open‑source word‑cloud solutions, identified gaps in geo‑ and shape‑based clouds, and engineered a performant front‑end layout algorithm that generates customizable shape word clouds for diverse business scenarios.
This presentation explains the fundamentals of financial risk control, outlines the data and AI technologies needed across the pre‑loan, in‑loan, and post‑loan stages, and showcases visual‑analytics and federated‑learning case studies that improve model interpretability, monitoring, and enterprise risk management.
This article introduces DataEase, an open‑source BI platform built with SpringBoot, Apache Doris, and Kettle, explains its system and functional architecture, provides step‑by‑step installation commands and configuration details, and demonstrates how to create datasets, views, and dashboards for data analysis.
Learn how to generate grayscale, random color, gradient, and custom visualizations using only NumPy (and optional Matplotlib ColorMaps) with concise Python code, covering array creation, image conversion, pixel manipulation, and advanced colormap techniques for powerful data art.
This article walks through a puzzling Pyecharts map issue where identical data produced one colored map and another blank one, reveals that the hidden cause is the use of numpy.int64 instead of plain int, and shows how casting fixes the visualization.
The talk outlines how to treat raw data like crude oil—using a robust big‑data platform, entity‑resolution techniques, secure data governance, and visualisation tools to transform disparate sources into reusable assets that drive rapid business insights and operational efficiency.
This tutorial introduces pyecharts, the Python wrapper for Baidu's Echarts library, explains how it generates a render.html file, and provides complete code examples for drawing various map visualizations—including basic maps, maps without labels, visual‑mapped maps, world maps, and regional maps—while also showing how to replace the dummy data with your own dataset.
This article introduces the ZRender canvas rendering engine, explains its underlying Canvas2D API, demonstrates how to integrate ZRender into projects, and provides detailed code examples for creating shapes, handling events, adding animations, and extending the library with custom elements such as video support.
This guide introduces Uber's open‑source Kepler.gl library for geospatial visualization in Jupyter notebooks, covering installation via pip, a simple map example, adding CSV or DataFrame data, customizing through the UI, retrieving configurations, and exporting the map to an HTML file.
Metabase is a fast, open‑source BI tool that can be set up in about five minutes using Docker, offering a user‑friendly interface for creating dashboards, custom SQL queries, data pivots, and multi‑table analysis without requiring deep SQL knowledge, making data visualization accessible to all.
This tutorial explains how to generate donut (ring) charts using Python's Matplotlib library, covering basic pie‑to‑donut conversion, custom colors, label positioning, background styling, annotation markers, and multi‑level donut visualizations with complete code examples.
This guide introduces Kepler.gl, Uber’s open‑source geospatial visualization library, shows how to install it in a Jupyter notebook, demonstrates creating maps from CSV, GeoJSON or DataFrame sources, customizing charts, extracting configurations, and exporting the result to an HTML file for easy sharing.
This article explores the origins of data visualization from John Snow's cholera map, explains why visual representation is essential for decision‑making in the big‑data era, outlines its historical development, and provides practical guidelines and checklists for creating effective visual analytics.
This guide walks you through installing the open‑source BI platform DataEase, explains its architecture built on SpringBoot, Apache Doris, and Kettle, and demonstrates how to create data sources, datasets, and visual dashboards from Excel and MySQL using Docker containers.
This tutorial demonstrates how NumPy can be used to generate and display grayscale, color, gradient, and colormap‑enhanced images in Python with just a few lines of code, covering module import, array creation, image conversion, and advanced color mapping techniques.
This guide demonstrates how to use a second‑hand housing dataset to create various reports—frequency tables, cross‑tables, and summary statistics—and visualise them with bar and stacked bar charts using Python's pandas library.
This tutorial walks you through downloading the past decade of Chinese earthquake records, cleaning and enriching the dataset with coordinate conversion, timestamp parsing, and location extraction, and then visualizing statistics and maps—including province counts, city heatmaps, and dynamic monthly distributions—using Python, pandas, and Folium.
This tutorial explains how to install Streamlit, use its text, data, chart, and input components, manage layout, control flow, and caching, and demonstrates a full garbage‑classification web app that integrates a third‑party image‑recognition API, providing complete Python code examples.
