[PR #1107] [CLOSED] Add pyecharts #7713

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GiteaMirror · 2026-04-18T22:11:13-05:00

GiteaMirror commented

2026-04-18 22:11:13 -05:00

📋 Pull Request Information

Original PR: https://github.com/vinta/awesome-python/pull/1107
Author: @chfw
Created: 8/15/2018
Status: ❌ Closed

Base: master ← Head: master

📝 Commits (1)

cb52b8d 🔖 add pyecharts, https://github.com/pyecharts/pyecharts

📊 Changes

1 file changed (+1 additions, -0 deletions)

View changed files

📝 README.md (+1 -0)

📄 Description

What is this Python project?

Pyecharts is a python interface to Echarts.js visualization library. It supports 28+ charts ranging from 2D, 3D and geo mapping. It supports data scientists using Jupyter, nteract and supports web developers with django and flask integration.

What's the difference between this Python project and similar ones?

It is similar to plotly.py and bokeh.

Comparing with plotly.py, pyecharts offers free exports of svg, pdf and html format whereas plotly.py offer them under a premium version. And pyecharts does not really care about the number of exports to other file formats whereas ploly.py allows up to100 exports per 24h.

Comparing with bokeh, user group are different. Bokeh serves primarily scientific community whereas pyecharts serves both web developers and data analysts. Bokeh's api emulates matplotlib whereas pyecharts api emulates pygal.

When it comes to geomapping, pyecharts has strong support for Chinese regions.

--

Anyone who agrees with this pull request could vote for it by adding a 👍 to it, and usually, the maintainer will merge it when votes reach 20.

_{🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.}

## 📋 Pull Request Information **Original PR:** https://github.com/vinta/awesome-python/pull/1107 **Author:** [@chfw](https://github.com/chfw) **Created:** 8/15/2018 **Status:** ❌ Closed **Base:** `master` ← **Head:** `master` --- ### 📝 Commits (1) - [`cb52b8d`](https://github.com/vinta/awesome-python/commit/cb52b8d551d6162931178f07cf47a10505cb417a) :bookmark: add pyecharts, https://github.com/pyecharts/pyecharts ### 📊 Changes **1 file changed** (+1 additions, -0 deletions) <details> <summary>View changed files</summary> 📝 `README.md` (+1 -0) </details> ### 📄 Description ## What is this Python project? Pyecharts is a python interface to [Echarts.js](https://github.com/apache/incubator-echarts) visualization library. It supports [28+ charts ranging from 2D, 3D and geo mapping](http://nbviewer.jupyter.org/github/pyecharts/pyecharts-users-cases/blob/master/notebook-users-cases/notebook-user-cases.ipynb). It supports data scientists using Jupyter, [nteract](http://pyecharts.org/#/zh-cn/jupyter_notebook?id=nteract) and supports web developers with [django ](http://pyecharts.org/#/zh-cn/django)and [flask](http://pyecharts.org/#/zh-cn/flask) integration. ## What's the difference between this Python project and similar ones? It is similar to plotly.py and bokeh. Comparing with plotly.py, pyecharts offers free exports of svg, pdf and html format whereas plotly.py offer them under a premium version. And pyecharts does not really care about the number of exports to other file formats whereas ploly.py allows up to[100 exports per 24h](https://plot.ly/products/cloud/). Comparing with bokeh, user group are different. Bokeh serves primarily scientific community whereas pyecharts serves both web developers and data analysts. Bokeh's api emulates matplotlib whereas pyecharts api emulates pygal. When it comes to geomapping, pyecharts has strong support for [Chinese regions](https://github.com/echarts-maps). -- Anyone who agrees with this pull request could vote for it by adding a :+1: to it, and usually, the maintainer will merge it when votes reach **20**. --- <sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>

GiteaMirror added the pull-request label 2026-04-18 22:11:13 -05:00

GiteaMirror closed this issue

2026-04-18 22:11:13 -05:00

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Reference: github-starred/awesome-python#7713