echarts
pygal
| echarts | pygal | |
|---|---|---|
| 1 | 3 | |
| 55,034 | 2,605 | |
| - | 0.3% | |
| 9.5 | 7.7 | |
| about 1 year ago | 3 months ago | |
| TypeScript | Python | |
| Apache License 2.0 |
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echarts
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ECharts for Python
ECharts was originally a Baidu project, released under https://github.com/ecomfe/echarts (ecomfe = "E-commerce frontend"?). They still maintain some of the auxiliary libraries.
I've been using it since around ... 2018-ish[0]? ... as a replacement for Google Charts. It was my first time using a big library from one of the Chinese tech giants -- basic docs and tutorials in English, then all the advanced stuff (and comments) written in Chinese. I was impressed by how comprehensive the charts library was, and how they'd obviously invested a lot of brainwork into the configuration system. IMO it's one of the highest-quality data visualization libraries in JavaScript unless you're willing to dive deep into something like d3.js.
The blog post's author describes running echarts in a headless Chrome, though, which seems insane to me. It's JavaScript rendering to a -- can't it run in Node with https://github.com/Automattic/node-canvas ?
[0] A small publicly-accessible example: https://john-millikin.com/reddit-front-page-2018#by-domain
pygal
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ECharts for Python
> There is a snapshot library for pyecharts that allows you to convert the HTML produced by the library into formats like JPEG, PNG, PDF and SVG.
One alternative is Pygal: https://github.com/Kozea/pygal/
Even though the library is not actively "developed" but it is a complete library in my opinion.
I feel like with d3.js and eCharts, modern data visualization requires you to run analytics processes first then outputting a JSON then writing the visualization code with JavaScript.
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Homebrew Crafting rules and analysis
I used Pygal to generate the charts, and it uses a unique colour per dataset, so 20 colours for each level. I just didn't see a need to change it.
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[OC] I created graphs that show the page count per chapter for the top 20 most popular manga on MyAnimeList. (Notes and interactive charts in comments)
pygal (To generate the png and interactive charts)
What are some alternatives?
mini-canvas-editor - JavaScript image editor as component. Integrate with any front-end framework.
matplotlib - matplotlib: plotting with Python
amcharts4 - The most advanced amCharts charting library for JavaScript and TypeScript apps.
plotly - The interactive graphing library for Python :sparkles: This project now includes Plotly Express!
vue-svg-pan-zoom - Vue component using SvgPanZoom
bokeh - Interactive Data Visualization in the browser, from Python
echarts-readymade - Make echarts come in handy for React. Based on echarts-for-react
seaborn - Statistical data visualization in Python
mod_harbour.v2 - mod_harbour.v2 - Harbour module for Apache
Altair - Declarative statistical visualization library for Python
node-canvas - Node canvas is a Cairo backed Canvas implementation for NodeJS.
bqplot - Plotting library for IPython/Jupyter notebooks