0 views collected around this technical thread.
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 consolidates the fragmented knowledge about using Echarts in UniApp mini‑programs, detailing library choices, installation steps, complete code examples for both Vue2 and Vue3 projects, and common pitfalls with practical solutions.
This tutorial explains how to install Python, Pandas, and Matplotlib, prepare an Excel file, and then create ten different chart types—including bar, scatter, pie, line, box, area, heatmap, polar, contour, and 3D charts—using concise code examples to visualize data effectively.
This article provides a detailed overview of multiple Python libraries for Excel manipulation—including xlrd, xlwt, xlutils, xlwings, openpyxl, XlsxWriter, win32com, and pandas—covering installation, basic operations, reading, writing, formatting, chart generation, and practical code examples for each tool.
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.
This article introduces Altair, a simple yet powerful Python library built on the Vega‑Lite JSON specification, explains its declarative syntax, showcases basic and advanced visualizations with code examples, and highlights its advantages for efficient data analysis and presentation.
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.
The G2 3.2 release from AntV introduces a richer visual cognition system with tail‑legend support, region‑filter color guides, new data marker guides, more flexible component configurations, and chart‑level theme customization, while providing installation options via npm or CDN for developers to try the latest features.