Python interactive web charting libraries (2026): 6 tools ranked

Python libraries that output interactive charts for Jupyter and the browser, with zooming, panning and tooltips. Plotly (Python) and Bokeh are the most widely used, bqplot is built on Jupyter widgets, and pyecharts and ipychart wrap the ECharts and Chart.js JavaScript libraries.

By GitHub stars, the most popular Python interactive charting libraries here are Bokeh (20.5K stars), Plotly (Python) (18.9K stars), and pyecharts (15.8K stars). 3 of 6 had commits in the last 90 days.

Python interactive web charting libraries, ranked by GitHub stars

1	Bokeh Interactive Web Plotting for Python.	20.5K	423	yesterday	BSD-3-Clause
2	Plotly (Python) Interactive web based visualization built on top of plotly.js	18.9K	816	yesterday	MIT
3	pyecharts Python binding for Echarts library.	15.8K	1	9 months ago	MIT
4	bqplot Plotting library for IPython/Jupyter notebooks.	3.7K	31	5 days ago	Apache-2.0
5	Chartify Inactive Bokeh wrapper that makes it easy for data scientists to create charts.	3.7K	0	2 years ago	Apache-2.0
6	ipychart Inactive The power of Chart.js in Jupyter Notebook.	133	0	2 years ago	MIT

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Frequently asked questions

What is the most popular Python interactive charting library?

By GitHub stars, Bokeh is the most popular, with 20.5K stars, followed by Plotly (Python) (18.9K) and pyecharts (15.8K).

Which Python interactive charting libraries are actively maintained?

4 of the 6 tools listed had commits in the last 12 months, including Bokeh, Plotly (Python), pyecharts, and bqplot. Each tool page shows monthly commit activity.

Are these Python interactive charting libraries free and open source?

6 of the 6 tools listed use an OSI-approved open-source license. The most common license here is MIT.

Missing a tool? Add it with a one-line pull request to the README.