---
id: pyecharts
version: "2.1.0"
license: MIT
license_treatment: permissive
maintenance: active
---

# pyecharts — Python options, make charting easier

License: permissive · Maintenance: active · Downloads: 1.1M/mo

## What it is and what it does

pyecharts is a Python binding for Apache ECharts, a data visualization library originally developed by Baidu. It lets you build interactive charts and maps in Python and export them as standalone HTML files or images. The library supports chain-style method calls for fluent API design, making it natural to build visualizations incrementally. It includes over 30 common chart types (bar, line, scatter, pie, heatmap, and others) and extensive map support for geographic data.

The package integrates with Jupyter Notebook, JupyterLab, and marimo for inline visualization, and can be embedded into Flask, Sanic, Django, and other web frameworks. It depends on jinja2 for template rendering, prettytable for table formatting, and simplejson for JSON serialization. Version 2 is based on ECharts 5.4.1+ and requires Python 3.7 or later.

Use it for:

- Build interactive sales dashboards or business intelligence reports in Jupyter notebooks for exploratory data analysis
- Generate standalone HTML visualizations for embedding in web applications or sharing via email
- Create geographic heatmaps or choropleth maps using the 400+ map files for location-based data analysis
- Render time-series or multi-series charts with custom styling and configuration for presentations or reports
- Integrate real-time or batch-generated charts into Flask or Django web applications for live dashboards

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

pyecharts wraps Apache ECharts to generate interactive data visualizations from Python, rendering charts as HTML or images with support for 30+ chart types and 400+ map files.

Yes. pyecharts is actively maintained, has low install friction, carries no known vulnerabilities, and offers a permissive MIT license. It is well-suited for anyone needing interactive charts in Python, especially for Jupyter workflows or web integration. The large ecosystem of chart types and map support makes it a practical choice for data visualization across multiple contexts.

## Install

pip install pyecharts
uv add pyecharts
poetry add pyecharts

## Installing pyecharts

Before you install: Low install friction with only three runtime dependencies (jinja2, prettytable, simplejson). Active maintenance with a recent release and 15775 repository stars; last commit on 2026-08-04.

License in practice: MIT license is permissive, allowing free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.

Quickstart:

pip install pyecharts

from pyecharts.charts import Bar
from pyecharts import options as opts

bar = (Bar()
    .add_xaxis(["A", "B", "C"])
    .add_yaxis("Series", [10, 20, 30])
    .set_global_opts(title_opts=opts.TitleOpts(title="Sample"))
)
bar.render()

Verify before relying:

- Whether the 400+ map files require separate download or are bundled with the package
- Performance characteristics when rendering large datasets or complex multi-series charts
- Specific integration patterns with Flask, Sanic, and Django beyond basic HTML rendering

## Package facts

- License: MIT (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: active
- Downloads: 1.1M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

python data visualization charts, echarts python wrapper, interactive plotting library, html chart generation, geographic data visualization, notebook charting tool, web framework visualization, data-visualization, jupyter-friendly, web-integration

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