This article walks through using Python to scrape Wang Leehom’s divorce‑related Weibo comments, clean the noisy dataset, visualize hourly comment trends, compare with his ex‑wife’s posts, generate word‑clouds and emoji frequency charts, and provides full code and data for reproducible analysis.
This tutorial introduces the Python pyecharts library for creating various map visualizations with Echarts, explains how the code generates HTML files, and provides complete example functions for basic, label‑less, continuous and piecewise visual maps, world and regional maps, plus guidance for using real data.
This tutorial introduces Plotly for Python, showing how to install it, configure offline mode in Jupyter, and create line, scatter, and bar charts with complete code examples and explanations of each visualization type.
This tutorial demonstrates how to use Python on Windows to crawl Maoyan movie rankings, extract details such as title, rating, genre, region, and duration, store them in a CSV file, and then perform comprehensive data cleaning, analysis, and visualization with pandas, matplotlib, and WordCloud.
This guide walks you through downloading Kweichow Moutai's historical stock data as a CSV file, handling encoding issues, and using Python's pandas and matplotlib libraries to create line charts of trading volume and OHLC prices for clear visual analysis.
This article demonstrates how to use Python's requests and BeautifulSoup to crawl text from a news site, process it with collections, numpy, and jieba for word‑frequency analysis, and then visualize the top terms using pyecharts, providing complete code snippets and explanations.
This tutorial walks through installing the Mayavi library, explains its core mlab module and API, and provides a series of Python code examples—including mesh, points3d, plot3d, imshow, surf, contour3d, and quiver3d—to demonstrate how to create various 3‑D visualizations.
This tutorial demonstrates how to crawl approximately 40,000 Maoyan movie reviews for the film “Chinese Doctors,” preprocess the data, and create visualizations such as rating pie charts, city distribution maps, top‑viewer bar charts, and a word cloud using Python libraries like requests, pyecharts, and wordcloud.
This tutorial explains what data visualization is, why it matters, common front‑end visualization libraries, and provides step‑by‑step guidance on drawing lines and curves with Canvas—including segment generation, Bézier and B‑spline curves, animation techniques, and interaction (picking) methods—so developers can build interactive charts efficiently.
This article examines the limitations of traditional Sankey diagrams for visualizing entity state transitions and proposes a card‑based data flow visualization that offers customizable start/end states, improved readability for disparate data scales, and enhanced user experience through interactive layouts and clear styling.
This tutorial explains how to use the Python Bokeh library to create interactive scatter, bar, and multi‑panel visualizations, covering installation, figure configuration, hover tooltips, data sources, color mapping, looping over categories, and linking plots with shared ranges.
This tutorial demonstrates how to install Plotly, import necessary modules, and create various visualizations—including scatter, bar, histogram, pie, 3D, and density map charts—using both the graph_objects and express APIs with sample CSV data and code snippets.
Learn step-by-step how to collect worldwide electricity generation data from the IEA using Python's requests and pandas, extract country names, retrieve historical consumption figures, and turn the results into animated visualizations with online tools, while avoiding manual entry and ensuring reproducible analysis.
This article provides a comprehensive overview of data visualization large screens, covering their purpose, classification, design styles, device considerations, layout strategies, adaptive techniques, chart selection, visual appeal, and interaction guidelines to help designers create effective and engaging dashboard experiences.
This tutorial demonstrates how to use Python to scrape Weibo for the most common National Day leave reasons, extract comment data via Ajax requests, store it in CSV files, and create visualizations such as top‑liked users and word‑clouds to reveal popular motifs.
This article explains how to simulate and draw the parabolic path of a horizontally launched projectile using Python, covering the physics formulas, point‑generation logic, and Matplotlib visualization with a complete, runnable code example.
This article explains how to compute and visualize the parabolic trajectory of a horizontally launched projectile using Python’s physics formulas and Matplotlib, providing step‑by‑step reasoning, code implementation, and sample outputs to help readers replicate the plot.
Learn step‑by‑step how to crawl 46,000+ rental listings from Ziroom using Python, extract house details with regex, clean and transform the data with pandas, and visualize distribution, pricing and location insights through pyecharts, matplotlib and seaborn, revealing rental market patterns in Beijing.
