pyecharts
Python options, make charting easier
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
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 · maintenance · safety
MIT (permissive) — MIT license is permissive, allowing free use, modification, and distribution with minimal restrictions—suitable for both open-source and commercial projects.
last release 2026-02-10 (185 days) · last repo commit 2026-08-04 · 15,775 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,139,358 downloads/mo, #4,310 on PyPI
Alternatives
Verify before relying
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()- 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
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 on this page. Verify before relying on it.
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
pyecharts on PyPI
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 | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagesjinja2prettytablesimplejson |
| Maintenance | Actively maintained 185 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,139,358 / month, #4,310 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: DevelopersLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Software Development :: Libraries |
Evidence: pyecharts-2.1.0-py3-none-any.whl
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See also streamlit-echarts · plotly · reflex-components-recharts · highcharts-core · reflex-components-plotly · highcharts-maps · chart-studio · leather · jupyter-leaflet