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 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 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 guide explains how to generate HTML, XML, and JSON reports with pytest, customize the HTML report by adding custom fields and execution time, and embed Matplotlib charts into the report, providing configuration snippets and code examples for each step.
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 introduces nine practical Vue.js plugins—including Vuetify, Vue Router, Vuex, Vue-i18n, Vue-lazyload, Vue-awesome-swiper, Vue-chartjs, Vue-meta, and Vue-awesome—detailing their features and providing code examples to help front‑end developers improve efficiency and code elegance.
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.
This tutorial explains what Helm is, how to create and customize Helm charts, and demonstrates practical commands for installing, upgrading, rolling back, and uninstalling applications on Kubernetes, highlighting Helm's benefits for simplifying deployments, version control, and CI/CD workflows.
This guide explains why data visualization matters, how to create and save graphics in R, adjust graphical parameters, and use core plotting functions such as barplot, pie, hist, boxplot, and scatter plots to produce clear, informative charts.
This comprehensive tutorial explains Helm as the Kubernetes package manager, covering its architecture, installation steps, core concepts like charts, releases, repositories, chart structure, dependencies, templating syntax, built‑in objects, common functions, control structures, named templates, NOTES.txt usage, and debugging techniques for effective Helm chart development.
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.
go-charts is a Go library that simplifies creating line, bar, and pie charts as SVG or PNG images, supports light, dark, and grafana themes, offers Apache ECharts‑compatible configuration, provides performance benchmarks, and includes guidance for handling Chinese characters.
This tutorial explains how to use the pyecharts library—a Python wrapper for Baidu's ECharts—to generate a wide range of interactive visualizations, including bar, line, pie, map, heatmap, scatter, 3D, and combined charts, with complete code examples and rendering instructions for Jupyter notebooks.
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 article introduces the lightweight Python visualization library cutecharts, explains its installation, and provides step‑by‑step code examples for creating line, bar, pie, radar, scatter charts and combined page charts, demonstrating how to customize options such as labels, legends, colors, and inner radius.
Ant Design Charts, the official AntV React chart component library, offers out‑of‑the‑box high‑quality visualizations, easy configuration, strong stability, and seamless React integration, making it a powerful and maintainable solution for building rich data dashboards in modern frontend applications.
This article introduces the Python library pyecharts, explains its background from ECharts, shows how to install it, and provides step‑by‑step examples—including creating bar and pie charts from Chengdu statistical data—while detailing the underlying code and rendering process for effective data visualization.
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 tutorial introduces pyecharts, a Python library that integrates with ECharts, covering installation via pip, handling network issues with a Tsinghua mirror, and detailed code examples for creating bar, pie, boxplot, line, radar, scatter, grid, and overlapping charts to visualize monthly precipitation and evaporation data.
This article provides a comprehensive overview of 56 data visualization tools ranging from simple spreadsheet options to advanced JavaScript libraries, mapping frameworks, and statistical packages, helping developers choose the right solution for creating interactive and insightful visual representations of their data.