This article introduces five essential Matplotlib chart types—scatter plot, line plot, histogram, bar plot, and box plot—explaining their strengths, typical use‑cases, and providing ready‑to‑run Python functions for each visualization.
This tutorial demonstrates how to collect, clean, and visualize historical Chinese National Games medal data using Python, covering data extraction with regular expressions, organization into pandas DataFrames, and creating both static line charts with Matplotlib and interactive charts with PyEcharts, complete with code snippets and practical tips.
Learn how to build eye‑catching lollipop (棒棒糖) charts using both Excel and Python’s matplotlib, covering data preparation, combining bar and scatter plots, styling axes, employing the stem function, and handling large datasets, with full code snippets and visual examples.
This tutorial demonstrates how to use the powerful pyecharts library to generate eye‑catching 2D maps, a province‑level map of Guizhou, and a rotating globe, all rendered as interactive HTML pages that can be viewed in a browser.
This article introduces Matplotlib as a versatile, open‑source Python plotting library, showcasing its 2D and 3D capabilities, various chart types, color‑customization options, LaTeX support, and integration with tools like Seaborn, while also providing installation tips and concise code examples.
This tutorial walks you through using Python to collect JD.com product comments, parse the JSON responses, store the data in Excel, and then perform simple visual analysis such as color‑distribution bar charts and a word‑cloud of review content.
This article introduces Ryven, a Python visual scripting tool for creating high-quality data visualizations, explains its data-flow and execution-flow models, provides setup instructions, and demonstrates how to import nodes and use control features, offering a practical tutorial for developers.
Apache Superset is a modern, enterprise‑ready open‑source business intelligence web application that offers rich data visualizations, an easy‑to‑use interface, dashboard sharing, robust security, a semantic layer, and integration with a wide range of relational and analytical databases.
This tutorial demonstrates how to create a radar chart in Python with Matplotlib to visualize cluster center values, providing the necessary data preparation steps, complete plotting code, and example output images for an airline customer value analysis.
This article walks you through cleaning messy Taobao product data using pandas and jieba, then visualizing ingredient and shelf‑life information with interactive Pyecharts charts—including pie, bar, table, funnel, and polar graphs—while showing how to combine multiple charts into a single draggable HTML page.
This tutorial walks through creating a stock sentiment analysis pipeline in Python by scraping news from a financial website, applying Baidu AI's sentiment API to classify headlines, aggregating positive and negative ratios per stock, and visualizing the results with matplotlib.
This guide walks you through using Python’s pandas and matplotlib libraries to generate daily bar‑chart images, combine them into an animated GIF, and embed the animation with custom QR code and HTML to create a heartfelt, data‑driven celebration for the Qixi festival.
This article explains how to identify Chinese prefecture‑level cities with populations over five million, classify them by tier, and create an animated bubble map using Python libraries such as geopandas, pandas, matplotlib and imageio, finally exporting the result as an HTML page and GIF.
This tutorial walks through collecting China’s Olympic medal data from 1984 to 2016, reshaping it into wide and long tables, and creating a series of interactive visualizations—including line charts, scatter plots, radar charts, and sunburst diagrams—using Python's pandas and Plotly libraries.
This tutorial demonstrates a complete Python data‑analysis workflow that scrapes complaint records for a rental‑apartment company, cleans and merges the data, visualizes complaint trends, and generates word‑clouds to reveal common issues and user demands.
This article introduces the open‑source Plotly library (with Cufflinks) for Python, explains how to install it, and demonstrates with concise code examples how to create a wide range of interactive charts—including bar, box, scatter, time‑series, 3D, and custom‑themed visualizations—directly in Jupyter notebooks.
This article explores practical guidelines for B‑end table design, covering optimal row height and column width, flexible horizontal space, efficient scanning techniques, and concise presentation strategies to create tables that are dense enough for information yet easy to read and interact with.
This tutorial walks data analysts through setting up a Python environment with Jupyter and Anaconda, importing diverse datasets via Pandas, cleaning and reshaping data, performing calculations, filtering, visualizing results, and finally merging and grouping data to produce insightful analyses comparable to SQL and Excel workflows.
The article explains how Airbnb’s visx library, built on React and D3 primitives, addresses the need for a lightweight, highly expressive, and performant front‑end visualization solution by offering modular packages, TypeScript safety, and seamless integration with existing React codebases.
